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Slope entropy (SlE) is an effective nonlinear dynamic analysis method, which has been used in mechanical fault diagnosis field. However, SlE only analyzes the time series on a single scale with much important information on other scales being ignored. Inspired by the multiscale analysis, the multiscale slope entropy (MSlE) is developed to extract the multiscale features of time series. Nevertheless, MSlE is susceptible to the loss of important information of original time series due to insufficient coarse-graining. In this paper, a novel algorithm termed refined time-shift multiscale slope entropy (RTSMSlE) is further proposed for enhancing the performance of MSlE. RTSMSlE changes the original coarse-grained computation and effectively improves the nonlinear analysis performance of MSlE, which has higher discriminating power and is less affected by mutant signals. After that, a novel fault diagnosis method for rotating machinery is proposed based on the RTSMSlE and DBO–SVM classifier. The effectiveness and superiority of the proposed fault diagnosis method is verified via the simulated signals and the measured data analysis with comparison to the MSlE, refined time-shift multiscale sample entropy (RTSMSE), refined time-shift multiscale fuzzy entropy (RTSMFE), refined composite multiscale sample entropy (RCMSE) and refined composite multiscale dispersion entropy (RCMDE). The analysis results show that the proposed method provides better diagnostic effect and more stable performance in analyzing the vibration signals of rotating machinery than the compared methods. [ABSTRACT FROM AUTHOR]

Unexpected faults in rotating machinery can lead to cascading disruptions of the entire work process, emphasizing the importance of early detection of performance degradation and identification of the current state. To accurately assess the health of a machine, this study introduces an FFT‐based raw vibration data preprocessing and graph representation technique, which analyses changes in frequency bands to detect early degradation trends in vibration data that may appear normal. The approach proposes a methodology that utilizes a graph convolutional autoencoder trained using only normal data to extract health indicators using the differences in the vectors as degradation progresses. This approach has the advantage of using only normal data to detect subtle performance degradation early and effectively represent health indicators accordingly. [ABSTRACT FROM AUTHOR]

Heavy-duty centrifugal fans account for a significant share of energy consumption in the process and manufacturing industries. As a result, these machines are under increasing pressure to operate at maximum efficiency to reduce costs, pollutants and noise: only combined optimization is considered competitive for future generations of fans. Preliminary studies have shown that applying structured porosity to aerofoil rear parts can lead to a reduction in self noise and trailing edge shedding noise in the mid-to-high frequency range. With this in mind, a porous surface cover is applied to a prototype centrifugal fan to evaluate the aeroacoustic potential in a complex rotating machinery. The optimal geometric characteristics of the perforation are derived from experiments with single aerofoils, while the perimeter of the covered area is varied in eight steps. The centrifugal fan specimen is rapid-prototyped and tested at different fan speeds along the complete characteristic curves, while both aerodynamic and aeroacoustic performances are simultaneously recorded. The results obtained show a significant reduction in overall noise level while aerodynamic performance is maintained. Spectral analysis shows that the noise reduction is due to a broadband effect, where the upper and lower cut-off frequencies are determined by the rotational speed and the location of the applied porosity along the blade chord. However, the maximum noise reduction is obtained as a clear function of the minimum distance between the perforation and the trailing edge of the blade, indicating that the underlying working mechanisms are a combination of broadband dissipation effects due to porosity and destructive interference. [ABSTRACT FROM AUTHOR]

Data-driven intelligent fault diagnosis methods have been extensively researched and applied in rotating machinery. In practical application scenarios, factors such as variable operating conditions and scarcity of labeled samples in rotating machinery hinder the engineering application and promotion of diagnostic models. To address these challenges, this paper proposes an unsupervised domain adaptation network called the Multi-scale Hybrid Domain Adaptation with Attention (MHDAA). Firstly, a multi-scale convolutional module was developed to extract fault features at different scales. Secondly, a multi-channel attention mechanism was proposed to enable the convolution layers of different convolution kernels fully extract feature information. Finally, a hybrid domain adaptation was constructed to dynamically extract invariant features from both the source and target domains. The method was evaluated in multiple transfer scenarios of planetary gearboxes and bearings. Experimental results demonstrate that the proposed method can effectively utilize fault features with high correlation from multiple source domains to complete fault diagnosis with unknown data labels in the target domain. Moreover, the proposed method exhibits superior diagnostic performance. [ABSTRACT FROM AUTHOR]

Insufficient training data often leads to overfitting, posing a significant challenge in diagnosing faults in mechanical devices, particularly rotating machinery. To address this issue, this paper introduces a novel approach employing a graph neural network (GNN) with one-shot learning for fault diagnosis in rotating machinery. Firstly, the Short-Time Fourier Transform (STFT) is utilized for data preprocessing to convert the one-dimensional data into two-dimensional pictures. Subsequently, Feature extraction a convolutional neural network (CNN) is utilized to perform feature extraction. By introducing the adjacency matrix to explore the spatial information within data, a graph neural network (GNN) method is proposed to achieve the fault classification of rotating machinery with small sample. The method utilizes GNN to process structural information between, transferring the distance metric from Euclidean space to non-Euclidean space. Classification accuracy is thereby improved based on information processing in non-Euclidean space.Experiments were implemented on two datasets to verify the proposed method, including an open dataset of the rolling bearing and an experimental rig of the rotate vector (RV) reducer in an industrial robot. Siamese Net, Matching Net, and sparse auto-encoder with random forest (SAE + RF) wereemployed as the comparisons to further prove the effectiveness of the proposed method. Results indicate that the proposed method outperforms all the comparative methods in both rotating machineries. [ABSTRACT FROM AUTHOR]

In rotating machinery, the condition of rolling bearings is paramount, directly influencing operational integrity. However, the literature on the fault evolution of rolling bearings in their nascent stages is notably limited. Addressing this gap, our study establishes an innovative nonlinear dynamic model for early fault evolution of rolling bearings based on collision impact. Firstly, considering the fault evolution characteristics, the influence of the rolling element and fault structure, the dynamic model of early fault evolution between the rolling element and the local fault is established. Secondly, according to the Hertzian contact deformation theory, a nonlinear dynamic model of rolling bearings expressed as mass-spring is established. Thirdly, the energy contribution method is used to integrate the fault evolution model and the nonlinear dynamic model of the rolling bearing. A nonlinear dynamic model of early fault evolution of the rolling bearing is proposed by using the Lagrangian equation. Comparing the simulation results of the nonlinear dynamic with the experimental results, it can be seen that the numerical model can effectively predict the evolution process and vibration characteristics of the fault evolution of rolling bearings in the early stage. [ABSTRACT FROM AUTHOR]

The reliable operation of scroll compressors is crucial for the efficiency of rotating machinery and refrigeration systems. To address the need for efficient and accurate fault diagnosis in scroll compressor technology under varying operating states, diverse failure modes, and different operating conditions, a multi-branch convolutional neural network fault diagnosis method (SSG-Net) has been developed. This method is based on the Swin Transformer, the Global Attention Mechanism (GAM), and the ResNet architecture. Initially, the one-dimensional time-series signal is converted into a two-dimensional image using the Short-Time Fourier Transform, thereby enriching the feature set for deep learning analysis. Subsequently, the method integrates the window attention mechanism of the Swin Transformer, the 2D convolution of GAM attention, and the shallow ResNet's two-dimensional convolution feature extraction branch network. This integration further optimizes the feature extraction process, enhancing the accuracy of fault feature recognition and sensitivity to data variability. Consequently, by combining the global and local features extracted from these three branch networks, the model significantly improves feature representation capability and robustness. Finally, experimental results on scroll compressor datasets and the CWRU dataset demonstrate diagnostic accuracies of 97.44% and 99.78%, respectively. These results surpass existing comparative models and confirm the model's superior recognition precision and rapid convergence capabilities in complex fault environments. [ABSTRACT FROM AUTHOR]

The "Virtual Rotor Kit (VRK) Project" is a lightweight and standalone application developed in response to the challenges posed by the COVID-19 outbreak in the field of rotating machinery dynamics. This alternative emulates a rotor test rig, enabling virtual balancing tests on a single-plane rotor and offering students a comprehensive learning experience that simulates on-site balancing. The Virtual Rotor Kit utilizes a three-dimensional model based on a well-known rotor test rig widely used in educational and research settings. To simulate the dynamics of the rotor test rig, the application employs PyChrono, a dedicated Python library that wraps Chrono, a validated open-source multi-physics simulation engine. A detailed usability assessment involving 41 senior mechanical engineering students yielded promising results, indicating the Virtual Rotor Kit's potential to enhance comprehension of vibration phenomena in unbalanced rotating machines. Despite revealing certain issues related to the graphical user interface and the application's execution time, three defined achievement indicators demonstrated positive outcomes for a virtual balancing laboratory experience using the Virtual Rotor Kit application. By focusing on the dynamic behavior of a rotor test rig instead of simulating a specific laboratory experience, the application offers instructors greater flexibility in developing learning experiences. Unlike other virtual testbeds, the Virtual Rotor Kit allows students to conduct both steady-state and transient experiments, offering a comprehensive understanding of rotating machinery dynamics. Additionally, the application is distributed under the BSD-3 license and can serve as a complement to on-site laboratory experiences. The results presented in this study inspire further research and development to enhance its efficacy. [ABSTRACT FROM AUTHOR]

The effects of hypopressive exercise (HE) on dynamic balance have never been studied. We aimed to study the effects of a HE program on dynamic balance, posterior chain kinematics and expiratory peak flow on female competitive roller skaters over a 6-week training period. Twenty competitive female roller-skaters (13-22 years of age, SD 2.25) performed a 30-minute HE session once weekly before the regular roller-skating practice for 6 weeks. The HE program consisted of breathing and postural awareness exercises in addition to 5 basic HE poses performed three times each. Dynamic neuromuscular control was assessed with the Y-Balance Test (YBT), posterior back chain kinematics with the sit and reach test and peak expiratory flow rate with a digital spirometer. Paired t-test revealed significant differences between the measurement periods for all YBT leg directions and composite score (p ≤ 0.01). Significant differences were also revealed between baseline and after the intervention for the sit and reach test (p ≤ 0.01) and peak expiratory flow (p = 0.01). No differences in forced expiratory volume in the first second were found (p = 0.04). These preliminary findings suggest that a 6-week HE program could be a feasible neuromuscular option for training dynamic balance, posterior back chain kinematics and peak expiratory flow in female roller-skaters. [ABSTRACT FROM AUTHOR]

Rotating machinery is an important component of mechanical equipment, and misaligned rotor systems are a key factor leading to equipment failures. If there is a misalignment fault during operation, it may have various adverse effects on the stable operation of mechanical equipment, such as causing dynamic and static friction between the rotor and stator, bearing friction damage, shaft bending deformation, oil film oscillation, vibration, etc., all of which may seriously interfere with the stable operation of mechanical equipment. Studying the mechanism and vibration characteristics of misalignment faults in rotor systems is particularly crucial for accurately judging and mastering the operating conditions of the rotor system. This article focuses on the parallel misalignment fault of the rotor system of a hydroelectric generator set and the collision friction coupling fault between the rotor system of the generator set. Based on nonlinear rotor dynamics theory and the motion mechanism of parallel misalignment faults, a nonlinear dynamic model of the rotor system of a hydroelectric generator set is constructed, which considers the influence of parallel misalignment and collision friction coupling faults. The model explores the nonlinear dynamic performance of the generator rotor system under multiple parameter changes such as unit speed, eccentricity, and parallel misalignment. [ABSTRACT FROM AUTHOR]

Current unsupervised domain adaptation methods for intelligent fault diagnosis focus on learning domain-invariant representations under covariate shift assumption. However, the covariate shift assumption is usually unsatisfied when each fault class in different domains consists of multiple modes with skewed proportions, which is common in industrial scenarios. Imbalanced data from multiple modes cause the presence of within-domain class imbalance and between-domain label distributional shift. This paper introduces a novel subpopulation shift that further considers the domain shift from a subpopulation perspective, i.e., that covariate shift and label distributional shift across domains are caused by shifts in the multiple modes. To address this issue, a label propagation-based unsupervised domain adaptation is proposed based on a realistic expansion assumption. We apply the theoretical analysis of the proposed method with a bi-level optimization strategy adapted from meta-learning. Using joint optimization of a teacher model and a student model, the label propagation-based model-agnostic meta-learning (LPMAML) not only propagates supervision information from the source to the target but also adjusts the teacher's strategy throughout the student's learning process. To alleviate the noise caused by label distributional shift, we integrate a sampling-based alignment method that aligns the empirical label distributions across the two domains into LPMAML. Experimental results on three bearing datasets show that the proposed method has impressive generalization ability under covariate, label distributional, and subpopulation shifts. The proposed method offers consistent improvements to unsupervised domain adaptation (UDA) methods. Compared with the vanilla UDA methods, the average diagnosis accuracies of the proposed method on the label distributional shift benchmark and subpopulation shift benchmark are improved by 8.21% and 7.63%, respectively. [ABSTRACT FROM AUTHOR]

Mechanical system fault diagnosis is essential to save costs and ensure safety. Generally, rotating machinery operates in nonstationary cases, which makes fault features complex and difficult to identify. However, existing fault diagnosis methods have the following limitations. (1) Consider only time or frequency domain features fusion. (2) Extract the fusion features representation only in the Euclidean domain. Based on that, a novel method based on multisensory time-frequency features fusion and graph attention network is proposed for rotating machinery fault diagnosis under the nonstationary case. First, the multi-sensor time series are converted into multi-sensor time-frequency maps by image-to-matrix, matrix concatenation, and matrix-to-image operations. Then, simple linear iterative clustering is applied to make the superpixels in the multi-sensor time-frequency maps into nodes and form graphs based on color and texture features. Finally, the graph attention network with residual connection is applied to distinguish the rotating machinery's health status. The proposed method is verified using gearbox test rig data and bearing public data, respectively. The experimental results indicate that the proposed method can provide more reliable and accurate fault diagnosis results for rotating machinery than other methods. [ABSTRACT FROM AUTHOR]

Vibration data from mechanical equipment contain extensive information distributed across multiple dimensions. Single‐scale analysis fails to comprehensively reflect its damage characteristics, thereby reducing fault diagnosis accuracy. This study proposes a novel signal vibration feature extraction method called hierarchical refined composite generalized multiscale fluctuation dispersion entropy (HRCGMFDE). This method simultaneously extracts fault information from both low‐frequency and high‐frequency components of the data, addressing the drawback of high‐frequency information loss in refined composite generalized multiscale fluctuation dispersion entropy (RCMFDE). Comparative results on two simulated signals demonstrate the method's advantages of high stability and more accurate complexity measurement. Furthermore, low‐frequency and high‐frequency components of the data are comprehensively extracted using dual‐tree complex wavelet packet transform (DTCWPT), and high‐dimensional features are downscaled using t‐distributed stochastic neighbor embedding (t‐SNE) to obtain low‐dimensional sensitive fault features. Subsequently, a Random Forest (RF) classifier is employed for fault identification. Finally, the effectiveness of the proposed method is validated using three typical mechanical datasets. Results confirm the method's capability to effectively determine the fault states of bearings, gearboxes, and centrifugal pumps, showcasing significant advantages over comparative methods. [ABSTRACT FROM AUTHOR]

Double planetary gear trains (DPGTs) have high loading capacity and transmission, making them indispensable in various heavy rotating machinery applications. However, the presence of planetary gear-set radial bearing roller dimension deviation (PBRDD) from real-world manufacturing discrepancies poses a significant challenge to the dynamics of the DPGTs. Previous dynamic models often overlooked the internal excitations of the planetary gear-set radial bearing, focusing only on the single planetary gear train dynamics, rendering them inadequate for studying the impact of the PBRDD on DPGTs. Therefore, this paper proposes a coupling method integrating the PBRDD model with the DPGT dynamic model, taking into consideration the planetary gear-set radial bearing’s internal excitations, to reveal the impact of PBRDD on DPGTs’ dynamics. Specifically, the changes in roller–ring contact force and roller–cage collision force induced by PBRDD are considered. Through comprehensive simulation and experimental analyses, the accuracy of the dynamic model is assessed and its efficacy in capturing the effects of PBRDD on DPGTs’ dynamics is validated. The results show that the DPGTs’ dynamics increment with the increment of PBRDD. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Dynamic loads are inevitably generated during the operation of rotating machinery. In situ monitoring the dynamic loads is of great significance for assessing shafting health. Triboelectric nanogenerator (TENG) is sensitive to motion but not suitable for load monitoring, while piezoelectric nanogenerator (PENG) is just the opposite. Therefore, the combination of the two can complement each other. From this, a dynamic load monitoring smart bearing (DLMSB) integrated with TENG and PENG is proposed. The specially designed TENG can produce an “M‐waveform”, which can be used to extract rotation frequency and distinguish the quadrant of the rotor. Meanwhile, applying PENG to capture the load condition of the bearing, and referring to the main frequency of M‐waveform, it is convenient to obtain the dynamic loads of shafting. Furthermore, a real‐time monitoring system is developed, which can realize not only real‐time monitoring of the rotation speed and dynamic loads magnitude but also the quadrant discriminating of dynamic loads. The results have shown that the monitoring error for dynamic loads is 3 N or 5.5%, and the accuracy rate for determining the quadrant of the dynamic loads can reach 93%. This study provides a novel approach for the in situ monitoring of mechanical operating status via nanogenerators. [ABSTRACT FROM AUTHOR]

AbstractAs an important component of rotating machinery, the rotor system (RSM) has a decisive effect on the system dynamics. Most previous works established the RSM dynamic model without the bearing slip, ball dynamics, and cage dynamics. This work proposes a flexible rotor system (FRSM) dynamic model with the bearing slip, ball dynamics, and cage dynamics. The effect of the bearing slip on the FRSM dynamic is analyzed. The FRSM consists of the rotor, rigid disk, and ball bearings. The rotor is modeled using the Timoshenko beam elements. The dynamic models of the rigid disk and ball bearing are established by using the lumped parameter method. The bearing clearance and installation method effects are considered. An experimental verification is developed to demonstrate the validity of the proposed dynamic model. The results obtained by the proposed model and the previous model without the bearing slip are compared. Moreover, the effects of the rotor rotational speed and rigid disk unbalance eccentric distance on the FRSM dynamics and bearing slip are analyzed. The results shows that rotor rotation frequency (fs) is not obvious for health FRSM and the amplitudes of fs and nfs increase with rotation speed and unbalance eccentric distance, which can provide guidance for fault diagnosis. [ABSTRACT FROM AUTHOR]

Bearing‐fault diagnosis in rotating machinery is essential for ensuring the safety and reliability of mechanical systems. However, under complicated working conditions, the number of normal mechanical equipment samples can far exceed the number of faulty ones. When the data are so imbalanced, data fault diagnosis cannot be easily conducted using conventional deep learning methods. This study proposes a fault diagnosis method based on a dual‐branch interactive fusion network, which improves the accuracy and stability of bearing‐fault diagnosis. First, a dual‐branch feature representation network comprising an iterative attention‐feature fusion residual neural network and a long short‐term memory network is designed for extracting different modal features. Meanwhile, intermodal fusion of the extracted features is performed through multilayer perception. Based on the cost‐sensitive regularization loss, a new joint loss function is then designed for network training. Finally, the effectiveness of the proposed method is verified through comparative experiments, visualization analyses, ablation experiments, and generalization performance experiments. [ABSTRACT FROM AUTHOR]

The rotor-bearing system is a crucial component of rotating machinery, such as turbines, pumps, compressors, and turbogenerators, which are widely used in various advanced engineering fields. This work presented a methodology for studying the behavior of an elastic Jeffcott rotor supported by two similar fluid film journal bearings. A finite element model using consistent matrix formulation was employed to simulate the shaft, including the external load, with four degrees of freedom per node. The small perturbation method was used to evaluate the second-order bearing coefficients of a journal bearing of finite length. These coefficients were further integrated with the finite element model to evaluate the dynamic response of the flexible rotors. Moreover, the system equations of motion were presented in dimensional form. The results of the second-order bearing coefficients analysis agreed with nonlinear analysis when the speed was less than the threshold speed, while there was a pronounced difference in second-order analysis when the speed was above the threshold speed. The behavior of the rotor-bearing system was studied using dynamic response and orbit diagrams, revealing that changes in rotational speed significantly affected the rotor's stability. [ABSTRACT FROM AUTHOR]

Rotating machinery is widely used in modern industrial systems, and its health status can directly impact the operation of the entire system. Timely and accurate diagnosis of rotating machinery faults is crucial for ensuring production safety, reducing economic losses, and improving efficiency. Traditional deep learning methods can only extract features from the vertices of the input data, thereby overlooking the information contained in the relationships between vertices. This paper proposes a Legendre graph convolutional network (LGCN) integrated with a self-attention graph pooling method, which is applied to fault diagnosis of rotating machinery. The SA-LGCN model converts vibration signals from Euclidean space into graph signals in non-Euclidean space, employing a fast local spectral filter based on Legendre polynomials and a self-attention graph pooling method, significantly improving the model's stability and computational efficiency. By applying the proposed method to 10 different planetary gearbox fault tasks, we verify that it offers significant advantages in fault diagnosis accuracy and load adaptability under various working conditions. [ABSTRACT FROM AUTHOR]

Rotating machinery is critical functional part of industrial mechanical equipment, and health status of rotating machinery is closely related to equipment stability, reliability and safety. Vibration signals for health prediction are often collected under operating conditions with variable loads and speeds, which makes health prediction more challenging. STFT-based time-frequency representation methods are widely used for the health prediction of rotating machinery. However, these methods lack specific learning mechanisms to effectively distinguish the time-frequency representations at different time points and frequency bands and highlight important feature information. To vanquish the weakness, this paper develops a novel dynamic adaptive time-frequency attention residual network (TFARNet) for rotating machinery intelligent health prediction. Firstly, a new adaptive STFT time-frequency attention (TFA) unit is developed to recalibrate time-frequency features, thereby enhancing important information and suppressing redundant information. Secondly, the TFA unit is inserted into the residual network, by stacking multiple residual blocks and TFA units to establish TFARNet and efficiently learn more discriminative features. Thirdly, label smoothing regularization and dynamic learning rate are employed to accelerate model convergence and optimize the model training process. Finally, three cases are carried out to evaluate the developed method. Compared with the other seven health prediction methods, the developed method can achieve higher prediction accuracy. [ABSTRACT FROM AUTHOR]

Rolling bearing is the key component of rotating machinery, and its vibration signal usually exhibits nonlinear and nonstationary characteristics when failure occurs. Multiscale permutation entropy (MPE) is an effective nonlinear dynamics analysis tool, which has been successfully applied to rolling bearing fault diagnosis in recent years. However, MPE ignores the deep amplitude information when measuring the complexity of the time series and the original multiscale coarse-graining is insufficient, which requires further research and improvement. In order to protect the integrity of information structure, a novel nonlinear dynamic analysis method termed refined composite multiscale slope entropy (RCMSlE) is proposed in this paper, which introduced the concept of refined composite to further boost the performance of MPE in nonlinear dynamical complexity analysis. Furthermore, RCMSlE utilizes a novel symbolic representation that takes full account of mode and amplitude information, which overcomes the weaknesses in describing the complexity and regularity of bearing signals. Based on this, a GWO-SVM multi-classifier is introduced to fulfill mode recognition, and then a new intelligent fault diagnosis method for rolling bearing based on RCMSlE and GWO-SVM is proposed. The experimental results show that the proposed method can not only accurately identify different fault types and degrees of rolling bearing, but also has a short computation time and better performance than other comparative methods. • Refined composite multiscale slope entropy (RCMSlE) is proposed for complexity measure of time series. • The feature extraction performance of RCMSlE outperforms the compared algorithms is confirmed by simulation experiments. • A novel fault diagnosis method for rolling bearing based on RCMSlE and GWO-SVM is proposed. • The effectiveness and superiority of the proposed method is verified by bearing test data analysis. [ABSTRACT FROM AUTHOR]

The operation of rolling bearings is directly related to the reliability of the whole rotating machinery. If the rolling bearing failure cannot be diagnosed and repaired in time, it will probably lead to equipment shutdown. The feature extraction process is an important part of bearing fault diagnosis. Some existing feature extraction methods are sensitive to noise and interference, and cannot fully explore and characterize useful information in nonlinear and non-stationary signals in complex scenes, and sometimes even lose important information contained in the data, resulting in low diagnostic accuracy. Therefore, a new method combining Maximum Second-order Cyclostationarity Blind Deconvolution (CYCBD) and Complementary Ensemble Empirical Mode Decomposition (CEEMD) was proposed for feature extraction of rolling bearing faults. Firstly, the cyclic frequency is set according to the failure frequency, and the original signal is filtered by CYCBD. Then, the filtered signal is decomposed into multiple Intrinsic Mode Function (IMF) by CEEMD, and the effective IMF components are selected by kurtosis criterion for reconstruction. Finally, the Teager energy operator is used to enhance the transient impact of the reconstructed signals. The results of simulation and comparison experiments show that the proposed method can extract bearing characteristics in different fault states more effectively, and the accuracy of network model diagnosis is improved compared with the traditional methods. [ABSTRACT FROM AUTHOR]

Wind resistance optimization is crucial for enhancing the rotational speed of supergravity centrifuges. We conducted a study using computational fluid dynamics on the Centrifugal Hypergravity and Interdisciplinary Experiment Facility (CHIEF) under construction at Zhejiang University and validated it experimentally using a ZJU400gt centrifuge. Our findings indicate significant reductions in wind resistance through structural modifications of the CHIEF. Reducing the outer radius from 4650 to 4150 mm decreased wind resistance by 16%, primarily due to reduced effective viscosity in the wake region's gases. More substantial reductions were achieved by lowering the height of the outer wall from 2200 to 1400 mm, which cut wind resistance by 25%. This height reduction suppressed vortex shedding and Kármán vortex street development via the Venturi effect. Adjustments to the roughness height of wall surfaces further decreased wind resistance, with minimal impact from arm roughness. A critical roughness height was identified, below which no further reductions in wind resistance could be attained. Notably, using disc-shaped arms reduced wind resistance by approximately 73% because of their minimal pressure–resistance components and predominant frictional resistance, highlighting their potential in future high-speed centrifuge designs. [ABSTRACT FROM AUTHOR]

Abstract In the field of intelligent fault diagnosis, particularly concerning rotating machinery, convolutional neural networks (CNNs) face significant challenges when applied to real industrial vibration data. These data are not only contaminated by various types of noise but also exhibit fault features that vary across different scales. Consequently, the effective suppression of extraneous noise and accurate extraction of multi-scale fault features are crucial issues. To address these challenges, this study proposes a novel deep neural network framework, termed the Multidimensional Fusion Residual Attention Network (MFRANet), for gearbox fault diagnosis. The MFRANet employs a multi-scale deep separable convolution module to thoroughly investigate the fundamental characteristics of the original vibration signals in both the time and time-frequency domains. To enhance the detailed analysis of diagnostic data and mitigate the risks of overfitting and noise interference, an efficient residual channel attention module is incorporated to weight and denoise the feature maps. Additionally, an external attention module is introduced to create implicit connections between the denoised multi-scale feature maps and to highlight potential correlations within the sample data, thereby improving the accuracy of fault diagnosis. Experimental evaluations on a gearbox fault dataset demonstrate that the proposed method surpasses several benchmark and state-of-the-art techniques in terms of diagnostic performance, exhibiting robust noise resilience across various noise levels. This indicates enhanced reliability and accuracy in gearbox fault diagnosis, providing an innovative and efficient solution for fault diagnosis in rotating machinery. The study underscores the contributions of artificial intelligence through the innovative structure of the method and the integration of advanced deep learning modules, while its engineering application is evidenced by addressing practical challenges in rotating machinery fault diagnosis. This work meets the urgent need for reliable diagnostic methods in industrial environments.

As a typical active equipment, pump machinery is widely used in nuclear power plants. Although the mechanism of pump machinery in nuclear power plants is similar to that of conventional pumps, the safety and reliability requirements of nuclear pumps are higher in complex operating environments. Once there is significant performance degradation or failure, it may cause huge security risks and economic losses. There are many pumps mechanical parameters, and it is very important to explore the correlation between multi-dimensional variables and condition. Therefore, a condition monitoring model based on Deep Denoising Autoencoder (DDAE) is constructed in this paper. This model not only ensures low false positive rate, but also realizes early abnormal monitoring and location. In order to alleviate the influence of parameter time-varying effect on the model in long-term monitoring, this paper combined equidistant sampling strategy and DDAE model to enhance the monitoring efficiency. By using the simulation data of reactor coolant pump and the actual centrifugal pump data, the monitoring and positioning capabilities of the proposed scheme under normal and abnormal conditions were verified. This paper has important reference significance for improving the intelligent operation and maintenance efficiency of nuclear power plants.

This study investigates a novel approach for assessing the health status of rotating machinery transmission systems by analyzing the dynamic degradation of bearings. The proposed method generates multi-dimensional data by creating virtual states and constructs a multi-dimensional model using virtual state-space in conjunction with mechanism model analysis. Innovatively, the Hammerstein–Wiener (HW) modeling technique from control theory is applied to identify these dynamic multi-dimensional models. The modeling experiments are performed, focusing on the model's input and output types, the selection of nonlinear module estimators, the configuration of linear module transfer functions, and condition transfer. Dynamic degradation response signals are generated, and the method is validated using four widely recognized databases consisting of accurate measurement signals collected by vibration sensors. Experimental results demonstrated that the model achieved a modeling accuracy of 99% for multiple bearings under various conditions. The effectiveness of this dynamic modeling method is further confirmed through comparative experimental data and signal images. This approach offers a novel reference for evaluating the health status of transmission systems. [ABSTRACT FROM AUTHOR]

This article presents a comprehensive collection of formulas and calculations for hand-crafted feature extraction of condition monitoring signals. The documented features include 123 for the time domain and 46 for the frequency domain. Furthermore, a machine learning-based methodology is presented to evaluate the performance of features in fault classification tasks using seven data sets of different rotating machines. The evaluation methodology involves using seven ranking methods to select the best ten hand-crafted features per method for each database, to be subsequently evaluated by three types of classifiers. This process is applied exhaustively by evaluation groups, combining our databases with an external benchmark. A summary table of the performance results of the classifiers is also presented, including the percentage of classification and the number of features required to achieve that value. Through graphic resources, it has been possible to show the prevalence of certain features over others, how they are associated with the database, and the order of importance assigned by the ranking methods. In the same way, finding which features have the highest appearance percentages for each database in all experiments has been possible. The results suggest that hand-crafted feature extraction is an effective technique with low computational cost and high interpretability for fault identification and diagnosis. [ABSTRACT FROM AUTHOR]

The fault diagnosis of rotating machinery is vital in industry but traditionally depends on manual expertise, requiring substantial resources. To improve diagnostic accuracy, enable effective condition monitoring, and minimize the impact of faults on operations, advanced diagnostic techniques are essential. Hence, we propose an advanced fault diagnosis framework that leverages improved particle swarm optimization (IPSO), variational mode decomposition (VMD), and probabilistic neural networks (PNN) to accurately diagnose faults in rotating machinery using gear and rolling bearing vibration signals. Initially, the vibration signals are decomposed into intrinsic mode functions via VMD, enabling the capture of subtle but critical fault features. To address parameter selection challenges in VMD, we employed IPSO to optimize the VMD parameters, ensuring the optimal decomposition effect. Further, we refined the feature set by applying Laplace fraction optimization and feature dimensionality reduction, isolating sensitive features that serve as input to a PNN-based fault classification model. Experimental results demonstrated that this IPSO-VMD-PNN framework achieves high diagnostic accuracy for various fault types, establishing it as an effective tool for fault identification in rotating machinery. [ABSTRACT FROM AUTHOR]

In rotating machinery, aerodynamic forces and oil film forces often lead to cross-coupling stiffness. This paper is aimed at studying the stick-slip whirling vibrations induced by the piecewise smooth rotor/stator frictions in a modified flexible rotor subjected to cross-coupling stiffness. Governing equations determining the sliding region and boundaries of piecewise discontinuous friction are defined. This analytical study was conducted to discuss the complex vibrations and bifurcations. Various types of sliding motions (continuous pure rolling, continuous crossing, and grazing–sliding) were observed in this research. Further, as for discussing the impacts of the parameters on nonlinear sliding vibrations, a parametric study was conducted. The obtained results reveal that with an increase in the cross-coupling stiffness coefficient, continuous pure rolling occurs earlier, and the disk vibration time around the contact regime becomes shorter. For studying the self-excited backward whirling vibration of stick–slip nonlinear motions, analytical formulations are established. Detailed vibration amplitude and frequency studies of friction-induced backward whirling vibrations were carried out. Numerical simulations were performed to compare them with the analytical solutions and to validate the results as well. The proposed theory and results provide fresh perspectives for predicting friction-induced whirlings and creating proper designs for turbo machinery. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the traditional fault diagnosis methods only focus on the fault detection part and there are few studies on whether there are faults in the sample. A comprehensive fault diagnosis model for rotating machinery based on complementary ensemble empirical mode decomposition with adaptive noise ( CEEMDAN)-attention entropy ( AE) and slime mould algorithm optimized extreme learning machine (SMA-ELM) was proposed. Firstly, according to the complexity difference between normal samples and fault samples, the attention entropy threshold was established. The AE value of the rotating machine was calculated and compared with the threshold value. If the entropy value was less than the threshold value, it was indicated that the sample had a fault, otherwise, the sample was healthy. Then, CEEMDAN was used to decompose the vibration signal of the fault sample, and extract the AE of the first 6 order components. Finally, the fault features were input into the SMA-ELM model for fault identification. The reliability of the comprehensive fault diagnosis model was studied using three rotating machinery fault datasets. The research results show that the threshold setting method can detect whether there is a fault in the sample with 100% accuracy, and the subsequent fault diagnosis model can accurately detect the fault type of the sample with the recognition accuracy of 99. 44%, 100% and 98%, respectively. This comprehensive fault diagnosis model can prevent normal samples from being misjudged as faulty samples, providing a feasible approach for fault detection of rotating machinery. [ABSTRACT FROM AUTHOR]

Ball bearings play a pivotal role in rotating machinery, making it crucial to detect their wear condition in real time for preventive maintenance. This paper proposes a novel method, involving one-dimensional convolutional neural network (1DCNN) and lubricating oil, which is sensitive to early abnormal wear, to intelligently predict the wear condition of ball bearings. To achieve that, an industrial-level gas turbine is used in the test to simulate the practical working conditions. During the machine's operation, the lubricating oil is sampled at different time intervals for spectral oil analysis (SOA), which is later utilized as the input data set. The 1DCNN model is then constructed and fine-tuned to extract its wear characters, and finally outputs a one-dimensional time-sequence feature vector of the SOA data, providing wear information for the upcoming hours. The predicted results have shown that the 1DCNN model can effectively predict the trend of wear condition with higher accuracy of over 97% and better generalization performance compared with the BP neural networks. Additionally, the validation test results have also verified the reliability of the 1DCNN model, which can be useful for manufacturing industries in reducing costly maintenance fees by early detection of potential failure. [ABSTRACT FROM AUTHOR]

Rolling bearing fault diagnosis based on convolutional neural network is greatly effective for bearing maintenance, and it is of great significance for ensuring the safe operation of rotating machinery. However, the traditional convolutional neural network models only focus on the single-scale feature and ignore the multi-scale deep information, which results in low performance for conducting the complex fault diagnosis problem. Aiming at this problem, we propose an improved fault diagnosis method of rolling bearings based on multi-scale attention convolutional neural network. In the first stage, a one-dimensional convolutional neural network model integrating feature extraction and fault intelligence classification is established. Subsequently, a multi-scale structure with serial layer skipping connection is constructed to extract multi-scale features in different reception fields, and the "Concat" operator is used to fuse the extracted features of each layer with SE attention mechanism in order to obtain more important feature information. And then, the fault samples are input into the network model to realize the end-to-end fault diagnosis of rolling bearings result from the nonlinear fitting ability of deep learning. Extensive experiments on two well-known rolling bearing datasets validate that the proposed method not only achieves higher fault diagnosis accuracy on the fault data sets under constant load conditions, but also exhibits strong fault identification and transfer diagnosis ability on the fault data sets under variable load conditions. [ABSTRACT FROM AUTHOR]

This review focuses on the definitions, modalities, applications, and performance of various aspects of digital twins (DTs) in the context of transmission and industrial machinery. In this regard, the context around Industry 4.0 and even aspirations for Industry 5.0 are discussed. The many definitions and interpretations of DTs in this domain are first summarized. Subsequently, their adoption and performance levels for rotating and industrial machineries for manufacturing and lifetime performance are observed, along with the type of validations that are available. A significant focus on integrating fundamental operations of the system and scenarios over the lifetime, with sensors and advanced machine or deep learning, along with other statistical or data-driven methods are highlighted. This review summarizes how individual aspects around DTs are extremely helpful for lifetime design, manufacturing, or decision making even when a DT can remain incomplete or limited. [ABSTRACT FROM AUTHOR]

• A dynamic model of bolted joint rotor system considering rubbing and pedestal looseness fault is established. • The effect of the rubbing fault on dynamic properties of the bolted joint was evaluated. • The effect of the rubbing-pedestal-looseness coupled fault on dynamic properties of the bolted joint was studied. • Experimental studies is conducted on a bolted joint rotor-bearing test rig with pedestal and rubbing device. Rotor-stator rub and pedestal looseness are common faults in rotating machinery, such as large gas turbines that typically use bolted joint structures to fasten adjacent disks together. It is of great significance to investigate the vibration behaviors of the bolted joint rotor-bearing system with pedestal looseness and rubbing coupled fault, which can provide proofs for the fault diagnosis. In the present work, a dynamic model of the bolted joint rotor-bearing system with coupled fault is developed by using the finite element (FE) method, and then the vibration characteristic analysis is carried out. The rotor shafts that connected by the bolted joint element (BJE) are discretized as two sets of Timoshenko beam elements. The BJE is composed with four springs (two linear translational springs and two rotational springs with piecewise-linear stiffness). The interactions of rotor-stator contact are then modeled based on the Hertz contact theory and Coulomb's friction law, and the bearing forces are also calculated based on the Hertz contact theory. Moreover, the pedestal looseness fault is modeled through the piecewise-linear stiffness spring. The equations of motions of the bolted joint rotor-bearing system with coupled fault are then developed through FE theory and solved by the Newmark method. The dynamic responses are then calculated, and the frequency spectrums, bending stiffness of bolted joint, and vibration responses under different rotation speeds under the coupled fault are analyzed. The experimental study is finally conducted based on a bolted joint rotor test rig. Simulation results are in agreement with experiment results. The research results can provide a basis for understanding the rotor dynamic features of a bolted joint rotor system with coupled faults of rubbing and pedestal looseness. [ABSTRACT FROM AUTHOR]

Time series is a popular data type that is collected from various machines for fault diagnosis. Although most time-series models for fault diagnosis reflect local relations well, they cannot extract the global patterns that contain valuable information that can be used to recognize faults. To reflect the global structural information of a time series, many recent studies have used a graph constructed by visibility algorithms (VAs) that convert a time series into a graph. However, applying the VAs to high-frequency time series—which the machines typically generate—is challenging because the computational burden of the VAs increases with the length of a time series. Therefore, we propose a novel graph-based fault diagnosis framework for high-frequency time series. First, we propose an efficient VA (EVA) that extracts essential data points to characterize a time series and constructs a graph from a high-frequency time series. Not only do the EVAs convert a given time series faster into a graph than the VAs, but the resulting graphs also characterize the time-series structure with simplicity and clarity by selecting essential data points. Then, we adopt a graph convolutional network to analyze the resulting graphs and diagnose faults. We verified the characteristics of the EVAs and the fault diagnosis performance of the proposed framework using toy time series and public rotating machinery datasets, respectively. The results demonstrated that, compared to the VAs, the EVAs are efficient in terms of computational cost, and the proposed framework is effective for fault diagnosis. [ABSTRACT FROM AUTHOR]

In light of the complexity of failure mechanisms in rotating machinery and the significant differences in feature information, traditional diagnostic models often rely on prior knowledge, leading to low accuracy and poor adaptability. We propose a rotating machinery fault diagnosis method named "Randomized Quantization-Randomized LSTM Block Mapping Method-Stochastic Configuration Network" (RQ-RLBM-SCN). To address the challenges posed by limited feature information in failing machinery and insufficient training samples, we employ random quantization data augmentation to expand the original data samples from multiple sensors. This enhances the model's adaptability, accuracy and mitigating overfitting. Next, we employ a random LSTM block mapping method to extract features, solving the difficulty of extracting temporal data features in stochastic configuration network (SCN). Sub-sequently, we utilize a stochastic configuration network (SCN) for classification. SCN dynamically configures parameters without the need for backpropagation to update parameters, ensuring a proper learning rate while effectively avoiding issues such as gradient explosion or vanishing. Research results indicate that the RQ-RLBM-SCN method can accurately identify bearing and gear faults. In 10 repeated experiments, the average accuracy on the multi-sensor dataset for bearings and gears reaches 99.80% and 98.75% respectively which are higher than the original SCN, TSC-SCN, VMD-SCN, SVM and KNN fault diagnosis methods. This approach provides a dynamic method and diagnostic insight for establishing a health monitoring model for rotating machinery. [ABSTRACT FROM AUTHOR]

To tackle the issue of the traditional intelligent diagnostic algorithm's insufficient utilization of correlation characteristics within the time series of fault signals and to meet the challenges of accuracy and computational complexity in rotating machinery fault diagnosis, a novel approach based on a recurrence binary plot (RBP) and a lightweight, deep, separable, dilated convolutional neural network (DSD-CNN) is proposed. Firstly, a recursive encoding method is used to convert the fault vibration signals of rotating machinery into two-dimensional texture images, extracting feature information from the internal structure of the fault signals as the input for the model. Subsequently, leveraging the excellent feature extraction capabilities of a lightweight convolutional neural network embedded with attention modules, the fault diagnosis of rotating machinery is carried out. The experimental results using different datasets demonstrate that the proposed model achieves excellent diagnostic accuracy and computational efficiency. Additionally, compared with other representative fault diagnosis methods, this model shows better anti-noise performance under different noise test data, and it provides a reliable and efficient reference solution for rotating machinery fault-classification tasks. [ABSTRACT FROM AUTHOR]

For a variety of production and development processes, there is a need for precise and reliable measurements of rotating machinery or objects. In this article, an alternative approach is introduced to assess both rotational and translational movements in a rotor by employing the technique known as "visual encoder." This methodology combines principles of computer vision with visual components, operating analogously to conventional optical encoders. Non-contact measurements provided by visual encoders offer several advantages, including the ability to withstand free movements and oscillations along the rotation axis, as well as application in hostile environments, such as high-temperature conditions that might pose challenges for conventional measurement methods. The proposed method incorporates translational motion tracking into rotational velocity measurement using cost-effective conventional equipment. Experimental tests demonstrate that the developed system exhibited high precision and robustness under various operating conditions, successfully operating even at rotational frequencies close to the sampling rate. The results validate the developed technique as a viable alternative for measuring rotation and translational movement in rotor applications. [ABSTRACT FROM AUTHOR]

The periodical impulses caused by localized defects of components are the vital characteristic information for fault detection and diagnosis of rotating machines. In recent years, multitudinous spectrum analysis-based signal processing methods have been developed and authenticated as the powerful tools for excavating fault-related repetitive transients from the measured complex signals. Nonetheless, in practice, their applications can be severely confined by the constraints of limited system signal availability and incomplete information extraction under intricate noise interferences. To tackle the aforementioned issues, this paper proposes a periodic-modulation-oriented noise resistant correlation (PMONRC) method for target period detection and fault diagnosis of rotating machinery. Firstly, the envelope of raw signal is obtained via a novel sequential procedure of signal element-wise squaring, spectral Gini index-guided adaptive low-pass filtering, and signal element-wise square root computation, to highlight the modulated wave component that is more likely to be related to the potential fault-induced periods. Subsequently, a series of sub-signals, which can encode the fault-related repetitive information and enhance noise resistance, are constructed utilizing the envelope signal. Based upon the envelope signal and the obtained sub-signals, a weighted envelope noise resistant correlation function can be derived with the assistance of the L-moment ratio-based indicator and Sigmoid transformation. Finally, the specific fault type of the rotating machinery can be identified and affirmed accordingly. The proposed PMONRC method, which is nonparametric and completely adaptive to the signal being processed itself, overcomes the deficiencies of spectral analysis-based approaches, and is applicable for the engineering circumstances of system signal limitation and low signal-to-noise ratio (SNR), possessing immense practical merit. Both simulation analyses and experimental validations profoundly demonstrate that the proposed method is superior to other existing state-of-the-art time-domain correlation methods. Moreover, as an attempt as well as exemplar to apply this method, the PMONRC-based incipient fault diagnostic results of rolling bearing data from the well-known experimental platform PRONOSTIA are presented and discussed as well, to further elucidate the effectiveness and practical engineering significance of the proposed method. • A weighted envelope noise-resistant correlation approach coined PMONRC is developed. • An adaptive enveloping-demodulation method is proposed for AM-FM signature enhancing. • Fault information of sub-signals are appraised for more rational weights allocation. • The PMONRC is effective for fault-induced hidden periods detection and extraction. • The PMONRC is applicative for the scenarios of limited system signal availability. [ABSTRACT FROM AUTHOR]

In rotating machinery with a multi-stage assembled rotor, such as is found in aero engines, any unbalance present will undergo unknown changes at each stage when rotating the assembly phases of the rotor. Repeated disassembly and adjustments are often required to meet the rotor's residual unbalance specifications. Therefore, developing a prediction model of this two-sided unbalance for a multi-stage assembled rotor is crucial for improving the first-time assembly pass rate and assembly efficiency. In this paper, we propose a prediction model of the two-sided unbalance seen in the multi-stage assembled rotor of an aero engine. Firstly, a method was proposed to unify the mass feature parameters of each stage's rotor into a geometric measurement coordinate system, achieving the synchronous transmission of geometric and mass feature parameters during the assembly process of the multi-stage rotor. Building upon this, a linear parameter equation of the actual rotation axis of the multi-stage rotor was established. Based on this axis, the mass eccentricity errors of the rotor were calculated at each stage, further enabling the accurate prediction of two-sided unbalance and its action phase in a multi-stage rotor. The experimental results indicate that the maximum prediction errors of the two-sided unbalance and its action phase for a four-stage rotor are 9.6% and 2.5%, respectively, when using this model, which is a reduction of 53.0% and 38.1% compared to the existing model. [ABSTRACT FROM AUTHOR]

Conventional convolutional neural networks (CNNs) predominantly emphasize spatial features of signals and often fall short in prioritizing sequential features. As the number of layers increases, they are prone to issues such as vanishing or exploding gradients, leading to training instability and subsequent erratic fluctuations in loss values and recognition rates. To address this issue, a novel hybrid model, termed one-dimensional (1D) residual network with attention mechanism and bidirectional gated recurrent unit (BGRU) is developed for rotating machinery fault classification. First, a novel 1D residual network with optimized structure is constructed to obtain spatial features and mitigate the gradient vanishing or exploding. Second, the attention mechanism (AM) is designed to catch important impact characteristics for fault samples. Next, temporal features are mined through the BGRU. Finally, feature information is summarized through global average pooling, and the fully connected layer is utilized to output the final classification result for rotating machinery fault diagnosis. The developed technique which is tested on one set of planetary gear data and three different sets of bearing data, has achieved classification accuracy of 98.5%, 100%, 100%, and 100%, respectively. Compared with other methods, including CNN, CNN-BGRU, CNN-AM, and CNN with an AM-BGRU, the proposed technique has the highest recognition rate and stable diagnostic performance. [ABSTRACT FROM AUTHOR]

The application of active magnetic bearings (AMBs) in high-speed rotating machinery faces the challenge of micro-vibration. This research addresses the vibration control of a high-speed magnetically suspended turbo molecular pump (MSTMP) with rotor mass imbalance vibration and multi-stage-blade modal vibration. A novel integrated AMB controller consisting of parallel co-frequency adaptive notch filter (ANF) and cascaded multi-frequency improved double-T notch filters (DTNFs) is proposed. To suppress rotor mass imbalance vibration, a bandwidth factor rectification method of the ANF based on displacement stiffness perturbation is designed. To suppress multi-stage-blade modal vibration, a multi-objective constrained optimization method of cascaded improved DTNFs based on linear normalization is designed. Simulation and experimental results validate that the proposed structure improvement of the addition of an AMB controller and multi-parameter optimization of the algorithm can effectively improve not only the phase stability margin and the notch vibration performance of the magnetically suspended rotor (MSR) system but also the efficiency and practicability of the algorithm. At rotational speeds of 12,000 rpm , 15,000 rpm , 18,000 rpm , and 21,000 rpm , the suppression of co-frequency synchronous vibration is approximately maintained between −30.94 dB and −30.56 dB. At the rated speed of 24,000 rpm , compared with other algorithms, the value of the rotor displacement converges from 0.08 mm to 0.03 mm , a reduction of 62.50%. The convergence time decreases from 3.67 s to 2.85 s , a reduction of 22.34%. [ABSTRACT FROM AUTHOR]

The potential for rotor component shedding in rotating machinery poses significant risks, necessitating the development of an early and precise fault diagnosis technique to prevent catastrophic failures and reduce maintenance costs. This study introduces a data-driven approach to detect rotor component shedding at its inception, thereby enhancing operational safety and minimizing downtime. Utilizing frequency analysis, this research identifies harmonic amplitudes within rotor vibration data as key indicators of impending faults. The methodology employs principal component analysis (PCA) to orthogonalize and reduce the dimensionality of vibration data from rotor sensors, followed by k-fold cross-validation to select a subset of significant features, ensuring the detection algorithm's robustness and generalizability. These features are then integrated into a linear discriminant analysis (LDA) model, which serves as the diagnostic engine to predict the probability of rotor component shedding. The efficacy of the approach is demonstrated through its application to 16 industrial compressors and turbines, proving its value in providing timely fault warnings and enhancing operational reliability. [ABSTRACT FROM AUTHOR]

Rolling element bearings are widely used in aero engines, high-speed trains, precision machine tools, and other technical fields due to their high rotational accuracy and strong carrying capacity. With the development of rotating machinery in the direction of high speed, the working conditions of rolling element bearings are becoming more and more harsh, resulting in thermal problems that will seriously affect the mechanical properties of the bearing and the stability of the rotor system. In this paper, the four-degrees-of-freedom quasi-static model of cylindrical roller bearings was established based on the elastohydrodynamic lubrication theory combined with slicing technology. The local thermal approach and the thermal resistance network method were adopted to calculate the frictional heat generation and the transient temperature field of bearings. So the thermal-mechanical coupling model was obtained considering the correction of the thermal effect. On this basis, the influences of temperature on the vibration of the bearing-rotor system were researched. Finally, the temperature and stiffness of the bearings under various operating conditions were discussed. The coupling theory of thermal characteristics and mechanical properties of such bearing-rotor systems was improved. Practical Applications: Cylindrical roller bearings generally play a supporting role in rotating machinery and are widely used on high-speed occasions. For example, aero engines work under high-speed conditions, and high speeds in mechanical systems often bring serious thermal problems. As one of the core components, the spindle bearing must be able to overcome such a complex working environment. In this paper, cylindrical roller bearings and bearing-rotor systems are mathematically modeled for this feature in order to study their thermal and dynamic characteristics. The conclusions of this paper can provide relevant references for the design theory of this type of bearings applied in the fields of aero engines, high-speed precision machine tools, high-speed trains, etc., as well as theoretical guidance for the evaluation of their actual service performance. [ABSTRACT FROM AUTHOR]

Fault diagnosis of rolling bearings is among the most crucial links in the prognostic and health management of bearings. To solve the problem that cross-domain fault diagnosis cannot be performed due to the distribution differences between different working conditions, a transfer diagnosis method based on multi-representation adversarial neural network is proposed. First, the multi-representation neural network is applied to extract multiscale features. Second, the domain adversarial network is utilized to set the gradient inversion layer and extract the domain invariant features in the multiscale features. In terms of the loss function, the Wasserstein function and cross-entropy loss function are utilized to measure the distance between the source domain and the target domain. The experimental case of rolling bearing supports the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Enhancing the aerodynamic performance of airfoils is the key to optimizing the energy harvesting efficiency of rotating machinery such as wind turbines. Motivated by the bowl-shaped outline of the dolphin's fluke during the propulsion process, this paper proposes a local indentation method that generates a concave region on the pressure surface of the airfoil. The NACA 0018 airfoil is selected as the reference airfoil, and two types of treatments are applied near the trailing edge point: rigid deformation and flexible deformation. Based on the grid quantity independence and experimental results validation, the results demonstrate that compared with the original airfoil, the local indentation method can modify the pressure distribution of the indentation section itself and optimize the airfoil's overall aerodynamic performance. The lift coefficient of the whole airfoil increases gradually with the rise in the indentation depth and reaches a stable value eventually. Quantitative results reveal that when the indentation depth D = 0.020c, the lift coefficient of the whole airfoil can increase by up to 26.27%; when the indentation depth D = 0.010c, the airfoil's lift-to-drag ratio reaches the maximum, which is 16.39% higher than that of the original airfoil. When replacing the rigid indentation section with a flexible medium, the fluid flowing over the pressure surface interacts with the flexible medium. The method of local indentation proposed in this paper can provide valuable reference for optimizing the aerodynamic profile of airfoils and improving the energy harvesting efficiency of wind turbines. [ABSTRACT FROM AUTHOR]

The reliable operation of steam turbines is paramount for the sustained performance of electric power generation facilities. Any malfunction or breakdown in these high-speed rotating machines can lead to significant operational disruptions, causing energy production losses and jeopardizing safety. Lubricating oil leaks represent a common challenge faced by power plants, impacting operational efficiency, safety, and environmental sustainability. Despite extensive research efforts to mitigate oil leakage, challenges persist due to the unique design characteristics of power stations. This paper addresses the management of a major lube oil leak in Unit No. 2 of the Sumsel-5 Power Plant in Indonesia, focusing on innovative strategies for mitigation. The aim of this research is to assess the effectiveness of various rectification measures in reducing lubricating oil leaks in steam turbines. Methods employed include detailed inspections, analysis of oil quality, and implementation of targeted rectification strategies. The results demonstrate a significant reduction in the oil leakage rate, from approximately 67-68 liters per day to around 1.5-2 liters per day, following the implementation of rectification measures. This remarkable decrease of around 97.3% underscores the effectiveness of the strategies in mitigating the oil leakage problem. The implications of this study extend beyond operational efficiency to encompass safety and environmental protection. By reducing the volume of oil lost to leakage, safety hazards associated with fire accidents are mitigated, enhancing safety for plant personnel and equipment integrity. [ABSTRACT FROM AUTHOR]

Unsupervised domain adaptation has been extensively researched in rotating-machinery cross-domain fault diagnosis. A multi-source domain adaptive network based on local kernelized higher-order moment matching is constructed in this research for rotating-machinery fault diagnosis. Firstly, a multi-branch network is designed to map each source-target pair to a domain-specific shared space and to extract domain-invariant features using domain adversarial thought. Then, a local kernelized higher-order moment matching algorithm is proposed to perform fine-grained matching in shared category subspace. Finally, a feature fusion strategy based on the local domain distribution deviation is applied to synthesize the output features of multiple classifiers to obtain diagnostic results. The experimental validation of two-branch and three-branch networks on two public datasets is carried out and average diagnostic accuracies both exceed 99%. The results demonstrate the effectiveness and superiority of the approach. • A multi-source domain adaptive network based on the adversarial theory is built. • A subdomain alignment method based on local kernelized higher-order moment matching. • A feature fusion strategy based on multi-branch local domain deviation metric. [ABSTRACT FROM AUTHOR]