Search

Background: Deep learning-based fault diagnosis techniques are promising approaches that can eliminate the need for advanced skills in signal processing and diagnostic expertise. Purpose: The sparse filtering method is an unsupervised learning method whose parameters play a vital role in obtaining more accurate and reliable results. Thus, their appropriate selection is necessary to obtain more accurate diagnosis of faults in rotating machinery by adaptively collecting the features of the vibration signal. Method: In this paper, a novel unsupervised learning method called general normalized sparse filtering (GNSF) based on Wasserstein distance with maximum mean discrepancy (MMD) has been proposed for fault diagnosis. Initially, the objective function of generalized l r - p / q norm is optimized to compute the feature sparsity that enhances the regularization performance of the sparse filtering. Whereas, clustering of features is done by Wasserstein distance with MMD highlights the contribution of different features in fault clustering. Conclusion: Using GNSF based on Wasserstein distance with MMD, a new intelligent fault diagnosis method is planned and applied to the centrifugal pump and Pelton wheel vibration data under harsh/defect-induced operating conditions. Even with a lesser number of training samples, the proposed approach can successfully identify the various health conditions of the centrifugal pump and Pelton wheel. For instance, at 5% of training samples, the proposed fault identification scheme obtained 99.95% and 99.91% of accuracy. The results confirm the capabilities and effectiveness of the proposed method in terms of efficiency and computation time even for fewer training samples. [ABSTRACT FROM AUTHOR]

Rotary machinery plays an important role in industry. Combined faults can be observed in rotating machinery, making fault classification difficult. In this paper, the Minutiae algorithm is used to classify the faults from the frequency domain of a particular fault. This paper provides a fault classification technique based on image processing for fault analysis of rotating machinery, recognizing function extraction automatically. Minutiae algorithm, a rising method within the discipline of image processing for characteristic extraction, is utilized in this paper to classify specific faults from the converted recurrence plot. The results reveal the effectiveness of the proposed method, providing a rather powerful tool for fault diagnosis of rotating machinery. The proposed model achieved an accuracy of 100% for combined faults, 98.33% for loosened faults, and 95% for unbalanced faults proving its applicability. [ABSTRACT FROM AUTHOR]

Bearing is the critical basic component of rotating machinery and its remaining life prediction is very important for mechanical equipment's smooth and healthy operation. However, fast and accurate bearing life prediction has always been a difficult point in industry and academia. This paper proposes a new strategy for bearing health assessment based on a model-driven dynamic interval prediction model. Firstly, the mapping proportion algorithm is used to determine whether the measured data are in the degradation stage. After finding the starting point of prediction, the improved annealing algorithm is used to determine the shortest data interval that can be used for accurate prediction. Then, based on the bearing degradation curve and the information fusion inverse health index, the health index is obtained from 36 general indexes in the time domain and frequency domain through screening, fusion, and inversion. Finally, the state space equation is constructed based on the Paris-DSSM formula and the particle filter is used to iterate the state space equation parameters with the minimum interval data to construct the life prediction model. The proposed method is verified by XJTU-SY rolling bearing life data. The results show that the prediction accuracy of the proposed strategy for the remaining life of the bearing can reach more than 90%. It is verified that the improved simulated annealing algorithm selects limited interval data, reconstructs health indicators based on bearing degradation curve and information fusion, and updates the Paris-DSSM state space equation through the particle filter algorithm. The bearing life prediction model constructed on this basis is accurate and effective. [ABSTRACT FROM AUTHOR]

Fault diagnosis is an essential process for the health maintenance of rotating machinery. With the development of AI technology, many deep learning-based methods have been applied to fault diagnosis to enhance the intelligence level of equipment maintenance. Such methods normally need a large amount of labeled data for model training. However, label acquisition is a difficult task that requires extensive human labor. To address these issues, a fault diagnosis method based on feature extraction via an unsupervised graph neural network is proposed in this paper. In the proposed method, the K-nearest neighbor approach is adopted to construct a fault graph from the collected signals, thereby providing extra relationship information for fine feature mining. Then, the GraphSAGE model is trained on the constructed graph in an unsupervised way, that is, it does not need labeled data, to extract features of each signal sample. Based on the extracted features, some traditional classifiers are adopted to identify the fault types. The proposed model is evaluated on a rolling bearing dataset provided by the University of Paderborn and a motor rotor dataset collected by a constructed motor rotor system. Compared with some traditional deep learning-based fault diagnosis methods, the proposed model can achieve more accurate diagnoses even when there are only a few labeled samples. [ABSTRACT FROM AUTHOR]

To abstract the fault features from multivariate vibration signals of the rotating machinery under different speeds, an improved multivariate multiscale fuzzy distribution entropy (IMMFDE) is proposed. Based on multivariate empirical mode decomposition, the IMMFDE can determine the maximum scale adaptively, meanwhile eliminate the frequency aliasing and avoid the loss of potentially useful information in the multiscale process. The trait of IMMFDE is verified by calculating the sequences and their amplitude spectrums at each scale of the simulated multivariate signals. Further, the fault diagnosis method is proposed for the rotating machinery under different speeds based on IMMFDE. In the method, the statistical parameters and IMMFDE are calculated as fault feature set; then, support vector machine is used for fault diagnosis. Applying the method to two types of the rotating machinery multi-fault diagnosis under different speeds, the results show the proposed method can obtain better fault diagnosis results. [ABSTRACT FROM AUTHOR]

Due to the lack of fault data in the daily work of rotating machinery components, existing data-driven fault diagnosis procedures cannot accurately diagnose fault classes and are difficult to apply to most components. At the same time, the complex and variable working conditions of components pose a challenge to the feature extraction capability of the models. Therefore, a transferable pipeline is constructed to solve the fault diagnosis of multiple components in the presence of imbalanced data. Firstly, synchrosqueezed wavelet transforms (SWT) are improved to highlight the time-frequency feature of the signal and reduce the time-frequency differences between different signals. Secondly, we proposed a novel hierarchical window transformer model that obeys a dynamic seesaw (HWT-SS), which compensates for imbalanced samples while fully extracting key features of the samples. Finally, a transfer diagnosis between components provides a new approach to solving fault diagnosis with imbalanced data among multiple components. The comparison with the benchmark models in four datasets proves that the proposed model has the advantages of strong feature extraction capability and low influence from imbalanced data. The transfer tests between datasets and the visual interpretation of the model prove that the transfer diagnosis between components can further improve the diagnostic capability of the model for extremely imbalanced data. [ABSTRACT FROM AUTHOR]

Flat foot is associated with increased odds of the lower extremity injury and pour posture balance [1]. Orthotic insole is one of the effective treatments to attribute the abnormally distributed pressure and help improve posture balance. The aim of the study is to design orthotic insoles for children with flatfoot based on their arch morphology to improve posture balance, followed by a preliminary validation on the practice effects. Eighty-nine normal weight children aged 8-12 years were recruited, and classified into the flatfoot group (n = 49, AI > 0.26) and the control group (n = 40, 0.21 < AI < 0.26). Foot morphology and plantar pressure of the participants were collected by an Infoot® 3D scanning system and a Footscan® plate measurement system. Orthotic insoles were designed based on the highest point in the anatomical structure of the foot arch of flatfoot group, combined with their typical plantar pressure. Total 9 insoles with three arch heights (2.0 cm, 2.5 cm, 3.0 cm) and three 3D printing infill densities (25%, 30%, 35%) were printed (Fig. 1b). Three infill densities were used to achieve different softness. The weight of each insole was less than 41 grams. To test the effect of restoring balance, 3 participants with flatfoot were recruited to walk in a natural speed with and without the orthotic insoles. The AI of them were 28.9%, 30.4%, and 32.0%, respectively. The center of pressure (COP) trajectory and gait phase were recorded by Footscan® plate measurement system. In the meantime, to quantify the effect of orthotic insoles, a comprehensive scoring scheme was established including the subjective evaluation and balancing effect. All of the orthotic insoles have improved the COP and gait phase of the participants, regarding the pattern of normal foot (Fig. 1c-d). The COP trajectory shifted laterally and the forefoot contact phase decreased when wearing the orthotic insoles. According to the comprehensive scores, the suitable orthotic insole for the three participants was group 8 insole, group 3 insole, and group 3 insole, respectively. Fig. 1 (a) The design of orthotic insole for children with flatfoot. (b) 3D printing orthotic insoles for children with flatfoot. (c) COP trajectories of all participants. (d) Gait phase of all participants. [Display omitted] The orthotic insoles designed in this study improved the posture balance of participants with flat foot. The optimal parameters of orthotics vary with the dgree of flatness, considering the arch height and hardness. [ABSTRACT FROM AUTHOR]

Bearings are important components of rotating machinery and transmission systems, and are often damaged by wear, overload and shocks. Due to the low resolution of traditional time-frequency analysis for the diagnosis of bearing faults, a synchrosqueezed wavelet transform (SSWT) is proposed to improve the resolution. An improved convolutional neural network fault diagnosis model is proposed in this paper, and a Bayesian optimisation method is applied to automatically adjust the structure and hyperparameters of the model to improve the accuracy of bearing fault diagnosis. Experimental results from the accelerated life testing of bearings show that the proposed method is able to accurately identify various types of bearing fault and the different status of these faults under complex running conditions, while achieving very good generalisation ability. [ABSTRACT FROM AUTHOR]

Machine failure in modern industry leads to lost production and reduced competitiveness. Maintenance costs represent between 15% and 60% of the manufacturing cost of the final product, and in heavy industry, these costs can be as high as 50% of the total production cost. Predictive maintenance is an efficient technique to avoid unexpected maintenance stops during production in industry. Vibration measurement is the main non-invasive method for locating and predicting faults in rotating machine components. This paper reviews the techniques and tools used to collect and analyze vibration data, as well as the methods used to interpret and diagnose faults in rotating machinery. The main steps of this technique are discussed, including data acquisition, data transmission, signal processing, and fault detection. Predictive maintenance through vibration analysis is a key strategy for cost reduction and a mandatory application in modern industry. [ABSTRACT FROM AUTHOR]

The importance of the quality of life of rotating machinery increases the Bearing fault diagnosis. Deep learning models (DL)-based databases become increasingly smart in the field of fault diagnostics, the latest research has widely used CNNs (convolutional neural networks). This paper proposes a new way to diagnose bearing failures with CNN with Bilinear LSTM. Traditional CNNs are however not easy to detect defects due to the fixed geometry of complex fault diagnosis with different working conditions. Our primary and secondary classifiers at specified layers replace primitive shape convolutions with reconfigurable convolutions, resulting in classification results with stringent feature time-frequency incompatibility and a larger receptive field. To acquire more adaptive knowledge and insight into the proposed approach, we employ the CWRU (Case Western Reserve University) opensource dataset to compare classification accuracy. The bearing dataset has been subjected to comprehensive experiments and evaluations in order to confirm the efficacy of the suggested technique's diagnostic performance in a variety of settings. By comparing multiple perspectives on the same dataset with related tasks, the proposed method's superiority is proved. To limit the effect of noise and avoid temporal oscillations, degraded index sequences are matched with a CNN. Current and previous inspection data are fed into a new CNN-BiLSTM model, which is then used to predict the useful time and compatible power values of bearing RULs. When it comes to output, go with the lifetime percentage. The proposed method has been tested by accelerating bearing operation to failure, and the results show that the method has advantages in predicting RUL more accurately. The results of the experiments suggest that the proposed core distance measurement method is a viable new tool for intelligent rolling bearing diagnosis. The BiLSTM technique is more diagnostic than some generic models, according to experimental results using the 48 K and 12 K CWRU datasets, with overall accuracy of 99.80% and 98.3%, respectively. [ABSTRACT FROM AUTHOR]

Identification of multiple mechanical faults from vibration signals has always been one of the most challenging tasks in the field of condition monitoring and fault diagnosis. This study proposes a new fault-oriented vibration signal decomposition method, called adaptive feature mode decomposition (AFMD), to identify multiple localized faults in rotating machines interfered by strong periodic harmonics in a robust and effective manner. The autoregressive model is first introduced as a preprocessing technique for initially reducing deterministic components of the raw signal. Then, for signal decomposition, an adaptive finite impulse response (FIR) filter bank is designed utilizing the blind deconvolution theory. The filter coefficients of the FIR filter bank are iteratively updated to make each filtered sub-signal infinitely approach their deconvolution objective functions based on the correlated kurtosis. Meanwhile, the filter length is adaptively determined using the developed evaluation indicator termed as the weighted squared envelope harmonic-to-noise ratio. Finally, several decomposed modes focusing on fault signatures can be acquired automatically, using the newly proposed mode selection strategy that considers signal similarity across multiple domains. The proposed AFMD method can significantly reduce the likelihood of incorrect diagnoses, as demonstrated by two simulated and two experimental datasets with multiple localized bearing and gear faults. The analysis results show that the proposed method outperforms over the state-of-the-art feature mode decomposition and the most popular variational mode decomposition in multi-fault feature extraction and weak fault detection, when interfered by strong periodic harmonics as well as other background noise. [ABSTRACT FROM AUTHOR]

This paper analyzes vibration data that shows sudden amplitude changes due to non-stationary load conditions. The data were recorded in a wind turbine that operated under gusty winds and showed high peaks during short periods. Data were analyzed with the auto-regressive integrated moving average (ARIMA) algorithm, and the results were compared to the exponential forecasting method. Other methods have been applied for forecasting vibration data, but the simplicity of this method makes it suitable for rotating machinery with high variable loading conditions. The analysis of the method's parameters is included in this paper, and the results showed that the optimum configuration depends on the data variations and the existence of significant trends. Forecasting vibration data is challenging; it depends on the source data quality, the preprocessing algorithms, and the deterioration of the mechanical elements. Predictions become less accurate when the machine operates under sudden changes, and evaluating damaging effects caused by the sudden event is difficult to estimate. [ABSTRACT FROM AUTHOR]

The majority of companies using rotating machinery have implemented decarbonization strategies that systematically analyze every part of their organization to try to adopt more sustainable practices. Lubricants are an essential component in rotary equipment, so it makes sense to find optimal ways to manage these fluids in the most sustainable way, including improving their performance. Life Cycle Analysis (LCA) is the recognized tool for assessing the overall environmental impact of a product from cradle to grave and is useful to compare different liquid management strategies. This paper outlines several ways in which turbine oils can be managed more sustainably by considering the life cycle assessment of different practices. [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.)

To prevent critical failure of the functional machinery of a ship, condition monitoring technologies have been much studied in recent times. In this respect, securing a fault database is a top priority in technology development. In this paper, we developed a test bed that simulates the LNG (liquefied natural gas) re-liquefaction system installed on LNG carriers to obtain data in various types of faults of ship machinery. To maintain rotor-dynamics characteristics, the structure was scaled based on the critical speed margin of the dynamic system. The developed test bed includes a gearbox and multiple shafts. It can simulate mass imbalance, misalignment, bearing fault, gear fault and impeller fault. To verify the validity of the vibration data obtained from the developed test bed, experiments were conducted on three fault modes: main shaft imbalance, pinion shaft imbalance, and gear fault. The time series data and FFT results were analyzed, and time domain features were extracted and statistically validated. Additionally, a simple diagnosis model was developed using the acquired data to evaluate its performance. The test data show distinct data with respect to fault conditions, and we can expect that the diagnosis algorithm can be developed using the test data. The developed test bed can provide not only for the fault data of a single component of the rotating machine but also for the combined fault data of the total system. In addition, we expect that it will solve the problem of securing fault data in the development of condition diagnosis technology if reliability is verified by identifying correlations by comparing data from the real system and data from the scaled test bed.

Rotor unbalance is the most common cause of vibration in industrial machines. The unbalance can result in efficiency losses and decreased lifetime of bearings and other components, leading to system failure and significant safety risk. Many complex analytical techniques and specific classifiers algorithms have been developed to study rotor imbalance. The classifier algorithms, though simple to use, lack the flexibility to be used efficiently for both low and high numbers of classes. Therefore, a robust multiclass prediction algorithm is needed to efficiently classify the rotor imbalance problem during runtime and avoid the problem's escalation to failure. In this work, a new deep learning (DL) algorithm was developed for detecting the unbalance of a rotating shaft for both binary and multiclass identification. The model was developed by utilizing the depth and efficacy of ResNet and the feature extraction property of Convolutional Neural Network (CNN). The new algorithm outperforms both ResNet and CNN. Accelerometer data collected by a vibration sensor were used to train the algorithm. This time series data were preprocessed to extract important vibration signatures such as Fast Fourier Transform (FFT) and Short-Time Fourier Transform (STFT). STFT, being a feature-rich characteristic, performs better on our model. Two types of analyses were carried out: (i) balanced vs. unbalanced case detection (two output classes) and (ii) the level of unbalance detection (five output classes). The developed model gave a testing accuracy of 99.23% for the two-class classification and 95.15% for the multilevel unbalance classification. The results suggest that the proposed deep learning framework is robust for both binary and multiclass classification problems. This study provides a robust framework for detecting shaft unbalance of rotating machinery and can serve as a real-time fault detection mechanism in industrial applications. [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 China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering 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.)

The intelligent maintenance of rotating machinery is inseparable from the real-time condition detection and fault diagnosis system. The traditional blind source fault diagnosis method has low efficiency and high misdiagnosis rate, so this paper introduces the VS-Harmogram method with prior parameters. First, the Fourier spectrum should be re-cut by power spectral density and the information in each frequency band can be sequentially extracted. Secondly, the window width of power spectral density is gradually increased to expand the variability of the sweep frequency. Finally, the harmonic spectral kurtosis of each component should be calculated using the prior parameters of the rotating equipment. A variable spectral segmentation Harmogram (VS-Harmogram) with multiple sets of boundaries would be constructed. The validity of the method can be verified by using the fault signals of rolling bearings in rotating equipment. [ABSTRACT FROM AUTHOR]

The vibration signals from rotating machinery are constantly mixed with other noises during the acquisition process, which has a negative impact on the accuracy of signal feature extraction. For vibration signals from rotating machinery, the conventional linear filtering-based denoising method is ineffective. To address this issue, this paper suggests an enhanced signal denoising method based on maximum overlap discrete wavelet packet transform (MODWPT) and variational mode decomposition (VMD). VMD decomposes the vibration signal of rotating machinery to produce a set of intrinsic mode functions (IMFs). By computing the composite weighted entropy (CWE), the phantom IMF component is then removed. In the end, the sensitive component is obtained by computing the value of the degree of difference (DID) after the high-frequency noise component has been decomposed through MODWPT. The denoised signal reconstructs the signal's intrinsic characteristics as well as the denoised high-frequency IMF component. This technique was used to analyze the simulated and real-world signals of gear faults and it was compared to wavelet threshold denoising (WTD), empirical mode decomposition reconstruction denoising (EMD-RD), and ensemble empirical mode decomposition wavelet threshold denoising (EEMD-WTD). The outcomes demonstrate that this method can accurately extract the signal feature information while filtering out the noise components in the signal. [ABSTRACT FROM AUTHOR]

In order to overcome the low accuracy of traditional Extreme Learning Machine (ELM) network in the performance evaluation of Rotate Vector (RV) reducer, a pattern recognition model of ELM based on Ensemble Empirical Mode Decomposition (EEMD) fusion and Improved artificial Jellyfish Search (IJS) algorithm was proposed for RV reducer fault diagnosis. Firstly, it is theoretically proved that the torque transmission of RV reducer has periodicity during normal operation. The characteristics of data periodicity can be effectively reflected by using the test signal periodicity characteristics of rotating machinery and EEMD. Secondly, the Logistic chaotic mapping of population initialization in JS algorithm is replaced by tent mapping. At the same time, the competition mechanism is introduced to form a new IJS. The simulation results of standard test function show that the new algorithm has the characteristics of faster convergence and higher accuracy. The new algorithm was used to optimize the input layer weight of the ELM, and the pattern recognition model of IJS-ELM was established. The model performance was tested by XJTU-SY bearing experimental data set of Xi'an Jiaotong University. The results show that the new model is superior to JS-ELM and ELM in multi-classification performance. Finally, the new model is applied to the fault diagnosis of RV reducer. The results show that the proposed EEMD-IJS-ELM fault diagnosis model has higher accuracy and stability than other models. [ABSTRACT FROM AUTHOR]

A fault diagnosis method for the rotating machinery based on improved Convolutional Neural Network (CNN) with Gray-Level Transformation (GLT) is proposed to increase the accuracy of the recognition adopting the multiple sensors. The Symmetrized Dot Pattern (SDP) in this method is applied to fuse the data of the multiple sensors, and the multi-color value method is adopted to increase the feature dimension. The grayscale and GLT are used to reduce the dimension of the SDP image. The SDP grayscale image is finally input to the CNN network for training recognition. The research results show that the diagnosis accuracy of the rolling bearing system based on the novel method is up to 98.6 %. Compared with the method without the multi-color value and GLT, the recognition accuracy of the proposed method is improved by 22.3 %, and the training time is reduced by about one third. The research work reveals that the developed method has the potential application value under the multi-sensor working conditions for the fault diagnosis. [ABSTRACT FROM AUTHOR]

Ball bearings are the most critical components of rotating machinery in oil and gas companies. Typical research has focused on bearing failure detection based on bearing failure frequencies derived from the velocity spectrum. However, most bearing failures are caused by improper or insufficient lubrication. The current research utilizes a case study demonstrating when ball bearings must be replaced or relubricated due to poor lubrication conditions. Poor lubrication is the cause of natural frequency excitation in bearings, where rapid bearing damage is typically induced by poor lubrication film. According to experimental data in this study, the bearing failed due to natural frequency excitation. In addition, when analyzing a signal with the velocity spectrum, high frequencies are displayed. Bearing failure is detected without bearing failure frequencies using the natural frequencies of the bearing in the velocity spectrum signal. Moreover, an experimental investigation of the bearing failure of a liquid ring compressor was conducted utilizing a VIBXPERT II vibration analyzer and the Omni trend software. The velocity spectrum is derived based on a fast Fourier transform from a time signal. After lubricating natural frequencies must be disappeared from the velocity spectrum otherwise, the bearing is failed and must be changed. [ABSTRACT FROM AUTHOR]

Random impulsive noise is a special kind of noise, which has strong impact features and random disturbances with large amplitude, short duration, and long intervals. This type of noise often displays nonGaussianity, while common background noise obeys Gaussian distribution. Hence, random impulsive noise greatly differs from common background noise, which renders many commonly used approaches in bearing fault diagnosis inapplicable. In this work, we explore the challenge of bearing fault detection in the presence of random impulsive noise. To deal with this issue, an improved adaptive multipoint optimal minimum entropy deconvolution (IAMOMED) is introduced. In this IAMOMED, an envelope autocorrelation function is used to automatically estimate the cyclic impulse period instead of setting an approximate period range. Moreover, the target vector in the original MOMED is rearranged to enhance its practical applicability. Finally, particle swarm optimization is employed to determine the optimal filter length for selection purposes. According to these improvements, IAMOMED is more suitable for detecting bearing fault features in the case of random impulsive noise when compared to the original MOMED. The contrast experiments demonstrate that the proposed IAMOMED technique is capable of effectively identifying fault characteristics from the vibration signal with strong random impulsive noise and, in addition, it can accurately diagnose the fault types. Thus, the proposed method provides an alternative fault detection tool for rotating machinery in the presence of random impulsive noise. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology 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.)

Rotating machinery is one of the key components of marine equipment. Due to the complex and harsh offshore environment, the health status of rotating machinery is more likely to be affected. Therefore, fault diagnosis is of great significance to normal operation and maintenance of rotating machinery in marine equipment. Traditional data-driven fault diagnosis tasks require massive label data for training, and it takes time and manpower to obtain enough label samples. At the same time, it is considered that the noise can interfere with the performance of the fault diagnosis framework. To overcome the above two defects, this paper proposes a fault diagnosis framework based on semi-supervised learning, where the contractive stacked autoencoder (CSA) and the classifier multilayer perceptron (MLP) extract features from unlabeled data and realize fault classification respectively. Compared with the Stacked Autoencoder (SAE)-MLP and Stacked Denoising Autoencoder (SDAE)-MLP frameworks, the proposed learning framework has better fault diagnosis accuracy and robustness. [ABSTRACT FROM AUTHOR]

Tip leakage flow (TLF) is not only an important factor causing hydraulic loss in mixed-flow pump, but also one of the inceptions of rotating stall. At present, limiting the stall of rotating machinery by restraining the TLF has gradually become a research hotspot of rotating machinery. However, in the field of mixed-flow pump, it is still in the initial stage of exploration. With the development of hydraulic machinery towards large-scale and high-speed, it is necessary to study its rotating stall suppression methods and expand its efficient operation range. Therefore, based on the idea of passive suppression of rotating stall, four new impeller rim structures are proposed in this study to optimize the performance of mixed-flow pump and to suppress or delay the occurrence of stall. The research results show that the geometric structure of the rim clearance has a significant impact on the energy performance of the mixed-flow pump, Bulge_Tip structure and Double_Rib_Tip structure make the critical stall and deep stall operating points shift to the low flow rate operating points, which improves the "saddle area" performance of the mixed-flow pump to a certain extent. However, there are two stall segments in the performance curve of Groove_Tip and Single_Rib_Tip structures, which worsens the pump performance in the "saddle area". From the analysis of flow mechanism, it can be seen that Bulge_Tip structure and Double_Rib_Tip structure can restrain the loss of turbulent kinetic energy in the gap under stall condition to a certain extent, and limit the position and strength of unsteady secondary vortex (SV) structure at the impeller outlet, so as to shorten the stall range of mixed-flow pump without deteriorating the stall flow field in the impeller. Therefore, Bulge_Tip structure and Double_Rib_Tip structure has a light application prospect in stall suppression of hydraulic machinery. [ABSTRACT FROM AUTHOR]

Efficient and automatic fault feature extraction of rotating machinery, especially for incipient faults is a challenging task of great significance. In this article, an optimal periodicity-enhanced group sparse method is proposed. Firstly, a period sequence determination method without any prior information is proposed, and the amplitude is calculated by the numerical characteristics of the vibration signal to obtain period square waves. Secondly, the periodic square waves are embedded into the group sparse algorithm, to eliminate the influence of random impulses, and intensify the periodicity of the acquisition signal. Thirdly, a fault feature indicator reflecting both signal periodicity and sparsity within and across groups is proposed as the fitness of the marine predator algorithm for parameter automatic selection. In addition, the method proposed is evaluated and compared by simulation and experiment. The results show that it can effectively extract incipient fault features and outperforms traditional overlapping group shrinkage and Fast Kurtogram. [ABSTRACT FROM AUTHOR]

Stability analysis of lateral–torsional coupled vibration is obligatory for rotating machinery, such as aero-engines. However, the state-of-the-art method may lead to stability misjudgment under different coordinate systems. The cause of this misjudgment has not yet been well explored. The purpose of this paper is to clarify the error source of the stability analysis in a more comprehensive manner. A vertical Jeffcott rotor model including torsion vibration is built, and the Lagrange approach is applied to establish the motion equations. The coordinate transformation matrix is used to transfer the motion equations into the rotating coordinate system, making the coefficients of the motion equation constants. The differences in the unstable speed regions in the two coordinate systems are captured. The limitations of the Floquet theory and Hill's determinant analysis in the stability estimation of the lateral–torsional coupled vibration are explained. It is found that, for Hill's method, increasing the number of the harmonic truncation cannot correct the misjudgment, and the matrix truncation is the fundamental error source. The above research provides more accurate theoretical support for the analysis of the lateral–torsional coupling instability of rotors. [ABSTRACT FROM AUTHOR]

To address the limited data problem in real-world fault diagnosis, previous studies have primarily focused on semi-supervised learning and transfer learning methods. However, these approaches often struggle to obtain the necessary data, failing to fully leverage the potential of easily obtainable unlabeled data from other devices. In light of this, this paper proposes a novel network architecture, named Signal Bootstrap Your Own Latent (SBYOL), which utilizes unlabeled vibration signals to address the challenging issues of variable working conditions, strong noise, and limited data in rotating machinery fault diagnosis. The architecture consists of a self-supervised pre-training-based fault feature recognition network and a diagnosis network based on knowledge transfer. The fault feature recognition network uses ResNet-18 as the backbone network for self-supervised pre-training and transfers the trained fault feature extractor to the target diagnostic object. Additionally, a unique vibration signal data augmentation technique, time–frequency signal transformation (TFST), is proposed specifically for rotating machinery fault diagnosis, which addresses the key task of contrastive learning and achieves high-precision fault diagnosis with very few labeled samples. Experimental results demonstrate that the proposed diagnostic model outperforms other methods in both extremely limited sample and strong noise scenarios and can transfer unlabeled data utilization between similar and even different device types. [ABSTRACT FROM AUTHOR]

The energy of rotating machinery exists widely in the environment. It is of great significance to collect and utilize the energy of rotating machinery for sustainable development. In this paper, a novel piezoelectric and electromagnetic energy harvester, which is capable of generating electrical energy under rotary excitation, is proposed based on array magnetic coupling. The working principle of this kind of energy harvester is analyzed. And the energy output modeling of the harvester is developed and output results are simulated. Based on the experimental test platform built in the laboratory, the output characteristics of the piezoelectric and electromagnetic energy harvester are tested. Results show that the maximum output power of the proposed energy harvester reaches 182 mW when the excitation speed is 120 rpm. Furthermore, both the piezoelectric module and the electromagnetic module can reach the maximum output power at the excitation speed of 120 rpm. [ABSTRACT FROM AUTHOR]

Fault diagnosis is an important means to ensure the safe and reliable operation of mechanical equipment. In machinery fault diagnosis, collecting and mining the potential fault information of the vibration signal is the most commonly used method to reflect the operating status of the equipment. In engineering scenarios, in the face of rotating machinery with variable speed, simple time domain analysis or frequency domain analysis is difficult to solve the problem. The time-frequency analysis technology that combines time-frequency transformation and data analysis can solve practical engineering problems by capturing the transient information of the signal. At present, a large number of related literatures have been published in academic journals. This paper hopes to provide convenience for relevant researchers and motivate researchers to further explore by summarizing the published literature. First, this paper briefly explains the concept of time-frequency analysis and its development. Then, the time-frequency transformation method proposed for the characteristics of rotating machinery fault vibration signal and related works of literature are reviewed, and the key issues of the application of time-frequency transformation method in rotating machinery fault diagnosis are discussed. Next, this paper summarizes the relevant literature on the combination of data analysis technology and time-frequency transformation and sorts out its development route and prospects. The study reveals that time-frequency analysis technology is able to detect the rotating machinery fault effectively. The time-frequency analysis technology has made abundant achievements in the field of rotating machinery fault diagnosis. It is expected that this review would inspire researchers to explore the potential of time-frequency analysis as well as to develop advanced research in this field. [ABSTRACT FROM AUTHOR]

The mechanical vibration response in engineering is the superimposition of multi-frequency characteristic information. Therefore, it is of great necessity to utilize signal decomposition methods to extract fault characteristics for ultimate diagnosis. In the traditional variational mode decomposition (VMD) methods, the decomposition parameters (i.e. the mode number and quadratic penalty factor) are determined according to the principle of convenience and experience. This behavior reduces the performance of VMD methods to a great extent, and limits their decomposition accuracy and feature extraction capability. To resolve this problem, a novel VMD method for rotating machinery fault diagnosis is developed in this article. Firstly, a measurement index called weighted correlated kurtosis (WCK) is constructed by combining correlated kurtosis and Pearson correlation coefficient. Secondly, taking the maximum WCK as the goal function, salp swarm algorithm is utilized to find the optimum parameters. Lastly, the feature extraction is performed according to the selected sensitive mode possessing the maximum WCK. Two experimental examples demonstrate the effectiveness of the developed VMD method on mechanical vibration signal processing and fault diagnosis. Furthermore, by comparing with other two typical VMD methods, the superiority of the developed method is verified. [ABSTRACT FROM AUTHOR]

The detection of cracks in rotating machinery is an unresolved issue today. In this work, a methodology for condition monitoring of railway axles is presented, based on crack detection by means of the automatic selection of patterns from the vibration signal measurement. The time waveforms were processed using the Wavelet Packet Transform, and appropriate alarm values for diagnosis were calculated automatically using non-supervised learning techniques based on Change Point Analysis algorithms. The validation was performed using vibration signals obtained during fatigue tests of two identical railway axle specimens, one of which cracked during the test while the other did not. During the test in which the axle cracked, the results show trend changes in the energy of the vibration signal associated with theoretical defect frequencies, which were particularly evident in the direction of vibration that was parallel to the track. These results are contrasted with those obtained during the test in which the fatigue limit was not exceeded, and the test therefore ended with the axle intact, verifying that the effects that were related to the crack did not appear in this case. With the results obtained, an adjusted alarm value for a condition monitoring process was established. [ABSTRACT FROM AUTHOR]

This paper deals with scale-fullscale models of rotor-bearing systems on rotating condition. The investigation is focused on the gyroscopic effect, which causes forward and backward whirl frequencies. When the rotor-bearing system is scaled proportionally in its dimension (height, length, and width), the scaling factor of whirl frequencies can be derived. It depends on the ratio between a transverse and polar moment of inertia on its rotating axis called the gyroscopic factor. The experimental study is conducted to validate it on three scaled rotor-bearing systems, which shifts its disc from the middle of the shaft on the scale of 1:1, 2:1, and 3:1 to clearly show the gyroscopic effect on first bending natural frequency. The scaling factor is then validated using the Campbell diagram by finding its critical speed. From this critical speed, the whirl frequencies along the range of the full-scale model speed can be obtained. The result also shows that the scaling factor remains the same whether it is at rotation or rest condition. Consideration must be made on the effect of the structural design, that is, blade and support, because of its unsymmetric stiffness that can cause backward whirl frequencies. The bearing stiffness must be ensured to be scaled proportionally, especially on journal bearing cases. This finding can be used by engineers to deal with scaling method implementation on rotating machinery design. [ABSTRACT FROM AUTHOR]

The availability of computational power in the domain of Prognostics and Health Management (PHM) with deep learning (DL) applications has attracted researchers worldwide. Industrial robots are the prime mover of modern industry. Industrial robots comprise multiple forms of rotating machinery, like servo motors and numerous gears. Thus, the PHM of the rotating components of industrial robots is crucial to minimize the downtime in the industries. In recent times, deep learning has proved its mettle in different areas, like bio-medical, image recognition, speech recognition, and many more. PHM with DL applications is a rapidly growing field. It has helped achieve a better understanding of the different condition monitoring signals, like vibration, current, temperature, acoustic emission, partial discharge, and pressure. Most current review articles are component- (or system-)specific and have not been updated to reflect the new deep learning approaches. Also, a unified review paper for PHM strategies for industrial robots and their rotating machinery with DL applications has not previously been presented. This paper presents a review of the PHM strategies with various DL algorithms for industrial robots and rotating machinery, along with brief theoretical aspects of the algorithms. This paper presents a trend of the up-to-date advancements in PHM approaches using DL algorithms. Also, the restrictions and challenges associated with the available PHM approaches are discussed, paving the way for future studies. [ABSTRACT FROM AUTHOR]

Rolling-element bearings are one of the most important elements when predicting the noise of rotating machinery. As a major connecting point between the rotating and non-rotating parts, their dynamic properties have to be accurately known. In this investigation we present a bottom-up approach to characterising the dynamics of the rolling-element bearing. A special test device was designed and built to assess the quality of the well-established analytical modelling approach of Lim and Singh. Two types of bearings were tested, i.e., the ball and tapered-roller types. The dynamic properties were observed by investigating the frequency-response functions. In addition, non-rotating as well as rotating test scenarios were checked. It was shown that the ball bearing model adequately predicts the system's response, whereas the tapered-roller bearing model requires modifications. These results were further confirmed with a quasi-static load-displacement numerical evaluation, where a full finite-element model serves as the reference. [ABSTRACT FROM AUTHOR]

Rotating electrical machines have several physical phenomena. Vibration is one of the important phenomena in the operation of rotating electrical machines. In addition, the vibration signal is considered an important source to have good information on the state of rotating electrical machinery. But this signal is rich in noise, especially under the presence of the bearing fault. This paper proposes a bearing fault diagnosis method based on EEMD and a denoising method based on three-sigma rule. In the first step, the EEMD decomposed the vibration signal into several components called Intrinsic Mode Functions (IMFs). After the calculation of the kurtosis of each IMF component, the signal is reconstructed by choosing components with higher values. To enhance periodic impulses, the three-sigma rule de-noising is applied to the reconstructed signal. As a final step, the envelope spectrum is used to determine the fault characteristic frequency. As a result of testing the bearing with inner race fault and the bearing with outer race, it was verified that the proposed approach suppressed noise effectively and extracted rich fault information from the vibration signals of bearings compared to the EEMD. [ABSTRACT FROM AUTHOR]

A rolling bearing is one of the most important components in the rotating machinery and equipment of agricultural production. Most rolling bearings can often operate under complex conditions, such as strong noise and variable working conditions. It is very necessary to fully extract the characteristics of the fault signal in the process of fault diagnosis of rolling bearings. However, the convolutional neural network cannot fully extract the global feature information during the feature extraction under variable working conditions of rolling bearings. In this study, a multiscale convolutional neural network (MSCNN) - swin transformer (SwinT) was proposed for the rolling bearing fault diagnosis. Firstly, the continuous wavelet transform (CWT) was used in the data processing module to convert the one-dimensional vibration signal into a two-dimensional time-frequency image, which retained the time-frequency characteristics of the original signal. Secondly, the MSCNN was used to extract the local features of fault information in the local perception module. Then, the key information was extracted by the convolutional block attention module (CBAM). As such, the feature extraction module was further constructed. The residual connection was introduced to improve the utilization efficiency of the feature information before and after extraction. The global features of fault information were learned using the SwinT network. Finally, the global average pooling was used instead of the fully connected layer for fault identification. The bearing dataset was collected from the Case Western Reserve University and the self-made experimental data for the experimental verification. The test results showed that the fault identification accuracy in the visualization, variable working condition, and the test of different coding tests were 99.67%, 95.01%-99.66%, and 100%, respectively. The accuracy of fault diagnosis reached 99.18% in the self-made experimental datasets. Compared with the CWT-LeNet5, CWT-VGG16, CWT-ResNet18 and CWT-SwinT, the average fault identification accuracy was improved by 8.79, 8.64, 3.49, and 3.18 percentage points, respectively in the variable working condition test, while by 5.23, 2.74, 1.40, and 1.26 percentage points in the self-made experimental data set, respectively. The accurate and rapid identification was realized in the different fault states of rolling bearings under complex conditions, such as variable working conditions. The global characteristic information of bearing faults was fully extracted with high fault diagnosis accuracy and better generalization ability. This finding can also provide a strong reference for the rolling bearing fault diagnosis under variable working conditions. [ABSTRACT FROM AUTHOR]

This work presents an analysis of vibration signals for bearing defects using a proposed approach that includes several methods of signal processing. The goal of the approach is to efficiently divide the signal into two distinct components: a meticulously organized segment that contains relatively straightforward information, and an inherently disorganized segment that contains a wealth of intricately complex data. The separation of the two component is achieved by utilizing the weighted entropy index (WEI) and the SVMD algorithm. Information about the defects was extracted from the envelope spectrum of the ordered and disordered parts of the vibration signal. Upon applying the proposed approach to the bearing fault signals available in the Paderborn university database, a high amplitude peak can be observed in the outer ring fault frequency (45.9 Hz). Likewise, for the signals available in XJTU-SY, a peak is observed at the fault frequency (108.6 Hz). [ABSTRACT FROM AUTHOR]

Sensor techniques and emerging CNN models have greatly facilitated the development of collaborative fault diagnosis. Existing CNN models apply different fusion schemes to achieve reliable fault identification based on multisensor data. Few CNN models, however, take into account both the intrinsic correlations and the distribution gap between different signals, which may result in a limited exploration of multisource data. To address this issue, a novel convolutional fusion framework called a collaborative fusion convolutional neural network (CFCNN) is developed in this paper. More specifically, a multiscale shrinkage denoising module (MSDM) is developed first to extract multilevel modality-specific features from different mechanical signals. Then, drawing inspiration from the intermediate fusion scheme, a central fusion module (CFM) is introduced to explore the intrinsic correlations and integrate cross-modal features. Moreover, an online label smoothing training (OLST) strategy is applied to reduce overfitting and promote better classification performance of CFCNN. The developed CFCNN is expected to shed new light on collaborative fault diagnosis using the intermediate fusion scheme. The efficacy of the developed CFCNN is verified through the cylindrical rolling bearing dataset and the planetary gearbox dataset. • A novel convolutional fusion framework named CFCNN is developed for collaborative fault recognition. • A central fusion module (CFM) is put forward for effective feature fusion. • An online label smoothing (OLS) algorithm is introduced to reduce overfitting and facilitate model convergence. • Two case studies are conducted to verify the effectiveness of the proposed diagnostic framework. [ABSTRACT FROM AUTHOR]

Strong noise interference or compound fault coupling phenomenon may lead to the failure of fault diagnosis technology. This paper focuses on weak feature extraction and compound faults detection for rotating machinery fault diagnosis and proposes adaptive symplectic geometric mode decomposition (SGMD) using cycle kurtosis entropy. Firstly, an index named cycle kurtosis entropy (CKE) is presented to measure the strength of periodic impulses in a signal. The CKE uses the entropy value of calculating all delay cycle kurtosis (CK) to overcome the shortcomings of the CK in adaptive ability and obtain more stable values. Thirdly, CKE is applied to construct an adaptive slip window with optimal length. This process is called the adaptive window segmentation method, which is mainly used to dig out weak fault features in signals. Finally, CKE is used as the component selection criterion to select the components decomposed by SGMD. The selected components are reconstructed to obtain a de-noised signal. Hilbert envelope analysis is applied to the denoised signal to demodulate the fault characteristic frequency. Numerical simulations and experimental investigations using bearings and gears are performed to testify the property of the presented method. The results indicate that the adaptive slip window can enhance the decomposing ability of SGMD under strong noise condition. Moreover, for the strong periodic impulse identification ability, the cycle kurtosis entropy is suitable to determine the optimal components of SGMD. It is expected that the presented method will be effectively used for fault feature extractions in rotating machinery under stationary running conditions. [Display omitted] • An improved version of symplectic geometry mode decomposition is proposed for rotating machinery faults diagnosis. • The short time series can be obtained by adding a slip window. • Cycle kurtosis entropy is proposed to characterize the periodic impact. • Performance is validated by numerical and experimental investigations to detect faults in bearings and gears. [ABSTRACT FROM AUTHOR]

Accurate fault diagnosis technology is essential for ensuring reliable operation of rotating machinery. However, complex conditions and various damage forms bring challenges to present diagnosis technology. In this study, a novel fault diagnosis method is proposed utilizing the newly developed adaptive resize-residual deep neural networks. The usage of the proposed method consists of three steps. First, the continuous wavelet transform is used to transfer the acquired vibration signals into time–frequency images. Second, the histogram equalization algorithm is applied to enhance the contrast of these images. Finally, the enhanced images are used as the input of newly proposed adaptive resize-residual networks, in which the adaptive resize block can deduce the dimensions of input data by self-learning and feed them into the residual block for pattern recognition. Two experimental cases are designed to evaluate the performance of proposed method. The experimental results indicate that the proposed adaptive resize-residual network obtains superior recognition accuracy and outperforms many state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In rotating machinery components, it is difficult to extract incipient weak fault features directly from vibration signals. A fault feature extraction method based on refined multiscale symbolic dispersion entropy (RMSDE) is proposed. This method mainly combines symbolic coding and dynamic complexity evaluation to achieve multi-dimensional feature extraction. By constructing multiple sets of simulation signals to evaluate the correlation performance of the proposed method, the superior performance in complexity estimation, anti-noise interference and computational efficiency is mainly demonstrated. When RMSDE is combined with a self-organizing fuzzy logic classifier (SOF), the results show that the proposed RMSDE-SOF method can achieve high-precision fault diagnosis of rotating machinery. The effectiveness and robustness of the proposed method in fault feature extraction and diagnosis are verified by using the rolling bearing dataset and gear dataset, which contain multiple fault types, and the comprehensive performance of the proposed method is better than that of similar feature extraction methods. The final results show that the proposed method is superior to the other methods in rotating machinery fault diagnosis. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology 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.)

Fault diagnosis of bearings in rotating machinery is a critical task. Vibration signals are a valuable source of information, but they can be complex and noisy. A transformer model can capture distant relationships, which makes it a promising solution for fault diagnosis. However, its application in this field has been limited. This study aims to contribute to this growing area of research by proposing a novel deep-learning architecture that combines the strengths of CNNs and transformer models for effective fault diagnosis in rotating machinery. Thus, it captures both local and long-range temporal dependencies in the vibration signals. The architecture starts with CNN-based feature extraction, followed by temporal relationship modelling using the transformer. The transformed features are used for classification. Experimental evaluations are conducted on two datasets with six and ten health conditions. In both case studies, the proposed model achieves high accuracy, precision, recall, F1-score, and specificity all above 99% using different training dataset sizes. The results demonstrate the effectiveness of the proposed method in diagnosing bearing faults. The convolutional-transformer model proves to be a promising approach for bearing fault diagnosis. The method shows great potential for improving the accuracy and efficiency of fault diagnosis in rotating machinery. [ABSTRACT FROM AUTHOR]

The most powerful technology in the condition-based maintenance (CBM) framework for rotating machinery is fault detection (FD) and fault diagnosis (FDS). This paper investigates the broken rotor bar (BRB) FDS utilizing Hilbert transform (HT), discrete wavelet transform (DWT), and energy eigenvalue (EEV) computation with the induction motor (IM) drive handled by the indirect field orientation control (IFOC). The stator current spectrum, which the HT collects, is utilized to determine BRB degradation. The DWT decomposes the signal while the fast Fourier transform (FFT) recovers the signal's frequency and amplitude factors. The EEV of the motor current in the signal determines the degree of the malfunction and provides a better method for recognizing errors. The DWT is used to overcome the Fourier analysis's drawbacks and is primarily dedicated to non-stationary signals. While DWT is used, the malfunctioning BRB's stator current signal is restrained from its original amplitude. The results demonstrate that the proposed method can identify and diagnose faults in an IM drive even under different loads. [ABSTRACT FROM AUTHOR]

Feature extraction from vibration signals plays a vital role in rotating machinery fault diagnosis. The noise contained in the signals will interfere with the fault feature extraction result. Wavelet denoising (WD) is a commonly used method to reduce the noise, but its parameters are generally selected based on subjective experience. With this problem in mind, an adaptive wavelet denoising (AWD) method is proposed in this paper. Using permutation entropy to evaluate the signal noise level and taking its minimum value as the fitness function, the whale optimization algorithm is applied to optimize the WD parameters. Based on the AWD method and a synthetic detection index, a new feature extraction approach is proposed. Results from simulation experiments and engineering applications prove that the signal denoising performance of the AWD method and the fault feature extraction approach are satisfactory. [ABSTRACT FROM AUTHOR]