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Data-driven intelligent methods have been widely used in bearing fault diagnosis. However, it is observed that previous studies on bearing fault diagnosis always assume that the label samples are sufficient and that the number of normal and fault samples is the same or similar, which is challenging to meet in practical engineering applications. This assumption reduces the accuracy and stability of the semi-supervised imbalanced bearing data fault diagnosis model in practical working conditions. The complex training and weak interpretation problems of transfer learning methods are analyzed, and a center jumping boosting machine method for bearing intelligent fault recognition with semi-supervised and imbalanced data is proposed. First, a modified density peak clustering (DPC) algorithm is used to classify unlabeled samples and select subsamples, and aiming at the DPC problem, a γ DPC algorithm based on the γ jumping phenomenon is proposed to determine the number of clusters and intercept distance automatically. Second, combined with the synthetic minority oversampling technique, some minority class samples are added to achieve a balanced bearing dataset. Then, a few known faults are used to assign pseudo-labels to unknown samples. Finally, to diagnose the new data and reduce the amount of calculation in actual production, the balanced data after processing are used to train the bottom light gradient boosting machine model to solve intelligent classification and recognition of bearing vibration data. In addition, by using three bearing datasets with different balance ratios and comparing them with other methods, the superiority of the proposed method is verified in bearing condition identification. [ABSTRACT FROM AUTHOR]

Bearings are the most vulnerable component in low-voltage induction motors from a maintenance standpoint. Vibration monitoring is the benchmark technique for identifying mechanical faults in rotating machinery, including the diagnosis of bearing defects. The study of different bearing fault phenomena under induction motor transient conditions offers interesting capabilities to enhance classic fault detection techniques. This study analyzes the low-frequency localized bearing fault signatures in both the inner and outer races during the start-up and steady-state operation of inverter-fed and line-started induction motors. For this aim, the classic vibration envelope spectrum technique is explored in the time–frequency domain by using a simple, resampling-free, Short Time Fourier Transform (STFT) and a band-pass filtering stage. The vibration data are acquired in the motor housing in the radial direction for different load points. In addition, two different localized defect sizes are considered to explore the influence of the defect width. The analysis of extracted low-frequency characteristic frequencies conducted in this study demonstrates the feasibility of detecting early-stage localized bearing defects in induction motors across various operating conditions and actuation modes. [ABSTRACT FROM AUTHOR]

Predicting the remaining useful life (RUL) of mechanical bearings is crucial in the industry. Estimating the RUL enables the assessment of health bearing, maintenance planning, and significant cost reduction, thereby fostering industrial development. Existing models rely on traditional feature engineering with feature changes because operating conditions pose a major challenge to the generalization of RUL prediction models. This study focuses on neural network-based feature engineering and the downstream prediction of the RUL, eliminating the need for specific prior knowledge and simplifying the development and maintenance of models. Initially, a convolutional neural network (CNN) model is employed for feature engineering. Subsequently, a bidirectional long short-term memory network (Bi-LSTM) model is used to capture the time-series degradation characteristics of the engineered features and predict the RUL through regression. Finally, the study examines the influence of operating conditions in the model and integrates domain adaptation to minimize differences in feature distribution, thereby enhancing the model's generalizability for the RUL prediction. [ABSTRACT FROM AUTHOR]

Deep-learning robust feature learning ability makes it a valuable tool for automatic fault detection of rolling bearings in Industry 4.0. This work presents a novel approach for classifying faults in rolling bearings under highly noisy conditions using a deep learning algorithm. The proposed methodology utilizes a double modes decomposition method, specifically the complete ensemble empirical mode decomposition (CEEMD) and variational mode decomposition (VMD) techniques for signal denoising. The novelty of this study lies in its utilization of a double decomposition method, coupled with the selection of dominant intrinsic mode functions (IMFs), followed by continuous wavelet analysis (CWT) to generate magnitude scalogram images for input into a VGG16 deep learning architecture. First, bearing vibration signals are mixed with white Gaussian noise to simulate noisy real-world conditions. The noisy signal is then decomposed into intrinsic mode functions (IMFs) using the CEEMD technique, and a dominant IMF is selected based on its permutation entropy and correlation coefficient values. This dominant IMF is further decomposed into another set of IMFs using the VMD technique to obtain a final dominant IMF based on its CC value. After that, continuous wavelet analysis (CWT) is performed on selected IMF to obtain magnitude scalogram images, and these images are fed into VGG16 deep learning architecture for bearing fault classifications. The results obtained after applying the proposed methodology to the standard bearing dataset suggest that CEEMD-VMD-IMF with magnitude scalogram images perform well with deep learning technique and achieve a bearing fault classification accuracy above 99 % in extremely noisy conditions. [ABSTRACT FROM AUTHOR]

In an integrated steel plant, the pulverized coal injection (PCI) mill plays an important role in providing pulverized coal to the blast furnace. The main purpose of using PCI coal is to reduce the hot metal production cost through the utilization of non-coking coal and to extend the available coke oven life. Any unplanned outage of the PCI mill leads to increase in production cost. The three stationary grinding rollers are arranged equidistantly on the grinding plate. Each grinding roller is fixed to the pressure frame by means of a pressure yoke. Each grinding roller is borne by two roller bearings on the axle of the pressure yoke. The bearings are oil lubricated, and oil level cannot be checked in running condition due to design constraints. The bearing of new grinding roller assembly damaged within 1 service month. Bearing mode of failure was seizure, and it happened due to lack of oil. Therefore, root cause failure analysis has been carried out, and incorrect assembly was the responsible of this failure. Detailed investigation report is covered in this paper. The same type of failure can be avoided by implementing the recommendations which has been addressed in this paper. [ABSTRACT FROM AUTHOR]

Copyright of Rolling Stock (1002-7602) is the property of Rolling Stock Editorial Office 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 Journal of Drainage & Irrigation Machinery Engineering / Paiguan Jixie Gongcheng Xuebao is the property of Editorial Department of Drainage & Irrigation Machinery 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 majority of bearings in the world are lubricated by grease, and nearly 80% of premature bearing damage is attributed to lubrication issues. Accurate measurement and prediction of film thickness are crucial aspects of understanding the lubrication mechanism in grease-lubricated bearings. This work focuses on grease film thickness measurement using the capacitance method in real bearings. It comprehensively reviews the current status, identifies key challenges, and proposes solutions. Mechanisms of mainstream electronic components in capacitance measurement were reviewed for the first time. It enables more accurate capacitance measurement. A new capacitive model and electric network to measure film thickness in fully flooded, starved, and mixed regimes are developed. It is more comprehensive compared to current models. Classic dielectric models are reviewed, and suitable ones for lubricants are proposed. It facilitates a more precise film thickness measurement. Finally, a new grease film thickness model (bearing raceway) is proposed based on the 113 literature capacitive film thickness data points from five different authors. The satisfied R-squared value indicates a strong correlation. [ABSTRACT FROM AUTHOR]

In response to the challenges of bearing fault diagnosis under small sample sizes and variable operating conditions, this paper proposes a novel method based on the two-dimensional analysis of vibration acceleration signals and a Multi-Task Conditional Generative Adversarial Network (MTC-GAN). This method first constructs two-dimensional images of vibration signals by leveraging the physical properties of the bearing acceleration signals and employs Local Binary Patterns (LBP) to extract subtle texture features from these images, thereby generating fault feature signatures with high discriminative power across different operating conditions. Subsequently, MTC-GAN is utilized for data augmentation, and the trained discriminator is used to perform fault classification tasks, improving classification accuracy under conditions with small sample sizes. Experimental results demonstrate that the proposed method achieves excellent fault diagnosis accuracy and robustness under both small sample sizes and varying operating conditions. Compared to traditional methods, this approach exhibits higher efficiency and reliability in handling complex operating conditions and data scarcity. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny 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.)

Thermoplastic polycarbonate provides minimal structural weight and ductility and is a recyclable material. However, investigation of the characteristics of hybrid composites reinforced with woven glass and polycarbonate sheets is limited. This current study investigated the effect of integration of polycarbonate sheets on glass fiber‐epoxy hybrid composite performance. Non hybrid laminate was fabricated from eight layers of woven glass fiber impregnated with epoxy resin (NG). However, the hybrid samples utilized various symmetric stacking patterns which were made by alternating two processed polycarbonate (PC) sheets with six layers of woven glass fiber embedded with epoxy matrix. The manufacturing was made using the hand layup procedure. Mechanical testing includes compressive, tensile, bearing, and hardness was performed on hybrid and non‐hybrid composites. The results showed that employing PC sheets instead of fibers affect the laminate's strength, ductility, bearing capacity, and hardness. Utilization of PC sheets reduces both compressive and tensile strength. However, an enhancement in tensile, compressive, and bearing failure strain was achieved by 2.5%, 31.25% and 31.7%, respectively with hybrid composite having PC in the skin layers as compared with NG. The extent of failure damage is correlated with the position of the PC sheets in the tested samples. Highlights: Novel hybrid composites of woven glass fiber and polycarbonate sheet as reinforcement with various stacking orders were successfully prepared by the hand lay‐up method.The compressive strain of hybrid PC samples are higher than NG sample.The hybrid PC composites showed an improved bearing strength and strain performance.The influence of sample configuration on the damage mechanisms has been investigated. [ABSTRACT FROM AUTHOR]

The swing torque and rotational torque after the spherical bearing is installed directly affect the performance of the spherical bearing. At this stage, the friction torque detection equipment of the spherical bearing is mainly used to detect uninstalled bearings. A set of rotation and swing after the bearing is installed is designed. Torque detection system. The detection principles of rotational torque and swing torque required for flexibility detection were analyzed, the functional design requirements and main technical indicators of the detection system were clarified, and the overall design plan of the detection system was established; the host structure of the detection system was designed, including rotational torque detection system, swing torque detection system, clamping system and calibration system; completed the scheme design of the detection control system, selected the torque sensor and servo motor, designed the main electrical control circuit of the detector; conducted error analysis of the detector. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to propose a method for monitoring bearing health in the time–frequency domain, termed the Lock-in spectrum, to track the evolution of bearing faults over time and frequency. Design/methodology/approach: The Lock-in spectrum uses vibration signals captured by vibration sensors and uses a lock-in process to analyze specified frequency bands. It calculates the distribution of signal amplitudes around fault characteristic frequencies over short time intervals. Findings: Experimental results demonstrate that the Lock-in spectrum effectively captures the degradation process of bearings from fault inception to complete failure. It provides time-varying information on fault frequencies and amplitudes, enabling early detection of fault growth, even in the initial stages when fault signals are weak. Compared to the benchmark short-time Fourier transform method, the Lock-in spectrum exhibits superior expressive ability, allowing for higher-resolution, long-term monitoring of bearing condition. Originality/value: The proposed Lock-in spectrum offers a novel approach to bearing health monitoring by capturing the dynamic evolution of fault frequencies over time. It surpasses traditional methods by providing enhanced frequency resolution and early fault detection capabilities. [ABSTRACT FROM AUTHOR]

In order to research the influence of bearing arrangement on the seismic response of urban viaduct, the 4 × 30 m standard span and C ramp bridge of the main line are taken as the engineering background. The SeismoStruct software is used to establish and analyse the seismic responses. For the standard span of the main line, under the same ground motion input, the internal force of the pier for the seismic isolation system is reduced by about 1 to 4 times compared with the ductile seismic system, but the displacement of the piers or the bearings are relatively large, and the overall cost of the structure is high. For the C ramp bridge, the shear force, axial force and pier displacement with all lead-core rubber bearings are lower than those with fixed bearings. From the perspectives of structure seismic response, post-earthquake repair and engineering economy, it is suggested that the viaduct of the standard span should adopt the plate type rubber bearing, and the C ramp bridge should adopt the fixed bearing. [ABSTRACT FROM AUTHOR]

With the increasing complexity of mechanical equipment and diversification of deep learning models, vibration signals collected from such equipment are susceptible to noise interference. Moreover, traditional neural network models struggle to be effectively deployed in production environments with limited computational resources, severely impacting the accurate extraction and effective diagnosis of FK fault characteristics. In response to this challenge, this study proposes a fault diagnosis method for rolling bearings, integrating a lightweight ShuffleNetV2 network with variational mode decomposition (VMD) and the fast kurtogram (FK) algorithm. Initially, this paper introduces an enhanced FK method where the VMD algorithm is employed for data denoising, extracting FK post-denoising. These feature maps not only preserve critical signal information but also simplify data complexity. Subsequently, these feature maps are utilized to train and test the ShuffleNetV2 model, facilitating effective fault identification and classification. Ultimately, by conducting experimental comparisons with several mainstream lightweight network models, such as MobileNet and SqueezeNet, as well as traditional convolutional neural network models, this study validates the effectiveness of the proposed method in extracting fault characteristics from vibration signals, demonstrating superior diagnostic accuracy and computational efficiency. This provides a novel technical approach for health monitoring and fault diagnosis of industrial bearings and offers theoretical and experimental support for the deployment of lightweight networks in industrial applications. [ABSTRACT FROM AUTHOR]

Machine learning (ML) and artificial intelligence (AI) have emerged as the most advanced technologies today for solving issues as well as assessing and forecasting occurrences. The use of AI and ML in various organizations seeks to capitalize on the benefits of vast amounts of data based on scientific approaches, notably machine learning, which may identify patterns of decision-making and minimize the need for human intervention. The purpose of this research work is to develop a suitable neural network model, which is a component of AI and ML, to assess and forecast crack propagation in a bearing with a seeded crack. The bearing was continually run for many hours, and data were retrieved at time intervals that might be utilized to forecast crack growth. The variables root mean square (RMS), crest factor, signal-to-noise ratio (SNR), skewness, kurtosis, and Shannon entropy were collected from the continuously running bearing and utilized as input parameters, with the total crack area and crack width regarded as output parameters. Finally, utilizing several methodologies of the Neural Network tool in MATLAB, a realistic ANN model was trained to predict the crack area and crack width. It was observed that the ANN model performed admirably in predicting data with a better degree of accuracy. Through analysis, it was observed that the SNR was the most relevant parameter in anticipating data in bearing crack propagation, with an accuracy rate of 99.2% when evaluated as a single parameter, whereas in multiple parameter analysis, a combination of kurtosis and Shannon entropy gave a 99.39% accuracy rate. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Southeast University / Dongnan Daxue Xuebao is the property of Journal of Southeast University Editorial Office 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 existing PDC (polycrystalline diamond compact)–cone hybrid bit bearing adopts a unilateral support structure, which is prone to stress concentration in the journal area, resulting in fracture and wear failure of the bearing, thus reducing the service life of the hybrid bit. In this paper, a new type of double supported bearing hybrid bit is proposed. The static strength analysis of unilateral and bilateral support bearing structures is carried out by finite element simulation, and the stress and strain distribution of the two structures under loads of 20–100 kN is obtained. Experimental devices for unilateral and bilateral support bearing structures are designed and manufactured to complete 50–100 kN static pressure loading experiments. The results show that the stress and strain of unilateral and bilateral support bearing increased linearly with the increase of load. Compared with unilateral bearing, when the load was 100 kN, the maximum Mises stress of bilateral bearing decreased from 358.80 MPa to 211.10 MPa, with a decrease of 41.16%. The maximum contact stress decreased from 415.20 MPa to 378.10 MPa, a decreased of 8.94%, and the maximum principal strain decreased from 1.101 × 10−3 to 9.71 × 10−4, a decrease of 11.81%. The axial strain in the danger zone was reduced by 14.68% and 17.35%, respectively. It is found that the contact stress of the simulation data is highly correlated with the bearing life, and the service life of the bilateral bearing bit is increased by 8.94%. The simulation data and experimental results provide data support for the production of hybrid bits with bilateral bearing support. [ABSTRACT FROM AUTHOR]

The gearbox of an electric vehicle operates under the high load torque and axial load of electric vehicles. In particular, the bearings that support the shaft of the gearbox are subjected to several tons of axial load, and as the mileage increases, fault occurs on bearing rolling elements frequently. Such bearing fault has a serious impact on driving comfort and vehicle safety, however, bearing faults are diagnosed by human experts nowadays, and algorithm-based electric vehicle bearing fault diagnosis has not been implemented. Therefore, in this paper, a deep learning-based bearing vibration signal processing method to diagnose bearing fault in electric vehicle gearboxes is proposed. The proposed method consists of a deep neural network learning stage and an application stage of the pre-trained neural network. In the deep neural network learning stage, supervised learning is carried out based on two acceleration sensors. In the neural network application stage, signal processing of a single accelerometer signal is performed through a pre-trained neural network. In conclusion, the pre-trained neural network makes bearing fault signals stand out and can utilize these signals to extract frequency characteristics of bearing fault. [ABSTRACT FROM AUTHOR]

In this study, spindle precision bearings and rotary vector (RV) reducer thin-walled bearings were taken as research objects. Based on the analysis of the metamorphic layer of bearings, the multidimensional research on the surface topography of parts by multisource information fusion (grinding force, grinding temperature, grinding strain, grinding vibration, and grinding acoustic emission (AE)) was put forward. The research shows that there are "dark layers" in the outer ring and inner ring of spindle precision bearings, and "white layers" and "dark layers" in the outer ring and inner ring of RV reducer thin-walled bearings. Feed speed has the greatest influence on grinding force, wheel speed, and grinding depth have the greatest influence on grinding temperature, wheel speed has the greatest influence on grinding strain, feed speed, and wheel speed have the greatest influence on grinding vibration, and workpiece speed has the greatest influence on grinding AE. When the grinding force is minimum (x -axis: 1.3 N, y -axis: 0.9 N); the grinding temperature is highest (110.8°C), the grinding strain is maximum (46.6%), the grinding vibration is maximum (vibration range: 400 m/s2–400 m/s2) and the grinding AE is maximum (variation range: 2 V–1 V), the surface topography is most prominent (Ra: 0.97 μm). Grinding wheel speed and grinding depth have the greatest influence on surface topography, followed by feed speed, while workpiece speed has less influence. [ABSTRACT FROM AUTHOR]

In order to study the unstable vibration of generator unit in nuclear power plant, a rotor dynamics analysis model was established, in which the thermal effect induced by oil shear flow in bearing was considered. One-dimensional energy balance equation was used to solve the journal temperature difference caused by the flow of lubricating oil in the bearing small gap. The average journal temperature difference and its angle during a whirling cycle were obtained. The effect of cross-sectional temperature gradient on shaft bending deformation was calculated, and the equivalent bending moment was obtained. The equivalent bending moment was input to the shaft in finite element model, and the bending deformation of the generator shaft was calculated. The results show that the temperature difference of the journal is induced by the shear flow of lubricating oil in the bearing clearance. The temperature difference of the journal increases with the increase of journal eccentricity and bearing clearance. After the eccentricity of the journal increases to a certain extent and the bearing clearance decreases to a certain extent, the temperature difference of the journal section increases rapidly, exhibiting typical nonlinear characteristics. If the amplitude of synchronous whirling orbit increases, the dynamic eccentricity increases and the cross-sectional temperature difference increases. Journal temperature difference of a certain nuclear power generation unit was calculated using the model. Within a reasonable range of bearing parameters in nuclear power units, the temperature difference can reach 10 ℃. Its impact on vibration is equivalent to the impact of G2.5 level imbalance force on the generator. The periodic vibration fluctuation phenomenon that occurred on a certain type of nuclear power unit was introduced. Test shows that the fluctuation amplitude is related to the lubricating oil temperature. It is pointed that the journal temperature difference produces rotational unbalance, resulting in vibration fluctuation. Unstable vibration is related to lubricating oil viscosity, bearing clearance, eccentricity ratio and unbalanced force. The unstable vibration can be reduced by reducing the viscosity of lubricating oil, increasing the bearing clearance, reducing the eccentricity and unbalance. The engineering verification test of adjusting lubricating oil temperature was carried out. The test results show that increasing the temperature of lubricating oil to reduce the temperature viscosity of lubricating oil is helpful to reduce the unstable vibration. The analysis results by calculation are consistent with the engineering practice. The model established in this study can be used for the analysis of unstable vibration of nuclear power generator units.

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]

Predictive maintenance (PdM) is critical to ensure optimal operating efficiency and minimize costly failures of industrial machinery. The PdM leverages a machine learning (ML) method to predict remaining useful life (RUL) for implementing minimal-cost and reliable maintenance. RUL prediction involves multiple steps, such as data collection, data pre-processing, and RUL estimation, each of which incorporates various methods. This study conducted a critical review of RUL estimation and data pre-processing specifically for turbofan engines and bearings, categorizing existing models to offer a high-level perspective on RUL prediction. This review demonstrates that the indirect mapping method exhibits outstanding prediction accuracy compared to the direct mapping method. Moreover, it highlights that autoencoder techniques and their variants demonstrate commendable performance in extracting features from turbofan engines and bearing datasets. Furthermore, the paper proposes potential areas for future research to improve RUL prediction in this domain. [ABSTRACT FROM AUTHOR]

To improve the performance of roller bearing fault diagnosis, this paper proposes an algorithm based on subtraction average-based optimizer (SABO), variational mode decomposition (VMD), and weighted Manhattan-K nearest neighbor (WMH–KNN). Initially, the SABO algorithm uses a composite objective function, including permutation entropy and mutual information entropy, to optimize the input parameters of VMD. Subsequently, the optimized VMD is used to decompose the signal to obtain the optimal decomposition characteristics and the corresponding intrinsic mode function (IMF). Finally, the weighted Manhattan function (WMH) is used to enhance the classification distance of the KNN algorithm, and WMH–KNN is used for fault diagnosis based on the optimized IMF features. The performance of the SABO–VMD and WMH–KNN models is verified through two experimental cases and compared with traditional methods. The results show that the accuracy of motor-bearing fault diagnosis is significantly improved, reaching 97.22% in Dataset 1, 98.33% in Dataset 2, and 99.2% in Dataset 3. Compared with traditional methods, the proposed method significantly reduces the false positive rate. [ABSTRACT FROM AUTHOR]

In the bearing-based formation control of autonomous surface vehicles, the scaling maneuver capability is greatly limited when faced with actuator faults and uncertainties. Under these circumstances, to better realize the formation scaling maneuver, a concise adaptive fault-tolerant formation scaling control scheme is proposed for autonomous vehicles with bearing measurements. By means of dynamic surface control, parameter integration and the adaptive technique, the tedious derivative calculation of virtual control signals is avoided and the prescribed formation scaling maneuver is achieved without knowing specific information about the faults and models. It is shown that both yaw angle tracking errors and bearing errors are able, ultimately, to be made uniformly bounded using this scheme. Meanwhile, only one control parameter and one adaptive parameter need to be updated during the formation scaling process. Stability analysis and comparative results are provided to verify the validity of the developed scheme. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering 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.)

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 mitigate the impact of fault iconic feature shift and feature missing due to missing data values on bearing fault diagnosis, this paper proposes a fault diagnosis method based on a spatial frequency filter and a Multi-Scale feature recombination calibration network (MSRCN). First, the fault features are converted into frequency band features and feature enhancement is realized using Mel filters to weaken the effect of fault feature offset. Then, the spatial calibration module (SC) in the MSRCN is utilized to further improve the fault feature distribution and eliminate the fault feature offset problem. Next, to solve the fault feature missing problem, the remaining fault features are sampled by multi-scale reorganization using MSRCN to obtain new fault features, which overcomes the effect of fault feature missing on fault diagnosis. Finally, experiments are conducted on CWRU and XJTU-SY rolling bearing datasets to verify that the algorithm can effectively solve the fault feature offset and missing problem. Meanwhile, the experimental results prove that the algorithm proposed in this paper can realize high-precision fault diagnosis. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering for Thermal Energy & Power / Reneng Dongli Gongcheng is the property of Journal of Engineering for Thermal Energy & Power 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.)

Energy-saving technology has become increasingly significant as one of the carbon-neutral options for suppressing recent global warming. Greaselubricated bearings have been used in various automotive and industrial machinery, requiring low torque and long service life for energy-saving performance, which is greatly influenced by grease fluidity. A numerical approach for understanding grease fluidity is very complex since grease is a non-Newtonian fluid with thixotropic properties. Visualization technique is one of the helpful methods to understand the complex grease fluidity and apply it to practical use. This paper describes state-of-the-art visualization techniques, such as fluorescence method, particle imaging velocimetry, infrared spectroscopy, X-rays, and neutron beams. [ABSTRACT FROM AUTHOR]

Aiming at the unbalanced datasets of fault samples of bearing, a fault diagnosis method of bearing based on time-frequency DCGAN processing is proposed in this paper. Firstly, through STFT, the vibration signals are converted into the time-frequency images, and then the time-frequency images are input into DCGAN to expand the fault samples. Secondly, the expanded fault samples are evaluated for image quality through the comprehensive method of PSNR and SSIM. Thirdly, the Canny edge detection algorithm is used to extract features from the time-frequency image, and the obtained binary image is used as the feature. Finally, k-nearest neighbor algorithm is used for classification to testify the superiority of time-frequency DCGAN processing. The experimental results show that the expanded samples can effectively improve the unbalance of the samples and improve the accuracy of fault diagnosis of bearing. [ABSTRACT FROM AUTHOR]

In this paper it is presented a reconstruction of the bearing of the vortex turning unit on the LITOSTROJ PD 2000–6000 SM lathe, located in the maintenance plant of the hydro power plant Đerdap 1, which is used for processing the turbine blade sleeve as well as for processing other large machine parts. A newly designed bearing that transmits both types of loads (radial and axial) is shown through research process and finding a new solution. The bearing thus defined is monitored during exploitation by algorithmic software program C4.5 techniques with the aim of monitoring the operation of the new bearing solution, as well as estimating the remaining service life of the bearings. [ABSTRACT FROM AUTHOR]

This paper introduces a novel diagnostic approach for bearing ball failures: a synergistic implementation of a bidirectional Long Short-Term Memory (LSTM) network, empowered by Gradient Minimum Mean Square. This method leverages deep analysis of operational data from bearings, enabling the precise identification of incipient bearing ball failures at early stages, thus markedly improving prediction accuracy. Our empirical results underscore the superior performance of this composite methodology in accurately detecting a spectrum of five mechanical bearing ball failure types, achieving a substantial enhancement in diagnostic precision. [ABSTRACT FROM AUTHOR]

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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.)

Background: To address issues in current deep learning models for detecting defects on industrial bearing surfaces, such as large parameter sizes and low precision in identifying small defects, we propose a lightweight detection algorithm for small-sized bearing appearance defects. Methods: First, we introduce a large separable convolution attention module on the spatial pyramid pooling fusion module. The deep convolutional layer with large convolutional kernels effectively captures more extensive context information of small-sized bearing defects while reducing the computation burden and learns attention weights to adaptively select the importance of input features. Secondly, we integrate the SimAM (simple attention mechanism) into the model without increasing the original network parameters, thereby augmenting the capacity to extract small-sized features and enhancing the model's feature fusion capability. Finally, utilizing SIoU (Scylla IoU) as the regression loss and Soft-NMS (soft non-max suppression) for handling redundant boxes strengthens the model's capacity to identify overlapping areas. Results: Experimental results demonstrate that our improved YOLOv8n model, sized at 6.5 MB, outperforms the baseline in terms of precision, recall, and mAP (mean average precision), with FPS (frames per second) of 146.7 (f/s), significantly enhancing bearing defect recognition for industrial applications. [ABSTRACT FROM AUTHOR]

Copyright of Railway Standard Design is the property of Railway Standard Design Editorial Office 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.)

In view of the problem that source signals cannot be effectively separated in the process of blind source separation of similar nonstationary nonlinear signals, a spatiotemporal intrinsic mode decomposition method was proposed for bearing fault diagnosis. Spatiotemporal intrinsic mode decomposition can separate source signals and construct Fourier basis dictionaries and nonlinear signal models. The fault components can be separated by using this method in fault diagnosis. The proper initial phase function is selected for blind source separation of signals and signal decomposition components are obtained. By simulation analysis, spatiotemporal intrinsic mode decomposition than the fast independent component analysis method can more intuitive clearly separate the signals of admixed with large modulation components of correlation coefficient, and through the impact of component kurtosis index to judge fault, inherent modal decomposition method better prove time restore original bearing vibration signals and fault impact. Through the analysis and comparison of experimental data, the spatiotemporal intrinsic mode decomposition method has a significant effect on the fault diagnosis and analysis of rolling bearing outer ring, inner ring, and can intuitively express the fault characteristic frequency and frequency doubling through the analysis of envelope spectrum. In processing the industrial data, the spatiotemporal intrinsic mode decomposition method can also find the frequency characteristics of inner ring fault more clearly and accurately. Therefore, the spatiotemporal intrinsic mode decomposition method can solve the problem of blind source separation and realize fault diagnosis in rolling bearing fault field. [ABSTRACT FROM AUTHOR]

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Bearings are one of the main components of induction motors, machines widely employed in today's industries, making their monitoring a primordial task; however, most systems focus on measuring one physical magnitude to detect one kind of fault at a time. This research tackles the combination of two common faults, grease contamination and outer race damage, as lubricant contamination significantly impacts the life of the bearing and the emergence of other defects; as a contribution, this paper proposes a methodology for the diagnosis of this combination of faults based on a proprietary data acquisition system measuring vibration and current signals, from which time domain statistical and fractal features are computed and then fused using LDA for dimensionality reduction, ending with an SVM model for classification, achieving 97.1% accuracy, correctly diagnosing the combination of the contamination with different severities of the outer race damage, improving the classification results achieved when using vibration and current signals individually by 7.8% and 27.2%, respectively. [ABSTRACT FROM AUTHOR]

Purpose: Total hip arthroplasty (THA) in younger patients remains controversial due to concerns regarding long-term implant survival and potential complications. This study aimed to evaluate long-term clinical outcomes, complications, differences in complication and revision rates by bearing surfaces, and Kaplan–Meier survival curves for THA in patients under 20 years old. Methods: A retrospective review was conducted for 65 patients (78 hips) who underwent THA between 1991 and 2018. Their mean age was 18.9 years. Their clinical outcomes were assessed using the Harris Hip Score (HHS). Radiological outcomes were evaluated based on the presence of loosening, osteolysis, and heterotopic ossification. Complications such as dislocation, periprosthetic fractures, and infections were assessed. The mean follow-up period was 13.2 years (range, 5.0–31.2 years). Results: The mean HHS improved from 44.6 to 90.1. There were two cases of dislocation. However, no periprosthetic fracture, deep infection, or ceramic component fracture was noted. There were 19 revisions of implants. Eighteen of 19 hips were operated with hard-on-soft bearings in the index surgery (p < 0.01). The 23-year survivorship was 97.8% for THA using ceramic-on-ceramic bearings, while the 31-year survivorship was 36.7% using hard-on-soft bearings. Conclusion: THA in patients under 20 years old yielded promising clinical and radiological outcomes, although polyethylene-bearing-related concerns persisted. Previously operated patients with hard-on-soft bearing should be meticulously examined during the follow-up. As ceramic-on-ceramic bearing showed excellent survivorship in this particular cohort, we recommend the use of this articulation as the bearing of choice. [ABSTRACT FROM AUTHOR]

In 2007, the American Iron and Steel Institute (AISI) established a standard for cold-formed steel–special bolted moment frames (CFS-SBMFs). This structural system is designed to resist seismic forces. The CFS-SBMF system employs double-channel beams and square hollow structural section (HSS) columns that are bolted together to create a sturdy and robust structural frame. However, the CFS-SBMF system is only suitable for constructing one-storey buildings, and ASCE 7 prohibits its use in buildings with a height of over one storey. This study was conducted to expand the use of CFS-SBMFs to the construction of multi-storey low-rise buildings. Firstly, a new moment connection detail is proposed, and a design procedure is proposed to ensure that bolted connections, instead of beams or columns, have the ductility to withstand seismic forces. Secondly, the proposed design procedure for bolted connections was verified through full-scale cyclic testing. Finally, a comprehensive evaluation was undertaken to evaluate the proposed structural system's performance under seismic excitation. The evaluation included nonlinear dynamic analysis and incremental dynamic analysis (IDA) according to FEMA P695, which provided a detailed understanding of the seismic design factors (SDFs) in multi-storey low-rise CFS-SBMF buildings. [ABSTRACT FROM AUTHOR]

In order to reduce the vibration of bearings in the bearing rotor system, a set of adaptive preload device based on pneumatic control was proposed. The vibration suppression effect of preload on the rotor and bearing roller at different rotating speed was studied based on preload force and rotating speed. An active control system composed of data acquisition system and pneumatic system was developed to monitor the vibration state of the bearing in the process of rotation in real time, and the preload was adjusted timely to ensure that the vibration was suppressed to the greatest extent during the normal operation of the bearing. The rotor system equipped with an aligning roller bearing (22208CA) was tested to verify the function of the device. The experimental results show that the vibration suppression effect of the device is more than 30% when the rotor speed is less than the first critical speed, and the bearing vibration amplitude is greatly reduced. Compared with the existing intelligent bearing units, the device provides a more efficient and simple solution for vibration suppression of the rotor bearing system. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering for Thermal Energy & Power / Reneng Dongli Gongcheng is the property of Journal of Engineering for Thermal Energy & Power 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.)

Industry 4.0 drives the demand for cost-efficient and reliable process data and condition monitoring. Therefore, visualizing the state of tribological contacts becomes important, as they are regularly found in the center of many applications. Utilizing rolling element bearings as sensors and monitoring their health by the electrical impedance method are promising approaches as it allows, e.g., load sensing and detection of bearing failures. The impedance cannot be measured directly, but there are various methods available. This paper discusses advantages and disadvantages and suggests the AC Wheatstone bridge as a reliable way of measuring impedances with low phase angles at sampling rates in the kHz range. The corresponding equations are introduced, a simulation built, an uncertainty mode and effects analysis carried out and sample measurement results of real rolling elements shown. It can be demonstrated that the AC Wheatstone bridge meets the proposed requirements for sensory utilization and condition monitoring when the bearing is operated in the hydrodynamic regime.