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Machines
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Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction
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Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Electrical Machines and Drives".

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 34035

Special Issue Editors

Prof. Dr. Hongrui Cao

E-Mail Website
Guest Editor
School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: dynamnic modeling and fault diagnosis of machinery
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jianping Xuan

E-Mail Website
Guest Editor
School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: condition monitoring and fault diagnosis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yongqiang Liu

E-Mail Website
Guest Editor
School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
Interests: condition monitoring and fault diagnosis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A variety of industrial and household appliances are equipped with rotating systems. These are used in electric motors, pumps, rotary engines and compressors, turbines, automobiles, railways, steel industries, power plants, materials-handling devices, jet engines, and many more. Bearings constitute one of the most critical components in rotating machinery. In today’s competitive environment, due to the increase in demand on running accuracy and nonlinearity involved in such systems, condition-based and predictive maintenance of bearings are gaining more popularity.

The objective of this Special Issue is to discover the most recent and significant developments in bearing modeling, fault diagnosis, and remaining useful life (RUL) prediction. This Special Issue encourages and welcomes original research articles with a significant contribution to numerical, theoretical and experimental analysis. Review articles related to these application areas are also invited.

Potential topics include but are not limited to:

  • Modeling and simulation;
  • Failure mechanism analysis;
  • Intelligent sensors and flexible sensors;
  • Wireless sensors and sensor networks;
  • Signal processing theory and methods;
  • Data acquisition and measurement methods;
  • Bearing condition monitoring;
  • Machine learning and intelligent fault diagnosis;
  • Bearing RUL prediction;
  • Big data analytics in bearings;
  • Intelligent bearings.

Prof. Dr. Hongrui Cao
Prof. Dr. Jianping Xuan
Prof. Dr. Yongqiang Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Bearing modeling
  • Failure analysis
  • Signal processing
  • Condition monitoring
  • Fault diagnosis
  • RUL prediction
  • Big data analytics

Related Special Issue

Published Papers (16 papers)

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Research

21 pages, 5592 KiB  
Article
Deep Subdomain Transfer Learning with Spatial Attention ConvLSTM Network for Fault Diagnosis of Wheelset Bearing in High-Speed Trains
by Jiujian Wang, Shaopu Yang, Yongqiang Liu and Guilin Wen
Machines 2023, 11(2), 304; https://0-doi-org.brum.beds.ac.uk/10.3390/machines11020304 - 17 Feb 2023
Cited by 1 | Viewed by 1349
Abstract
High-speed trains operate under varying conditions, leading to different distributions of vibration data collected from the wheel bearings. To detect bearing faults in situations where the source and target domains exhibit differing data distributions, the technique of transfer learning can be applied to [...] Read more.
High-speed trains operate under varying conditions, leading to different distributions of vibration data collected from the wheel bearings. To detect bearing faults in situations where the source and target domains exhibit differing data distributions, the technique of transfer learning can be applied to move the distribution of features gleaned from unlabeled data in the source domain. However, traditional deep transfer learning techniques do not take into account the relationships between subdomains within the same class of different domains, resulting in suboptimal transfer learning performance and limiting the use of intelligent fault diagnosis for wheel bearings under various conditions. In order to tackle this problem, we have developed the Deep Subdomain Transfer Learning Network (DSTLN). This innovative approach transfers the distribution of features by harmonizing the subdomain distributions of layer activations specific to each domain through the implementation of the Local Maximum Mean Discrepancy (LMMD) method. The DSTLN consists of three modules: a feature extractor, fault category recognition, and domain adaptation. The feature extractor is constructed using a newly proposed SA-ConvLSTM model and CNNs, which aim to automatically learn features. The fault category recognition module is a classifier that categorizes the samples based on the extracted features. The domain adaptation module includes an adversarial domain classifier and subdomain distribution discrepancy metrics, making the learned features domain-invariant across both the global domain and subdomains. Through 210 transfer fault diagnosis experiments with wheel bearing data under 15 different operating conditions, the proposed method demonstrates its effectiveness. Full article
(This article belongs to the Special Issue Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction)
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15 pages, 6325 KiB  
Article
Fuzzy Broad Learning System Combined with Feature-Engineering-Based Fault Diagnosis for Bearings
by Jianmin Zhou, Xiaotong Yang, Lulu Liu, Yunqing Wang, Junjie Wang and Guanghao Hou
Machines 2022, 10(12), 1229; https://0-doi-org.brum.beds.ac.uk/10.3390/machines10121229 - 16 Dec 2022
Cited by 1 | Viewed by 1391
Abstract
Bearings are essential components of rotating machinery used in mechanical systems, and fault diagnosis of bearings is of great significance to the operation and maintenance of mechanical equipment. Deep learning is a popular method for bearing fault diagnosis, which can effectively extract the [...] Read more.
Bearings are essential components of rotating machinery used in mechanical systems, and fault diagnosis of bearings is of great significance to the operation and maintenance of mechanical equipment. Deep learning is a popular method for bearing fault diagnosis, which can effectively extract the in-depth information of fault signals, thus achieving high fault diagnosis accuracy. However, due to the complex deep structure of deep learning, most deep learning methods require more time and resources for bearing fault diagnosis. This paper proposes a bearing fault diagnosis method combining feature engineering and fuzzy broad learning. First, time domain, frequency domain, and time-frequency domain features are extracted from the bearing signals. Then the stability and robustness indexes of these features are evaluated to complete the feature engineering. The features obtained by feature engineering are used as the input of the fault diagnosis model, and three sets of experimental data validate the model. The experimental results show that the proposed method can achieve the bearing fault diagnosis accuracy of 96.43% on the experimental bench data, 100% on the Case Western Reserve University dataset, and 100% on the centrifugal pump bearing fault dataset, with a time of approximately 0.28 s. The results show that this method has the advantages of accuracy, rapidity, and stability of bearing fault diagnosis. Full article
(This article belongs to the Special Issue Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction)
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19 pages, 2566 KiB  
Article
Bearing Fault Diagnosis for Time-Varying System Using Vibration–Speed Fusion Network Based on Self-Attention and Sparse Feature Extraction
by Fulin Chi, Xinyu Yang, Siyu Shao and Qiang Zhang
Machines 2022, 10(10), 948; https://0-doi-org.brum.beds.ac.uk/10.3390/machines10100948 - 18 Oct 2022
Cited by 3 | Viewed by 1878
Abstract
Nowadays, most deep-learning-based bearing fault diagnosis methods are studied under the condition of steady speed, while the performance of these models cannot be fully played under time-varying conditions. Therefore, in order to facilitate the practical application of a deep learning model in bearing [...] Read more.
Nowadays, most deep-learning-based bearing fault diagnosis methods are studied under the condition of steady speed, while the performance of these models cannot be fully played under time-varying conditions. Therefore, in order to facilitate the practical application of a deep learning model in bearing fault diagnosis, a vibration–speed fusion network is proposed, which utilizes a transformer with a self-attention module to extract vibration features and utilizes a sparse autoencoder (SAE) network to extract sparse features from speed pulse signal. The vibration–speed fusion network enables the efficient fusion of different signals in a high-dimensional vector space with a high degree of model interpretability, without additional signal processing steps. After tuning the hyperparameters of the network, the key segments of the bearing’s time-domain vibration signals can be optimally extracted, the network performance is much better than traditional deep learning methods, and the classification accuracy can reach 95.18% and 99.85% on the two public bearing datasets from the Xi’an Jiaotong University and the University of Ottawa. Full article
(This article belongs to the Special Issue Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction)
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18 pages, 7227 KiB  
Article
A New Method of Bearing Remaining Useful Life Based on Life Evolution and SE-ConvLSTM Neural Network
by Shuai Yang, Yongqiang Liu, Yingying Liao and Kang Su
Machines 2022, 10(8), 639; https://0-doi-org.brum.beds.ac.uk/10.3390/machines10080639 - 01 Aug 2022
Cited by 4 | Viewed by 1832
Abstract
The degradation process of bearing performance in the whole life cycle is a complex and nonlinear process. However, the traditional neural network-based approaches usually consider the degradation process of bearing performance as linear, which does not accord with the actual situation of bearing [...] Read more.
The degradation process of bearing performance in the whole life cycle is a complex and nonlinear process. However, the traditional neural network-based approaches usually consider the degradation process of bearing performance as linear, which does not accord with the actual situation of bearing degradation. To overcome this shortcoming, a rolling bearing’s remaining useful life prediction method based on a Squeeze-and-Excitation-Convolutional long short-term memory (SE-ConvLSTM) neural network was proposed based on the construction of a new health index in the process of bearing life evolution. The proposed method considered the change rule of the health indicator during the whole life cycle evolution of bearings, then constructed the health indicator by using the SE-ConvLSTM neural network, effectively improving the model prediction accuracy and training efficiency. Firstly, the original data are filtered and denoised by Ensemble Empirical Mode Decomposition. Combined with Principal Component Analysis (PCA) dimensionality reduction and the Local Outlier Factor (LOF) algorithm, the bearing’s life evolution interval is divided. Then, the health indicator is constructed based on the proposed SE-ConvLSTM model, and the remaining useful life of rolling bearings is predicted by a particle filter and double exponential model. The proposed method is compared with other related methods with the PHM2012 dataset, and the results show that the proposed method has higher accuracy in remaining useful life predictions. Compared with the traditional method, the health index construction based on the division of the lifespan evolution interval has higher practical significance. Full article
(This article belongs to the Special Issue Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction)
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21 pages, 6367 KiB  
Article
A Rolling Bearing Fault Diagnosis Method Based on Enhanced Integrated Filter Network
by Kang Wu, Jie Tao, Dalian Yang, Hu Xie and Zhiying Li
Machines 2022, 10(6), 481; https://0-doi-org.brum.beds.ac.uk/10.3390/machines10060481 - 15 Jun 2022
Cited by 2 | Viewed by 1764
Abstract
Aiming at the difficulty of rolling bearing fault diagnosis in a strong noise environment, this paper proposes an enhanced integrated filter network. In the method, we firstly design an enhanced integrated filter, which includes the filter enhancement module and the expression enhancement module. [...] Read more.
Aiming at the difficulty of rolling bearing fault diagnosis in a strong noise environment, this paper proposes an enhanced integrated filter network. In the method, we firstly design an enhanced integrated filter, which includes the filter enhancement module and the expression enhancement module. The filter enhancement module can not only filter the high-frequency noise to extract useful features of medium and low-frequency signals but also maintain frequency and time resolution to some extent. On this basis, the expression enhancement module analyzes fault features intercepted by the upper network at multiple scales to get deep features. Then we introduce vector neurons to integrate scalar features into vector space, which mine the correlation between features. The feature vectors are transmitted by dynamic routing to establish the relationship between low-level capsules and high-level capsules. In order to verify the diagnostic performance of the model, CWRU and IMS bearing datasets are used for experimental verification. In the strong noise environment of SNR = −4 dB, the fault diagnosis precisions of the method on CWRU and IMS reach 94.85% and 92.45%, respectively. Compared with typical bearing fault diagnosis methods, the method has higher fault diagnosis precision and better generalization ability in a strong noise environment. Full article
(This article belongs to the Special Issue Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction)
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21 pages, 5317 KiB  
Article
Anomaly Data Detection of Rolling Element Bearings Vibration Signal Based on Parameter Optimization Isolation Forest
by Haiming Wang, Qiang Li, Yongqiang Liu and Shaopu Yang
Machines 2022, 10(6), 459; https://0-doi-org.brum.beds.ac.uk/10.3390/machines10060459 - 09 Jun 2022
Cited by 7 | Viewed by 2116
Abstract
Anomaly data detection is not only an important part of the condition monitoring process of rolling element bearings, but also the premise of data cleaning, compensation and mining. Aiming at the abnormal data segment detection of the vibration signals of a rolling element [...] Read more.
Anomaly data detection is not only an important part of the condition monitoring process of rolling element bearings, but also the premise of data cleaning, compensation and mining. Aiming at the abnormal data segment detection of the vibration signals of a rolling element bearing, this paper proposes an abnormal data detection model based on comprehensive features and parameter optimization isolation forest (CF-POIF), which can adaptively identify abnormal data segments. First, in order to extract the mutation feature of vibration signals more accurately, the concept of comprehensive feature is proposed, which integrates the time domain and wavelet packet energy features. Then, the particle swarm optimization (PSO) algorithm is used to optimize the rectangular window length and sub sample set capacity in the isolation forest for anomaly detection. Finally, three real cases concerning abnormal data are used to verify the effectiveness of the proposed method. The results demonstrate that the proposed method is able to detect missing data, drift data and external interference data effectively, and it has a higher F1 score and accuracy compared to other methods. Full article
(This article belongs to the Special Issue Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction)
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16 pages, 8316 KiB  
Article
A New Piecewise Nonlinear Asymmetry Bistable Stochastic Resonance Model for Weak Fault Extraction
by Li Cui and Wuzhen Xu
Machines 2022, 10(5), 373; https://0-doi-org.brum.beds.ac.uk/10.3390/machines10050373 - 14 May 2022
Viewed by 1411
Abstract
In order to solve output saturation problems found in traditional stochastic resonance methods and to improve the diagnosis ability of weak faults, a new piecewise nonlinear asymmetric bistable stochastic resonance (PNABSR) method is proposed. This model uses a left and right potential function [...] Read more.
In order to solve output saturation problems found in traditional stochastic resonance methods and to improve the diagnosis ability of weak faults, a new piecewise nonlinear asymmetric bistable stochastic resonance (PNABSR) method is proposed. This model uses a left and right potential function with an asymmetrical shape, which makes it easier to induce stochastic resonance phenomena. Based on the PNABSR model, the expression of the signal-to-noise ratio (SNR) is derived, and the changes in the SNR with different parameters in the PNABSR model are analyzed. Then, the parameters in the PNABSR model are optimized using the adaptive intelligent algorithm to enhance the diagnostic ability. The diagnosis properties of the weak fault are compared between the PNABSR model and the classical bistable stochastic resonance model (CBSR). The experimental results prove that the PNABSR model can effectively extract the weak fault characteristic frequency under a strong noise background, verifying the effectiveness of this method. Full article
(This article belongs to the Special Issue Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction)
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17 pages, 3506 KiB  
Article
Reliability Assessment Method Based on Condition Information by Using Improved Proportional Covariate Model
by Baojia Chen, Zhengkun Chen, Fafa Chen, Wenrong Xiao, Nengqi Xiao, Wenlong Fu and Gongfa Li
Machines 2022, 10(5), 337; https://0-doi-org.brum.beds.ac.uk/10.3390/machines10050337 - 05 May 2022
Cited by 2 | Viewed by 1380
Abstract
If sufficient historical failure life data exist, the failure distribution of the system can be estimated to identify the system initial hazard function. The conventional proportional covariate model (PCM) can reveal the dynamic relationship between the response covariates and the system hazard rate. [...] Read more.
If sufficient historical failure life data exist, the failure distribution of the system can be estimated to identify the system initial hazard function. The conventional proportional covariate model (PCM) can reveal the dynamic relationship between the response covariates and the system hazard rate. The system hazard rate function can be constantly updated by the response covariates through the basic covariate function (BCF). Under the circumstances of sparse or zero failure data, the key point of the PCM reliability assessment method is to determine the proportional factor between covariates and the hazard rate for getting BCF. Being devoid of experiments or abundant experience of the experts, it is very hard to determine the proportional factor accurately. In this paper, an improved PCM (IPCM) is put forward based on the logistic regression model (LRM). The salient features reflecting the equipment degradation process are extracted from the existing monitoring signals, which are considered as the input of the LRM. The equipment state data defined by the failure threshold are considered as the output of the LRM. The initial reliability can be first estimated by LRM. Combined with the responding covariates, the initial hazard function can be calculated. Then, it can be incorporated into conventional PCM to implement the reliability estimation process on other equipment. The conventional PCM and the IPCM methods are respectively applied to aero-engine rotor bearing reliability assessment. The comparative results show that the assessing accuracy of IPCM is superior to the conventional PCM for small failure sample. It provides a new method for reliability estimation under sparse or zero failure data conditions. Full article
(This article belongs to the Special Issue Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction)
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23 pages, 6051 KiB  
Article
Two-Stage Multi-Scale Fault Diagnosis Method for Rolling Bearings with Imbalanced Data
by Minglei Zheng, Qi Chang, Junfeng Man, Yi Liu and Yiping Shen
Machines 2022, 10(5), 336; https://0-doi-org.brum.beds.ac.uk/10.3390/machines10050336 - 04 May 2022
Cited by 9 | Viewed by 1688
Abstract
Intelligent bearing fault diagnosis is a necessary approach to ensure the stable operation of rotating machinery. However, it is usually difficult to collect fault data under actual working conditions, leading to a serious imbalance in training datasets, thus reducing the effectiveness of data-driven [...] Read more.
Intelligent bearing fault diagnosis is a necessary approach to ensure the stable operation of rotating machinery. However, it is usually difficult to collect fault data under actual working conditions, leading to a serious imbalance in training datasets, thus reducing the effectiveness of data-driven diagnostic methods. During the stage of data augmentation, a multi-scale progressive generative adversarial network (MS-PGAN) is used to learn the distribution mapping relationship from normal samples to fault samples with transfer learning, which stably generates fault samples at different scales for dataset augmentation through progressive adversarial training. During the stage of fault diagnosis, the MACNN-BiLSTM method is proposed, based on a multi-scale attention fusion mechanism that can adaptively fuse the local frequency features and global timing features extracted from the input signals of multiple scales to achieve fault diagnosis. Using the UConn and CWRU datasets, the proposed method achieves higher fault diagnosis accuracy than is achieved by several comparative methods on data augmentation and fault diagnosis. Experimental results demonstrate that the proposed method can stably generate high-quality spectrum signals and extract multi-scale features, with better classification accuracy, robustness, and generalization. Full article
(This article belongs to the Special Issue Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction)
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15 pages, 10818 KiB  
Article
Failure Analysis of a Cylindrical Roller Bearing Caused by Excessive Tightening Axial Force
by Xueqin Hou, Qing Diao, Yujian Liu, Changkui Liu, Zheng Zhang and Chunhu Tao
Machines 2022, 10(5), 322; https://0-doi-org.brum.beds.ac.uk/10.3390/machines10050322 - 29 Apr 2022
Cited by 4 | Viewed by 3526
Abstract
The premature failure of a cylindrical roller bearing took place during service, with a total operation time of 100 h. The failure cause was analyzed by macroscopic and microscopic observation, metallographic analysis, hardness testing, tightening axial force influence analysis, and test verification. The [...] Read more.
The premature failure of a cylindrical roller bearing took place during service, with a total operation time of 100 h. The failure cause was analyzed by macroscopic and microscopic observation, metallographic analysis, hardness testing, tightening axial force influence analysis, and test verification. The results show that failure modes of the bearing are contact fatigue spalling, wear, and fatigue fracture. The outer ring, inner ring, rollers, and cages all have suffered relatively heavy damage in the sides corresponding to the bearing side with laser marking. Excessive load, induced by the excessive tightening axial force, derived from the lock nut, is the cause of the bearing failure. The failure mechanism is that excessive tightening axial force caused a great deformation and cylindricity increase of the inner ring raceway, which induced high local contact stress between one side of the ring raceways, as well as the corresponding ends of the rollers, resulting in the bearing failure. At last, measures for prevention of this failure are put forward as follows: controlling the tightening axial force within the range of technical requirement, increasing the convexity of the inner ring raceway and rollers, and decreasing the grinding undercut size of the inner ring. Full article
(This article belongs to the Special Issue Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction)
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20 pages, 2731 KiB  
Article
Dynamic Reweighted Domain Adaption for Cross-Domain Bearing Fault Diagnosis
by Yu Meng, Jianping Xuan, Long Xu and Jie Liu
Machines 2022, 10(4), 245; https://0-doi-org.brum.beds.ac.uk/10.3390/machines10040245 - 30 Mar 2022
Cited by 5 | Viewed by 2067
Abstract
Certain progress has been made in fault diagnosis under cross-domain scenarios recently. Most researchers have paid almost all their attention to promoting domain adaptation in a common space. However, several challenges that will cause negative transfer have been ignored. In this paper, a [...] Read more.
Certain progress has been made in fault diagnosis under cross-domain scenarios recently. Most researchers have paid almost all their attention to promoting domain adaptation in a common space. However, several challenges that will cause negative transfer have been ignored. In this paper, a reweighting method is proposed to overcome this difficulty from two aspects. First, extracted features differ greatly from one another in promoting positive transfer, and measuring the difference is important. Measured by conditional entropy, the weight of adversarial losses for those well aligned features are reduced. Second, the balance between domain adaptation and class discrimination greatly influences the transferring task. Here, a dynamic weight strategy is adopted to compute the balance factor. Consideration is made from the perspective of maximum mean discrepancy and multiclass linear discriminant analysis. The first item is supposed to measure the degree of the domain adaptation between source and the target domain, and the second is supposed to show the classification performance of the classifier on the learned features in the current training epoch. Finally, extensive experiments on several bearing fault diagnosis datasets are conducted. The performance shows that our model has an obvious advantage compared with other common transferring algorithms. Full article
(This article belongs to the Special Issue Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction)
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19 pages, 3873 KiB  
Article
A Fault Diagnosis Method of Rolling Bearings Based on Parameter Optimization and Adaptive Generalized S-Transform
by Yuwei Peng and Xianghua Ma
Machines 2022, 10(3), 207; https://0-doi-org.brum.beds.ac.uk/10.3390/machines10030207 - 14 Mar 2022
Cited by 5 | Viewed by 2158
Abstract
As for the fault diagnosis of rolling bearings under strong background noises, whether the fault feature extraction is comprehensive and accurate is critical, especially for the data-driven fault diagnosis methods. To improve the comprehensiveness and accuracy of the fault feature extraction, a fault [...] Read more.
As for the fault diagnosis of rolling bearings under strong background noises, whether the fault feature extraction is comprehensive and accurate is critical, especially for the data-driven fault diagnosis methods. To improve the comprehensiveness and accuracy of the fault feature extraction, a fault diagnosis method of rolling bearings is proposed based on parameter optimization and Adaptive Generalized S-Transform (AGST). The AGST is used to solve the problem of incomplete feature extraction of bearing faults. The Particle Swarm Brain Storm Optimization algorithm based on the Discussion Mechanism (PSDMBSO) is used for the parameter optimization of VMD, which can better separate the complete fault components. The effectiveness of the fault diagnosis method proposed in this paper is verified by comparison with other methods. Full article
(This article belongs to the Special Issue Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction)
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16 pages, 9070 KiB  
Article
Micromechanism of Plastic Accumulation and Damage Initiation in Bearing Steels under Cyclic Shear Deformation: A Molecular Dynamics Study
by Yachao Sun, Hongrui Cao and Xunkai Wei
Machines 2022, 10(3), 199; https://0-doi-org.brum.beds.ac.uk/10.3390/machines10030199 - 10 Mar 2022
Viewed by 1952
Abstract
Fatigue failure usually occurs on the subsurface in rolling bearings due to multiaxial and non-proportional fatigue loadings between rolling elements. One of the main stress components is the alternating shear stress. This paper focuses on the micromechanism of plastic accumulation and damage initiation [...] Read more.
Fatigue failure usually occurs on the subsurface in rolling bearings due to multiaxial and non-proportional fatigue loadings between rolling elements. One of the main stress components is the alternating shear stress. This paper focuses on the micromechanism of plastic accumulation and damage initiation in bearing steels under cyclic shear deformation. The distribution of subsurface shear stress in bearings was firstly investigated by finite element simulation. An atomic model containing bcc-Fe and cementite phases was built by molecular dynamics (MD). Shear stress–strain characteristics were discussed to explore the mechanical properties of the atomic model. Ten alternating shear cycles were designed to explore the mechanism of cyclic plastic accumulation and damage initiation. Shear stress responses and evolutions of dislocaitons, defect meshes and high-strain atoms were discussed. The results show that cyclic softening occurs when the model is in the plastic stage. Severe cyclic shear deformation can accelerate plastic accumulation and result in an earlier shear slip of the cementite phase than that under monotonic shear deformation, which might be the initiation of microscopic damage in bearing steels. Full article
(This article belongs to the Special Issue Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction)
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18 pages, 5377 KiB  
Article
A Comparative Study to Predict Bearing Degradation Using Discrete Wavelet Transform (DWT), Tabular Generative Adversarial Networks (TGAN) and Machine Learning Models
by Keval Bhavsar, Vinay Vakharia, Rakesh Chaudhari, Jay Vora, Danil Yurievich Pimenov and Khaled Giasin
Machines 2022, 10(3), 176; https://0-doi-org.brum.beds.ac.uk/10.3390/machines10030176 - 26 Feb 2022
Cited by 27 | Viewed by 3865
Abstract
Prognostics and health management (PHM) is a framework to identify damage prior to its occurrence which leads to the reduction of both maintenance costs and safety hazards. Based on the data collected in condition monitoring, the degradation of the part is predicted. Studies [...] Read more.
Prognostics and health management (PHM) is a framework to identify damage prior to its occurrence which leads to the reduction of both maintenance costs and safety hazards. Based on the data collected in condition monitoring, the degradation of the part is predicted. Studies show that most failures are caused by faults in rolling element bearing, which highlights that a bearing is one of the most important mechanical components of any machine. Thus, it becomes important to monitor bearing degradation to make sure that it is utilized properly. Generally, machine learning (ML) or deep learning (DL) techniques are utilized to predict bearing degradation using a data-driven approach, where signals are captured from the machine. There should be a large amount of data to apply either ML or DL techniques, but it is difficult to collect that amount of data directly from any machine. In this study, health assessment is carried out using the correlation coefficient to divide the bearing life into two degradation stages. The raw signal is processed using discrete wavelet transform (DWT), where mutual information (MI) is used to rank and select the base wavelet, after which tabular generative adversarial networks (TGAN) are used to generate the artificial coefficients. Statistical features are calculated from the real data (DWT coefficients) and the artificial data (generated from TGAN). The constructed feature vector is then used as an input to train machine learning models, namely ensemble bagged tree (EBT) and Gaussian process regression with the squared exponential kernel function (SEGPR), to estimate bearing degradation conditions. Both the machine learning models were validated on the publicly available experimental data of FEMTO bearing. Obtained results showed that the developed EBT and SEGPR models accurately predicted the bearing degradation conditions with the average lowest RMSE value of 0.0045 and MAE value of 0.0037. Full article
(This article belongs to the Special Issue Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction)
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22 pages, 23429 KiB  
Article
Optimal Placement of Sensors Based on Data Fusion for Condition Monitoring of Pulley Group under Speed Variation Condition
by Jie Wu, Yanyang Zi, Hongru Ma, Yaochun Wu and Xiaofeng Xue
Machines 2022, 10(2), 148; https://0-doi-org.brum.beds.ac.uk/10.3390/machines10020148 - 17 Feb 2022
Viewed by 1431
Abstract
Pulley group plays an important role in the transmission of large mechanical equipment. To obtain informative data for condition monitoring, it is very important to optimize sensor placement on the pulley group. However, due to sharp speed fluctuation, heavy load and complex internal [...] Read more.
Pulley group plays an important role in the transmission of large mechanical equipment. To obtain informative data for condition monitoring, it is very important to optimize sensor placement on the pulley group. However, due to sharp speed fluctuation, heavy load and complex internal structure, sensor placement for acquiring optimal monitoring points is still a challenging task. Therefore, a novel sensor optimization method based on data fusion is proposed. In this method, the Kalman filter is firstly used to refine the collected signal for dealing with the variable noises. Subsequently, the variable periodicity strength of the signal is calculated to recognize the non-stationary characteristics of the measured signal. A data fusion technique based on maximum likelihood estimation (MLE) is then introduced to estimate sensitive components from the multi-source sensor signals for finding out optimal sensor placement points. The method is validated experimentally on a test rig of the pulley group with variable speed conditions. Analysis results show that the proposed method can recognize the optimal sensor placement points for the pulley group. Full article
(This article belongs to the Special Issue Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction)
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16 pages, 2280 KiB  
Article
Research on the Electromagnetic Conversion Method of Stator Current for Local Fault Detection of a Planetary Gearbox
by Xiangyang Xu, Guanrui Liu and Xihui Liang
Machines 2021, 9(11), 277; https://0-doi-org.brum.beds.ac.uk/10.3390/machines9110277 - 08 Nov 2021
Cited by 1 | Viewed by 1843
Abstract
Motor current signature analysis (MCSA) is a useful technique for planetary gear fault detection. Motor current signals have easier accessibility and are free from time-varying transfer path effects. If the fault symptoms in current signals are well understood, it will be more beneficial [...] Read more.
Motor current signature analysis (MCSA) is a useful technique for planetary gear fault detection. Motor current signals have easier accessibility and are free from time-varying transfer path effects. If the fault symptoms in current signals are well understood, it will be more beneficial to develop effective current signal processing methods. Some researchers have developed mathematical models to study the characteristics of current signals. However, no one has considered the coupling of rotor eccentricity and gear failures, resulting in an inaccurate analysis of the current signals. This study considers the sun gear failure of a planetary gearbox and the eccentricity of the motor rotor. An improved induction motor model is proposed based on the magnetomotive force (MMF) to simulate the stator current. By analyzing the current, the modulation relationships of gearbox meshing frequency, fault frequency, power supply frequency, and gear rotating frequency are obtained. The proposed model is validated to some extent using experimental data. Full article
(This article belongs to the Special Issue Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction)
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