Special Issue "Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction"

A special issue of Machines (ISSN 2075-1702).

Deadline for manuscript submissions: 15 September 2022 | Viewed by 5120

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
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 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 1800 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

Published Papers (10 papers)

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Research

Article
A New Piecewise Nonlinear Asymmetry Bistable Stochastic Resonance Model for Weak Fault Extraction
by and
Machines 2022, 10(5), 373; https://doi.org/10.3390/machines10050373 - 14 May 2022
Viewed by 260
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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Article
Reliability Assessment Method Based on Condition Information by Using Improved Proportional Covariate Model
Machines 2022, 10(5), 337; https://doi.org/10.3390/machines10050337 - 05 May 2022
Viewed by 295
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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Article
Two-Stage Multi-Scale Fault Diagnosis Method for Rolling Bearings with Imbalanced Data
Machines 2022, 10(5), 336; https://doi.org/10.3390/machines10050336 - 04 May 2022
Viewed by 335
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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Article
Failure Analysis of a Cylindrical Roller Bearing Caused by Excessive Tightening Axial Force
Machines 2022, 10(5), 322; https://doi.org/10.3390/machines10050322 - 29 Apr 2022
Viewed by 324
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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Article
Dynamic Reweighted Domain Adaption for Cross-Domain Bearing Fault Diagnosis
Machines 2022, 10(4), 245; https://doi.org/10.3390/machines10040245 - 30 Mar 2022
Cited by 1 | Viewed by 439
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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Article
A Fault Diagnosis Method of Rolling Bearings Based on Parameter Optimization and Adaptive Generalized S-Transform
Machines 2022, 10(3), 207; https://doi.org/10.3390/machines10030207 - 14 Mar 2022
Viewed by 510
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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Article
Micromechanism of Plastic Accumulation and Damage Initiation in Bearing Steels under Cyclic Shear Deformation: A Molecular Dynamics Study
Machines 2022, 10(3), 199; https://doi.org/10.3390/machines10030199 - 10 Mar 2022
Viewed by 471
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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Article
A Comparative Study to Predict Bearing Degradation Using Discrete Wavelet Transform (DWT), Tabular Generative Adversarial Networks (TGAN) and Machine Learning Models
Machines 2022, 10(3), 176; https://doi.org/10.3390/machines10030176 - 26 Feb 2022
Viewed by 779
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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Article
Optimal Placement of Sensors Based on Data Fusion for Condition Monitoring of Pulley Group under Speed Variation Condition
Machines 2022, 10(2), 148; https://doi.org/10.3390/machines10020148 - 17 Feb 2022
Viewed by 305
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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Article
Research on the Electromagnetic Conversion Method of Stator Current for Local Fault Detection of a Planetary Gearbox
Machines 2021, 9(11), 277; https://doi.org/10.3390/machines9110277 - 08 Nov 2021
Cited by 1 | Viewed by 485
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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