Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.5
Journals
Applied Sciences
Sections
Bearing Fault Detection and Diagnosis
applsci-logo
Submit to Topical Collection Review for Applied Sciences

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Bearing Fault Detection and Diagnosis

A topical collection in Applied Sciences (ISSN 2076-3417). This collection belongs to the section "Mechanical Engineering".

Viewed by 14848

Editor

Dr. Oscar Duque-Perez

E-Mail Website
Collection Editor
Department of Electrical Engineering, Universidad de Valladolid, 47011 Valladolid, Spain
Interests: electric machines condition monitoring; power systems reliability and power quality; electric energy efficiency
Special Issues, Collections and Topics in MDPI journals

Topical Collection Information

Dear Colleagues,

Bearings are an essential part of modern machinery, allowing more efficient operation, extending operating life, avoiding mechanical breakdown, and allowing the efficient transmission of power. However, the bearings are not free from failure. In fact, because of the demanding function they perform, they are one of the main headaches for maintenance engineers. For example, in induction motors, more than half of all failures are considered to be due to bearings.

It is logical, therefore, that a research effort is being made to develop procedures that will improve the detectability and diagnostic capacity of the various failures that can occur in the different types of equipment commonly used in a wide range of applications. Traditionally, vibrations have been the signal used in predictive maintenance of bearings, although there are also proposals such as the use of electric current, infrared thermography or axial flow, among others.

This Topical Collection focuses on the topic of bearing fault diagnosis and diagnosis. Researchers are invited to contribute original research papers related to fault detection and diagnosis of bearings considering, but not limited to, different applications, signal processing techniques for fault detection, AI applications to diagnosis, condition-based monitoring, bearing lubrication condition, and remaining life prognosis. Solutions in the context of Industry 4.0 are welcome.

Dr. Oscar Duque-Perez
Collection Editor

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 collection 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. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • acoustic monitoring
  • artificial intelligence-based methods
  • bearing fault detection
  • bearing diagnosis
  • bearing prognosis
  • big data feature learning
  • data-based techniques
  • deep learning
  • digital signal processing
  • feature extraction methods
  • industrial Internet of Things
  • intelligent sensors
  • machine current signature analysis
  • machine learning
  • model-based techniques
  • signal-based techniques
  • statistical diagnosis methods
  • stray flux monitoring
  • vibration monitoring

Published Papers (9 papers)

Download All Papers

2024

Jump to: 2023, 2022

16 pages, 4056 KiB  
Article
Research on High-Speed Train Bearing Fault Diagnosis Method Based on Domain-Adversarial Transfer Learning
by Yingyong Zou, Wenzhuo Zhao, Tao Liu, Xingkui Zhang and Yaochen Shi
Appl. Sci. 2024, 14(19), 8666; https://doi.org/10.3390/app14198666 - 26 Sep 2024
Viewed by 528
Abstract
Traditional bearing fault diagnosis methods struggle to effectively extract distinctive, domain-invariable characterizations from one-dimensional vibration signals of high-speed train (HST) bearings under variable load conditions. A deep migration fault diagnosis method based on the combination of a domain-adversarial network and signal reconstruction unit [...] Read more.
Traditional bearing fault diagnosis methods struggle to effectively extract distinctive, domain-invariable characterizations from one-dimensional vibration signals of high-speed train (HST) bearings under variable load conditions. A deep migration fault diagnosis method based on the combination of a domain-adversarial network and signal reconstruction unit (CRU) is proposed for this purpose. The feature extraction module, which includes a one-dimensional convolutional (Cov1d) layer, a normalization layer, a ReLU activation function, and a max-pooling layer, is integrated with the CRU to form a feature extractor capable of learning key fault-related features. Additionally, the fault identification module and domain discrimination module utilize a combination of fully connected layers and dropout to reduce model parameters and mitigate the risk of overfitting. It is experimentally validated on two sets of bearing datasets, and the results show that the performance of the proposed method is better than other diagnostic methods under cross-load conditions, and it can be used as an effective cross-load bearing fault diagnosis method. Full article
►▼ Show Figures

Figure 1

23 pages, 9394 KiB  
Article
Small-Sample Bearings Fault Diagnosis Based on ResNet18 with Pre-Trained and Fine-Tuned Method
by Junlin Niu, Jiafang Pan, Zhaohui Qin, Faguo Huang and Haihua Qin
Appl. Sci. 2024, 14(12), 5360; https://doi.org/10.3390/app14125360 - 20 Jun 2024
Cited by 1 | Viewed by 1383
Abstract
In actual production, bearings are usually in a normal working state, which results in a lack of data for fault diagnosis (FD). Yet, the majority of existing studies on FD of rolling bearings focus on scenarios with ample fault data, while research on [...] Read more.
In actual production, bearings are usually in a normal working state, which results in a lack of data for fault diagnosis (FD). Yet, the majority of existing studies on FD of rolling bearings focus on scenarios with ample fault data, while research on diagnosing small-sample bearings remains scarce. Therefore, this study presents an FD method for small-sample bearings, employing variational-mode decomposition and Symmetric Dot Pattern, combined with a pre-trained and fine-tuned Residual Network18 (VSDP-TLResNet18). The approach utilizes variational-mode decomposition (VMD) to break down the signal, determining the k value and the best Intrinsic-Mode Function (IMF) component based on center frequency and kurtosis criteria. Following this, the chosen IMF component is converted into a two-dimensional image using the Symmetric Dot Pattern (SDP) transform. In order to maximize the discrimination between two-dimensional fault images, Pearson correlation analysis is carried out on the parameters of SDP to select the optimal parameters. Finally, we use the pre-trained and fine-tuned method combined with ResNet18 for small-sample FD to improve the diagnosis accuracy of the model. Relative to alternative approaches, the suggested method demonstrates strong performance when dealing with small-sample FD. Full article
►▼ Show Figures

Figure 1

19 pages, 28563 KiB  
Article
Intelligent Fault Diagnosis of Rolling Bearings Based on Markov Transition Field and Mixed Attention Residual Network
by Anshi Tong, Jun Zhang, Danfeng Wang and Liyang Xie
Appl. Sci. 2024, 14(12), 5110; https://doi.org/10.3390/app14125110 - 12 Jun 2024
Viewed by 856
Abstract
To address the problems of existing methods that struggle to effectively extract fault features and unstable model training using unbalanced data, this paper proposes a new fault diagnosis method for rolling bearings based on a Markov Transition Field (MTF) and Mixed Attention Residual [...] Read more.
To address the problems of existing methods that struggle to effectively extract fault features and unstable model training using unbalanced data, this paper proposes a new fault diagnosis method for rolling bearings based on a Markov Transition Field (MTF) and Mixed Attention Residual Network (MARN). The acquired vibration signals are transformed into two-dimensional MTF feature images as network inputs to avoid the loss of the original signal information, while retaining the temporal correlation; then, the mixed attention mechanism is inserted into the residual structure to enhance the feature extraction capability, and finally, the network is trained and outputs diagnostic results. In order to validate the feasibility of the MARN, other popular deep learning (DL) methods are compared on balanced and unbalanced datasets divided by a CWRU fault bearing dataset, and the proposed method results in superior performance. Ultimately, the proposed method achieves an average recognition accuracy of 99.5% and 99.2% under the two categories of divided datasets, respectively. Full article
►▼ Show Figures

Figure 1

18 pages, 3870 KiB  
Article
Bearing Fault Diagnosis Based on Image Information Fusion and Vision Transformer Transfer Learning Model
by Zichen Zhang, Jing Li, Chaozhi Cai, Jianhua Ren and Yingfang Xue
Appl. Sci. 2024, 14(7), 2706; https://doi.org/10.3390/app14072706 - 23 Mar 2024
Cited by 1 | Viewed by 1230
Abstract
In order to improve the accuracy of bearing fault diagnosis under a small sample, variable load, and noise conditions, a new fault diagnosis method based on an image information fusion and Vision Transformer (ViT) transfer learning model is proposed in this paper. Firstly, [...] Read more.
In order to improve the accuracy of bearing fault diagnosis under a small sample, variable load, and noise conditions, a new fault diagnosis method based on an image information fusion and Vision Transformer (ViT) transfer learning model is proposed in this paper. Firstly, the method applies continuous wavelet transform (CWT), Gramian angular summation field (GASF), and Gramian angular difference field (GADF) to the time series data, and generates three grayscale images. Then, the generated three grayscale images are merged into an information fusion image (IFI) using image processing techniques. Finally, the obtained IFIs are fed into the advanced ViT model and trained based on transfer learning. In order to verify the effectiveness and superiority of the proposed method, the rolling bearing dataset from Case Western Reserve University (CWRU) is used to carry out experimental studies under different working conditions. Experimental results show that the method proposed in this paper is superior to other traditional methods in terms of accuracy, and the effect of ViT model based on transfer learning (TLViT) training is better than that of the Resnet50 model based on transfer learning training (TLResnet50) under variable loads and small sample conditions. In addition, the experimental results also prove that the IFI with multiple image information has better anti-noise ability than the single information image. Therefore, the method proposed in this paper can improve the accuracy of bearing fault diagnosis under small sample, variable load and noise conditions, and provide a new method for bearing fault diagnosis. Full article
►▼ Show Figures

Figure 1

2023

Jump to: 2024, 2022

18 pages, 4349 KiB  
Article
Legendre Multiwavelet Transform and Its Application in Bearing Fault Detection
by Xiaoyang Zheng, Zijian Lei, Zhixia Feng and Lei Chen
Appl. Sci. 2024, 14(1), 219; https://doi.org/10.3390/app14010219 - 26 Dec 2023
Cited by 2 | Viewed by 1148
Abstract
Bearing failures often result from compound faults, where the characteristics of these compound faults span across multiple domains. To tackle the challenge of extracting features from compound faults, this paper proposes a novel fault detection method based on the Legendre multiwavelet transform (LMWT) [...] Read more.
Bearing failures often result from compound faults, where the characteristics of these compound faults span across multiple domains. To tackle the challenge of extracting features from compound faults, this paper proposes a novel fault detection method based on the Legendre multiwavelet transform (LMWT) combined with envelope spectrum analysis. Additionally, to address the issue of identifying suitable wavelet decomposition coefficients, this paper introduces the concept of relative energy ratio. This ratio assists in identifying the most sensitive wavelet coefficients associated with fault frequency bands. To assess the performance of the proposed method, the results obtained from the LMWT method are compared with those derived from the empirical wavelet transform (EWT) method using different datasets. Experimental findings demonstrate that the proposed method exhibits more effective frequency spectrum segmentation and superior detection performance across various experimental conditions. Full article
►▼ Show Figures

Figure 1

24 pages, 10511 KiB  
Article
Bearing Fault Diagnosis Method Based on Deep Learning and Health State Division
by Lin Shi, Shaohui Su, Wanqiang Wang, Shang Gao and Changyong Chu
Appl. Sci. 2023, 13(13), 7424; https://doi.org/10.3390/app13137424 - 22 Jun 2023
Cited by 8 | Viewed by 2436
Abstract
As a key component of motion support, the rolling bearing is currently a popular research topic for accurate diagnosis of bearing faults and prediction of remaining bearing life. However, most existing methods still have difficulties in learning representative features from the raw data. [...] Read more.
As a key component of motion support, the rolling bearing is currently a popular research topic for accurate diagnosis of bearing faults and prediction of remaining bearing life. However, most existing methods still have difficulties in learning representative features from the raw data. In this paper, the Xi’an Jiaotong University (XJTU-SY) rolling bearing dataset is taken as the research object, and a deep learning technique is applied to carry out the bearing fault diagnosis research. The root mean square (RMS), kurtosis, and sum of frequency energy per unit acquisition period of the short-time Fourier transform are used as health factor indicators to divide the whole life cycle of bearings into two phases: the health phase and the fault phase. This division not only expands the bearing dataset but also improves the fault diagnosis efficiency. The Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN) network model is improved by introducing multi-scale large convolutional kernels and Gate Recurrent Unit (GRU) networks. The bearing signals with classified health states are trained and tested, and the training and testing process is visualized, then finally the experimental validation is performed for four failure locations in the dataset. The experimental results show that the proposed network model has excellent fault diagnosis and noise immunity, and can achieve the diagnosis of bearing faults under complex working conditions, with greater diagnostic accuracy and efficiency. Full article
►▼ Show Figures

Figure 1

18 pages, 4296 KiB  
Article
Research on a Rolling Bearing Fault Diagnosis Method Based on Multi-Source Deep Sub-Domain Adaptation
by Fengyun Xie, Linglan Wang, Haiyan Zhu and Sanmao Xie
Appl. Sci. 2023, 13(11), 6800; https://doi.org/10.3390/app13116800 - 3 Jun 2023
Cited by 2 | Viewed by 1325
Abstract
Rolling bearings are the core component of rotating machinery. In order to solve the problem that the distribution of collected rolling bearing data is inconsistent during the operation of bearings under complex working conditions, which results in poor fault identification effects, a fault [...] Read more.
Rolling bearings are the core component of rotating machinery. In order to solve the problem that the distribution of collected rolling bearing data is inconsistent during the operation of bearings under complex working conditions, which results in poor fault identification effects, a fault diagnosis method based on multi-source deep sub-domain adaptation (MSDSA) is proposed in this paper. The proposed method uses CMOR wavelet transform to transform the collected vibration signal into time–frequency maps and construct multiple sets of source–target domain data pairs, and a rolling bearing fault diagnosis network based on a multi-source deep sub-domain adaptive network is established. The network uses shared and domain-specific feature extraction networks to extract data features together. At the same time, the local maximum mean discrepancy (LMMD) was introduced to effectively capture the fine-grained information of the category. Each set of data was used to train the corresponding classifier. Finally, multiple sets of classifiers were combined to reduce the classification loss of the target domain samples at the classification boundary to achieve fault identification. In order to make the training process more stable, the network used the Ranger optimizer for parameter tuning. This paper verifies the effectiveness of the proposed method through two sets of comparative experiments. The proposed method achieves 97.78%, 99.65%, and 99.34% in three migration tasks. The experimental results show that the proposed method has a high recognition rate and generalization performance. Full article
►▼ Show Figures

Figure 1

2022

Jump to: 2024, 2023

18 pages, 773 KiB  
Article
Fault Diagnosis Method for Rolling Bearings Based on Two-Channel CNN under Unbalanced Datasets
by Yufeng Qin and Xianjun Shi
Appl. Sci. 2022, 12(17), 8474; https://doi.org/10.3390/app12178474 - 25 Aug 2022
Cited by 20 | Viewed by 2687
Abstract
As a critical component in industrial systems, timely and accurate fault diagnosis of rolling bearings is closely related to reliability and safety. Since the equipment usually operates in normal conditions with few fault samples, unbalanced data distribution problems lead to poor fault diagnosis [...] Read more.
As a critical component in industrial systems, timely and accurate fault diagnosis of rolling bearings is closely related to reliability and safety. Since the equipment usually operates in normal conditions with few fault samples, unbalanced data distribution problems lead to poor fault diagnosis ability. To address the above problems, a two-channel convolutional neural network (TC-CNN) model is proposed. Firstly, the frequency spectrum of the vibration signal is extracted using the Fast Fourier Transform (FFT), and the frequency spectrum is used as the input to the one-dimensional convolutional neural network (1D-CNN). Secondly, the time-frequency image of the vibration signal is extracted using generalized S-transform (GST), and the time-frequency image is used as the input to the two-dimensional convolutional neural network (2D-CNN). Then, feature extraction in the convolution and pooling layers is performed in the above two CNN channels, respectively. The feature vectors obtained from the two CNN models are stitched together in the fusion layer, and the fault classes are identified using an SVM classifier. Finally, using the rolling bearing experimental dataset of Case Western Reserve University (CWRU), the fault diagnosis effect of the proposed TC-CNN model under various data imbalance conditions is verified. In comparison with other related works, the experimental results demonstrate the better fault diagnosis results and robustness of the method. Full article
►▼ Show Figures

Figure 1

13 pages, 3447 KiB  
Article
Application of Convolutional Neural Network for Fault Diagnosis of Bearing Scratch of an Induction Motor
by Shrinathan Esaki Muthu Pandara Kone, Kenichi Yatsugi, Yukio Mizuno and Hisahide Nakamura
Appl. Sci. 2022, 12(11), 5513; https://doi.org/10.3390/app12115513 - 29 May 2022
Cited by 4 | Viewed by 2072
Abstract
The demand for the condition monitoring of induction motors is increasing in various fields, such as industry, transportation, and daily life. Bearing faults are the most common faults, and many fault diagnosis methods have been proposed using artificial pitting as the fault factor [...] Read more.
The demand for the condition monitoring of induction motors is increasing in various fields, such as industry, transportation, and daily life. Bearing faults are the most common faults, and many fault diagnosis methods have been proposed using artificial pitting as the fault factor in most cases. However, the validity of a fault diagnosis method for other kinds of faults does not seem to be evaluated. Considering onsite scenarios and other possibilities of faults, this paper introduces scratches on the outer raceways of bearings. A study was performed on the detection of several kinds of bearing scratches using a proposed method that was based on an auto-tuning convolutional neural network. The developed approach was also compared with other diagnostic methods for validation. The results showed that the proposed technique provides the possibility of diagnosing several kinds of scratches with acceptable accuracy rates. Full article
►▼ Show Figures

Figure 1

Back to TopTop