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Entropy
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Entropy Applications in Condition Monitoring and Fault Diagnosis
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Entropy Applications in Condition Monitoring and Fault Diagnosis

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".

Deadline for manuscript submissions: 20 July 2024 | Viewed by 4796

Special Issue Editors

Dr. Liansheng Liu

E-Mail Website
Guest Editor
Department of Test and Control Engineering, Harbin Institute of Technology, Harbin 150001, China
Interests: prognostics and health management; machine learning; electronic measurement; signal processing; intelligent computing
Dr. Yuqing Li

E-Mail Website
Guest Editor
Deep Space Exploration Research Center, Harbin Institute of Technology, Harbin 150001, China
Interests: condition monitoring; signal processing; anomaly detection; fault diagnosis; task optimization; swarm intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the increasing automation of industrial production, the complexity of the system has increased significantly. The study of complex system condition monitoring and fault diagnosis technology is of great importance to improve both the level of technology and productivity. The long-term development of information theory makes it possible to use information-theoretic methods for signal feature extraction analysis of complex systems. Entropy, as a characteristic indicator to measure the uncertainty of signal state distribution and signal complexity, can quantitatively describe the information contained inside the signal. The study of how to use entropy to reflect the operation state and characteristic information of complex systems has become one of the current research hotspots in the field of condition monitoring and fault diagnosis.

Dr. Liansheng Liu
Dr. Yuqing Li
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. Entropy 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 2600 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

  • information theory
  • condition monitoring
  • signal processing
  • complexity measure
  • fault diagnosis
  • anomaly detection
  • remaining useful life prediction
  • machine learning
  • intelligent maintenance.

Published Papers (4 papers)

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Research

18 pages, 9687 KiB  
Article
Research on Three-Phase Asynchronous Motor Fault Diagnosis Based on Multiscale Weibull Dispersion Entropy
by Fengyun Xie, Enguang Sun, Shengtong Zhou, Jiandong Shang, Yang Wang and Qiuyang Fan
Entropy 2023, 25(10), 1446; https://doi.org/10.3390/e25101446 - 13 Oct 2023
Viewed by 998
Abstract
Three-phase asynchronous motors have a wide range of applications in the machinery industry and fault diagnosis aids in the healthy operation of a motor. In order to improve the accuracy and generalization of fault diagnosis in three-phase asynchronous motors, this paper proposes a [...] Read more.
Three-phase asynchronous motors have a wide range of applications in the machinery industry and fault diagnosis aids in the healthy operation of a motor. In order to improve the accuracy and generalization of fault diagnosis in three-phase asynchronous motors, this paper proposes a three-phase asynchronous motor fault diagnosis method based on the combination of multiscale Weibull dispersive entropy (WB-MDE) and particle swarm optimization–support vector machine (PSO-SVM). Firstly, the Weibull distribution (WB) is used to linearize and smooth the vibration signals to obtain sharper information about the motor state. Secondly, the quantitative features of the regularity and orderliness of a given sequence are extracted using multiscale dispersion entropy (MDE). Then, a support vector machine (SVM) is used to construct a classifier, the parameters are optimized via the particle swarm optimization (PSO) algorithm, and the extracted feature vectors are fed into the optimized SVM model for classification and recognition. Finally, the accuracy and generalization of the model proposed in this paper are tested by adding raw data with Gaussian white noise with different signal-to-noise ratios and the CHIST-ERA SOON public dataset. This paper builds a three-phase asynchronous motor vibration signal experimental platform, through a piezoelectric acceleration sensor to discern the four states of the motor data, to verify the effectiveness of the proposed method. The accuracy of the collected data using the WB-MDE method proposed in this paper for feature extraction and the extracted features using the optimization of the PSO-SVM method for fault classification and identification is 100%. Additionally, the proposed model is tested for noise resistance and generalization. Finally, the superiority of the present method is verified through experiments as well as noise immunity and generalization tests. Full article
(This article belongs to the Special Issue Entropy Applications in Condition Monitoring and Fault Diagnosis)
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23 pages, 3241 KiB  
Article
Incorporating Heterogeneous Features into the Random Subspace Method for Bearing Fault Diagnosis
by Yan Chu, Syed Muhammad Ali, Mingfeng Lu and Yanan Zhang
Entropy 2023, 25(8), 1194; https://doi.org/10.3390/e25081194 - 11 Aug 2023
Viewed by 1046
Abstract
In bearing fault diagnosis, machine learning methods have been proven effective on the basis of the heterogeneous features extracted from multiple domains, including deep representation features. However, comparatively little research has been performed on fusing these multi-domain heterogeneous features while dealing with the [...] Read more.
In bearing fault diagnosis, machine learning methods have been proven effective on the basis of the heterogeneous features extracted from multiple domains, including deep representation features. However, comparatively little research has been performed on fusing these multi-domain heterogeneous features while dealing with the interrelation and redundant problems to precisely discover the bearing faults. Thus, in the current study, a novel diagnostic method, namely the method of incorporating heterogeneous representative features into the random subspace, or IHF-RS, is proposed for accurate bearing fault diagnosis. Primarily, via signal processing methods, statistical features are extracted, and via the deep stack autoencoder (DSAE), deep representation features are acquired. Next, considering the different levels of predictive power of features, a modified lasso method incorporating the random subspace method is introduced to measure the features and produce better base classifiers. Finally, the majority voting strategy is applied to aggregate the outputs of these various base classifiers to enhance the diagnostic performance of the bearing fault. For the proposed method’s validity, two bearing datasets provided by the Case Western Reserve University Bearing Data Center and Paderborn University were utilized for the experiments. The results of the experiment revealed that in bearing fault diagnosis, the proposed method of IHF-RS can be successfully utilized. Full article
(This article belongs to the Special Issue Entropy Applications in Condition Monitoring and Fault Diagnosis)
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21 pages, 7388 KiB  
Article
Fault Diagnosis for Rolling Bearing of Combine Harvester Based on Composite-Scale-Variable Dispersion Entropy and Self-Optimization Variational Mode Decomposition Algorithm
by Wei Jiang, Yahui Shan, Xiaoming Xue, Jianpeng Ma, Zhong Chen and Nan Zhang
Entropy 2023, 25(8), 1111; https://doi.org/10.3390/e25081111 - 25 Jul 2023
Cited by 4 | Viewed by 804
Abstract
Because of the influence of harsh and variable working environments, the vibration signals of rolling bearings for combine harvesters usually show obvious characteristics of strong non-stationarity and nonlinearity. Accomplishing accurate fault diagnosis using these signals for rolling bearings is a challenging subject. In [...] Read more.
Because of the influence of harsh and variable working environments, the vibration signals of rolling bearings for combine harvesters usually show obvious characteristics of strong non-stationarity and nonlinearity. Accomplishing accurate fault diagnosis using these signals for rolling bearings is a challenging subject. In this paper, a novel fault diagnosis method based on composite-scale-variable dispersion entropy (CSvDE) and self-optimization variational mode decomposition (SoVMD) is proposed, systematically combining the nonstationary signal analysis approach and machine learning technology. Firstly, an improved SoVMD algorithm is developed to realize adaptive parameter optimization and to further extract multiscale frequency components from original signals. Subsequently, a CSvDE-based feature learning model is established to generate the multiscale fault feature space (MsFFS) of frequency components for the improvement of fault feature learning ability. Finally, the generated MsFFS can serve as the inputs of the Softmax classifier for fault category identification. Extensive experiments on the vibration datasets collected from rolling bearings of combine harvesters are conducted, and the experimental results demonstrate the more superior and robust fault diagnosis performance of the proposed method compared to other existing approaches. Full article
(This article belongs to the Special Issue Entropy Applications in Condition Monitoring and Fault Diagnosis)
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16 pages, 12616 KiB  
Article
Multi-Fault Classification and Diagnosis of Rolling Bearing Based on Improved Convolution Neural Network
by Xiong Zhang, Jialu Li, Wenbo Wu, Fan Dong and Shuting Wan
Entropy 2023, 25(5), 737; https://doi.org/10.3390/e25050737 - 29 Apr 2023
Cited by 6 | Viewed by 1379
Abstract
At present, the fault diagnosis methods for rolling bearings are all based on research with fewer fault categories, without considering the problem of multiple faults. In practical applications, the coexistence of multiple operating conditions and faults can lead to an increase in classification [...] Read more.
At present, the fault diagnosis methods for rolling bearings are all based on research with fewer fault categories, without considering the problem of multiple faults. In practical applications, the coexistence of multiple operating conditions and faults can lead to an increase in classification difficulty and a decrease in diagnostic accuracy. To solve this problem, a fault diagnosis method based on an improved convolution neural network is proposed. The convolution neural network adopts a simple structure of three-layer convolution. The average pooling layer is used to replace the common maximum pooling layer, and the global average pooling layer is used to replace the full connection layer. The BN layer is used to optimize the model. The collected multi-class signals are used as the input of the model, and the improved convolution neural network is used for fault identification and classification of the input signals. The experimental data of XJTU-SY and Paderborn University show that the method proposed in this paper has a good effect on the multi-classification of bearing faults. Full article
(This article belongs to the Special Issue Entropy Applications in Condition Monitoring and Fault Diagnosis)
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