A hybrid fault diagnosis approach based on mixed-domain state features for rotating machinery

Xue, Xiaoming; Zhou, Jing

doi:10.1016/j.isatra.2016.10.014

Cited by 82 publications

(44 citation statements)

References 31 publications

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“…The study demonstrated that the proposed PCA technique is effective in classifying bearing faults with a higher accuracy and a lower number of input features when compared to using all of the original feature. Similarly, the rest of the papers based on PCA [39]- [42] take advantage of its data mining capability to facilitate the manual feature selection process and generate more representative features.…”

Section: B Principle Component Analysis (Pca)mentioning

confidence: 99%

Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review

et al. 2020

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In this survey paper, we systematically summarize existing literature on bearing fault diagnostics with machine learning (ML) and data mining techniques. While conventional ML methods, including artificial neural network (ANN), principal component analysis (PCA), support vector machines (SVM), etc., have been successfully applied to the detection and categorization of bearing faults for decades, recent developments in deep learning (DL) algorithms in the last five years have sparked renewed interest in both industry and academia for intelligent machine health monitoring. In this paper, we first provide a brief review of conventional ML methods, before taking a deep dive into the state-of-the-art DL algorithms for bearing fault applications. Specifically, the superiority of DL based methods over conventional ML methods are analyzed in terms of fault feature extraction and classification performances; many new functionalities enabled by DL techniques are also summarized. In addition, to obtain a more intuitive insight, a comparative study is conducted on the classification accuracy of different algorithms utilizing the open source Case Western Reserve University (CWRU) bearing dataset. Finally, to facilitate the transition on applying various DL algorithms to bearing fault diagnostics, detailed recommendations and suggestions are provided for specific application conditions such as the setup environment, the data size, and the number of sensors and sensor types. Future research directions to further enhance the performance of DL algorithms on health monitoring are also discussed.

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Section: B Principle Component Analysis (Pca)mentioning

confidence: 99%

Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review

et al. 2020

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“…As a metric of non-linear behavior measurement, permutation entropy has been utilized to realize condition monitoring data analysis. The most widely used strategy is the combination of permutation entropy and decomposition methods, as given in [64][65][66][67][68][69][70]. Similar to those works in the category of energy entropy, these achieved features are mainly the metrics in time domain, frequency domain, and their combination.…”

Section: Application Of Permutation Entropy On Bearingmentioning

confidence: 99%

“…Li et al [65] local mean decomposition + multiscale permutation entropy 2 An et al [64] variational mode decomposition + permutation entropy 3 Liu et al [66] variational mode decomposition + multiscale permutation entropy 4 Shi et al [67] local mean decomposition + permutation entropy 5 Xue et al [68] ensemble empirical mode decomposition + permutation entropy 6 Yao et al [69] ensemble empirical mode decomposition + multiscale permutation entropy 7…”

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confidence: 99%

Related Entropy Theories Application in Condition Monitoring of Rotating Machineries

Liu

Zhi

Zhang

et al. 2019

Entropy

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Rotating machinery plays an important role in various kinds of industrial engineering. How to assess their conditions is a key problem for operating safety and condition-based maintenance. The potential anomaly, fault and failure information can be obtained by analyzing the collected condition monitoring data of the previously deployed sensors in rotating machinery. Among the available methods of analyzing sensors data, entropy and its variants can provide quantitative information contained in these sensing data. For implementing fault detection, diagnosis, and prognostics, this information can be utilized for feature extraction and selecting appropriate training data for machine learning methods. This article aims to review the related entropy theories which have been applied for condition monitoring of rotating machinery. This review consists of typical entropy theories presentation, application, summary, and discussion.

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“…If no effective actions are taken, device faults will inevitably occur, and such faults may lead to serious casualties and enormous pecuniary loss [5]. Thus, it is of significance to identity bearing faults to maintain safety of the device and reduce maintenance cost.…”

Section: Introductionmentioning

confidence: 99%

“…In the phase of signal processing and features extraction, due to the complexity of equipment structure and variety of operation conditions [5], the signals collected from rolling bearings often exhibit strong nonlinearity and nonstationarity. Therefore, the time-domain and frequency-domain analysis approaches cannot have essential effects [9].…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection

Dong

Ding

et al. 2018

Shock and Vibration

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In order to enhance the performance of bearing fault diagnosis and classification, features extraction and features dimensionality reduction have become more important. The original statistical feature set was calculated from single branch reconstruction vibration signals obtained by using maximal overlap discrete wavelet packet transform (MODWPT). In order to reduce redundancy information of original statistical feature set, features selection by adjusted rand index and sum of within-class mean deviations (FSASD) was proposed to select fault sensitive features. Furthermore, a modified features dimensionality reduction method, supervised neighborhood preserving embedding with label information (SNPEL), was proposed to realize low-dimensional representations for high-dimensional feature space. Finally, vibration signals collected from two experimental test rigs were employed to evaluate the performance of the proposed procedure. The results show that the effectiveness, adaptability, and superiority of the proposed procedure can serve as an intelligent bearing fault diagnosis system.

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A hybrid fault diagnosis approach based on mixed-domain state features for rotating machinery

Cited by 82 publications

References 31 publications

Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review

Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review

Related Entropy Theories Application in Condition Monitoring of Rotating Machineries

Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection

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