Cross-Domain Intelligent Fault Diagnosis Method of Rotating Machinery Using Multi-Scale Transfer Fuzzy Entropy

Zheng, Dangdang; Han, Bing; Luo, Geng; Li, Yongbo; Yu, Huangchao

doi:10.1109/access.2021.3063743

Cited by 7 publications

(5 citation statements)

References 27 publications

(51 reference statements)

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“…34 Meanwhile, different vibration signals show differences in their characteristic frequency bands and complexity at different scales, and extraction of their FE value at different scales can make the results much more accurate. 35 Since each component obtained by MRDMD decomposition corresponds to low-rank or sparse components of the original signal, fault feature extraction of signals is a process of retaining low-rank components and removing sparse components. The MFE values of each layer of mode components decomposed by MRDMD are calculated and the threshold is set.…”

Section: Methodologiesmentioning

confidence: 99%

Section: Multiscale Fuzzy Entropymentioning

confidence: 99%

“…(3) Update the particle displacement and velocity in formula (33) and (34). (4) Update weight in formula (35).…”

Section: The Improved Particle Swarm Optimization Algorithmmentioning

confidence: 99%

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Improved particle swarm optimization-based adaptive multiresolution dynamic mode decomposition with application to fault diagnosis of rolling bearing

Cai

Dang

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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It is very important to detect fault and extract fault features of mechanical systems at an early stage, because the above two steps promise normal operation of mechanical systems. However, they are also very challenging. In this context, this article has put forward improved particle swarm optimization-based adaptive multiresolution dynamic mode decomposition of rolling bearing (IPSO-AMDMD). Multiresolution dynamic mode decomposition (MRDMD) is used to decompose signals of rolling bearing at the early stage, multiscale fuzzy entropy (MFE) is employed to divide low-rank components and sparse components. In order to make up for the shortcomings of the above two methods, namely truncated rank of MRDMD and inaccurate selection in threshold of MFE, this paper has proposed a new fitness function, which is called synthetic envelope kurtosis characteristic energy difference ratio, and adopted the improved particle swarm optimization algorithm (IPSO) to select the optimal parameters adaptively. With these two steps, signals can be decomposed perfectly. Finally, reconstructed signals, which are obtained through the combination of signals from each layer according to a certain weight, go through DMD again, thus getting the final recovered signal. Through simulation experiment and in-field experiment, it has proved that IPSO-AMDMD is viable and sound in accurately extracting features from fault signals.

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Section: Methodologiesmentioning

confidence: 99%

Section: Multiscale Fuzzy Entropymentioning

confidence: 99%

See 1 more Smart Citation

Improved particle swarm optimization-based adaptive multiresolution dynamic mode decomposition with application to fault diagnosis of rolling bearing

Cai

Dang

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

View full text Add to dashboard Cite

show abstract

“…Instance-based [25], [26], [27] Feature-based [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75],…”

Section: Approach Referencesmentioning

confidence: 99%

A Systematic Literature Review on Transfer Learning for Predictive Maintenance in Industry 4.0

et al. 2023

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The advent of Industry 4.0 has resulted in the widespread usage of novel paradigms and digital technologies within industrial production and manufacturing systems. The objective of making industrial operations monitoring easier also implied the usage of more effective data-driven predictive maintenance approaches, including those based on machine learning. Although those approaches are becoming increasingly popular, most of the traditional machine learning and deep learning algorithms experience the following three major challenges: 1) lack of training data (especially faulty data), 2) incompatible computation power, and 3) discrepancy in data distribution. A new data-driven technique, such as transfer learning, can be developed to overcome the issues related to traditional machine learning and deep learning for predictive maintenance. Motivated by the recent big interest towards transfer learning within computer science and artificial intelligence, in this paper we provide a systematic literature review addressing related research with a focus on predictive maintenance. The review aims to define transfer learning in the context of predictive maintenance by introducing a specific taxonomy based on relevant perspectives. We also discuss current advances, challenges, open-source datasets, and future directions of transfer learning applications in predictive maintenance from both theoretical and practical viewpoints.

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“…Wang et al [15] constructed statistical features based on multiscale sample entropy (MSE) to reflect the information of rotating machinery. Zheng et al [16] combined multiscale fuzzy entropy (MFE) with the SVM to construct an intelligent diagnosis model. Chen et al [17] combined local mean decomposition and multiscale permutation entropy (MPE) to enhance the feature extraction ability of MPE through pre-processing of the initial signal.…”

Section: Introductionmentioning

confidence: 99%

Gearbox Fault Diagnosis Based on Refined Time-Shift Multiscale Reverse Dispersion Entropy and Optimised Support Vector Machine

Wang

Han

2023

Machines

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The fault diagnosis of a gearbox is crucial to ensure its safe operation. Entropy has become a common tool for measuring the complexity of time series. However, entropy bias may occur when the data are not long enough or the scale becomes larger. This paper proposes a gearbox fault diagnosis method based on Refined Time-Shifted Multiscale Reverse Dispersion Entropy (RTSMRDE), t-distributed Stochastic Neighbour Embedding (t-SNE), and the Sparrow Search Algorithm Support Vector Machine (SSA-SVM). First, the proposed RTSMRDE was used to calculate the multiscale fault features. By incorporating the refined time-shift method into Multiscale Reverse Dispersion Entropy (MRDE), errors that arose during the processing of complex time series could be effectively reduced. Second, the t-SNE algorithm was utilized to extract sensitive features from the multiscale, high-dimensional fault features. Finally, the low-dimensional feature matrix was input into SSA-SVM for fault diagnosis. Two gearbox experiments showed that the diagnostic model proposed in this paper had an accuracy rate of 100%, and the proposed model performed better than other methods in terms of diagnostic performance.

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Cross-Domain Intelligent Fault Diagnosis Method of Rotating Machinery Using Multi-Scale Transfer Fuzzy Entropy

Cited by 7 publications

References 27 publications

Improved particle swarm optimization-based adaptive multiresolution dynamic mode decomposition with application to fault diagnosis of rolling bearing

Improved particle swarm optimization-based adaptive multiresolution dynamic mode decomposition with application to fault diagnosis of rolling bearing

A Systematic Literature Review on Transfer Learning for Predictive Maintenance in Industry 4.0

Gearbox Fault Diagnosis Based on Refined Time-Shift Multiscale Reverse Dispersion Entropy and Optimised Support Vector Machine

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