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Open AccessArticle

Rotating Machinery Fault Diagnosis Based on Improved Multiscale Amplitude-Aware Permutation Entropy and Multiclass Relevance Vector Machine

1
School of Measurement and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China
2
School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150080, China
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(20), 4542; https://doi.org/10.3390/s19204542
Received: 4 September 2019 / Revised: 4 October 2019 / Accepted: 17 October 2019 / Published: 18 October 2019
(This article belongs to the Special Issue Sensors for Fault Diagnosis)
The health state of rotating machinery directly affects the overall performance of the mechanical system. The monitoring of the operation condition is very important to reduce the downtime and improve the production efficiency. This paper presents a novel rotating machinery fault diagnosis method based on the improved multiscale amplitude-aware permutation entropy (IMAAPE) and the multiclass relevance vector machine (mRVM) to provide the necessary information for maintenance decisions. Once the fault occurs, the vibration amplitude and frequency of rotating machinery obviously changes and therefore, the vibration signal contains a considerable amount of fault information. In order to effectively extract the fault features from the vibration signals, the intrinsic time-scale decomposition (ITD) was used to highlight the fault characteristics of the vibration signal by extracting the optimum proper rotation (PR) component. Subsequently, the IMAAPE was utilized to realize the fault feature extraction from the PR component. In the IMAAPE algorithm, the coarse-graining procedures in the multi-scale analysis were improved and the stability of fault feature extraction was promoted. The coarse-grained time series of vibration signals at different time scales were firstly obtained, and the sensitivity of the amplitude-aware permutation entropy (AAPE) to signal amplitude and frequency was adopted to realize the fault feature extraction of coarse-grained time series. The multi-classifier based on the mRVM was established by the fault feature set to identify the fault type and analyze the fault severity of rotating machinery. In order to demonstrate the effectiveness and feasibility of the proposed method, the experimental datasets of the rolling bearing and gearbox were used to verify the proposed fault diagnosis method respectively. The experimental results show that the proposed method can be applied to the fault type identification and the fault severity analysis of rotating machinery with high accuracy. View Full-Text
Keywords: rotating machinery; fault diagnosis; fault severity; intrinsic time-scale decomposition; amplitude-aware permutation entropy; multiclass relevance vector machine rotating machinery; fault diagnosis; fault severity; intrinsic time-scale decomposition; amplitude-aware permutation entropy; multiclass relevance vector machine
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MDPI and ACS Style

Chen, Y.; Zhang, T.; Zhao, W.; Luo, Z.; Lin, H. Rotating Machinery Fault Diagnosis Based on Improved Multiscale Amplitude-Aware Permutation Entropy and Multiclass Relevance Vector Machine. Sensors 2019, 19, 4542.

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