Rotating Machinery Fault Classification Method using Multi-Sensor Feature Extraction and Fusion

doi:10.23940/ijpe.20.04.p9.577586

Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (4): 577-586.doi: 10.23940/ijpe.20.04.p9.577586

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Rotating Machinery Fault Classification Method using Multi-Sensor Feature Extraction and Fusion

Qinyao Zhang^a,*, and Chenglin Wen^b

^aCollege of Electrical Engineering, Henan University of Technology, Zhengzhou, 450000, China
^bSchool of Automation, Hangzhou Dianzi University, Hangzhou, 310000, China

Submitted on ; Revised on ; Accepted on
Contact: Zhang Qinyao
Supported by:
This work is supported by the Natural Science Foundation of China (No. U1509203, 61673160, 61703385, and 61603366).

Abstract

Abstract:

This paper focuses on data information preprocessing methods in the fault classification of rotating machinery. In order to avoid the information loss caused by the weighted fusion method, a merging fusion method is provided to obtain the final feature information. Furthermore, a direct fusion method that synchronizes the extraction and fusion of multi-sensor feature information is also proposed. The artificial neural network is used to test the three proposed information preprocessing methods and obtain rotary machinery fault classification methods. A final comparative experiment is given to compare the three methods proposed above in the fault classification of rotating machinery.

Key words: rotating machinery, fault classification, multi-sensor, data preprocessing, weighted fusion, merge fusion, direct fusion

Qinyao Zhang and Chenglin Wen. Rotating Machinery Fault Classification Method using Multi-Sensor Feature Extraction and Fusion [J]. Int J Performability Eng, 2020, 16(4): 577-586.

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Rotating Machinery Fault Classification Method using Multi-Sensor Feature Extraction and Fusion

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