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

• Orginal Article • Previous Articles     Next Articles

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

Qinyao Zhanga,*, and Chenglin Wenb   

  1. 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).


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