1. Institute of Sound and Vibration Research, Hefei University of Technology, Hefei, Anhui 230009, China
2. Automotive NVH Engineering & Technology Research Center Anhui Province, Hefei,Anhui 230009, China
Abstract：Aiming at the problem that it is difficult to identify the weak vibration signal feature of rolling bearing faults, a rolling bearing fault diagnosis method based on improved intrinsic time scale decomposition (IITD) and fuzzy entropy (FE) input random forest (RF) pattern recognition was proposed. First, the bearing test bench was used to collect the vibration signals of the bearing in four states: normal, rolling body failure, inner ring failure, and outer ring failure; the collected vibration signals were decomposed into a set of proper rotation components (PRC) by IITD decomposition, Then selected the effective component that represents the main information of the fault to calculate its fuzzy entropy value and constructed the feature vector, which was input to a random forest classifier model for identification and classification. The experimental data analysis results show that this method can effectively realize the diagnosis of rolling bearing fault categories.
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2021, Vol. 42 
