Rolling Bearing Fault Feature Extraction Method Based on Adaptive Enhanced Difference Product Morphological Filter

doi:10.3901/JME.2021.09.078

Abstract

Abstract: In order to extract weak fault feature information of rolling bearings under strong background noise. A new adaptive enhanced different product operation morphological filter (AEDPO) is proposed for early fault diagnosis of rolling bearings. First, combining the existing filtering capabilities of the four morphological filtering operators, an improved enhanced differential product morphological filtering operator (EDPO) is proposed, which has the ability to extract periodic pulse features under strong background noise. Then, a new adaptive selection strategy is proposed for the optimal selection of structuring elements (SE) scale in the morphological filtering process, which is called the kurtosis feature energy product (KF). Finally, the EDPO operator performs filtering based on the optimal SE scale to extract early fault characteristics of rolling bearings. Through the analysis of the simulation signal and the actual fault signal of the inner ring of the rolling bearing, the results show that the AEDPO method can effectively extract the weak fault characteristics of rolling bearings from strong background noise, and it can better reflect its accuracy and superiority than the traditional morphological filtering method.

Key words: morphological filtering, rolling bearing, fault feature extraction, fault diagnosis

CLC Number:

TK83
TH133

MIAO Baoquan, CHEN Changzheng, LUO Yuanqing, ZHAO Siyu. Rolling Bearing Fault Feature Extraction Method Based on Adaptive Enhanced Difference Product Morphological Filter[J]. Journal of Mechanical Engineering, 2021, 57(9): 78-88.

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