Fault diagnosis of rolling bearing based on SEMD and ISSA-KELMC

Abstract

Enhancing the operational reliability of rotary machinery relies significantly on the effective diagnosis of faults in rolling bearings. This study introduces an innovative method to improve the accuracy of fault diagnosis of rolling bearings during operation. First, we propose a sine empirical mode decomposition (SEMD) designed to effectively mitigate mode mixing and decompose the vibration signals of rolling bearings into a series of intrinsic mode functions. Subsequently, we constructed and optimized a kernel extreme learning machine classifier (KELMC) using the improved sparrow search algorithm (ISSA). Within ISSA, the opposition-based Learning method is refined and applied to enhance the optimization performance of the sparrow search algorithm. Finally, the paper presents a novel method for the fault diagnosis of rolling bearings based on SEMD and ISSA-KELMC, which can effectively extract the fault features and accurately recognize the fault types of rolling bearings by taking advantage of the SEMD and ISSA-KELMC. The effectiveness of the proposed method was verified through two simulation and fault diagnosis experiments. The results demonstrated the efficiency of the method in diagnosing faults in rolling bearings under both consistent and variable working conditions. This approach is valuable for fault diagnosis and condition monitoring of rotating machinery.