Research on rolling bearing fault diagnosis method based on ARMA and optimized MOMEDA

Abstract

In actual operating conditions, rolling bearings vibration signals are easily covered by heavy noise, increasing the difficulty of fault diagnosis. A fault diagnosis method based on auto regressive moving average (ARMA) model and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) algorithm is proposed to address this issue. Firstly, ARMA model is used to remove the intrinsic components and pre-whitening the signals. Then parameters of MOMEDA are optimized by Sparrow Search Algorithm (SSA), the periodic fault signals are recovered by the optimized MOMEDA and the secondary noise reduction of the signals is realized. Finally, a class of time-domain average dimensionless features, namely average pulse factor, average kurtosis factor and average margin factor, are proposed and combined with the Gini index as fault diagnosis indexes then input into ELM classifier to identify fault types. Experimental results show the proposed method can identify fault types effectively and achieve accurate diagnosis of rolling bearings.