Fault Diagnosis Method of Rolling Bearing Based on Ensemble Local Mean Decomposition and Neural Network

Abstract

For a problem of mode mixing occurs in implementation process of local mean decomposition (LMD) method, an analytical method based on ensemble local mean decomposition (ELMD) and neural network is proposed to apply to fault diagnosis of rolling bearing, the vibrational signal of rolling bearing is decomposed into a series of product functions(PF) by ELMD method. The PF components which contain main fault information are selected to perform a further analysis. The kurtosis coefficient and energy characteristic parameters extracted from these PF components can be used as the input parameters of the neural network to identify the working status and fault types of rolling bearing. Through the analysis of rolling bearing with fault-free, inner-race fault and outer-race fault, the results indicate that the method based on ELMD and neural network has a higher failure recognition rate than the method based on wavelet packet analysis and neural network, and the working status and fault types of rolling bearing can be identified accurately and effectively.

An SDP Characteristic Information Fusion‐Based CNN Vibration Fault Diagnosis Method

“…With the development of data mining and artificial intelligence, shallow machine learning-based fault diagnosis models, such as ANN, SVM, and fuzzy recognition [10][11][12], have been widely applied to the fault diagnosis of rotary machinery. However, diagnostic precision depends largely on the accuracy of characteristic extraction.…”