A fault diagnosis method based on local mean decomposition and multi-scale entropy for roller bearings

A novel fault feature extraction method based on the local mean decomposition technology and multi-scale entropy is proposed in this paper. When fault occurs in roller bearings, the vibration signals picked up would exactly display non-stationary characteristics. It is not easy to make an accurate evaluation on the working condition of the roller bearings only through traditional time-domain methods or frequency-domain methods. Therefore, local mean decomposition method, a new self-adaptive time-frequency method, is used as a pretreatment to decompose the non-stationary vibration signal of a roller bearing into a number of product functions. Furthermore, the multi-scale entropy, referring to the calculation of sample entropy across a sequence of scales, is introduced here. The multi-scale entropy of each product function can be calculated as the feature vectors. The analysis results from practical bearing vibration signals demonstrate that the proposed method is effective. (C) 2014 Elsevier Ltd. All rights reserved.