Introduction - If you have any usage issues, please Google them yourself
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 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.