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.-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.