Rolling Bearing Fault Diagnosis Based on ELCD Permutation Entropy and RVM

Abstract EN

Aiming at the nonstationary characteristic of a gear fault vibration signal, a recognition method based on permutation entropy of ensemble local characteristic-scale decomposition (ELCD) and relevance vector machine (RVM) is proposed.

First, the vibration signal was decomposed by ELCD; then a series of intrinsic scale components (ISCs) were obtained.

Second, according to the kurtosis of ISCs, principal ISCs were selected and then the permutation entropy of principal ISCs was calculated and they were combined into a feature vector.

Finally, the feature vectors were input in RVM classifier to train and test and identify the type of rolling bearing faults.

Experimental results show that this method can effectively diagnose four kinds of working condition, and the effect is better than local characteristic-scale decomposition (LCD) method.