A sample entropy inspired affinity propagation method for bearing fault signal classification

Abstract

For bearing fault signal classification, the performance of the affinity propagation (AP) method is limited by its way of measuring similarity. In this paper, we have proposed an improved version for the AP method, which is termed as IMFSE-AP. The key point of the proposed IMFSE-AP method is the improvement on the way of measuring signal similarity. Instead of the Euclidean distance, a novel method based on the sample entropy of IMFs (IMFSE) is proposed, which can measure signal similarities better with respect to data complexity. In the proposed similarity measurement method, signals are first decomposed into IMFs by the EEMD method. Then the distances of sample entropy vectors of IMFs are computed to evaluate similarities between signals. Numerical experiments conducted on synthetic signals and real bearing fault signals illustrate the good performance of the proposed method.