Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, multiscale permutation entropy (MPE) was introduced for feature extraction from faulty bearing vibration signals. After extracting feature vectors by MPE, the support vector machine (SVM) was applied to automate the fault diagnosis procedure. Simulation results demonstrated that the proposed method is a very powerful algorithm for bearing fault diagnosis and has much better performance than the methods based on single scale permutation entropy (PE) and multiscale entropy (MSE).
For instance, Wu et al. REF investigated the feasibility and performance of MPE for bearing fault diagnosis, in which MPE was used for feature extraction from bearing vibration signals.
Bearing Fault Diagnosis Based on Multiscale Permutation Entropy and Support Vector Machine
Entropy
Entropy
Mathematics Computer Science