Step-by-step Fault Diagnosis of Rolling Bearings Based on EMD and Random Forest

Abstract

A step-by-step fault diagnosis method based on Empirical Mode Decomposition (EMD) combined with Random Forest algorithm was proposed for actual requirements of rolling bearing vibration fault diagnosis. Firstly, the preliminary fault monitoring was carried out, and a Linear Support Vector Machine model was established by extracting the Permutation Entropy of vibration signals as characteristic parameters to judge whether the bearing was faulty or not. Then, the fault location identification and the fault degree determination were carried out, and high-dimensional characteristic parameters in time domain, frequency domain and time-frequency domain are respectively extracted as inputs of the Random Forest algorithm. Finally, through the step-by-step diagnostic test of rolling bearing vibration data, the results show that each step of diagnosis can achieve 100% diagnostic accuracy and appropriate training time, which proves that EMD and Random Forest have good effect on step-by-step fault diagnosis of rolling bearing.