A Double Feature Extraction Method for Rolling Bearing Fault Diagnosis Based on Slope Entropy and Fuzzy Entropy

Abstract

Rolling bearings are the key components for the safe operation of mechanical equipment. It plays an irreplaceable role in the normal operation of mechanical equipment. Higher load makes higher failure rate of rolling bearing. Accurate identification of the fault location is an important step in the diagnosis of the rolling bearing fault. In recent years, the entropy features of rolling bearing vibration signals are usually extracted to identify fault. In this paper, a double feature extraction method based on slope entropy (SlE) and fuzzy entropy (FE) is proposed to recognize the fault state of rolling bearing through rolling bearing signals. In the single feature extraction experiment, the recognition rate of these two kinds of entropy is not high. Through the improvement of the single feature extraction experiment, SlE and FE are selected as two feature combinations. After combining approximate entropy (AE), SlE, FE, permutation entropy (PE), and sample entropy (SE). The identification rate of these combinations was calculated using k nearest neighbor (KNN). The result shows that the recognition rate of this combination is 98% and 3.3% higher than other combinations.

Fault Diagnosis of Rolling Bearings in VMD and GWO-ELM

“…Fuzzy entropy [6] is a time series complexity prediction method. Fuzzy entropy not only has similar theoretical characteristics to sample entropy, but also has the advantage that the entropy value is more stable under parameter changes.…”