Fault diagnosis of bearing based on Symbolic Aggregate approXimation and Lempel-Ziv

Abstract

Aiming at the problem that the traditional Lempel-Ziv encoding method can’t accurately reflect the bearing modulation information in bearing fault diagnosis, an improved Lempel-Ziv method based on Symbolic Aggregate approXimation is proposed to improve the coding accuracy and the computational efficiency of Lempel-Ziv indicator. According to the Piecewise Aggregate Approximation, the normalized bearing signal is divided into w equal-sized segments. The equal-sized segments are symbolized by determined breakpoints. The complexity value of the symbol sequence is calculated by Lempel-Ziv indicator. The proposed method is verified by a single-point defect dataset and a run-to-failure dataset of bearing, respectively. The results show that the proposed method can effectively reflect the modulation information of bearings. While the proposed method has higher computational efficiency than the traditional Lempel-Ziv method and can realize the early fault diagnosis of bearings.