Early Fault Diagnosis Technology for Bearings Based on Quantile Multiscale Permutation Entropy

Yufeng Long,Xianjun Shi,Zhicai Xiao,Qiangqiang Chen,Yufeng Qin,Jiapeng Lv,Marcelo Ra L Risk

doi:10.1155/2021/7718074

Abstract

Early fault diagnosis of bearings is the basis of condition-based maintenance. To overcome the difficulty of early fault diagnosis for the mechanical system, a new conception named quantile multiscale permutation entropy (QMPE) is defined, and a new feature extraction method based on QMPE is proposed. On the basis of the multiscale entropy, the multiscale permutation entropy for the gathered vibration signal of equipment is obtained, and the sample quantile is calculated, which is employed to analyze the weak change of the variation signal. The proposed method is verified with the full lifetime datasets of a certain bearing, which proves that signal features extracted by the QMPE method can not only truly express the bearing detailed condition changing from normal to fault but also duly detect the early fault of the bearing. Comparing with other methods for early fault diagnosis, the proposed method can advance the finding time of the early fault obviously.

Highlights

Rolling bearings are one of the most basic structures in a mechanical system, as well as an important part of the mechanical system. e running state of the rolling bearing is directly related to the safe and stable operation of the system [1]
If the degradation of rolling bearings can be quantitatively evaluated during this period, equipment maintenance programs can be formulated in a targeted manner
quantile multiscale permutation entropy (QMPE) extracts sample quantiles of permutation entropy values at multiple scales, which effectively reflects the aggregation characteristics of data at a certain quantile point, thereby deeply extracting the characteristic state information of the full life cycle of rolling bearings, which can be effectively used for abnormal detection and early fault diagnosis

Summary

Introduction

Rolling bearings are one of the most basic structures in a mechanical system, as well as an important part of the mechanical system. e running state of the rolling bearing is directly related to the safe and stable operation of the system [1]. In order to make effective use of these characteristic parameters, multidimensional features are usually selected to evaluate the running state of rolling bearings. E algorithm performs bearing abnormality detection; Feng et al [10] used the wavelet correlation filtering method to decompose and filter the rolling bearing signal and expressed the early fault characteristics of the bearing through permutation entropy. Zhang et al [13] introduced multiscale entropy partial mean information to evaluate the degree of bearing failure, which improved the performance of fault diagnosis to a certain extent. QMPE extracts sample quantiles of permutation entropy values at multiple scales, which effectively reflects the aggregation characteristics of data at a certain quantile point, thereby deeply extracting the characteristic state information of the full life cycle of rolling bearings, which can be effectively used for abnormal detection and early fault diagnosis. Studies have shown that this method can effectively express fault characteristic information and significantly advance the discovery time of early bearing faults

Multiscale Permutation Entropy

QMPE Feature Extraction Method

Experiment Analysis