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Affiliation of Author(s):能源与动力学院

Title of Paper:Study on nature of crossover phenomena with application to gearbox fault diagnosis

Journal:MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Key Words:DFA Feature extraction Crossover phenomenon Piecewise-linear regression model Gearbox Fault diagnosis

Abstract:Detrended Fluctuation Analysis (DFA) is a robust tool for uncovering long-range correlations hidden in the non-stationary data. Recently, crossover properties of the scaling-law curve obtained by DFA have been applied to diagnose gearbox faults. However, the nature of the crossover phenomena has not been well-explained. In this paper, an explanation for the nature of crossover phenomena is specifically given, which is conducive to discovering novel features for gearbox fault diagnosis. Firstly, an explicit exposition of the crossover phenomena is provided by analyzing the gearbox vibration signal. Secondly, the nature of crossover phenomena is specifically disclosed. Thirdly, the features with clear physical meaning are proposed to describe operating conditions of a gearbox. Then, to overcome the deficiency of feature extraction through visual observation, a piecewise-linear regression model is utilized to extract the features automatically. Lastly, several combinations of these features are used to classify the fault types. As a consequence, the proposed novel features are verified that they can well-distinguish the gearbox operating conditions with different fault types and severities, and deliver a better performance than the existing method depending on the sensitive index (SI). (C) 2016 Elsevier Ltd. All rights reserved.

ISSN No.:0888-3270

Translation or Not:no

Date of Publication:2017-01-15

Co-author:江星星,王勇

Correspondence Author:江星星,lsz