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所属单位：能源与动力学院

发表刊物：MECHANICAL SYSTEMS AND SIGNAL PROCESSING

关键字：DFA Feature extraction Crossover phenomenon Piecewise-linear regression model Gearbox Fault diagnosis

摘要：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号：0888-3270

是否译文：否

发表时间：2017-01-15

合写作者：江星星,王勇

通讯作者：江星星,李舜酩