Fault Diagnosis Approach for Rotating Machinery Based on Feature Importance Ranking and Selection

Autor: Zong Yuan, Taotao Zhou, Jie Liu, Changhe Zhang, Yong Liu
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Shock and Vibration, Vol 2021 (2021)
Druh dokumentu: article
ISSN: 1070-9622
1875-9203
DOI: 10.1155/2021/8899188
Popis: The key to fault diagnosis of rotating machinery is to extract fault features effectively and select the appropriate classification algorithm. As a common signal decomposition method, the effect of wavelet packet decomposition (WPD) largely depends on the applicability of the wavelet basis function (WBF). In this paper, a novel fault diagnosis approach for rotating machinery based on feature importance ranking and selection is proposed. Firstly, a two-step principle is proposed to select the most suitable WBF for the vibration signal, based on which an optimized WPD (OWPD) method is proposed to decompose the vibration signal and extract the fault information in the frequency domain. Secondly, FE is utilized to extract fault features of the decomposed subsignals of OWPD. Thirdly, the categorical boosting (CatBoost) algorithm is introduced to rank the fault features by a certain strategy, and the optimal feature set is further utilized to identify and diagnose the fault types. A hybrid dataset of bearing and rotor faults and an actual dataset of the one-stage reduction gearbox are utilized for experimental verification. Experimental results indicate that the proposed approach can achieve higher fault diagnosis accuracy using fewer features under complex working conditions.
Databáze: Directory of Open Access Journals
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