ACCUGRAM: A novel approach based on classification to frequency band selection for rotating machinery fault diagnosis. (December 2019)

• Record Type: Journal Article
• Authors: Liu, Zhiliang; Jin, Yaqiang; Zuo, Ming J.; Peng, Dandan
• Is Part Of: ISA transactions. Volume 95(2019)
• Page Start: 346
• Page End: 357
• Publication Date: 2019-12
• Subjects: Fault transient -- Frequency band selection -- Health reference -- Classification -- Rotating machinery fault diagnosis; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2019.05.007
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: Frequency band selection (FBS) in rotating machinery fault diagnosis aims to recognize frequency band location including a fault transient out of a full band spectrum, and thus fault diagnosis can suppress noise influence from other frequency components. Impulsiveness and cyclostationarity have been recently recognized as two distinctive signatures of a transient. Thus, many studies have focused on developing quantification metrics of the two signatures and using them as indicators to guide FBS. However, most previous studies almost ignore another aspect of FBS, i.e. health reference, which significantly affect FBS performance. To address this issue, this paper investigates importance of a health reference and recognize it as the third critical aspect in FBS. With help of the health reference, the frequency band where the fault transient exists could be located. A novel approach based on classification is proposed to integrate all three aspects (impulsiveness, cyclostationarity, and health reference) for FBS. Classification accuracy is developed as a novel indicator to select the most sensitive frequency band for rotating machinery fault diagnosis. The proposed method (coined by accugram) has been validated on benchmark and experiment datasets. Comparison results show its effectiveness and robustness over conventional envelope analysis, the kurtogram, and the infogram. Highlights: Importance of health reference has been revealed in FBS. Health reference, impulsiveness and cyclostationarity are firstly integrated. Classification accuracy is newly developed to indicate FBS for fault diagnosis.