An image-based feature extraction method for fault diagnosis of variable-speed rotating machinery. (15th March 2022)

• Record Type: Journal Article
• Authors: Park, Jungho; Kim, Yunhan; Na, Kyumin; Youn, Byeng D.; Chen, Yuejian; Zuo, Ming J.; Bae, Yong-Chae
• Is Part Of: Mechanical systems and signal processing. Volume 167:Part B(2022)
• Publication Date: 2022-03-15
• Subjects: Fault diagnosis -- Rotating machines -- Time-frequency analysis -- Vibration signal -- Prognostics and health management; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.108524
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A novel method is proposed to diagnose faults of rotating machinery under variable speed conditions. The method can extract fault-related features from time-frequency image data. The proposed method is validated by experiment data from the planetary and spur gearboxes. The proposed method shows better sensitivity than the previous methods, and consistent behaviors under different phases of speed profiles. Abstract: This paper proposes a new feature extraction method using time–frequency image data for fault diagnosis of variable-speed rotating machinery. Time-frequency representation (TFR) is widely used to analyze time-varying behaviors of rotating machinery. Recently, methods have been developed to extract fault-related features from TFR image data. However, these methods can be only applied to in-phase TFR image data, or have limited sensitivity because they cannot utilize the characteristics of faults in rotating machinery. Therefore, the research outlined in this paper proposes a new fault feature for rotating machinery under variable-speed conditions. The proposed feature enhances sensitivity by exploiting faulty behaviors in the TFR image data. Two experimental case studies are presented to demonstrate the performance of the proposed method: a planetary gearbox and a spur gearbox. From the results, we conclude that the proposed method shows higher fault sensitivity than the previous image-based features, while showing consistent behavior under different phases of TFR image data.