An image-based feature extraction method for fault diagnosis of variable-speed rotating machinery.

• 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. 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. [ABSTRACT FROM AUTHOR]