Novel Hilbert Huang transform techniques for bearing fault detection

Abstract

Bearings are commonly used in rotary machinery; while up to half of machinery malfunctions could be related to bearing defects. A reliable bearing fault detection technique becomes vital to a wide array of industries to recognize an incipient bearing defect to prevent machinery performance degradation, malfunction, and unexpected breakdown. Many signal processing techniques have been suggested in literature to extract fault-related signatures for bearing fault detection, but most of them are not robust in real-world bearing health condition monitoring when signal properties vary with time. Vibration signals generated from bearings can be either stationary or nonstationary. If bearing defect-related signature is stationary, it is relatively easy to analyze using these classical data analysis techniques. However, bearing nonstationary signals are much more complex to analyze using these classical signal processing techniques, especially when slippage has occurred. Reliable fault detection still remains a challenging task, especially when bearing defect-related features are nonstationary. Two alternative approaches are proposed in this work for bearing fault detection: The first technique is based on analytical normality test, named Normalized Hilbert Haung Transform (NHHT). The second technique is based on information domain analysis, named enhanced Hilbert Haung Transform (eHHT). In the proposed NHHT technique, a novel strategy based on d’Agostino-Pearson normality analysis is suggested to demodulate feature functions and highlight feature characteristics for bearing fault detection. In the proposed eHHT, a novel strategy is proposed to enhance feature extraction based on the analysis of correlation and mutual information. The effectiveness of the proposed techniques is verified by a series of experimental tests corresponding to different bearing health conditions. Their robustness in bearing fault diagnostic is examined by the use of data sets from a different experimental setup.