Fault detection of a wheelset bearing in a high-speed train using the shock-response convolutional sparse-coding technique

Abstract

The wheelset bearing is an essential mechanical component of a high-speed train, and its fault detection is of great importance for ensuring the safety of high-speed train service. However, the strong nonlinear and non-stationary modulation characteristics of the impulses induced by bearing faults, noises and interferences make fault detection of the wheelset bearing difficult and complex. The impulses induced by bearing faults have convolutional sparse characteristics, and can be described as the convolution of shock responses and time location coefficients. This paper presents a new fault detection method based on the shock-response convolutional sparse-coding technique (SRCSCT) to resolve the two inverse problems related to convolution characteristics. One of the problems is that shock responses are learned directly from vibration signals via the shift-invariant dictionary learning (SIDL) of SRCSCT; the other is that the time location coefficients are inferred from vibration signals via shift-invariant sparse coding (SISC) of SRCSCT. Shock responses can reflect the high-level structures of impulses embedded in vibration signals. Time location coefficients can be used to determine the time of impulse occurrence. The convolutions of learned shock responses and inferred corresponding time location coefficients are used to extract impulses submerged in vibration signals and are convenient for post-processing methods, such as Hilbert envelope analysis. Both the simulated signals and real vibration signals collected from a wheelset bearing test bench are used to verify the proposed method. The results show that SRCSCT not only effectively detects faults of the wheelset bearing but also accurately characterizes the fault dynamic behaviours of the bearing defects.