Fault Diagnosis of a Helical Gearbox based on Empirical Wavelet Transform

Abstract

A gearbox is an essential power transmission component for the rotating machine systems, in which working conditions will directly affect the performance of the whole mechanical equipment. The development of condition monitoring and fault diagnosis systems for Gearboxes has received considerable attention in recent years. Extracting fault feature information from a gearbox’s vibration signal by using the signal processing methods has been the key to the fault diagnosis for the gearbox. Therefore, reliable fault detection is necessary to ensure productive and safe operations. Generally, when a gearbox is damaged, accurate identification of the side-band features can be used to detect the condition of machinery equipment to reduce financial losses. However, the side-band features of damaged gears that are constantly disturbed by strong jamming are embedded in the background noise. In this paper, a hybrid signal-processing method is proposed based on a spectral subtraction (SS) denoising algorithm combined with an empirical wavelet transform (EWT) to extract the side-band feature of gear faults. Firstly, SS is used to estimate the real-time noise information, which is used to enhance the fault signal of a helical gearbox from a vibration signal with strong noise disturbance. The empirical wavelet transform can extract amplitude-modulated/frequency-modulated (AM-FM) components of a signal using different filter bands that are designed in accordance with the signal properties. Secondly, the fault signal is obtained by building a multi-body dynamics model of the gearbox system with ADAMS software. The influence of flexible body deformation is considered in the process of dynamic simulation, so a flexible gear is used to simulate the gear fault dynamic characteristics. The experiment shows the feasibility and availability of the multi-body dynamics model with different algorithms. Then, the signal processing techniques viz empirical decomposition (EMD), local mean decomposition (LMD) and discrete wavelet transform (DWT) for diagnostics of a gearbox, are majorly employed. The spectral subtraction-based adaptive empirical wavelet transform (SS-AEWT) method was applied to estimate the gear side-band feature for different tooth breakages and the strong background noise. The verification results show that the proposed method gives a clearer indication of gear fault characteristics with different tooth breakages and the different signal-noise ratio (SNR) than the conventional EMD and LMD methods. Finally, the fault characteristic frequency of a damaged gear suggests that the proposed SS-AEWT method can accurately and reliably diagnose faults of a gearbox. Furthermore, the simulated model is an efficient approach to test the performance of a new algorithm working in the different conditions, in which the variables would be limited and the model is easy to be modified. Therefore, this simulation model would be useful in the development of new algorithms.