A practical methodology for enhancement and detection of transient faults in a gearbox without prior fault feature information. (17th December 2020) - Zhou, Xiaoqin; He, Xiuzhi; Peng, Dikang; Hou, Yixuan; Liu, Qiang
Abstract: Gear fault diagnosis has been the focus of research in both academia and industry in the past. Due to the complicated structures of gearboxes, the vibration signals collected from the gearbox casings are comprised of multiple sources, in which the fault-related components may be easily masked by the Gaussian and non-Gaussian noises (e.g. random external shocks and gear meshing harmonics). Moreover, fault feature frequencies of interest cannot always be obtained in advance in practical applications. Therefore, it is necessary to choose an appropriate signal processing method for gear fault diagnosis. In this study, a new practical methodology is proposed to extract the transient fault features from a gearbox vibration signal corrupted by complicated interference without prior fault feature information. In the proposed method, a modified self-adaptive noise cancellation (MSANC) algorithm is first developed for removing the interferences of gear meshing harmonics adaptively, which can overcome the shortcomings of the traditional SANC in parameter selection and convergence performance. Based on the de-noised signal, a cyclic spectral analysis tool called the fast spectral correlation and assisted by the multipoint optimal minimum entropy deconvolution adjusted algorithm is then applied to enhance and detect the certain and potential gear faults. The effectiveness of the proposed method is demonstrated by a numerical simulation and two experimental scenarios that are compared to an advanced blind optimal demodulation band selection technique. The results reveal that the proposed methodology has good capability to detect mono- and multiple gear faults under complicated interferences, and can be regarded as an efficient method for practical gear fault diagnosis, especially for applications where the fault feature frequencies are unknown in advance.