A novel compound fault diagnosis method using intrinsic component filtering

Abstract

The compound fault diagnosis of gearboxes, which can prevent breakdown accidents and minimize production loss, has become a challenging hotspot. In practical applications, the early fault signal is weak and often concealed by environmental noise. Therefore, separating the different fault components from signals with weak faults and strong noise is the key to performing compound fault diagnosis. In this paper, a novel compound fault diagnosis method based on intrinsic component filtering is proposed for compound fault diagnosis, which can also be regarded as a multi-dimension blind deconvolution method. Firstly, a Hankel matrix is constructed from the collected vibration data. Secondly, the filter matrix is trained by intrinsic component filtering in an unsupervised way without any time-consuming preprocessing or a prior basis. Finally, Hilbert demodulation analysis is conducted on the filtered data. Different fault components can be diagnosed according to the envelope spectra of the fault components. The separation performance of the proposed method is validated by the simulation data and the experiment signals in a noisy environment. The results show that the proposed method can train the different filters using the compound fault adaptively, demonstrating superior performance to existing methods.