Early gear tooth crack detection based on singular value decomposition

Abstract

Detection of gear tooth crack fault through vibration analysis relies on extracting the fault induced periodic impulses. Singular value decomposition (SVD)-based methods have been used for periodic impulse extraction. Reported reweighted SVD-based method did not consider interferences from non-fault related vibration components on the periodic modulation intensity (PMI) criteria, leading to the selection of incorrect signal component(s) for reconstruction. This paper proposes an improved SVD-based method by adopting autoregression model-based baseline removal approach. SVD is applied to decompose the residual signal, instead of the raw signal. The interferences from non-fault related vibration components on the PMI are therefore eliminated. Simulation study has shown that the improved method outperforms the reported method in detecting early tooth crack fault.