A new noise reduction method based on re-weighted group sparse decomposition and its application in gear fault feature detection

Abstract

Abstract Aiming at the problem that gear vibration signals are susceptible to noise and the difficulty of extracting fault features, this paper proposes a new noise reduction method based on re-weighted group sparse decomposition (RWGSD). RWGSD introduces group sparse mode decomposition theory to protect the structural information of signal components in the frequency domain. On this basis, vital components are screened according to the time-domain characteristics of fault information, and the re-weighted enhancement is carried out. The fault characteristics are easy to identify in the final noise reduction result. In addition, RWGSD defines two new indicators, cyclic re-weighted kurtosis (CRWK) and re-weighted cyclic intensity (RWCI). CRWK can assess the intensity of periodic characteristic components and has some resistance to strong impact interference. RWCI can evaluate the magnitude of fault information, overcoming the limitations of traditional noise reduction techniques that screen out vital components based on energy size. Numerical simulation and real-world experiment results show that the proposed method has excellent performance in noise removal, increases the reliability of gear fault feature detection, and has certain practical values.