Resonance-Based Sparse Decomposition Application in Extraction of Rolling Bearing Weak Fault Information

Abstract

It is significant to detect the fault type and assess the fault level as early as possible for avoiding catastrophic accidents. In the early fault diagnosis of rolling bearing, the vibration signal is mixed with a lot of noise, resulting in the difficulties in analysis of early fault weak signal. This chapter introduces resonance-based signal sparse decomposition (RSSD) into rolling bearing weak fault diagnosis, and presents a technical route to extract rolling bearing weak fault information. On this basis, we studied the fault information contained in high-resonance and low-resonance components. Finally, we combine the main sub-bands of the two resonance components to extract fault information and achieve good results. The proposed method is applied to analyze the fault of rolling element bearing with an approximate hemisphere pit on inner race. The results show that the proposed method could enhance the ability of weak fault detection of mechanical equipment.