Rolling bearing fault diagnosis is of great significance to ensuring the safe operation of rotating machinery, and vibration analysis based signal processing methods have become a mainstream of rolling bearing fault diagnosis technologies. Aiming at the separation of different signal components induced by rolling bearing composite defects, a novel signal decomposition based on linear time-invariant (LTI) filtering and multiple resonance is proposed in this paper, which can decompose the fault vibration signal with composite defects into high-, middle-, low-resonance components and the low-frequency component. The high- and middle-resonance components sparsely represent the damped responses induced by severe and slight defects, respectively. The low-resonance component represents transient component induced by some random interferences, and the low-frequency component contains the components of shaft rotation rate and harmonics caused by shaft bending or imbalance. Compared with conventional dual-Q-factor resonance-based signal sparse decomposition (RSSD), this method can not only detect the feature frequency, realize semi-quantitative analysis of defects’ amounts and severities, but also provide a monitor for shaft bending and imbalance. The effectiveness and practicability of this method has been validated by the experimental signal with dual defects on outer race, which explores a new way to apply RSSD to the diagnosis of rolling bearing composite defects.
Rolling bearing fault diagnosis based on GMCP sparse enhanced signal decomposition and TFM
