Integrated fault diagnosis of rolling bearings based on improved multichannel singular spectrum analysis and frequency-spatial domain decomposition

Extensive fault information can be obtained from the vibration signals of rotating machines with faulty rolling bearings. However, the diagnosis of compound faults is challenging because of their easy mix-ups, which can lead to faulty diagnosis and judgment. This study improves the multichannel singular spectrum analysis (MSSA) by using convex optimization. In addition, an integrated fault diagnosis technology for rolling bearings using an improved MSSA and frequency-spatial domain decomposition was developed. This approach involves two primary stages: signal preprocessing and fault diagnosis. The proposed method was tested to diagnose faults in the rolling bearings of pellet mills. Signal preprocessing can significantly improve the quality of a vibration signal and preserve modal information that characterizes a fault. Fault diagnosis identifies the modal parameters entirely and accurately from the reconstructed vibration signal, and determines the degree of damage. The proposed method can aid in the robust diagnosis of faulty rolling bearings under severe operating conditions.