Fault severity assessment of rolling bearing based on optimized multi-dictionaries matching pursuit and Lempel-Ziv complexity.

ABSTRACT

For the safe working of rolling bearing, this paper presents a fault severity assessment method through optimized multi-dictionaries matching pursuit (OMMP) and Lempel-Ziv (LZ) complexity. To solve the redundancy problem of over-complete dictionary, the OMMP is proposed by introducing the quantum particle swarm optimization into matching pursuit for best representing the original vibration signal. And then, LZ complexity is calculated as an index of fault severity assessment by reconstructed signal. The performance of assessment method is verified through the measured signals of three bearing tests, and the comparisons with various methods are specifically described. The results indicate that the OMMP method averagely takes the shortest running time for the vibration signal decomposition. The assessment method is able to effectively evaluate different fault sizes of rolling bearing, and has a great applicability to in the condition-based maintenance of rotating machineries.