Jixie qiangdu (Jan 2019)
GEAR FAULT IDENTIFICATION OF RVM BASED ON LCD BASE-SCALE ENTROPY
Abstract
Aiming at the fact that the gear vibration signal would exactly display non-stationary characteristics and fault features is hard to extracted, a fault extraction method of gear based on multiscale base-scale entropy of LCD was proposed. The vibration signal was decomposed adaptively with local characteristic-scale decomposition（LCD） to obtain the components in different scales of the original signal. Considering the ability of the base-scale entropy in distinguishing the complexity of different signals effectively, the base-scale entropy of intrinsic scale components（ISCs） by LCD was calculated. Thus the complexity metric in different scales of the original signal was gained, which was consequently taken as the feature parameter to describe different gear states. The feature parameters were then put into relevance vector machine（RVM） for diagnosing the gear faults. Experiment results of gear show that the proposed method can classify typical fault of gear exactly and has certain superiority when compared with some other methods.
