Incipient Fault Detection Method of Rolling Bearing Based on DDPCA
This paper proposed a deep difference principal component analysis(DDPCA)method for early fault detection of rolling bearings in extracting and dividing fault features of multimodal conditions.The parameters were adjusted according to the demand,which lead to the rolling bearing being in variable speed and multi-modal working conditions.In view of the dynamic process characteristics of rolling bearings in transition mode,the fault characteristics were difficult to extract and classify,the failure detection of rolling bearings in incipient stage cannot be carried out using the uniform detection model This method uses differential method to process original data,classifies the transition mode data with similar variable characteristics into the same transition submodes by K-means clustering method,establishes fault detection model for each transition submode in combination with depth decomposition theory.The layer rate of the outer ring fault detection was 17.2%,8.6%and 6.6%.The multi-layer decomposition of the transition submodes extracts the fault characteristics of the transition submodes accurately,the model is established to improve accuracy of the incipient failure detection of the rolling bearing in the transition mode.
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