A SVR-Based Remaining Life Prediction for Rolling Element Bearings
A new approach is proposed to construct a reasonable prediction model for prognostic. The Gaussian mixture model-based health indicator is used for degradation performance and help to determine the threshold of the incipient fault. The support vector regression is joined with least mean square algorithm for the construction of the adaptive prediction model based on the historical data and the online monitoring data. According to the failure threshold, the remaining life can be obtained. Through experimental verification, the results show that the selected health index is able to effectively reflect the degradation of rolling bearings, and the proposed model shows great prediction accuracy in comparison to the common one.
KeywordsSVR Rolling element bearings Remaining life prediction Gaussian mixture model An adaptive prediction model
The work described in this paper was supported by a grant from the National Defense Researching Fund (No. 9140A27020413JB11076).
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