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Abstract
Run-to-failure experiment is efficient and effective to investigate bearing deterioration process. Periodic transient waveform carries rich information of health conditions of bearings but the transient waveform matching is a challenging problem for evaluating bearing fatigue life because the shapes and parameters of the waveform vary with the evolution of the bearing degradation. A wavelet function such as a Morlet wavelet is able to extract essential features from the transient waveform but limited to a single transient component. The multi-wavelet may provide a solution to fit a variety of primary components in the transient waveform, so as to track the degradation trend of the bearing; however, very limited work has been done to address this issue. To bridge the research gap in the transient waveform matching, a novel ascension multi-wavelet method is proposed in this paper for diagnosing the undergoing degradation state and predicting the remaining useful life (R ....

Bearing Deterioration , Ensemble Learning , Ulti Wavelet , Ransient Waveform ,