The method of grey Markov remaining service life prediction specific to generalized mathematical morphological particle

Abstract

In the domain of rolling bearing condition monitor and fault prognosis, to solve the key problem of rolling bearing degenerate feature extraction, a new approach based on generalized mathematical morphological particle is proposed in the paper, the new approach, which is founded on the mathematical morphological particle analysis, introduces corrosion and dilation operators in morphological calculation and takes the calculated generalized mathematical morphological particle as feature indicator, therefore, the performance degenerate degree could be reflected in quantity. The effectiveness of this approach is test and verified with simulation and actual signal. On this basis, in order to describe the whole tendency and random fluctuating feature for rolling bearings, grey Markov model is applied in the remaining service life prediction for rolling bearing, A method of remaining service life prediction based on generalized mathematical morphological particle and grey Markov model is proposed thereby. Rolling bearing fatigued life testing was proceeded with Hangzhou Bearing Test & Research Center, the approach is proved effective with the collecting bearing inner race whole life data in fatigued life testing.