Reliability Prediction of Bearing Based on Feature Selection and ELM Neural Network Network
Aiming at the problem of rolling bearing reliability forecast,a reliability forecast approach based on feature selection and ELM network is devised.Firstly,the features of vibration signals are extracted to form a preliminary selection of characteris-tic parameters;secondly,three characteristic evaluation indexes of monotonicity,correlation and robustness were introduced to evaluate the initial selection of characteristic parameters,and a new restrictive index was defined to obtain the parameters that could reflect the bearing degradation process,which constituted the degradation characteristic parameter set;thirdly,the dimen-sion of the degraded feature parameter set is reduced to form the low dimensional feature vector set;finally,the degenerate fea-ture parameter set and feature vector set are used as input data and labels respectively,and are brought into the ELM network for reliability forecast.Its validity is verified by the vibration signal data set of bearing in Xi'an Jiaotong University.
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