Evaluation and Prediction of Rolling Bearing Reliability Based on SSA-RVM
In order to solve the problem of rolling bearing operation reliability prediction,a rolling bearing reliability prediction method based on sparrow search algorithm and relevance vector machine is proposed in this paper.Firstly,principal component analysis(PCA)was used to reduce the dimensionality of the higher dimensional vector set composed of bearing vibration signals in time domain,frequency domain and time-frequency domain.Then,the feature set after dimensionality reduction was input into the logistic regression model as the degenerate state features of the bearing to evaluate the reliability of the rolling bearing.Then,the performance degradation state characteristics of the bearing were taken as the input of the sparrow search algorithm-relevance vector machine model to obtain the prediction results.Finally,the results are put into the logistic regression model to predict the operating reliability of bearings.Experimental results show that the proposed algorithm has obvious advantages in predicting the operational reliability of rolling bearings.
NETL
NSTL
万方数据
