A comparative study of pick degradation prediction based on grey prediction and Bayesian algorithm

Abstract

The performance of the pick gradually degrades under the cyclic complex load, so it is difficult to realize the state prediction, in order to be able to accurately predict the pick degradation state, this paper develops a test system for the wear degradation of pick, the method of experimental analysis and numerical simulation is adopted, Multi-signal fusion model of vibration and acoustic emission was constructed, the grey prediction and the Gamma method of Bayesian parameter updating are used to realize the application of the degenerate data, the results prove that the relative error of grey prediction under vibration signal is only 0.45%, the relative error of the Gamma model was 0.57%, the relative error of the Gamma model under Bayesian updating was 0.22%, the three models have good prediction accuracy, the prediction error of the Gamma model under Bayesian updating is minimal.