Open AccessOn Condition Maintenance Model for Complex Electromechanical Equipments Based on Remaining Useful Life and Wiener Process
Author(s) -
Minghui Wu,
Xuemin Wang,
Xianzhong Liu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1678/1/012014
Subject(s) - wiener process , residual , condition based maintenance , reliability (semiconductor) , process (computing) , particle swarm optimization , optimal maintenance , reliability engineering , computer science , gamma process , engineering , mathematical optimization , algorithm , mathematics , statistics , power (physics) , physics , quantum mechanics , operating system
Aiming at the problem of condition based maintenance modeling for complex electromechanical equipment, a condition based maintenance model based on residual life prediction and improved Wiener process is proposed; first of all, for the degradation process character of complex electromechanical equipment, this paper proposes a residual life prediction model based on random coefficient Wiener process; To estimate the parameters of degradation process, a prior distribution construction method based on state data is proposed; And the posterior distribution and estimation of degradation process parameters are constructed by Bayes theory, and the remaining life prediction of equipment in operation is realized. Secondly, the decision model of condition based maintenance is established by balancing the reliability and economy targets with the information of remaining life; And the immune particle swarm optimization algorithm is used to solve the model. The case study shows that the residual life prediction model proposed in this paper has higher prediction accuracy and lower prediction uncertainty, the condition based maintenance decision-making model is more practical, has higher operational reliability and economy, and the convergence accuracy of the model solving algorithm is higher.