Optimal Decision Of Condition-based Maintenance With The Human Factor

Equipment maintenance has been concern and research by enterprises for a long time.With the rapid development of science and technology,the equipment in safety and reliability have been greatly improved,but the cost of the equipment is not significantly reduced.The study found that the human error in maintenance is an important reason for the increase of cost.The increasing types of equipment,the expansion of the scale,and increase the degree of complexity force the equipment maintenance personnel to improve the corresponding professional knowledge and skills.But in practice,the personnel's professional knowledge and skills of maintenance updates slowly,leading to the high maintenance human error rate.We can see that the influence of human factors on equipment maintenance is becoming more and more obvious.When considering human factors during making maintenance decision,we can make a more reasonable maintenance plan and choose a more suitable maintenance mode.Therefore,this paper purposes an optimization model of maintenance decision for the integration of human reliability model with condition based maintenance(CBM)optimization.Compared to the current maintenance decision optimization model,it is that considering the human reliability is more precise.First of all,this paper develops a framework which enables to analyze human error in five functions of CBM system with the Systemic Accident Models.It is shown that human performance plays a significant role during the CBM,especially in data collection and diagnostics and prognostics function.Then,calculation of human error involves data collection and diagnostics and prognostics.Through the AHP-SLIM model,we found that the probability of human error in diagnostics and prognostics functions is higher than in data collection.The main reason is the lack of experience,skills and professional knowledge.Finally,We develop an integrated systemic model for the integration of human reliability model with condition based maintenance optimization and observe the effects of human errors in condition monitoring(diagnostics and prognostics functions and data collection functions)on equipment cost.