Development of Simulation for Condition Monitoring and Evaluation of Manufacturing Systems

Abstract

Equipment Condition Management used for predicting the performance parameters required for maintenance decision making was developed. This program predicts the state probabilities and maintenance action recommendation based the predetermined alert levels. The maintenance program software was developed from the derived stable state probability models using algebraic substitution and computation of the breakdown data and operational data of the MTTF, MTTR, λ and µ of these equipment/component(s) at PM and CM states with implementation algorithm. The models were derived using mechanistic modeling technique such that all the relevant variables of the reliability process were accounted for. Validation analysis of this simulation revealed its prediction accuracy of over 99%. Therefore, its use in the monitoring and evaluation of the health conditions of production systems remains very essential.

Keywords: Mechanistic model, process parameter, stable state probabilities, prediction algorithm, Equipment Condition Management