RAM analysis of repairable industrial systems utilizing uncertain data

Industrial systems are mostly complex and considered as repairable. Also data, either collected or available (historical), reflecting their failure and repair patterns are limited, vague and imprecise due to various practical constraints. In such circumstances, their reliability, availability and maintainability (RAM) analysis may play an important role in any design modifications, if required, for achieving its optimum performance. However, it is difficult to estimate the RAM parameters of these systems up to a desired degree of accuracy by utilizing available information and uncertain data. This paper provides an idea, how can we estimate the RAM parameters of these systems by utilizing available information and uncertain data. For this purpose, Genetic Algorithms based Lambda–Tau (GABLT) technique is used. In this technique, expressions for the RAM parameters of the system are obtained by using traditional Lambda–Tau methodology and genetic algorithm is used to compute these parameters in the form of fuzzy membership functions utilizing quantified data in the form of triangular fuzzy numbers. A general RAM-Index is used for post RAM analysis to rank the subunits of the system on the basis of their performance. The approach has been applied to the press (series system) and washing (series–parallel system) units of a typical paper mill. The results may be helpful for the plant personnel for analyzing the systems’ behavior and to improve their performance by adopting suitable maintenance strategies.