Abstract
For an industrial system reliability, availability and maintainability (RAM) analysis play an important role in any design modification for achieving its optimum performance. However, it is difficult to predict these parameters by using available and imprecise data up to a desired degree of accuracy. For this, a novel technique named as an artificial bee colony based Lambda-Tau has been presented for computing these parameters by utilizing available or collected data up to a desired degree of accuracy. In this technique expression of RAM parameters are calculated by Lambda-Tau methodology and their corresponding membership functions are computed by formulating a nonlinear programming problem. A generalized RAM-Index has been used for ranking the components of the system based on its performance for improving the system productivity. The presented approach has been investigated through a case study of washing unit of paper industry and computed results are compared with existing Lambda-Tau and evolutionary algorithm techniques.
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