An approach for analyzing the reliability of industrial systems using soft-computing based technique

• Novel technique named as ABCBLT has been presented.
• Various reliability parameters are analyzed in the form of membership functions.
• Sensitivity as well as performance analysis has also addressed.
• Ranking the component of the system based on its performance.
• Technique shown to be outperform as compared to existing techniques.

The purpose of this paper is to present a novel technique for analyzing the behavior of an industrial system by utilizing vague, imprecise, and uncertain data. In this, two important tools namely traditional Lambda–Tau and artificial bee colony algorithm have been used to build a technique named as an artificial bee colony (ABC) algorithm based Lambda–Tau (ABCBLT). In real-life situation, data collected from various resources contains a large amount of uncertainties due to human errors and hence it is not easy to analyze the behavior of such system up to a desired accuracy. If somehow behavior of these systems has been calculated, then they have a high range of uncertainty. For handling this situation, a fuzzy set theory has been used in the analysis and an artificial bee colony has been used for determining their corresponding membership functions. To strengthen the analysis, various reliability parameters, which affects the system performance directly, have been computed in the form of fuzzy membership functions. Sensitivity as well as performance analysis has also been analyzed and their computed results are compared with the existing techniques result. The butter–oil processing plant, a complex repairable industrial system has been taken to demonstrate the approach.