The objective of my research work is to analyze the behavior and performance of repairable industrial systems by utilizing available information and uncertain data. For analyzing this a hybridized technique named as Particle swarm optimization based Lambda-Tau (PSOBLT) has been proposed. Fuzzy set theory has been used in the technique for handling the uncertainties in the data and then behavior of the system are analyzed in the form of fuzzy membership functions of various reliability parameters namely systems failure rate, repair time, mean time between failures (MTBF), expected number of failures (ENOF), reliability and availability which affects the system performance. Major advantages of proposed technique is that it gives compressed range of prediction for all computed reliability parameters by utilizing uncertain data. Further using these results, system performance has been analyzed by formulating a nonlinear fuzzy optimization problem by considering reliability and cost as objectives. Also the technique has been applied for a time varying failure and repair rate model instead of constant failure rate model.
The present thesis is organized into nine chapters which are briefly summarized as follows:
A brief account of the related work of various authors in evaluation of system reliability by using conventional, fuzzy and optimization techniques is presented in the first chapter. In Chapter 2, the basics and preliminaries related to the reliability analysis and to be used in subsequent chapters are given.
Chapter 3 presents a hybridized technique named as particle swarm optimization based lambda-tau (PSOBLT) technique for analyzing the behavior of an industrial system by utilizing the uncertain,vague and limited data. The proposed technique has been applied to various subsystems/units of a paper mill, a complex repairable industrial system, and analyzed their behavior in terms of various reliability parameters/indices in the form of fuzzy membership functions. Computed results are compared with the existing results as obtained by other researchers. Sensitivity and performance analysis have also been done on system availability for ranking the sensitive components of each subsystem of the system which helps the system analyst or DM to maintain the behavior of the system as per his preferential order.
In Chapter 4, the behavior analysis of the urea fertilizer plant, a complex repairable industrial system, have been dealt by using PSOBLT technique. Using their behavior analysis results, fuzzy multi-objective optimization problem (FMOOP) is formulated for each subsystems of the plant by taking system's reliability and cost as objectives. An interactive method for solving multi-objective reliability optimization problems modeled in crisp and fuzzy environments is presented. In this, the conflicting nature of the objectives are resolved by defining their fuzzy goals (linear membership functions only) and using the preference of decision makers (DMs) towards the objectives, the problem is reformulated to single objective optimization problem and solved by using PSO algorithm. During the interactive phase, based on the outcomes of previous iteration, the DM has the option to change his or her preferences in view of the importance being given by him to different objectives.
Chapter 5 deals with the reliability-redundancy allocation problem (RRAP) of the series system (a Pharmaceutical plant) under the fuzzy environment. In this formulation, decision variables are treated as uncertain and hence corresponding problem is solved under fuzzy environment. The linear as well as nonlinear (sigmoidal) membership functions are defined corresponding to their fuzzy goals and then problem is solved by iterative process as described in chapter 4.
In Chapter 6, performance analysis of repairable industrial systems has been done by incorporating their behavior analysis results. For this FMOOP is formulated by considering all the reliability indices as obtained during their behavior analysis. Exponential distribution membership functions have been fitted corresponding to each of the objective function and then based on the preference of DM towards the objective, a problem is reformulated to an equivalent crisp optimization problem and then solved by using PSO. The presented approach has been applied to optimize the performance of an industrial system namely a cattle-feed plant.
Chapter 7 describes an approach for reliability and maintainability analysis of an industrial system by using the collected field failure and repair data. The best fit of the failure and repair data between the common theoretical distributions are found by Anderson Darling (AD) goodness-of-fit test. The respective parameters are obtained by optimizing their likelihood functions. Furthermore, the survival and hazard rate models of the entire system are calculated. The reliability, availability and maintainability analysis of the crank - case manufacturing plant over a period of one year was investigated.
In Chapter 8 PSOBLT technique has been used to obtain fuzzy reliability parameters by utilizing the results as obtained in chapter 7. The parameters related to failure and repair rate distributions are time varying and follow Weibull and Normal distributions respectively instead of constant distribution as discussed in previous chapters. Behavior of the system has been analyzed in the form of crisp and defuzzified values at $\pm$15\%, $\pm$25\% and $\pm$50\% spreads. Sensitivity analysis has also been done to reflect the effect of components failure and repair rate on system performance and hence, based on their performance as per preferential order, ranking of the components has been presented.
Chapter 9 deals with the overall concluding observations of this study and a brief discussion on the scope for future work.