Abstract
The main objective of the present study is to permit the reliability analyst or system manager to analyze the failure behavior of the system in a more consistent and logical manner. As the collected or available data from various resources are uncertain and imprecise due to various practical constraints and hence the performance of the system cannot be made up to desired levels. To cope with such situations and subjective information in a consistent and logical manner, fuzzy methodology is one of the most vital and effective tool. To this effect a structural framework has been developed by the authors for analyzing and predicting the system behavior. The pulping unit of paper industry has been taken as an illustration. The failure rates and repair times for all the constituent components are obtained by solving availability-cost optimization model using particle swarm optimization and genetic algorithm. To increase the performance of the system, various reliability parameters are computed with the obtained results using a confidence interval based fuzzy lambda-tau methodology. Sensitivity as well as performance analysis of the system performance has been done for ranking the critical component of the system as per preferential order. The computed results are compared with existing fuzzy lambda-tau and traditional (crisp) methodology results.
Top1. Introduction
Due to continuous advancement in technology, industrial systems are becoming complex and expensive to operate and maintain. In the modern day electronic, manufacturing and industrial systems, reliability and safety analysis and assessment of complex systems is becoming more and more difficult task due to the fact that the reliability and safety of manufacturing systems depend not only on all failed states of system components, but also on the sequence of occurrences of those failures (Birolini, 2007). However, failure is an unavoidable phenomenon associated with the technological advancement of the equipments used in all industries. Any unfortunate consequences of unreliable behavior of such equipments or systems have led to the desire for reliability analysis. Therefore. In recent years system reliability becomes an important issue in evaluating the performance of an engineering system by eliminating or reducing the likelihood of failures and thus increasing their desired life and operational availability.
To maintain the reliability of sophisticated systems to a higher level, the systems’ optimum structural design or highly reliable components of these systems are required, rather both of them may be sought simultaneously. Implementation of these methods to improve the system availability or reliability will normally consume resources such as cost, weight, volume etc. So the system reliability cannot be further improved effectively by considering these constraints. Replacement of lesser reliable components with highly reliable components can improve the system reliability but the cost constraints may violate. While improving the reliability of systems and their components, the associated cost also increases. Increasing attention needs to be given to reduce the cost during production, operation and maintenance of the systems. These objectives can be achieved with the reliability based design of the systems and optimization of the maintenance and operational activities in the industrial systems to ensure their full utilization. Thus. In the present scenario of global competition and faster delivery times, it is an important topic for decision-makers to fully consider the actual business and the quality requirement together. This is the reason why there is a growing interest in implementation and investigation of reliability principles for industrial systems.
Key Terms in this Chapter
Evolutionary Algorithm (EA): A collective term for all variants of (probabilistic) optimization and approximation algorithms that are inspired by Darwinian evolution. Optimal states are approximated by successive improvements based on the variation-selectionparadigm. Thereby, the variation operators produce genetic diversity and the selection directs the evolutionary search.
Fuzzy Set: A fuzzy set is any set that allows its members to have different grades of membership (membership function) in the interval [0,1]. A numerical value between 0 and 1 that represents the degree to which an element belongs to a particular set, also referred to as membership value.
Fitness Function: A process which evaluates a member of a population and gives a score or fitness. In most cases the goal is to find an individual with the maximum (or minimum) fitness.
Fuzzy Logic: Traditional logic systems assume that things are either in one category or another. Yet in everyday life, we know this is often not precisely so. Fuzzy logic, introduced in the year 1965 by Lofti A. Zadeh, is a mathematical tool for dealing with uncertainty. Unlike Boolean logic, fuzzy logic is multi-valued and handles the concept of partial truth (truth values between completely true and completely false). Dr. Zadeh states that the principle of complexity and imprecision are correlated: The closer one looks at a real world problem, the fuzzier becomes its solution. The fuzzy theory provides a mechanism for representing linguistic constructs such as high, low, medium, tall, many. In general, fuzzy logic provides an inference structure that enables appropriate human reasoning capabilities. On the contrary, the traditional binary set theory describes crisp events that is, events that either do or do not occur.
Reliability: It is a characteristic of an item (component or system), expressed by the probability that the item (component/system) will perform its required function under given conditions for a stated time interval.
Fuzzification: It is the process of transforming a crisp set to a fuzzy set or a fuzzy set to a fuzzifier i.e., crisp quantities are converted to fuzzy quantities. This operation translates accurate crisp input values into linguistic variables.
Defuzzification: In fuzzification process, the crisp quantities are converted into fuzzy quantities, however in several applications as well as most of actions or decisions implemented by human or machines are binary or crisp in nature. So it is necessary to defuzzified the fuzzy results that have generated through fuzzy analysis. The process of converting the fuzzy output to a crisp value is said to be defuzzification. The input for the defuzzification process is the aggregate set and the output is a single number.