Abstract
The present paper investigates the reliability analysis of industrial systems by using vague lambda-tau methodology in which information related to system components is uncertain and imprecise in nature. The uncertainties in the data are handled with the help of intuitionistic fuzzy set (IFS) theory rather than fuzzy set theory. Various reliability parameters are addressed for strengthening the analysis in terms of degree of acceptance and rejection of IFS. Performance as well as sensitivity analysis of the system parameter has been investigated for accessing the impact of taking wrong combinations on its performance. Finally results are compared with the existing traditional crisp and fuzzy methodologies results. The technique has been demonstrated through a case study of bleaching unit of a paper mill.
Highlights
Engineering systems have become complicating day by day, and rapidly increasing the cost of the equipment challenges the plant personnel or job analyst that to maintain the system performance so that to produce the desirable profit under a predetermined time
The failure is an inevitable phenomenon in an industrial system
It follows from the tables that results computed by proposing an approach follows the same trend as the fuzzy lambdatau (FLT) results, and results are conservative in nature
Summary
Engineering systems have become complicating day by day, and rapidly increasing the cost of the equipment challenges the plant personnel or job analyst that to maintain the system performance so that to produce the desirable profit under a predetermined time. The effects of product failures range from those that cause minor nuisances to catastrophic failures involving loss of life and property It is difficult for the system analyst to maintain the performance of the system for a longer period of time. It is usually impossible to obtain such a large quantity of data from any particular plant From this point of view, fuzzy reliability is a novel concept in system engineering as fuzzy set can capture subjective, uncertain, and ambiguous information. Out of existence of several extensions, intuitionistic fuzzy set theory (IFS) defined by Atanassov [11] is one of the most successful extensions and has been found to be well suited for dealing with problems concerning vagueness.
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