Reliability analysis of complex robotic system using Petri nets and fuzzy lambda‐tau methodology

Abstract

PurposeThe purpose of this paper is to evaluate various reliability parameters of a multi‐robot system, arranged in a complex configuration. The effects of failures and course of action on the system performance have also been investigated.Design/methodology/approachThe present work is based on a multi‐robotic system, in which two robots are working independently with a conveyer unit. Petri net (PN) tool is applied to represent the asynchronous and concurrent processing of the system. To enhance the relevance of the reliability study, fuzzy numbers are developed from available data of the components, using fuzzy possibility theory to define membership functions. The use of fuzzy arithmetic in the PN model increases the flexibility for application to various systems and conditions. Various reliability parameters (such as mean time between failures, ENOF, reliability, availability, etc.) are computed using fuzzy lambda‐tau methodology. As the available data are imprecise, incomplete, vague and conflicting, the fuzzy methodology can deal easily with approximations.FindingsThe adopted methodology in the present work improves the shortcomings/drawbacks of the existing probabilistic approaches and gives a better understanding of the system behavior through its graphical representation.Originality/valueIn an earlier study failure behavior of a single robot was analyzed. This paper is an extension of the previous work, in which failure behavior of multi‐robotic system is analyzed. Also, the interactions among the working units of multi‐robotic system are deeply studied. The paper contains a new idea about the reliability analysis of robotic system. Fuzzy lambda‐tau methodology, a fuzzy PROBIST technique, is used for the proposed robotic system and the results obtained are compared with crisp results. Reliability analysis of a multi‐robotic system is presented in this paper, which may help the system analysts to analyze and predict the system behavior and to reallocate the required resources.