Robot Reliability Using Petri Nets and Fuzzy Lambda-Tau Methodology

TL;DR

This paper presents a method combining Petri Nets and Fuzzy Lambda-Tau techniques to analyze robot reliability, effectively handling imprecise data and uncertainties in failure prediction within hazardous environments.

Contribution

It introduces a novel approach integrating Petri Nets with Fuzzy Lambda-Tau methodology to quantify reliability parameters under uncertainty, enhancing decision-making in robot systems.

Findings

Reliability parameters computed using fuzzy data

Efficient generation of minimal cut and path sets

Handling of imprecise failure data

Abstract

Robot reliability has become an increasingly important issue in the last few years due to increased application of robots in many industries (like automobile industry) under hazardous and unstructured environment. As the component failure behavior is dependent on configuration and environment, the available information about the constituent component of robots is most of the time imprecise, incomplete, vague and conflicting and so it is very difficult to analyze their behavior and to predict their failure pattern. The reliability analysis of any system provides an understanding about the likelihood of failures occurring in the system/component and the increased insight about its inherent weakness. The objective of this paper is to quantify the uncertainties that makes the decision more realistic, generic and extendable to application domain. In this paper various reliability parameters (such as mean time between failures, expected number of failures, reliability, availability etc.) are computed using Fuzzy Lambda-Tau methodology. Triangular fuzzy numbers are used to represent failure rates and repair times as they allow expert opinion, linguistic variables, operating conditions, uncertainty and imprecision in reliability information, to be incorporated into system model. Petri Nets are used because unlike the fault tree methodology, the use of Petri Nets allows efficient simultaneous generation of minimal cut and path sets.