Reliability analysis of complex multi-robotic system using GA and fuzzy methodology

Abstract

The objective of the study is to compute various reliability parameters for multi-robotic system, using Real Coded Genetic Algorithms (RCGAs) and Fuzzy Lambda-Tau Methodology (FLTM). The paper contains a new idea about the reliability analysis of robotic system. The optimal values of mean time between failures (MTBF) and mean time to repair (MTTR) are obtained using GAs. Petri Net (PN) tool is applied to represent the interactions among the working components of multi-robotic system. To enhance the relevance of the reliability study, triangular fuzzy numbers (TFNs) are developed from the computed data, using possibility theory. The use of fuzzy arithmetic in the PN model increases the flexibility for application to various systems and conditions. Various reliability parameters, namely failure rate, repair time, MTBF, expected number of failures (ENOF), reliability and availability, are computed using FLTM. Sensitivity analysis has also been performed and the effects on system MTBF are addressed. The adopted methodology improves the shortcomings/drawbacks of the existing probabilistic approaches and gives a better understanding of the system behavior through its graphical representation. The analysis presented, may be helpful for the system analyst to analyze and predict the system behavior and to reallocate the required resources.

Introduction

A wide use of robotic systems has increased the importance of robot reliability and quality. The problem becomes more important for robots, which are used in hazardous environments. Reliability is an important factor for industrial and medical robots. The subject of robot reliability is very complex and there are numerous interlocking variables in evaluating and accomplishing various reliability levels. A successful robot installation has to be safe and reliable. A robot with poor reliability leads to many problems such as high maintenance cost, unsafe conditions and inconvenience.

The reliability of robotic system can be maintained to a higher level using the structural design of the system/components of higher reliability or both of them may be performed simultaneously [1]. When the components of higher reliability are used, the associated cost of components also increases. This is an important issue to be considered for industrial application purpose. Thus, the decision-makers have to consider both the profit and the quality requirements. Reliability and performance of robotic systems may be improved if failure analysis techniques are used during the design process. An industrial robotic system consists of numerous components and the probability that the system survives, depends directly on each of its constituent components. For analyzing the performance of complex robotic systems, it is required to develop a suitable methodology so that timely actions may be initiated for achieving the goal of high production.

The present work is an extension of the work, earlier done by Sharma et al. [2], [3], in which the cost factor was not considered in mathematical modeling. In this study, various reliability parameters have been evaluated for a multi-robotic system, arranged in a complex configuration. Reliability block diagram (RBD) of the system is drawn and based on it, availability model is constructed by considering availability function, manufacturing cost and repair cost, and optimal values of MTBF and MTTR are obtained using GA. With reference to the availability and cost factors, it is possible to find out maximum overall efficiency of the entire system [4]. The computed parameters have been used to calculate various fuzzy reliability parameters (failure rate, repair time, MTBF, expected number of failures, reliability and availability). In the quantitative framework the quantification of system parameters is important for effective managerial decision-making with respect to maintenance planning and it is done in terms of fuzzy, crisp and defuzzified values. First, the PN model of the system is drawn and the system failure rates and repair times are computed from the optimal MTBF and MTTR. To remove the uncertainty in data, the fuzzification of failure rate and repair time data is done using triangular fuzzy numbers (TFNs). After knowing the input TFNs for all the components, the corresponding values for failure rate (λ) and repair time (τ) for the system at different confidence levels (α) are determined using fuzzy transition expressions. The mission time for the calculation of reliability parameters is taken to be t = 100 h. To study the failure behavior of the system, crisp and defuzzified values are obtained at ±15%, ±25% and ±50% spreads. The effects of failures and course of action on the system performance have also been investigated.