Fuzzy reliability analysis of dual-fuel steam turbine propulsion system in LNG carriers considering data uncertainty

Abstract

This paper presents an approach to analyze the fuzzy reliability of dual-fuel steam turbine mechanical propulsion conventional (DFSMC) system of LNG carriers utilizing best available data collected from various sources which may have some sort of uncertainties. Quantification of uncertainties present in collected data has been done through data fuzzification using triangular fuzzy numbers with known spreads as suggested by system experts. In these conditions, if available techniques such as FLT (fuzzy lambda-tau) and GABLT (genetic algorithms based lambda-tau) are applied for DFSMC system reliability assessment using quantified data then the computed reliability indices have wide ranges of prediction due to the complexity of the system. Using these results, decision-maker may suggest some impressive corrections which may improve the system performance. However, it may also be possible that after incorporating suggested corrections, system performance may not improve upto the desired level because suggestions are inappropriate due to the wide ranges of prediction. In order to reduce the prediction ranges of computed fuzzy reliability indices, and to select suitable and effective strategic decision making, this study applies the weakest t-norm (T-omega) based approximate arithmetic operations for evaluating some very important fuzzy reliability indices of the system. Sensitivity analysis has also been conducted in an ideal condition for analysing the effects of various reliability parameters on system performance. To rank the critical components of the system, Tanaka et al. (1983) approach has been extended for repairable systems and then utilized for this purpose. The analysis can help maintenance personnel to understand and plan suitable maintenance strategy to improve the overall performance of the system. Based on results some influential suggestions are given for improving system performance.