Fuzzy reliability analysis of the compressor house unit (CHU) system in a coal fired thermal power plant using TBGFLT technique

Abstract

Purpose The purpose of this paper is to analyze the fuzzy reliability of the compressor house unit (CHU) system in a coal fired thermal power plant under vague environment by reducing the accumulating phenomenon of fuzziness and accelerating the computation process. This paper uses different fuzzy membership functions to quantify uncertainty and access the system reliability in terms of different fuzzy reliability indices having symmetric shapes. Design/methodology/approach This study analyses the fuzzy reliability of the CHU system in a coal fired thermal power plant using Tω-based generalized fuzzy Lambda-Tau (TBGFLT) technique. This approach applies fault tree, Lambda-Tau method, different fuzzy membership functions and α-cut coupled Tω-based approximate arithmetic operations to compute various reliability parameters (such as failure rate, repair time, mean time between failures, expected number of failures, availability and reliability) of the system. The effectiveness of TBGFLT technique has been demonstrated by comparing the results with results obtained from four different existing techniques. Moreover, this paper applies the extended Tanaka et al. (1983) approach to rank the critical components of the system when different membership functions are used. Findings The adopted TBGFLT technique in the present study improves the shortcomings of the existing approaches by reducing the accumulating phenomenon of fuzziness, accelerating the computation process and getting symmetric shapes for computed reliability parameters when different membership functions are used to quantify data uncertainty. Originality/value In existing fuzzy reliability techniques which are developed for repairable systems either triangular fuzzy numbers, triangle vague sets or triangle intuitionistic fuzzy sets have been used for quantifying uncertainty. These approaches do not examine the systems for components with different membership functions. The present study is an effort in this direction and evaluates the fuzzy reliability of the CHU system in a coal fired thermal power plant for components with different membership functions. This is the main contribution of the paper.

On the optimization of maintenance storage cost in industry a fuzzy logic application

“…Typically, in industry, users prefer systems that remain reasonably valid with limited data and possibly compromised accuracy of inputs. For this reason, the fuzzy logic method first developed by Lotfy Zada (Zada, 1965) has been widely implemented in a diversity of fields (Komal, 2019), (Samima, Sarma, Samanta, & Prasad, 2019), (Zare, Malakoutikhah, & Alimohammadou, 2019) and (Jatoba, Cesar Bellas, Koster, Burns, & Vidal, 2018). Fuzzy logic was also implemented heavily in the field of industrial maintenance and reliability (Rashid, Place, & Braithwaite, 2014), (Sene, Kamsu-Foguem, & Rumeau, 2018) and (Tillement & Hayes, 2019).…”

Fuzzy fault tree analysis for controlling robot-related accidents involving humans in industrial plants: a case study

“…A convex and normal fuzzy subset is called a fuzzy number (Cheng and Mon, 1993). In the literature, there are different types of fuzzy numbers which are used to quantify different types of data uncertainties (Mon and Cheng, 1994;Chen, 1996;Komal, 2017Komal, , 2019Zimmermann, 2011). Some commonly used fuzzy numbers are triangular, trapezoidal, FFTA to control robot-related accidents Cauchy, sharp gamma, normal, etc.…”

“…Some commonly used fuzzy numbers are triangular, trapezoidal, FFTA to control robot-related accidents Cauchy, sharp gamma, normal, etc. (Mon and Cheng, 1994;Chen, 1996;Komal, 2017Komal, , 2019). An imprecise real number "around a 2 ∈ R" can be expressed by a fuzzy setà using different fuzzy membership functions in the range (support) a 1 ≤ a 2 ≤ a 3 as followings Mon and Cheng (1994).…”

“…to quantify uncertainty. For computation point of view, these techniques apply appropriate fuzzy arithmetic operations either based on α-cut and interval arithmetic (Cheng and Mon, 1993;Fuh et al, 2014); simplified arithmetic operations (Tanaka et al, 1983;Singer, 1990;Chen, 1994;Chang et al, 2006;Shu et al, 2006;Chang et al, 2008); simplified fuzzy number arithmetic and T ω -norm (the weakest t-norm) (Hong and Do, 1997;Komal, 2015;Kumar and Yadav, 2012;Komal, 2019); Posbist approach (Huang et al, 2004); operator theory (Misra and Weber, 1989); Monte Carlo simulation Ferdous et al (2009); etc. These techniques in context of robot fuzzy reliability assessment have following shortcomings:…”

“…In this paper, different fuzzy numbers are used for estimating robot fuzzy reliability by quantifying different uncertainty factors which is the main contribution of the paper. In the presented method, different fuzzy membership functions are used to quantify data uncertainty, and α-cut, interval arithmetic coupled T ω based approximate fuzzy arithmetic operations are employed for evaluating robot fuzzy reliability (Komal, 2017(Komal, , 2019. This paper also uses V-index introduced by Tanaka et al (1983) for ranking critical basic events as per their criticality.…”