A Novel FFTA for Handwashing Process to Maintain Hygiene with Events Following Different Membership Function

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

Purpose

In recent years, the application of robots in different industrial sectors such as nuclear power generation, construction, automobile, firefighting and medicine, etc. is increasing day by day. In large industrial plants generally humans and robots work together to accomplish several tasks and lead to the problem of safety and reliability because any malfunction event of robots may cause human injury or even death. To access the reliability of a robot, sufficient amount of failure data is required which is sometimes very difficult to collect due to rare events of any robot failures. Also, different types of their failure pattern increase the difficulty which finally leads to the problem of uncertainty. To overcome these difficulties, this paper presents a case study by assessing fuzzy fault tree analysis (FFTA) to control robot-related accidents to provide safe working environment to human beings in any industrial plant.

Design/methodology/approach

Presented FFTA method uses different fuzzy membership functions to quantify different uncertainty factors and applies alpha-cut coupled weakest t-norm (Tω) based approximate fuzzy arithmetic operations to obtain fuzzy failure probability of robot-human interaction fault event which is the main contribution of the paper.

Findings

The result obtained from presented FFTA method is compared with other listing approaches. Critical basic events are also ranked using V-index for making suitable action plan to control robot-related accidents. Study indicates that the presented FFTA is a good alternative method to analyze fault in robot-human interaction for providing safe working environment in an industrial plant.

Originality/value

Existing fuzzy reliability assessment techniques designed for robots mainly use triangular fuzzy numbers (TFNs), triangle vague sets (TVS) or triangle intuitionistic fuzzy sets (IFS) to quantify data uncertainty. Present study overcomes this shortcoming and generalizes the idea of fuzzy reliability assessment for robots by adopting different IFS to control robot-related accidents to provide safe working environment to human. This is the main contribution of the paper.

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

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.

Reliability analysis of a phaser measurement unit using a generalized fuzzy lambda-tau(GFLT) technique

Nowadays power consumption is increasing day-by-day. To fulfill failure free power requirement, planning and implementation of an effective and reliable power management system is essential. Phasor measurement unit(PMU) is one of the key device in wide area measurement and control systems. The reliable performance of PMU assures failure free power supply for any power system. So, the purpose of the present study is to analyse the reliability of a PMU used for controllability and observability of power systems utilizing available uncertain data. In this paper, a generalized fuzzy lambda-tau (GFLT) technique has been proposed for this purpose. In GFLT, system components' uncertain failure and repair rates are fuzzified using fuzzy numbers having different shapes such as triangular, normal, cauchy, sharp gamma and trapezoidal. To select a suitable fuzzy number for quantifying data uncertainty, system experts' opinion have been considered. The GFLT technique applies fault tree, lambda-tau method, fuzzified data using different membership functions, alpha-cut based fuzzy arithmetic operations to compute some important reliability indices. Furthermore, in this study ranking of critical components of the system using RAM-Index and sensitivity analysis have also been performed. The developed technique may be helpful to improve system performance significantly and can be applied to analyse fuzzy reliability of other engineering systems.