Fuzzy-Reasoning-Based Approach to Qualitative Railway Risk Assessment

Abstract

This article presents the development of a fuzzy-reasoning-based railway risk assessment system. The proposed method can evaluate qualitative and quantitative risk data in a uniform manner for railway risk assessment. It permits risk analysts to assess the risks associated with the failure modes directly using linguistic terms. The proposed railway risk assessment system is capable of assessing the risks at component level, subsystem level, and system level. The outcomes of the risk assessment are represented in two formats, risk score and risk category with a belief of percentage, which provide very useful risk information to railway designers, operators, engineers, and maintainers. A case study on rolling stock asset risk assessment is used to demonstrate the proposed railway risk assessment system. The results indicate that by using this system, risks associated with a railway system can be assessed effectively and efficiently.

An Intelligent Railway Safety Risk Assessment Support System for Railway Operation and Maintenance Analysis

“…[8]. However, the triangular and trapezoidal MFs are the most frequently used in safety risk analysis practice [1][2][3][11][12][13].…”

“…The centroid of area method, which determines the centre of gravity of an aggregated fuzzy set, is the most frequently used method in the fuzzy reasoning systems [3,8,11,13,23], defined as…”

“…Fuzzy qualitative descriptors are extensions of numerical variables in the sense that they are able to represent the condition of an attribute at a given interval by taking fuzzy sets as their values [1][2][3]. The values obtained in the development of fuzzy qualitative descriptors are considered as fuzzy-measuring attributes of objects, in this case, RLs.…”

“…However, there are serious issues involved in both maintaining this position in reality and sustaining the public perception of railway safety excellence [1][2][3]. The UK railway now finds itself in a situation where actual and perceived safeties are real issues, to be dealt with in a new public culture of rapid change, short-term pressures, and instant communications [4,5].…”

“…Furthermore, in many situations, the data of probability of failure of a system do not exist and it must be estimated based on the expert knowledge and experience or engineering judgement from similar items. In this context, a safety risk model using fuzzy reasoning-based approach may be more appropriate to analyse the risks of the system where there is incomplete safety risk information [3,12,13]. A fuzzy reasoning approach allows imprecision or approximate information in its analysis process, which helps to restore integrity to safety risk analysis.…”