Abstract
Critical types of infrastructure are provided by the state to maintain the people’s livelihood, ensure economic development, and systematic government operations. Given the development of ever more complicated critical infrastructure systems, increasing importance is being attached to the protection of the components of this infrastructure to reduce the risk of failure. Power facilities are one of the most important kinds of critical infrastructure. Developing an effective risk detection system to identify potential failure modes (FMs) of power supply equipment is crucial. This study seeks to improve upon prior approaches for risk assessment by proposing a hybrid risk‐assessment model using the concepts of failure mode and effect analysis (FMEA) and multiple‐criteria decision‐making (MCDM). The proposed model includes a cost‐based factor for decision‐makers. The subjectivity and uncertainty in FM assessment are adjusted through the rough number method. The original risk priority number (RPN) can be expanded by including the entropy weights in the risk index. Furthermore, to rank the risk priorities in a rational manner, a modified technique for order preference by similarity to ideal solution (modified TOPSIS) is adopted. The applicability and effectiveness of the proposed method were demonstrated by considering an example of a turbine steam engine in a nuclear power plant.
Highlights
Critical infrastructure networks, such as technological networks, information and communication technology systems, transport networks, health care systems, and finance and government systems, are vital assets for every country [1, 2]
In mid-August 2017, a large-scale unexpected power outage occurred in Taiwan. e primary reason for this incident was that the supply pipeline for the power supply plant stopped operating, which resulted in a large number of generator sets being shut down. e resultant blackout indirectly caused one death and multiple injuries. e area affected by the incident included a metropolitan area with a high population concentration. e main effects of the blackout included the suspension of business operations in the area and loss of road lighting leading to traffic congestion
According to the information provided by the 24 experts, the results of our analysis indicate that rotor breakdown (FM9), fracture of the vane (FM4), foreign objects (FM3), a clogged lubricating oil system (FM2), bearing damage (FM6), and mechanical transmission breakdown (FM8) are the top 6 failure modes (FMs) leading to the failure of steam turbines. e modified TOPSIS can provide the relative risk level of the FMs through the ranking index (RCi)
Summary
Critical infrastructure networks, such as technological networks, information and communication technology systems, transport networks, health care systems, and finance and government systems, are vital assets for every country [1, 2]. (i) e three elements applied in FMEA analysis do not encompass the entire range of causal risk elements (ii) e measurement of S and D is relative and subjective, with no holistic characterization of group judgment (iii) e S, O, and D are difficult to evaluate precisely in numerical form (iv) e S, O, and D are often given no or equal importance weights (v) RPN values are not continuous, and there exists no mechanism to interpret the meaning of the differences among different RPNs (vi) Different combinations of S, O, and D may produce the same RPN, thereby causing some highrisk FMs to be ignored (vii) Many numbers in the 1–1,000 range cannot be formed from the product of S, O, and D (viii) Small variations in each rating may lead to considerably different effects on the RPN e conventional FMEA method has been proven to be one of the most important early preventative initiatives for systems, processes, and services; the aforementioned limitations may reduce the reliability of the conventional FMEA model.
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