A decision support system for identifying abnormal operating procedures in a nuclear power plant

Hsieh, Meng-Long; Hwang, Sheue‐Ling; Liu, Kang-Hong; Liang, Sheau-Farn Max; Chuang, Chia-Chin

doi:10.1016/j.nucengdes.2012.04.009

Cited by 34 publications

(14 citation statements)

References 6 publications

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“…Although they have made great progress, they are often ineffective when dealing with increasingly complex decision-making environments and rapidly increasing data volumes/dimensions [49]. Hsieh et al [50] proposed a decision support system for identifying abnormal operating procedures of NPPs, which can reduce program selection errors and the psychological burden of subjects. However, the system state combination formed by the dynamic and complex parameter changes of nuclear power safety is very complicated, and the human-computer interaction scenarios are difficult to predict, which may lead to unstable states of the system and cause faults eventually.…”

Section: A Current Limitations In Nppsmentioning

confidence: 99%

“…This research puts its focus on human engineering while the decision-making process itself is of complexity in NPPs. In order to deal with that issue, a decision support system for identifying abnormal operating procedures (AOPs) is introduced where a database for abnormal symptoms is constructed and similarity analysis is included [50]. The system could reduce errors of human operators and decision-making time.…”

Section: ) Decision-making Assistancementioning

confidence: 99%

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Nuclear Power Plants With Artificial Intelligence in Industry 4.0 Era: Top-Level Design and Current Applications—A Systemic Review

Lyu

Zhang

et al. 2020

IEEE Access

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Nuclear energy can make an important contribution to low-carbon energy supply for Industry 4.0, while Industry 4.0 can reform this industry in return. As a typical and complex man-machine-network integration system, various faults, insufficient automation and stressed human operators limit the further popularization of nuclear power plants (NPPs) while these issues can be addressed by the aid of artificial intelligence (AI) technologies. In this work, we try to present a systemic review of how AI can benefit NPPs in a top-to-down fashion. We discuss limitations in current NPPs and introduce the concept of Nuclear Power Plant Human-Cyber-Physical System (NPPHCPS) as the top-level design. Then, we category AI-related nuclear power applications into Physical-Plant-Centered and Human-Operator-Centered technologies and review research works from 7 typical NPP functional scenarios in the recent two decades. In each NPP functional scenario, how researchers integrate AI into NPPs is presented following timeline. We hope this review can be used as the guideline for NPPs' Design in the future and contribute to green Industry 4.0.

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Section: A Current Limitations In Nppsmentioning

confidence: 99%

Section: ) Decision-making Assistancementioning

confidence: 99%

Nuclear Power Plants With Artificial Intelligence in Industry 4.0 Era: Top-Level Design and Current Applications—A Systemic Review

Lyu

Zhang

et al. 2020

IEEE Access

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“…If several abnormal situations occur at once, decisions have to be made in even less time. Operators are usually unable to judge what situation should be given priority when confronted with complex abnormal situations such as these [6,7].…”

Section: Introductionmentioning

confidence: 99%

An abnormal situation modeling method to assist operators in safety-critical systems

Naderpour

Zhang

2015

Reliability Engineering & System Safety

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One of the main causes of accidents in safety-critical systems is human error. In order to reduce human errors in the process of handling abnormal situations that are highly complex and mentally taxing activities, operators need to be supported, from a cognitive perspective, in order to reduce their workload, stress, and the consequent error rate. Of the various cognitive activities, a correct understanding of the situation, i.e. situation awareness (SA), is a crucial factor in improving performance and reducing errors. Despite the importance of SA in decision-making in time-and safety-critical situations, the difficulty of SA modeling and assessment means that very few methods have as yet been developed. This study confronts this challenge, and develops an innovative abnormal situation modeling (ASM) method that exploits the capabilities of risk indicators, Bayesian networks and fuzzy logic systems. The risk indicators are used to identify abnormal situations, Bayesian networks are utilized to model them and a fuzzy logic system is developed to assess them. The ASM method can be used in the development of situation assessment decision support systems that underlie the achievement of SA. The performance of the ASM method is tested through a real case study at a chemical plant.

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“…For this reason, most NPPs provide a large volume of AOPs (abnormal operating procedures), through which human operators are able to effectively identify the list of actions to be done and how to actually conduct them [10,17,22]. This implies that AOPs should have a sufficient coverage so that human operators are able to determine the required actions to cope with a given abnormal condition.…”

Section: Introductionmentioning

confidence: 99%

A systematic framework to investigate the coverage of abnormal operating procedures in nuclear power plants

Park¹,

Jung²

2015

Reliability Engineering & System Safety

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A decision support system for identifying abnormal operating procedures in a nuclear power plant

Cited by 34 publications

References 6 publications

Nuclear Power Plants With Artificial Intelligence in Industry 4.0 Era: Top-Level Design and Current Applications—A Systemic Review

Nuclear Power Plants With Artificial Intelligence in Industry 4.0 Era: Top-Level Design and Current Applications—A Systemic Review

An abnormal situation modeling method to assist operators in safety-critical systems

A systematic framework to investigate the coverage of abnormal operating procedures in nuclear power plants

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