A fuzzy dynamic bayesian network-based situation assessment approach

Situation awareness (SA), a state in the mind of a human, is essential to conduct decision-making activities. It is about the perception of the elements in the environment, the comprehension of their meaning, and the projection of their status in the near future. Two decades of investigation and analysis of accidents have showed that SA was behind of many serious large-scale technological systems' accidents. This emphasizes the importance of SA support systems development for complex and dynamic environments. This paper presents a fuzzy dynamic Bayesian network-based situation assessment approach to support the operators in decision making process in hazardous situations. The approach includes a dynamic Bayesian network-based situational network to model the hazardous situations where the existence of the situations can be inferred by sensor observations through the SCADA monitoring system using a fuzzy quantizer method. In addition to generate the assessment result, a fuzzy risk estimation method is proposed to show the risk level of situations. Ultimately a hazardous environment from U.S. Chemical Safety Board investigation reports has been used to illustrate the application of proposed approach. © 2013 IEEE

FACTORS INFLUENCING HUMAN RELIABILITY OF HIGH TEMPERATURE GAS COOLED REACTOR OPERATION

ABSTRACT Operator roles and intervene actions on the operation of gas cooled reactor would be different compared to their roles in other reactor types. Analysis of operator performance and the influencing factors can be conducted comprehensively in Human Reliability Analysis (HRA). Using HRA, the impact of human errors on the system and the ways to reduce human error impact and frequency can be idenfified. The paper discusses factors influencing reactor operator performance to response to the cooling accident of the high temperature gas cooled reactor (HTGR). Analysis and qualification of influencing factors, which are performance shaping factors (PSF), were conducted based on time reliability curve and Cognitive Reliability and Error Analysis Method (CREAM). Based on time reliability curve, results showed that time variable contributes to the improvement of operator performance (PSF<1), especially when the safety features of the system properly work as in the design. Based on CREAM, it can be identified that in addition to the time variable, human machine interface design and sufficiently training also contribute to the improvement of operator performance. This study found that total PSF equals to 0.25, in which the positive dominant factor is time variable whose PSF is 0.01 and the negative dominant factors are procedure and working cycle whose PSF is 5. Those PSF values reflected the multiplier factors to the human error probability. The analysis of performance shaping factors should be developed on the other operation and accident scenarios of HTGRs prior to be further applied for a comprehensive assessment and analysis of human reliability and for the design of human machine interface system at control room. Keywords: PSF, HTGR, human operator, control room, human reliability  ABSTRAK Peran dan tindakan operator pada reaktor berpendingin gas akan berbeda dengan peran operator pada operasi tipe reaktor lain. Analisis unjuk kerja operator dan faktor yang berpengaruh dapat dilakukan secara komprehensif melalui analisis keandalan manusia(HRA). Melalui HRA dampak dari kesalahan manusia pada sistem maupun cara untuk mengurangi dampak dan frekuensi kesalahan dapat diketahui. Makalah membahas faktor yang berpengaruh pada tindakan operator, yaitu pada kejadian kecelakaan pendingin reaktor gas bersuhu tinggi-HTGR. Analisis untuk kualifikasi faktor pembentuk kinerja(PSF) dilakukan berdasarkan kurva keandalan fungsi waktu, dan metode keandalan manusia yang dikembangkan berdasar pada aspek kognitif yaitu Cognitive Reliability and Error Analysis Method (CREAM). Hasil analisis berdasar kurva keandalan fungsi waktu menunjukkan komponen waktu berkontribusi positif pada peningkatan keandalan operator (PSF<1) pada kondisi semua fitur keselamatan berfungsi sesuai rancangan. Sedangkan pada metoda analisis dengan pendekatan kognitif CREAM diketahui selain faktor ketersediaan waktu, faktor pelatihan dan rancangan HMI juga berkontribusi meningkatkan keandalan operator. Faktor pembentuk kinerja keseluruhan diketahui sebesar 0,25 dengan faktor kontribusi positif dominan atau berpengaruh pada penurunan kesalahan manusia adalah ketersediaan waktu (PSF=0,01), dan faktor kontribusi negatif dominan adalah prosedur dan siklus kerja (PSF=5). Nilai PSF tersebut sebagai faktor pengali dalam perhitungan probabilitas kesalahan manusia. Analisis faktor pembentuk kinerja perlu dikembangkan pada skenario kejadian lain untuk selanjutnya digunakan untuk perhitungan dan analisis keandalan manusia yang komprehensif dan perancangan sistem interaksi manusia mesin di ruang kendali. Kata kunci: PSF, HTGR, operator, ruang kendali, keandalan manusia

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

© 2014 Elsevier Ltd. 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

A human-system interface risk assessment method based on mental models

© 2015 Elsevier Ltd. In many safety-critical systems, it is necessary to maintain operators' situation awareness at a high level to ensure the safety of operations. Today, in many such systems, operators have to rely on the principles and design of human-system interfaces (HSIs) to observe and comprehend the overwhelming amount of process data. Thus, poor HSIs may cause serious consequences, such as occupational accidents and diseases including stress, and they have therefore been considered an emerging risk. Despite the importance of this, very few methods have as yet been developed to assess the risk of HSIs. This paper presents a new risk assessment method that relies upon operators' mental models, human reliability analysis (HRA) event tree, and the situation awareness global assessment technique (SAGAT) to produce a risk profile for the intended HSI. In the proposed method, the operator's understanding (i.e. mental models) about possible abnormal situations in the intended plant is modeled on the basis of the capabilities of Bayesian networks. The situation models are combined with the HRA event tree, which paves the way for the incorporation of operator responses in the assessment method. Probe questions in line with the SAGAT through simulated scenarios in a virtual environment are then administrated to gather operator responses. Finally, the proposed method determines a risk level for the HSI by assigning the operator responses to the developed situational networks. The performance of the proposed method is investigated through a case study at a chemical plant

An intelligent situation awareness support system for safety-critical environments

Operators handling abnormal situations in safety-critical environments need to be supported from a cognitive perspective to reduce their workload, stress, and 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 error. However, existing system safety researches focus mainly on technical issues and often neglect SA. This study presents an innovative cognition-driven decision support system called the situation awareness support system (SASS) to manage abnormal situations in safety-critical environments in which the effect of situational complexity on human decision-makers is a concern. To achieve this objective, a situational network modeling process and a situation assessment model that exploits the specific capabilities of dynamic Bayesian networks and risk indicators are first proposed. The SASS is then developed and consists of four major elements: 1) a situation data collection component that provides the current state of the observable variables based on online conditions and monitoring systems, 2) a situation assessment component based on dynamic Bayesian networks (DBN) to model the hazardous situations in a situational network and a fuzzy risk estimation method to generate the assessment result, 3) a situation recovery component that provides a basis for decision-making to reduce the risk level of situations to an acceptable level, and 4) a human-computer interface. The SASS is partially evaluated by a sensitivity analysis, which is carried out to validate DBN-based situational networks, and SA measurements are suggested for a full evaluation of the proposed system. The performance of the SASS is demonstrated by a case taken from US Chemical Safety Board reports, and the results demonstrate that the SASS provides a useful graphical, mathematically consistent system for dealing with incomplete and uncertain information to help operators maintain the risk of dynamic situations at an acceptable level. © 2014 Elsevier B.V. All rights reserved

A situation risk awareness approach for process systems safety

Promoting situation awareness is an important design objective for a wide variety of domains, especially for process systems where the information flow is quite high and poor decisions may lead to serious consequences. In today's process systems, operators are often moved to a control room far away from the physical environment, and increasing amounts of information are passed to them via automated systems, they therefore need a greater level of support to control and maintain the facilities in safe conditions. This paper proposes a situation risk awareness approach for process systems safety where the effect of ever-increasing situational complexity on human decision-makers is a concern. To develop the approach, two important aspects - addressing hazards that arise from hardware failure and reducing human error through decision-making - have been considered. The proposed situation risk awareness approach includes two major elements: an evidence preparation component and a situation assessment component. The evidence preparation component provides the soft evidence, using a fuzzy partitioning method, that is used in the subsequent situation assessment component. The situation assessment component includes a situational network based on dynamic Bayesian networks to model the abnormal situations, and a fuzzy risk estimation method to generate the assessment result. A case from US Chemical Safety Board investigation reports has been used to illustrate the application of the proposed approach. © 2013 Elsevier Ltd

Insights on Accident Information and System Operations during Fukushima Events

As part of the development of an integrated perspective on lessons learned from the Fukushima Daiichi nuclear accident, this paper highlights lessons learned and implications relating to the accident information and system operational aspects during the events. Our analysis clearly indicates that the plant was neither designed nor prepared to withstand such an unexpected event, which included a complete loss of electrical power sources for a long period. The author focused on the accident information and system operational aspects of the Fukushima event, including lack of information, provision of wrong information, operator performance in life-threatening environments, and improvisation given lack of procedures and training. Suggestions for further improvement of the nuclear plant safety are then made with respect to preparation for beyond design basis events, provision of reliable essential information to operators, development of guidelines/procedures, training of operators, and development of operator support systems with consideration of severe accidents caused by unexpected events. It is hoped that the lessons learned from the accident will significantly contribute to the enhancement of nuclear plant safety

Modeling and simulation of offshore workers' behavior

The offshore oil and gas industry functions in a team work culture in which operations depend not only on individuals’ competency, but also on team skills. Team skills are even more necessary when it comes to handling emergency conditions. Emergency conditions are dynamic in nature and personnel on board are challenged with evolving high-risk situations, time pressure, and uncertainty. One way to effectively handle emergencies is to train personnel to a competency level, both individually and as a part of a team. This would increase the chance of achieving safety in a timely manner using the available resources such as information, equipment, and people. Such training involves enhancing team members' understanding of human performance, in particular, the social and cognitive aspects of effective teamwork and good decision making. Post-accident analysis of offshore accidents shows that conventional training programs are often too generic, and that they are not designed to identify and tackle the human factors that are critical for evolving offshore emergency situations. Recognition of the importance of human factors on operator performance raises the need for training that goes beyond conventional training programs and incorporates non-technical training focusing on leadership, command, decision making, communication, and teamwork. A major difficulty to design such training is that it involves practicing emergency exercises with a potentially large number of participants, each playing the appropriate role in a given scenario. Such large-scale team exercises suffer from both organizational and educational drawbacks. The amount of human and financial resources needed for such a training exercise is difficult to organize. Furthermore, it is very hard, if not impossible, to get all team members together at the same time and location. Also, the team members may have variability in the competency levels (novice versus advanced trainees) and hence different training needs. One effective and flexible solution to this problem is to use intelligent artificial agents, or ‘virtual workers’, in a virtual environment (VE) to play different roles in the team. Virtual workers are artificially intelligent agents that can reproduce behaviors that are similar to or compatible with those of a real worker. This research proposes to develop a human behavior simulation model (HBM) that can be used to create such virtual workers in the context of offshore emergency egress. The goal of this research is to develop a human behavior model that can simulate offshore workers’ emergency response under the influence of performance influencing factors (PIFs). The first part of the work focuses on understanding human behavior during offshore emergency situations. A two level, three factor experiment was conducted in a virtual environment (VE) to investigate the relationships between the PIFs and human behavior. Influence of both internal and external PIFs were investigated. Knowledge acquisition and inference processes of individuals were also investigated in the experimental study. In the second part, a computational model was developed to capture the across-subject variability observed during the experiment. Interviews with subject matter experts (SME) were conducted at this step to ensure that the model is able to produce a realistic range of human behaviors. The final step was to validate the developed behavior model. All high-level tasks to validate the HBM were performed. Special emphasis was given on acceptability criteria testing to ensure that the integrated HBM performs adequately under different operating conditions

Desarrollo de una metodología de simulación de secuencias en accidente en centrales nucleares de agua ligera considerando actuaciones del operador

This thesis presents the work carried out to develop a simulation system for nuclear power plants, denominated TRETA / COPMA-III integrated simulator, which allows the simulation of the thermalhydraulic processes that take place in this type of facilities, in normal operation and emergency operation, as well as the control room crew actions related with the management of the emergency situations. The simulation of the thermalhydraulic processes is carried out by means of the TRETA (PWR) or TIZONA (BWR) simulators, both developed by the Spanish Nuclear Council (CSN). In what concerns to the simulation of the human performance, and taking into account the fact that in this type of facilities the management of the emergencies is strongly proceduralized, the COPMA-III procedures simulator is used. This simulator has been developed by the Halden Reactor Project (HRP), and it was adapted by the HRP development team for its use in the integrated simulator. This new tool is characterized mainly by its modular structure and its interconnection ability with other codes. In an individual way, the different codes that compose the simulator present advanced capacities in its models. Firstly, the TRETA simulator presents great versatility in defining the grade of complexity in the simulation of the processes. On the other hand, regarding to the COPMA-III simulator, it enables the automatic simulation of human actions proceduralized or planned, that means, all of those manual performances of which it is possible to develop a deterministic scheme, including aspects of timing and work load. Concerning the simulator package modular structure, it makes possible even the substitution of the process or procedures simulators and the implementation of any other simulator that it is considered more appropriate for specific necessities. This simulation system not only could be applied to validate the procedures design, but rather could be use in the verification of the consistency of the analyses made in the safety analysis. This last aspect is specially relevant, because these studies don’t include, except for seldom cases, the dynamic evaluation of the operator actions impact in the analysis of the accidental sequences