Cognitive Reliability And Error Analysis Method Research Articles

Safety Analysis of Aviation Flight-Deck Procedures Using Systemic Accident Model

Abstract: Analyzing the feasibility of procedures is important for ensuring safety when using systems that feature human operations and highly developed automation. This is certainly the case with flight-deck procedures due to their complexity, which sometimes leads to deviation from standard operation procedures (SOPs) and other serious outcomes (e.g., air crash accidents). To analyze the feasibility of procedures, we adopt the functional resonance analysis method (FRAM) (Hollganel, 2004) to examine the safety management of flight-deck procedures. However, FRAM is essentially a theoretic method, and there are currently no specific approaches or supportive tools to bridge the gap between theory and practice. In this paper, we propose an adaptation of the cognitive reliability and error analysis method (CREAM) (Hollnagel (1998)) that we call Fuzzy CREAM for systematic and quantitative FRAM analysis. We applied the proposed method to an actual air crash accident that occurred near Cali Airport, Colombia in 1995 and conclude that the accident was due to deviation from SOPs. On the basis of our analysis, we show that FRAM can identify potential hazardous paths that may lead to an accident. We also propose a new method using FRAM for pre-analysis of the safety of designed procedures.

The Mechanism of how Design Failures cause Unsafe Behavior: The Cognitive Reliability and Error Analysis Method (CREAM)

Studies have indicated that prevention through design is the most economic and effective approach to enhance safety. Although the importance of prevention through design has been argued for many years, the mechanism between design and unsafe behavior is still unclear, which impedes the promotion of design for safety. This study aims to reduce accidents in the construction industry and promote safety design by understanding the mechanism through design leads to unsafe behavior and quantify the probability that design failure will cause unsafe behavior. Real inspection data were combined with a Bayesian network, and Bayesian parameter learning was used to identify how design lead to unsafe behavior and calculate the probability of the unsafe behavior. The result highlighted the significance of engineering design specify the mechanism of design on behavior, which outlined a theoretical foundation for prevention through design.

A Hybrid Model for Human Error Probability Analysis

The aim of the present research is to propose a hybrid model for human error probability analysis based on Cognitive Reliability and Error Analysis Method (CREAM) and Systematic Human Error Reduction and Prediction Approach (SHERPA) Method. The model aims to provide a theoretical framework to understand human behavior and to predict error probability. This model is based on a fundamental distinction between competence and control that offers a way of describing how performance depends on context. The most important result of this work is to provide a model that can be used in the preventive phase of an accident in order to mitigate the damages.

Human Reliability Analysis by Cognitive Approach for Unloading Process in an ALDS (Auto LPG Dispensing Station)

Background/Objectives: Accident dynamics always considers the role of humans in order to ensure effective prevention of dangerous events in engineering industries where the execution of highly repetitive and standardized tasks occurs. The centrality and responsibility of the role entrusted to the human operators are exalted because it requires problem solving and decision making ability. Methods/Statistical Analysis: Human operator is the core of a cognitive process that leads to decisions, influencing the safety of the whole system in function of their reliability. The main focus of the research is to systematically predict human error potentials (Failure probability) during unloading process of LPG from truck tank to the storage tank and to determine the required safety control levels. The paper adopted CREAM (Cognitive Reliability and Error Analysis Method) basic and extended versions in order to assess human reliability and is demonstrated with the above stated operator activity. Findings: The probability value for most of the failures are included in the 'tactical'control mode range 1.2E-3<P<8E-2 (1.0E-3 < p < 1E-1), as shown by the basic version of methodology. The overall human error probability value is assigned as the maximum value of the sub-tasks which is 8E-2. Application/Improvements: Quantification of failure probability is HRA is a highly difficult task. Availability of failure data helps to take various maintenance and management decisions. Consequently, the research contributes to safety at work and prevention of human injury and loss of life.

Human Error Probability Estimation in Atex-hmi Area Classification: from Therp to Fuzzy Cream

ATEX (explosive atmosphere) risk assessment is required when any equipment or system can potentially cause explosive atmospheres. Although many operations are performed by operators, influences from human and organizational factors (HOFs) are mostly neglected. In order to address HOFs influence, the ATEX-HMI (Human-Machine Interaction) methodology is proposed: it aims at integrating HOFs into each step of the standard ATEX risk assessment. The first step faced is Area classification: the ATEX-HMI methodology introduces the Human Error Probability (HEP) into the zone calculation procedure. Human Reliability Analysis (HRA) is the main method for the HEP assessment, thus two HRA methods were applied for the ATEX-HMI methodology. The first one, THERP, demonstrated an efficient quantitative precision, but it was not easily applicable to real industries. Then, the Cognitive Reliability and Error Analysis Method (CREAM) was adopted, together with a complementary FUZZY application for quantitative analysis. The present paper introduces the FUZZY CREAM application, also showing a comparative analysis of THERP and FUZZY CREAM, on the basis of a food plant case study.

HUMAN RELIABILITY ANALYSIS DENGAN PENDEKATAN COGNITIVE RELIABILITY AND ERROR ANALYSIS METHOD (CREAM)

The accidents in grinding and welding sectors were the highest cases over the last five years in PT. X and it caused by human error. Human error occurs due to the influence of working environment both physically and non-physically. This study will implement an approaching scenario called CREAM (Cognitive Reliability and Error Analysis Method). CREAM is one of human reliability analysis methods which purposely employed to gain a Cognitive Failure Probability (CFP) value which can be conducted with basic and extended method. An application of basic method will result a general value of failure probability whereas a more specific CFP value for every task will be resulted when the extended method is utilized. This study showed that numbers of factors that shall be applied to mitigate error on grinding and welding sector are application of; adequacy of organization, adequacy of Man Machine Interface (MMI) & operational support, availability of procedure /plans and adequacy of training and preparation. This study exhibites that planning has the highest erroneous value of cognitive aspect on grinding task (by CFP value of 0.3). Furthermore, CFP value of 0.18 of cognitive aspect is shown for execution on welding task. To summarize, this study suggests numerous method to trim cognitive erroneous value on grinding and welding work, which are by committing a periodical training, applying more detail work instruction and giving education to operate the equipment.

Application of CREAM human reliability model to cargo loading process of LPG tankers

The storage and handling processes of liquefied petroleum gas (LPG) constitutes a complex operational environment in the maritime mode of transportation. The LPG cargo is carried by specially designed ships called LPG tankers. The LPG cargo loading and discharging operations have always potential hazards. Thus, the crew on-board LPG tankers should be fully aware of operational risks during the cargo handling process, which includes various critical tasks such as drying, inerting, gassing-up, cooling, and reliquefaction. During these stages, human reliability (operation without failure) plays a crucial role in sustainable transportation of cargo. Human reliability analysis (HRA), related to various parameters such as the human factor, technology, and ergonomics, is always a critical consideration as regards maritime safety and environment. The main focus of the research is to systematically predict human error potentials for designated tasks and to determine the required safety control levels on-board LPG ships. The paper adopted CREAM (Cognitive reliability and error analysis method) basic and extended versions in order to assess human reliability along with the cargo loading process on-board LPG tanker ships. Specifically, the model is demonstrated with an operational case study. Consequently, the research provides should contribute to maritime safety at sea and prevention of human injury and loss of life on-board LPG ship.

The comparative study of evaluating human error assessment and reduction technique and cognitive reliability and error analysis method techniques in the control room of the cement industry

Aims: The present study aimed to evaluate the assessment methods of human errors and compare the results of these techniques in order to introduce the precise method of human error assessment, and recognize the factors affecting the occurrence of these errors. Materials and Methods: This case study was done at three workstation control room of a cement industry in 2014. After determining the responsibilities and critical jobs by hierarchical task analysis, cognitive reliability and error analysis method (CREAM) and human error assessment and reduction technique (HEART) were used in order to analyze the human errors. Results: The results showed that in the CREAM method, the highest probability of error occurrence is related to monitoring and control (operator) with a probability of 0.207, and that of in the HEART method, is related to control signs (operator) with a probability of 0.416. The number of errors detected by CREAM and HEART method were 85 and 80, respectively. Time and cost of applying the CREAM methods were 235 h and 1175($), while those in the HEART techniques were 215 h and 1075($). Conclusion: We concluded that the highest probability of calculated errors relates to monitoring and control (operator), controlling warning signs (operators), and cooperation in solving the problem (supervisor) for both techniques. By considering the time and cost factors, HEART has superiority, while CREAM is better due to its extensive evaluation and the number of detected errors.

Quantitative human reliability analysis for crew during shipping operation

A simplified bi-variable human error probability calculation method is developed by incorporating two common performance condition (CPC) factors, which are modified from factors employed in cognitive reliability and error analysis method (CREAM) to take into account the characteristics of shipping operations. After the influencing factors are identified, Markov method is used to calculate the values of human reliability. The proposed method does not rely on the involvement of experts in the field of human factor nor depend on historical accidents or human error statistics. It is applied to the case of the crew on board of an ocean going dry bulk carrier. The caculated results agree with the actual case, which verifies the validity of the model.

The Design and Realization of HRA Service System for the Engine Assembly

Human error is a main factor causing the engine assembly quality defects. This paper presented the application of the Root Cause Retrospective Analysis (RCRA) of the Cognitive Reliability and Error Analysis Method (CREAM) in the engine assembly process, proposed a new model of the RCRA for engine assembly failure within the framework of HRA (Human Reliability Analysis) service system for the engine assembly. The application of the model was illustrated by an example. The research belongs to one of the core functions of HRA service system, which can be used to perform the human error analysis in the process of the engine assembly, so as to provide guidance for production.

Common patterns in aggregated accident analysis charts from human fatigue-related groundings and collisions at sea

Research has shown that there are potentially disastrous outcomes of human fatigue at sea. The conditions in which the seafarers have to operate are becoming more and more demanding. The study in this article attempts to aggregate accident charts derived from in-depth studies of human fatigue-related accidents to determine common patterns of interlinked fatigue factors. The accidents are analyzed by means of the Cognitive Reliability and Error Analysis Method (CREAM), which in the article has been modified for maritime accidents. The main fatigue factors identified are ‘shift work’, ‘irregular working hours’, ‘inadequate task allocation’, and ‘excessive demands’. The study reveals several differences between ship collision and grounding accidents and their corresponding fatigue factors. Human fatigue-related collision accidents are characterized by wrong/badly timed decisions, misconceptions, and poor communication between the vessels. Right before the collision the crew is often panicking and mistakes are easily made. In human fatigue-related groundings, the conditions are often monotonous and the navigating officer has either overlooked the upcoming seabed or simply fallen asleep. Safety climate issues are also identified as important contributors to human fatigue.

An Application of CREAM for Human Reliability Analysis in Power System Switching Operation

Reliability is always one of the most focal points for the power system researchers. With the improvement of equipment reliability, human error has become a great threat to the power system security. But the human reliability analysis in power system does not get as much attention as it perhaps deserve. As a representative HRA method, the Cognitive Reliability and Error Analysis Method (CREAM) is cited in this paper. And it is applied to evaluate the human error probability of a simple switching operation case. This practice is a beneficial attempt to introduce HRA method into the power system reliability research and lay a foundation for the further explorations.

An empirical study on the basic human error probabilities for NPP advanced main control room operation using soft control

By adopting new human–system interfaces that are based on computer-based technologies, the operation environment of main control rooms (MCRs) in nuclear power plants (NPPs) has changed. The MCRs that include these digital and computer technologies, such as large display panels, computerized procedures, soft controls, and so on, are called Advanced MCRs. Among the many features in Advanced MCRs, soft controls are an important feature because the operation action in NPP Advanced MCRs is performed by soft control. Using soft controls such as mouse control, touch screens, and so on, operators can select a specific screen, then choose the controller, and finally manipulate the devices.However, because of the different interfaces between soft control and hardwired conventional type control, different basic human error probabilities (BHEPs) should be considered in the Human Reliability Analysis (HRA) for advanced MCRs. Although there are many HRA methods to assess human reliabilities, such as Technique for Human Error Rate Prediction (THERP), Accident Sequence Evaluation Program (ASEP), Human Error Assessment and Reduction Technique (HEART), Human Event Repository and Analysis (HERA), Nuclear Computerized Library for Assessing Reactor Reliability (NUCLARR), Cognitive Reliability and Error Analysis Method (CREAM), and so on, these methods have been applied to conventional MCRs, and they do not consider the new features of advance MCRs such as soft controls. As a result, there is an insufficient database for assessing human reliabilities in advanced MCRs.In this paper, BHEPs in a soft control operation environment are investigated empirically for BHEPs to apply advanced MCRs. A soft control operation environment is constructed by using a compact nuclear simulator (CNS), which is a mockup for advanced MCRs. Before the experiments, all tasks that should be performed by subjects are analyzed using one of the task analysis methods, Systematic Human Error Reduction and Prediction Approach (SHERPA). Human errors are then checked to analyze BHEPs, human error mode, and the cause of human error when using soft control.

A modified CREAM to human reliability quantification in marine engineering

Modern shipping activities are carried out via a highly sophisticated man–machine system within which technological, social and environmental factors often contribute to the occurrence of human failures. Due to the high risks caused by such failures, human reliability analysis (HRA) has always been a serious concern in marine engineering safety. However, the problem of lack of data, together with the complexity of marine engineers' behaviour, has weakened the applicability of well-established HRA methods (i.e., cognitive reliability and error analysis method (CREAM)) in the maritime context. This paper proposes a modified CREAM to facilitate human reliability quantification in marine engineering by incorporating fuzzy evidential reasoning and Bayesian inference logic. The core of the new method is to use evidential reasoning to establish fuzzy IF–THEN rule bases with belief structures, and to employ a Bayesian inference mechanism to aggregate all the rules associated with a marine engineer's task for estimating its failure probability. Consequently, the outcomes of this work can also provide safety engineers with a transparent tool to realise the instant estimation of human reliability performance for a specific scenario/task.

Quantitative Evaluation of Human-Reliability Based on Fuzzy-Clonal Selection

Human reliability analysis (HRA) is necessary for system safety assessment as well as equipment reliability analysis. The cognitive reliability and error analysis method (CREAM) as a representative HRA method provides nine common performance conditions (CPCs) to represent the contextual conditions under which a given action is performed. With a scarcity of empirical data, a high uncertainty in analyzing results is produced by subjective judgment. To obtain more objective and effective HRA results, this paper presents an optimized quantification method to evaluate the human error probability (HEP) according to CREAM, and its linguistic variables. The starting point for the quantification is an introduced fuzzy version of CREAM. However, many fuzzy rules are redundant, and occupy extensive computing time. The clonal selection algorithm combined with the fuzzy model is proposed to optimize the rule set. The evaluation method using the fuzzy-clonal selection algorithm is aimed to support possible applications for prospective and retrospective studies in the domain of safety assessment of power systems. The simulations are carried out on four presumed contexts, and a practical power system. The conclusions illuminate the feasibility of the proposed quantitative method.

Modeling of Errors Realized by a Human Learner in Virtual Environment for Training

This study focuses on the notion of erroneous actions realized by human learners in Virtual Environments for Training. Our principal objective is to develop an Intelligent Tutoring System (ITS) suggesting pedagogical assistances to the human teacher. For that, the ITS must obviously detect and classify erroneous actions produced by learners during the realization of procedural and collaborative work. Further, in order to better support human teacher and facilitate his comprehension, it is necessary to show the teacher why learner made an error. Addressing this issue, we firstly modeling the Cognitive Reliability and Error Analysis Method (CREAM). Then, we integrate the retrospective analysis mechanism of CREAM into our existing ITS, thus enable the system to indicate the path of probable cause-effect explaining reasons why errors have occurred.

Evaluation of significant transitions in the influencing factors of human reliability

This paper describes sensitivity analysis of a previously developed model for the estimation of the probability of human erroneous actions in specific industrial and working contexts. The model has been built using the fuzzy logic theory. The fuzzy model developed includes nine input parameters according to the CREAM (cognitive reliability and error analysis method) methodology. The model has been used in order to detect the critical transitions in the optimization of human reliability, for corresponding changes in each of the nine input parameters. Changes in the working context have been evaluated through their relevant reduction to the action failure probability of the human operator. The results of the fuzzy model, after the defuzzification process, are human error probabilities in the form of crisp numbers, which can eventually be used in probabilistic assessments and cost—benefit analyses in order to compare the parameters’ adjustment cost with the impact this adjustment has on the performance and reliability of the human operator.

Evaluation of a Software Implementation of the Cognitive Reliability and Error Analysis Method (CREAM)

The Cognitive Reliability and Error Analysis Method (CREAM) represents a second-generation approach to human reliability analysis (HRA). The method, however, is very tedious to apply manually and not yet in widespread use and therefore largely untested. To allow for rapid and systematic evaluation of the CREAM method, a software tool for its application was developed. Results from several analyses undertaken to evaluate the method and the tool are also presented. The simplicity of our CREAM software allowed novices to analyze events in much detail but also revealed some critical shortcomings in both the method and the software tool. Several conclusions could be drawn from this allow for drafting of specific design guidelines for future upgrades of the CREAM software. These conclusions and recommendations can be further generalized to all software applications of HRA methods

A simplified CREAM prospective quantification process and its application

Cognitive reliability and error analysis method (CREAM), with a sound cognitive model and framework with an emphasis of the whole characteristics of context, is a representative method of the so-called second generation human reliability analysis (HRA) methods and can be used in both retrospective and prospective analysis. For prospective analysis in the context of PSA/HRA, the need of CREAM to provide the results in quantitative terms is seemingly unavoidable. One remaining problem of CREAM as HRA is how to provide an easily practicable process to get the numeric results. This paper provides a simplified CREAM prospective quantification process including the basic method and extended method and their applications in the SGTR event of Qinshan 1 NPP. A comparison is made between the CREAM and THERP in the quantification and a data collection system is proposed to provide a more validated data source for future application of CREAM method.

Human Reliability Analysis by Fuzzy “CREAM”

This article proposes a systematic procedure for computing probabilities of operator action failure in the cognitive reliability and error analysis method (CREAM). The starting point for the quantification is a previously introduced fuzzy version of the CREAM paradigm that is here further extended to account for: (1) the ambiguity in the qualification of the conditions under which the action is performed (common performance conditions, CPCs) and (2) the fact that the effects of such conditions on human performance reliability may not all be equal.