Cognitive Reliability And Error Analysis Method Research Articles

Human Reliability Analysis for Fishing Vessels in Korea Using Cognitive Reliability and Error Analysis Method (CREAM)

In this paper, we introduce a model designed to predict human error probability (HEP) in the context of fishing boat operations utilizing the cognitive reliability and error analysis method (CREAM). We conducted an analysis of potential accidents on fishing boats and calculated the cognitive failure probability (CFP) for each identified accident. The common performance conditions (CPCs) from the original CREAM were adapted to better reflect the conditions on fishing boats, with the adapted CPCs’ validity confirmed through expert consultations. To apply CREAM, data were gathered via a survey of fishermen, with the uncertainty in the collected data addressed through the application of fuzzy set theory (FST). We then established a Bayesian network (BN) model to elucidate the relationship between the fuzzy data and HEP, utilizing a weighted sum algorithm to determine conditional probabilities within the BN. Both basic and extended versions of CREAM were applied to analyze the most common accidents among fishermen, calculating the CFP for each type of accident. According to our analysis, the poorer the dynamic CPC, the higher the probability that a fall accident will occur inside the boat due to human error, necessitating a countermeasure. The paper proposes safety enhancements for small fishing boats and illustrates the increased precision of human reliability analysis (HRA) models in forecasting human error by incorporating quantitative methods. It calls for further data collection and refinement of the model for more accurate operational risk assessments.

Extended cognitive reliability and error analysis method for advanced control rooms of nuclear power plants

This study proposes a modified extended cognitive reliability and error analysis method (CREAM) for achieving a more accurate human error probability (HEP) in advanced control rooms. The traditional approach lacks failure data and does not consider the common performance condition (CPC) weights in different cognitive functions. The modified extended CREAM decomposes tasks using a method that combines structured information analysis (SIA) and the extended CREAM. The modified extended CREAM performs the weight analysis of CPCs in different cognitive functions, and the weights include cognitive, correlative, and important weights. We used the extended CREAM to obtain the cognitive weight. We determined the correlative weights of the CPCs for different cognitive functions using the triangular fuzzy decision-making trial and evaluation laboratory (TF-DEMATEL), and evaluated the importance weight of CPCs based on the interval 2-tuple linguistic approach and ensured the value of the importance weight using the entropy method in the different cognitive functions. Finally, we obtained the comprehensive weights of the different cognitive functions and calculated the HEPs. The accuracy and sensitivity of the modified extended CREAM were compared with those of the basic CREAM. The results demonstrate that the modified extended CREAM calculates the HEP more effectively in advanced control rooms.

An investigation into accidents in laboratories in universities in China caused by human error: A study based on improved CREAM and SPAR-H

Although considerable research has been devoted to improving safety in university laboratories, accidents, in that environment, have still occurred frequently at the cost of serious injury or even death of laboratory personnel. Currently, few Human Reliability Analyses (HRA) have been conducted with respect to a university laboratory. The aim of the research was to conduct a reliability study relating to human behaviour in a university laboratory to explore quantitatively the causes and influencing factors relating to the frequency of laboratory accidents. Improved Cognitive Reliability and Error Analysis Method (CREAM) and improved Standardized Plant Analysis Risk HRA (SPAR-H) were employed to assess Human Error Probability (HEP) of 23 subjects. The HEP calculated through improved CREAM proved more accurate than results obtained through improved SPAR-H. Unexpectedly, the results demonstrated that under similar environmental conditions, the HEP of subjects did not decrease with an increase in educational background, including additional experimental time and experience. Moreover, environmental conditions exerted greater impact on personnel reliability than Human Inherent Factors (HIFs) in laboratories. It is anticipated that the study would provide valuable insights, in respect of research methods, and to serve as a practical basis for lowering the accident rate in university laboratories.

A hybrid human reliability analysis approach for a remotely-controlled maritime autonomous surface ship (MASS- degree 3) operation

Maritime autonomous surface ships (MASS) are one of the hot topics in maritime transportation even though they bring many challenges in terms of safety, security, and environment. This paper tackles the safety-related challenges of remotely controlled ships without seafarers on board but controlled at the shore. In this context, the reliability of the operator is of paramount importance for safe and efficient MASS operation. This paper performs systematic human reliability analysis for the operator of MASS (for degree 3) under the Bayesian belief network (BBN) and evidential reasoning (ER)- cognitive reliability and error analysis method (CREAM) approach. In the model, BBN is capable of determining the probability distribution of Contextual Control Modes in CREAM, while ER tackles the uncertainty and subjectivity of expert judgments. The outcome of the paper shows that the human reliability for remote control MASS operation is 8.88E-01. Besides its robust theoretical background, the paper will provide the utmost contributions to operators, managers, safety inspectors, and ship owners of MASS for safer and reliable operations.

Analyzing human reliability for the operation of cargo oil pump using fuzzy CREAM extended Bayesian Network (BN)

Crude oil cargo discharging operations can be performed by cargo oil pumps in tanker ships. Considering the harmful effects of crude oil, any failure during the cargo oil pump operation may pose acute hazards such as temperature and pressure increase in pipelines, leakage, fire, and following undesired events that lead to marine environmental pollution and toxic effects to humans. The process is managed by the ship's crew, and it requires following the operational steps in order. This paper aims at performing a detailed human reliability analysis (HRA) for the operational process of cargo oil pumps on a tanker ship. In the paper, the Cognitive Reliability and Error Analysis Method (CREAM) is used to calculate human error probability under a fuzzy set which deals with the uncertainty and ambiguity of the CPC (Common Performance Condition). The Bayesian Network (BN) approach is adopted to determine the probability distribution of the control modes in reliability assessment. In view of the findings, the human reliability for the entire process of cargo oil pump operation on tanker ship is found 8.82E-01. The outcomes of the paper provide valuable insight into the improvement of tanker safety in shipboard operation and performance reliability assessment.

Operational reliability analysis of remote operated vehicle based on dynamic Bayesian network synthesis method

In this study, we propose a reliability framework that combines the GO method with fault tree analysis and integrates human factor reliability analysis. Taking the application of remotely operated vehicle (ROV) in underwater oil and gas production as an example, a reliability model of ROV operation process is established. The human reliability in the ROV operation process is analyzed on the basis of Cognitive Reliability and Error Analysis Method (CREAM) extension approach. The influence of human factor reliability and equipment reliability on the system is compared and analyzed. The importance of each operation step is also compared and analyzed. A sensitivity analysis of the ROV during operation is performed. This study provides a theoretical basis for the improvement and maintenance of the operational reliability of ROV. The proposed reliability modeling approach solves the reliability analysis problem of large and complex operating systems.

A new model to optimize the human reliability based on CREAM and group decision making

AbstractThe Cognitive Reliability and Error Analysis Method (CREAM) represents one of the second‐generation approaches to human reliability assessment, taking into account the influence of environmental conditions on human error probability (HEP). In the context of CREAM, the Common Performance Conditions (CPCs) influence error probabilities. Since not all CPCs have equal impacts, this study employs the Bayesian Best Worst Method (BWM), a novel approach in group decision‐making, to assign weights to these factors. Subsequently, two techniques based on basic CREAM are proposed. The current control mode is determined in the first technique according to the experts' opinions. Then the probability of human error is calculated based on the amount of control. It is possible to provide solutions for improving control mode, based on obtained results. Therefore, in this study, the second method has been used to make suggestions to enhance human reliability. For this purpose, in the second technique, an optimization problem is formulated to select the best applicable programs for managers to enhance human reliability. The proposed bi‐objective model tries to increase the reliability of human resources by reducing human error and costs. The proposed bi‐objective model seeks to bolster the reliability of human resources by concurrently minimizing HEP and associated costs. The efficiency of the presented methods is verified through a case study in the control room of the cement factory. The results of the first technique reveal an opportunistic control mode with a corresponding HEP of 0.0198. On the other hand, the outcomes of our proposed model underscore the greater impact of improving CPC levels in reducing the probability of human error. Ultimately, the practical programs derived from our mathematical model provide decision‐makers with valuable insights to reduce the probability of human error to a mere 0.000172 through the transition from opportunistic to strategic control.

A Risk-Data-Based Human Reliability Analysis for Chemical Experiments with Hazardous Processes

In recent years, chemical experiment accidents have frequently occurred, resulting in injuries and fatalities among researchers. It is crucial to address this issue to improve laboratory safety. Based on many publications, it is clear that human error makes a major contribution to many laboratory accidents which contain hazardous processes. However, there is limited research focusing on human error in laboratory safety, and there is also a lack of effective measures to assess Human Error Probability (HEP) for experimental process safety. Therefore, we propose an improved Cognitive Reliability and Error Analysis Method (CREAM) which is based on risk data to assess the HEP during hazardous processes in chemical experiments. The proposed method adjusts nine Common Performance Conditions (CPCs) in conventional CREAM to make them suitable to describe chemical experiments. Then, in contrast to the traditional approach, this study uses the definition of risk as the support to collect CPC data from the perspectives of possibility and severity, so as to improve the rationality of the data and decrease the subjectivity of expert judgment. Afterwards, the weight value of each CPC is calculated through Gray Relation Analysis (GRA) based on the collected risk data of each CPC. Meanwhile, the collected risk data are used to determine the fuzzy degrees of each CPC, the activated fuzzy If-Then rules, and the corresponding rule weights. Finally, the CPCs’ membership degrees, the CPCs’ weights, and If-Then rule weights are integrated together to acquire the HEP by defuzzification. In short, the proposed method changes the CPCs to ensure they are suitable, and then it innovatively uses risk data as the source to directly and indirectly determine the CPC’s fuzzy degree, the CPC’s importance weight, and the If-Then rule weight by fuzzy theory and GRA for collecting final HEP results. This method was tested on a selected chemical experiment, “preparation of active ferrous sulfide”, which contains hazardous processes. Through the proposed method, the HEP of each procedure in the selected risky chemical experiment could be determined, and among the procedures, the highest HEP was 1.51 × 10−3. In addition, with the HEP results, several subtasks with a high risk of human error could be identified. The results matched the real situations.

Evaluation of human error in workers of an Iranian copper mine during the COVID-19 pandemic using the CREAM.

The outbreak of COVID-19 has adversely affected both global economy and public health around the world. These effects have also been observed in many workplaces, including mines. This study aimed to examine the human error of copper miners during the pandemic. This descriptive-analytical, cross-sectional study was performed on 192 workers of a copper mine in Iran. For this, occupation tasks were firstly analyzed using the Hierarchical Task Analysis (HTA), and then the human error in different subunits was assessed using the basic Cognitive Reliability and Error Analysis Method (CREAM). The prevalence of COVID-19 among miners was determined by assessing positive PCR test records. The probability of human error in the operational subunits including mining, crushing, processing, and support subunits was estimated to be 0.0056, 0.056, 0.0315, and 0.0177, respectively. All three operational units were found to be in the scrambling control mode. The support unit was determined to be in the tactical control mode. Approximately 50% of all workers had been infected with COVID-19, with the highest prevalence in support units. The results suggest that during the COVID-19 pandemic, copper miners are at higher risk of human error induced by poor working conditions. Therefore, it is recommended to employ some management strategies such as promotion of safety, health monitoring, and adopting supportive measures to control occupational stresses and therefore the probability of human error in the mine's operational units.

Human reliability assessment on building construction work at height: The case of scaffolding work

The hybrid approach of CREAM (cognitive reliability and error analysis method), fuzzy theory and Bayesian network (BN) was constructed to analyze the reliability of scaffolding operators in this work. A construction site in Fuzhou City (Fujian Province, China) was targeted as an example. Firstly, seven operational and five cognitive subtasks were developed by the characteristics of scaffolding operations. Then, the relational network of the common performance conditions (CPCs) was determined by a BN. Expert evaluation can obtain a priori probabilities. Fuzzy languages were transformed into conditional probabilities by fuzzy theory. The probability distribution of the control model can be determined based on the relational network. Secondly, the human error probabilities (HEPs) of the operational subtasks were calculated based on the center of the area (COA). Based on the influence of the operational subtask on the cognitive subtask, the HEPs of the cognitive subtasks were calculated with BNs. Finally, the total HEP of the scaffolding operation was identified using a series–parallel hybrid structure. The results show that the total HEP of the scaffolding operation at this construction site is 4.30E-03. The scaffolding dismantling and supervisory inspection have a significant impact on human error. Also, site management should strengthen training and education on work methods. This method can provide a new idea for human reliability analysis, as well as a new perspective for the safety of work at height.

Investigating human error in Iran’s copper mines using the CREAM based on human cognitive reliability analysis

Objectives Human error has been recognized as one of the top causes of most accidents in mines. This study aimed to identify and assess cognitive errors among copper miners using the cognitive reliability and error analysis method (CREAM). Methods. This cross-sectional study was carried out in one of the copper mines in Iran. First, all tasks were analyzed by hierarchical task analysis (HTA). Then cognitive errors were assessed using the CREAM. Results. With respect to the basic CREAM results in the operational units, including extraction, crushing and processing, human error probability (HEP) was obtained as 0.056, 0.0315 and 0.0177, respectively. Based on the results of the extended CREAM, the types of errors identified for all tasks in the three operational units were mainly associated with execution (53.4%), observation (40%), interpretation (5%) and planning (1.6%). Also, the cognitive errors were related to execution, monitoring, observation, communication, diagnosis, recording and planning, respectively. Conclusion. The results showed that human error is very high in the operational units of the mine. Therefore, immediate actions are needed to improve safety performance in the mine units, which determine the role of various factors in human errors and can provide the possibility of implementing more effective interventions.

Quantitative Study on Human Error in Emergency Activities of Road Transportation Leakage Accidents of Hazardous Chemicals.

The emergency rescue process of road transportation leakage accidents involving hazardous chemicals is complex and includes various emergency activities. A quantitative study of human errors in emergency activities is conducive to seeking the focus of the emergency rescue process. To quantitatively analyze human error in emergency activities during the emergency rescue process of road transportation leakage accidents of hazardous chemicals, sequentially timed events plotting (STEP) and the cognitive reliability and error analysis method (CREAM), were used. First, STEP was used to analyze six laws, regulations and standards, as well as 54 accident cases, to derive 24 emergency activities in the emergency rescue process. Then, CREAM was used to analyze and obtain the probability of human error for each emergency activity. Two high error level emergency activities, five medium error level emergency activities, and seventeen low error level emergency activities were identified after the human error levels of the emergency activities were classified. The results show that two emergency activities, the initial handling of the accident, and cleanup of the leakage site, should be prioritized in the emergency rescue process of road transportation leakage accidents of hazardous chemicals.

A Quantitative HAZOP Risk Analysis Under Extended CREAM Approach for Maritime Autonomous Surface Ship (MASS) Operation

Abstract Globally, there has been a substantial growth in interest in autonomous surface ships. This new domain presents numerous concerns for enhanced reader comprehension. The most crucial topic to consider is the safety of autonomous ships and the risks they present. To enable the use of autonomous ships, they must be as safe for the environment and for people as manned surface ships. However, as it has not yet been widely implemented, acquiring real-time data restricts the scope of the study. The objective of this paper is to conduct a comprehensive quantitative risk analysis for maritime autonomous surface ships (MASS). Therefore, it focuses on the operations to be performed by MASS and the factors that may pose a risk. In this context, Hazard and Operability Analysis (HAZOP) and Cognitive Reliability and Error Analysis Method (CREAM) approaches will be used in risk analysis assessment. While the HAZOP method will be used to determine the deviations, causes, possible consequences, and measurements that autonomous surface ships will be exposed to, the extended CREAM will be used to determine the likelihood of occurrence and risk levels of the MASS operation. The outcomes of the paper are anticipated to enhance the safety operational safety level of autonomous ships as well as mitigate risks.

Human Operating Risk Assessment for Outdoor Terminal Box of Electric Power

The outdoor terminal box is the basic node of the power Internet of Things as an intermediate link between outdoor electrical equipment and indoor equipment, like measurement and control, protection, communication and other equipment. Terminal boxes and terminals need to be regularly checked on site, loop tests, transformations, and equipment replacement. Operator errors have become one of the main factors in outdoor terminal box accidents. In terms of human risk assessment of outdoor terminal boxes, few papers are involved. In order to correctly evaluate the influence of human factors on the failure of outdoor terminal boxes, the paper first analyzes the human behavior factors of outdoor terminal box operators, the common performance conditions of the CREAM (Cognitive Reliability and Error Analysis Method) model is used to analyze the human behavior mechanism and behavior reliability factors during the operation of the terminal box. Then, the SLIM (Success Likelihood Index Method) model is used to calculate the probability of human error. The proportional failure model is used to calculate the failure rate of the outdoor terminal box itself. Finally, taking a circuit breaker terminal box as an example for simulation, the probability of human error is 1.56%, the equipment failure rate is 0.84%, the risk value of the system is 10.7%, and the risk level of the system is 3. From the probabilistic perspective, it shows that human factors have a greater influence on the causes of accidents.

Human Error Probability Assessment for LNG Bunkering Based on Fuzzy Bayesian Network-CREAM Model

Liquified natural gas (LNG) as a marine fuel has gained momentum as the maritime industry moves towards a sustainable future. Since unwanted LNG release may lead to severe consequences, performing quantitative risk assessment (QRA) for LNG bunkering operations has become mandatory according to some regulations. Human error is a main contributor to the risks, and the human error probabilities (HEPs) are essential for inclusion in a QRA. However, HEPs data are unavailable in the LNG bunkering industry so far. Therefore, this study attempts to infer HEPs through on-site safety philosophical factors (SPFs). The cognitive reliability and error analysis method (CREAM) was adopted as a basic model and modified to make it suitable for HEP assessment in LNG bunkering. Nine common performance condition (CPC) indicators were identified based on the fuzzy ranking of 23 SPF indicators (SPFIs). A Bayesian network (BN) was built to simulate the occurrence probabilities of different contextual control modes (COCOMs), and a conditional probability table (CPT) for the COCOM node with 19,683 possible combinations in the BN was developed according to the CREAM’s COCOM matrix. The prior probabilities of CPCs were evaluated using the fuzzy set theory (FST) based on data acquired from an online questionnaire survey. The results showed that the prior HEP for LNG bunkering is 0.009841. This value can be updated based on the re-evaluation of on-site SPFIs for a specific LNG bunkering project to capture the dynamics of HEP. The main innovation of this work is realizing the efficient quantification of HEP for LNG bunkering operations by using the proposed fuzzy BN-CREAM model.

An HFM‐CREAM model for the assessment of human reliability and quantification

AbstractHuman reliability analysis (HRA) is a proactive approach to model and evaluate systematic human errors and has been extensively implemented in various complicated systems. The assessment of human errors relies heavily on the knowledge and experience of experts in real‐world cases. Moreover, there are usually specific sorts of uncertainty while experts use linguistic labels to evaluate human failure events. In this context, this paper seeks to establish a new model based on the hesitant fuzzy matrix (HFM) and the cognitive reliability and error analysis method (CREAM) to conduct a quantitative analysis of human errors. This model handles the multiple crisp scores of the common performance conditions (CPCs) given by experts according to the context description in terms of CPCs, determines the weights of CPCs by the HFM, and elicits the human error probability (HEP) point estimation formula considering consequences based on the CREAM. Finally, the effectiveness and practicality of the presented HFM‐CREAM model are demonstrated through the emergency response analysis of the steam generator tube rupture (SGTR) in nuclear power plant.

Fuzzy Logic in HEART and CREAM Methods to Assess Human Error and Find an Optimum Method Using a Hierarchical Fuzzy System: A Case Study in a Steel Factory

Numerous studies have been conducted to assess the role of human errors in accidents in different industries. Human reliability analysis (HRA) has drawn a great deal of attention among safety engineers and risk assessment analyzers. Despite all technical advances and the development of processes, damaging and catastrophic accidents still happen in many industries. Human Error Assessment and Reduction Technique (HEART) and Cognitive Reliability and Error Analysis Method (CREAM) methods were compared with the hierarchical fuzzy system in a steel industry to investigate the human error. This study was carried out in a rolling unit of the steel industry, which has four control rooms, three shifts, and a total of 46 technicians and operators. After observing the work process, reviewing the documents, and interviewing each of the operators, the worksheets of each research method were completed. CREAM and HEART methods were defined in the hierarchical fuzzy system and the necessary rules were analyzed. The findings of the study indicated that CREAM was more successful than HEART in showing a better capability to capture task interactions and dependencies as well as logical estimation of the HEP in the plant studied. Given the nature of the tasks in the studied plant and interactions and dependencies among tasks, it seems that CREAM is a better method in comparison with the HEART method to identify errors and calculate the HEP.

Evaluation of the Operation Process of Medical Equipment to Enhance Ergonomic Reliability Based on FRAM–Moran’s I and CREAM

To improve the ergonomic reliability of medical equipment design during the operation process, a method for evaluating the operating procedure of a medical equipment interface according to functional resonance analysis method (FRAM)-Moran’s I and cognitive reliability and error analysis method (CREAM) is proposed in this study. The novelty of this research is to analyze the ergonomic reliability of medical equipment in a more systematic manner and to minimize the impact of human subjectivity and individual differences on the evaluation results of the operation process. To solve the calculation problem of functional resonance in FRAM and to make the evaluation results more objective, Moran’s I was introduced to quantify the deviation degree caused by the individual differences of the subjects. By giving weights based on Moran’s I, the influence of individual differences and subjectivity on the evaluation results can be minimized, to a certain extent. Considering the importance of a special environment, which is not fully considered by the conventional CREAM, the weighting values based on Moran’s I, Delphi survey, and technique for order preference by similarity to an ideal solution (TOPSIS) were adopted to assign weights to common performance conditions (CPCs) in CREAM. The optimal design scheme was selected more objectively than in the conventional method. The validity and practicability of this operation process evaluation method was verified by a statistical method based on ergonomic reliability experiments.

Evaluation of human factor engineering influence in nuclear safety using probabilistic safety assessment techniques

Abstract This paper provides the empirical basis to support predictions of the Human Factor Engineering (HFE) influences in Human Reliability Analysis (HRA). A few methods were analyzed to identify HFE concepts in approaches of Performance Shaping Factors (PSFs): Technique for Human Error Rate Prediction (THERP), Human Cognitive Reliability (HCR) and Cognitive Reliability and Error Analysis Method (CREAM), Success Likelihood Index Method (SLIM) Plant Analysis Risk – Human Reliability Analysis (SPAR-H), A Technique for Human Error Rate Prediction (ATHEANA) and Man-Machine-Organization System Analysis (MMOSA). Also, in order to identify other necessary PSFs in HFE, an additional investigation process of human performance (HPIP) in event occurrences was used. Thus, the human error probability could be reduced and its evaluating can give out the information for error detection and recovery. The HFE analysis model developed using BHEP values (maximum and pessimistic) is based on the simplifying assumption that all specific circumstances of HFE characteristics are equal in importance and have the same value of influence on human performance. This model is incorporated into the PSA through the HRA methodology. Finally, a clarification of the relationships between task analysis and the HFE is performed, ie between potential human errors and design requirements.

Quantitative failure analysis for static electricity-related explosion and fire accidents on tanker vessels under fuzzy bow-tie CREAM approach

Cargo operations on tanker vessels involve high risks to crew and the marine environment. At the time of loading and unloading of cargoes and tank cleaning operations, consequences of accidents may be severe such as loss of lives, property loss and marine pollution. One of the most critical accidents is an explosion which may advance to a large fire. An explosion may occur due to flammable and combustible materials, electric arcs, dangerous operations, like hot work on deck, or static electricity. This study focuses on the issue of static electricity, which is one of the serious threats onboard and can cause large explosions under hazardous conditions if a discharge is in contact with nearby flammable gases or air mixture. In a bow-tie structure, static electricity-related explosion and fire accident was determined as the top event. In the fault tree diagram, intermediate events were considered as the flammable atmosphere in a tank, operational failures, technical failures, and unsafe practices which cover direct human errors. Fuzzy logic and Cognitive Reliability and Error Analysis Method (CREAM) were used for quantitative failure analysis, and the probability of accident risk was found as 6.31E-02. Findings show that the most critical contributions to the top event are minimal cut sets of lack of monitoring cargo tank atmosphere, inadequate actions for reducing the O2 level in tank, failure to understand safety data sheets, failure to follow procedures, non-bonded ullage & sample apparatus. The research provides a significant contribution to the literature and gives information regarding static electricity risks to ship officers, tanker safety superintendents and other maritime authorities to improve the safety of cargo operations.