Common Performance Conditions 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.

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.

Risk Assessment of Launching Airbags Using Functional Resonance Accident Model

‘Ship Launching Airbags’ can greatly increase the economic benefits of a new gravitational launching operation. Gravitational launching of a new ship with marine airbags takes into account the resistance force of rolling friction. However, a launching operation is a high-risk process, as it involves many risk factors. In this paper, Functional Resonance Accident Model (FRAM) was used systematically to identify potential risks and carry out the risk analysis of the ship launching operation. The human factor, technical factors, and organisational factors were identified based on the common performance conditions of FRAM. Functional performance changes, prevention, and hazard control barriers were evaluated to identify key operations. The results show that the ship launching operation is characterized by high collision, decreasing stability, and the need for the use of airbags.

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.

Human Reliability Assessment method applied to investigate human factors in NDT - The case of the interpretation of radiograms in the French nuclear sector

This communication reports on a study carried out in the context of the collaborative FOEHN project (Human and Organizational Factors in Non-Destructive Evaluation) supported by the French National Research Agency. The motivation of this project comes from the observation that human and Organizational factors (HOF) are not sufficiently considered by the NDT community. Its goal is to analyse and model the influence of the HOF on selected cases of study in the perspective of a better evaluation of the performance of inspections. The communication is focused on a radiographic test (RT) case of study in which it appeared that several successive inspections had failed to detect an existing in-service defect. The analysis and modelling of HOF related to interpretation of films has been achieved in the framework of the CREAM (Cognitive and Reliability and Error Analysis Method). A survey has been conducted during the training and the maintaining of the proficiency of NDT (Non Destructive Testing) operators. This was followed by a non-participant observation of operators on site and several individual interviews including a sample of people covering the main organizational and hierarchical roles (eg. project management, management, operations, invigilation). The exchange with the HOF experts resulted in a hierarchical analysis of "radiogram interpretation" tasks (31 sub-tasks) and a list of contextual and organizational factors that may affect the performance of interpretation of films by the operator. From such a description the CREAM method allows to determine critical tasks and probability of “errors” linked to a limited set of “Common Performance Conditions” (CPC). The first conclusions of this study are that the model CREAM seems well-adapted to the estimation of the impact of HOF on NDT performances. The next phases should be to apply it to other tasks (here only radiograph interpretation) and techniques. The expected benefit of this study is to provide tools for the evaluation and optimisation of NDT implementation.

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.

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.

Human reliability assessment and risk prediction for deep submergence operating system of manned submersible under the influence of cognitive performance

The manned submersible is the main platform for precise deep-sea exploration. The cognitive states of different levels generated when the submerged members in the vehicle are faced with changes in external factors, such as environmental characteristics and operational tasks at different operation stages, will have a dynamic impact on work efficiency and human reliability. Based on the CREAM method and Bayesian network, this study proposed a human reliability assessment method and risk prediction technology for deep submergence operating system. Firstly, the extended CREAM method was corrected again by introducing the cognitive performance correction coefficient. Secondly, the weights of common performance conditions (CPCs) and cognitive effectiveness influence system (CEIS) were adaptively calibrated by using the DEMATEL method. Finally, the Bayesian assessment network for human reliability of deep submergence operating system was constructed to assess the human reliability of each operation stage and the overall system. This method is helpful for the overall design of the manned deep submergence research institute, and it can accurately identify and interdict the key risk points for relevant technicians of system security engineering, so as to reduce the safety risk of deep submergence operating system and improve the working efficiency of the man-machine system in cabin.

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.

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.

A Fuzzy CREAM Method for Human Reliability Analysis in Digital Main Control Room of Nuclear Power Plants

Human error is an important factor leading to nuclear power plant (NPP) accidents. The increasing of the amount of information improves the operators’ human error probability (HEP) in the digital main control room of NPPs. Human reliability analysis (HRA) is considered to be an effective method to reduce human error. The Cognitive Reliability and Error Analysis Method (CREAM) is one of the widely accepted HRA methods. However, there are shortcomings that weaken the applicability of this method. Therefore, this research proposes a fuzzy CREAM method based on a combination of fuzzy logic theory and the CREAM method. The study considers the weight of common performance conditions (CPCs) and constructs a logical relationship between CPCs and control modes. Finally, the effectiveness of the proposed method is determined using a widely accepted method to validate the evaluation results. The validated results showed that the evaluation result has a consistency between the fuzzy CREAM method and the traditional CREAM method. They indicated that the fuzzy CREAM method can obtain reliable HEP.

Human reliability analysis of high-temperature molten metal operation based on fuzzy CREAM and Bayesian network.

Human errors are considered to be the main causation factors of high-temperature molten metal accidents in metallurgical enterprises. The complex working environment of high- temperature molten metal in metallurgical enterprises has an important influence on the reliability of human behavior. A review of current human reliability techniques confirms that there is a lack of quantitative analysis of human errors in high-temperature molten metal operating environments. In this paper, a model was proposed to support the human reliability analysis of high-temperature molten metal operation in the metallurgy industry based on cognitive reliability and error analysis method (CREAM), fuzzy logic theory, and Bayesian network (BN). The comprehensive rules of common performance conditions in conventional CREAM approach were provided to evaluate various conditions for high-temperature molten metal operation in the metallurgy industry. This study adopted fuzzy CREAM to consider the uncertainties and used the BN to determine the control mode and calculate human error probability (HEP). The HEP for workers involved in high-temperature melting in steelmaking production process was calculated in a case with 13 operators being engaged in different high-temperature molten metal operations. The human error probability of two operators with different control modes was compared with the calculation result of basic CREAM, and the result showed that the method proposed in this paper is validated. This paper quantified point values of human error probability in high-temperature molten metal operation for the first time, which can be used as input in the risk evaluation of metallurgical industry.

Prediction of human error probability during the hydrocarbon road tanker loading operation using a hybrid technique of fuzzy sets, Bayesian network and CREAM

Objectives. The hydrocarbon road tanker loading operation is vulnerable to human error. The present study aimed to develop a methodology for predicting human error probabilities (HEPs) in various subtasks of this operation. Methods. First, task analysis was performed using hierarchal task analysis. Then, HEP was calculated using a hybrid technique of fuzzy set theory (FST), Bayesian network (BN) and cognitive reliability and error analysis method (CREAM). FST was used for handling uncertainties regarding common performance conditions (CPCs) and the BN was employed for modeling the interrelationships among CPCs and HEPs. The weighted sum algorithm was used for quantifying conditional probability tables in the network. Results. Twenty-six subtasks were required for completing the road tanker loading operation. Investigating the internal parts of the tanker before the loading operation and attaching the ground rode clamp were the subtasks with highest HEPs. Working conditions and crew collaboration were the CPCs with the highest contribution to these errors. HEP was most sensitive to crew collaboration. Conclusion. Improving collaboration among the driver, site operators and control room operators, as well as increasing the knowledge of the road tanker driver regarding the hazards of incompatible chemicals, are the best practices for reducing HEPs in this operation.

Ergonomic Reliability Assessment for Operation Process of Medical Device Interface Design Based on OAT and CREAM

This paper uses OAT (Operator Action Tree) and CREAM (Cognitive Reliability and Error Analysis Method) to analyze the interface design of medical equipment according to human error, discuss how to accurately describe the operator’s failure behavior, determine the development sequence of important human factors events and the feasible calculation method according to the system response, establish a complete ergonomic reliability assessment modeling by example demonstrations. This study can better describe the relationship between software interface reliability and ergonomic reliability. In addition, this paper makes improvement on the cognitive reliability and error analysis. In this paper, based on the basic concept and core idea of CREAM method, MADM (Multi-attribute Decision-making) is used to improve the weighting factor value of CPCs (Common Performance Conditions) provided by CREAM method. After the OAT model building based on the operation process of medical device interface design, the adjusted failure probability prediction analysis method is applied to obtain the failure probability of OAT basic events, which makes CREAM method more suitable for the medical device accident analysis and risk assessment in complex environment.

A modified human reliability analysis method for the estimation of human error probability in the offloading operations at oil terminals

AbstractHuman error is a significant factor contributing to accidents during oil offloading at oil terminals. Fuzzy cognitive reliability and error analysis method (CREAM) is one of the applicable methods to estimate human error probability (HEP). However, the currently used fuzzy CREAM models have issues which weaken the quality of HEP results. Both the max‐min fuzzy CREAM and the weighted fuzzy CREAM neglect the compatibility degree of each activated fuzzy if‐then rule; besides, the max‐min fuzzy CREAM focuses only on the maximum and minimum input data and ignores their weights. Therefore, this paper presents a modified fuzzy CREAM to address all these issues to strengthen the quality of HEP estimation. The proposed approach employs fuzzy degree of each common performance condition (CPC), the weight of each CPC, and the compatibility degree of each activated fuzzy if‐then rule together to calculate HEP. The proposed approach is applied for HEP estimation in an oil offloading operation at the selected oil terminal. The final HEP value is estimated at 8.929 × 10−3. It is concluded that the proposed fuzzy CREAM model can provide more reliable results.

An extended CREAM model based on analytic network process under the type‐2 fuzzy environment for human reliability analysis in the high‐speed train operation

AbstractHuman error is one of the largest contributing factors to unsafe operation and accidents in high‐speed train operation. As a well‐known second‐generation human reliability analysis (HRA) technique, the cognitive reliability and error analysis method (CREAM) has been introduced to address HRA problems in various fields. Nevertheless, current CREAM models are insufficient to deal with the HRA problem that need to consider the interdependencies between the Common Performance Conditions (CPCs) and determine the weights of these CPCs, simultaneously. Hence, the purpose of this paper is to develop a hybrid HRA model by integrating CREAM, the interval type‐2 fuzzy sets, and analytic network process (ANP) to overcome this drawback. Firstly, the interval type‐2 fuzzy sets are utilized to express the highly uncertain information of CPCs. Secondly, the ANP is incorporated into the CREAM to depict the interdependencies between the CPCs and determine their weights. Furthermore, human error probability (HEP) can be calculated based on the obtained weights. Finally, an illustrative example of the HRA problem in high‐speed train operation is proposed to demonstrate the application and validity of the proposed HRA model. The results indicate that experts prefer to express their preferences by fuzzy sets rather than crisp values, and the interdependences between the CPCs can be better depicted in the proposed model.

Methodology for assessing dependencies between factors influencing airline pilot performance reliability: A case of taxiing tasks

Pilot performance reliability is critical to civil air transportation safety. Internal and external factors influencing pilot performance reliability are important elements to estimate human error probability. However, dependencies between these factors, including state dependencies and effect dependencies, are always ignored or only partially considered in most human reliability analysis (HRA) method. This paper attempts to develop a methodology incorporating both state and effect dependencies between contributory factors into HRA for airline pilot tasks based on fuzzy logic and the Cognitive Reliability and Error Analysis Method (CREAM). Anchor points were determined for each common performance condition (CPC) state levels through airline pilot elicitation process and then corresponding state and effect fuzzy sets were constructed for each CPC. The CPC dependency fuzzy inference rules were constructed and transformed with reference to the CPC dependency assessing rule in CREAM. And a cognitive failure probability (CFP) calculation method based on the final CPC effect fuzzy sets and the extended CREAM was proposed. To illustrate the use of the proposed methodology, a case study about commercial airline pilot taxiing task was given. The results demonstrate the validity of the proposed methodology and its capability of representing the ambiguity and fuzziness of expert judgement.