•A new dependency assessment method was developed for human reliability analysis.•The method mathematically quantifies the conditional probability of a subsequent event.•The method analyzes six ...features of dependency between two events.•Quantitative bases for estimating the conditional probability are provided.•Two application studies demonstrate the feasibility of the proposed method.
Dependency assessment is an aspect of human reliability analysis that identifies the causal relationship between two human events and quantifies the conditional probability of the successor event when two or more events exist in an accident sequence. Despite broad recognition of the impact of dependency on the overall system risk, many experts have been concerned that most current methods are rooted in the THERP method without a sufficient theoretical and empirical basis for dependency models. In this study, we propose a method that calculates the conditional failure probability of a successor event based on quantitative evidence of the dependency between two human events. Quantitative assessment is performed by evaluating six features and integrating the failure probabilities due to the features into the assessment based on an arithmetic equation. The estimates obtained from this empirical data analysis, a statistical function for time insufficiency, and a sequence alignment algorithm were employed to support the basis of the calculation with several assumptions. Two case studies are presented to show the feasibility of the study and the result differences between the proposed and existing methods. Since this study presents a new approach to dependency assessment, additional issues to be tackled regarding the assumptions and technical bases used are discussed with further research directions.
Shipping Liquefied Natural Gas (LNG) has become a popular method for transporting LNG. However, offloading work poses significant risks, many of which are attributed to human errors. Considering that ...most accidents are associated with human errors, the Human Reliability Analysis (HRA) method is a critical option to prevent accidents and to predict Human Error Probability (HEP). One such method is fuzzy CREAM, a well-known HRA methods. However, this method has some limitations. The method uses Common Performance Conditions (CPCs) to estimate HEP, but the source of CPC data is insufficient. Without enough and reliable CPC data, the process and the result of fuzzy CREAM are questionable and criticized. Therefore, this study proposes a modified approach to address this issue. The proposed method uses the definition of “Risk” as the support to collect each CPC's data from aspects of likelihood and impact. Then, using the collected risk data as the source to determine each CPC's fuzzy degree, to determine each CPC's weight by combining with Grey Relationship Analysis (GRA), and to identify each activated fuzzy If-Then rules and the rule weight. Afterwards, the proposed method integrates the fuzzy degree of each CPC, the weight of each CPC, and the weight of each activated If-Then rule together to estimate HEP. Finally, the proposed method is validated through a real engineering case of shipping LNG offloading work.
•The concept of risk is innovatively used as the support for Common Performance Condition (CPC) data collection.•The collected risk data of each CPC are used as the source data to determine CPC's fuzzy degree, weight, and the weight of activated if-then rules.•The proposed approach develops a hybrid operation which integrates CPC's fuzzy degree and weight, and the weight of each activated if-then rule.•The proposed method is practicable to real engineering cases.
•Analyse the primary data to estimate the appearance frequencies of risk factors resulting in maritime accidents.•Evaluate the joint impact of human factors and other risk factors on different types ...of maritime accidents.•Develop an accident data-driven Bayesian network to realise human factor oriented maritime accident analysis.•Conduct an empirical study to provide insights for the prevention of a particular type of accidents involving human errors.
A data-driven Bayesian network (BN) is used to investigate the effect of human factors on maritime safety through maritime accident analysis. Its novelties consist of (1) manual collection and analysis of the primary data representing frequencies of risk factors directly derived from maritime accident reports, (2) incorporation of human factors into causational analysis with respect to different maritime accident types, and (3) modelling by a historical accident data-driven approach, to generate new insights on critical human factors contributing to different types of accidents. The modelling of the interdependency among the risk influencing factors is structured by Tree Augmented Network (TAN), and validated by both sensitivity analysis and past accident records. Our findings reveal that the critical risk factors for all accident types are ship age, ship operation, voyage segment, information, and vessel condition. More importantly, the findings also present the differentiation among the vital human factors against different types of accidents. Most probable explanation (MPE) is used to provide a specific scenario in which the beliefs are upheld, observing the most probable configuration. The work pioneers the analysis of various impacts of human factors on different maritime accident types. It helps provide specific recommendations for the prevention of a particular type of accidents involving human errors.
•Autonomous ships will probably be monitored by humans from an onshore control center.•Task analysis of operators for collision avoidance was performed.•Interactions between operators and system for ...collision avoidance must the considered.•Humans can be an ultimate safety barrier for successful collision avoidance.•Risk assessment of autonomous ships need to consider the human-system interaction.
Numerous research and industry initiatives have increasingly aimed at developing maritime surface autonomous ships (MASS). Among the motivations for the use of MASS is the potential increase in safety when compared to traditional manned ships – particularly regarding human error. However, in spite of having less human intervention, MASS will rely on humans working on an onshore control center for their operation. There have been great advances in investigating the technical aspects of MASS operation, such as collision avoidance algorithms and detection sensors; nevertheless, possible human tasks and their deriving failures have rarely been addressed. This paper thus explores how humans can be a key factor for successful collision avoidance in future MASS operations. It presents a task analysis for collision avoidance through Hierarchical Task Analysis and making use of a cognitive model for categorizing the tasks. The failures in accomplishing these tasks are further analyzed, and human failure events are identified. The results provide valuable information for the design stage of the system; which must acknowledge the operators’ tasks to ensure a safe voyage. The conclusions of this paper are also a starting point for the implementation of a Human Reliability Analysis for this operation.
Since reliability and security of man-machine system increasingly depend on reliability of human, human reliability analysis (HRA) has attracted a lot of attention in many fields especially in ...nuclear engineering. Dependence assessment among human tasks is a important part in HRA which contributes to an appropriate evaluation result. Most of methods in HRA are based on experts’ opinions which are subjective and uncertain. Also, the dependence influencing factors are usually considered to be constant, which is unrealistic. In this paper, a new model based on Dempster–Shafer evidence theory (DSET) and fuzzy number is proposed to handle the dependence between two tasks in HRA under uncertain and dynamic situations. First, the dependence influencing factors are identified and the judgments on the factors are represented as basic belief assignments (BBAs). Second, the BBAs of the factors that varying with time are reconstructed based on the correction BBA derived from time value. Then, BBAs of all factors are combined to gain the fused BBA. Finally, conditional human error probability (CHEP) is derived based on the fused BBA. The proposed method can deal with uncertainties in the judgments and dynamics of the dependence influencing factors. A case study is illustrated to show the effectiveness and the flexibility of the proposed method.
•Holistic use of fNIRS and maritime simulation to conduct HPM objectively.•Development of a hybrid assessment model using haemoglobin data and ANN.•Pioneering psychophysiological data-driven machine ...learning for seafarers’ HPM.•Real case analysis for classifying seafarers of different qualifications.
Human errors significantly contribute to transport accidents. Human performance measurement (HPM) is crucial to ensure human reliability and reduce human errors. However, how to address and reduce the subjective bias introduced by assessors in HPM and seafarer certification remains a key research challenge. This paper aims to develop a new psychophysiological data-driven machine learning method to realize the effective HPM in the maritime sector. It conducts experiments using a functional Near-Infrared Spectroscopy (fNIRS) technology and compares the performance of two groups in a maritime case (i.e. experienced and inexperienced seafarers in terms of different qualifications by certificates), via an Artificial Neural Network (ANN) model. The results have generated insightful implications and new contributions, including (1) the introduction of an objective criterion for assessors to monitor, assess, and support seafarer training and certification for maritime authorities; (2) the quantification of human response under specific missions, which serves as an index for a shipping company to evaluate seafarer reliability; (3) a supportive tool to evaluate human performance in complex emerging systems (e.g. Maritime Autonomous Surface Ship (MASS)) design for ship manufactures and shipbuilders.
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.
► Review the best practice of human reliability in marine engineering. ► Identify the shortcomings of traditional human reliability analysis methods. ► Develop a quantitative human reliability analysis method using fuzzy Bayesian. ► Realise real time monitoring of marine engineers' failures under uncertainty. ► Demonstrate advantages of the new risk model through a real case analysis
Control room operators on offshore platforms play an important role in realizing the safety instrumentation functions of the high integrity pressure protection system (HIPPS) during overpressure ...emergencies. Human reliability analysis is critical to the overpressure operation of offshore HIPPS, yet there are few studies. To remedy this gap, the paper proposes an integrated human reliability analysis method to analyse the impact of human intervention on offshore HIPPS safety. Considering the effects of context and cognitive performance on human reliability comprehensively, the improved cognitive reliability and error analysis model (CREAM) is established; The correction effect of external environmental factors on response failure probability is quantified, and the improved human cognitive reliability (HCR) model is developed; The two improved models are organically combined to form an integrated methodology. Based on the behaviour characteristics of personnel emergency response, human error probability is calculated in stages to analyse human reliability. The findings indicate that the overall human error probability for HIPPS overpressure operations is 0.124 and particular attention needs to be paid to observation and decision-making operations. Besides the theoretical background, the paper provides practical contributions to personnel training, mission planning, and risk management of HIPPS to minimize the possibility of human error.
•An integrated human reliability analysis method for offshore HIPPS is proposed.•The cognitive performance correction coefficient is introduced to improve CREAM.•The modification effect of external environmental factors is quantified to improve the HCR model.•Human error probabilities are calculated in stages based on personnel task characteristics.
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