Human error probabilities computation for manufacturing system simulation using CREAM

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An Integrated Human Reliability Based Decision Pool Generating and Decision Making Method for Power Supply System in LNG Terminal

Acknowledgement We would like to give sincerely thank to Zhonghe Zhang, the principle expert in Sinopec and other relevant staff in Beihai LNG terminal for their valuable and constructive support during the development of this work. We would also like to express our very great appreciation to the respected reviewers. Their valuable suggestions and comments have enhanced the strength of this paper.Peer reviewedPostprin

A new hybrid approach to human error probability quantification-applications in maritime operations

Human Reliability Analysis (HRA) has always been an essential research issue in safety critical systems. Cognitive Reliability Error Analysis Method (CREAM), as a well-known second generation HRA method is capable of conducting both retrospective and prospective analysis, thus being widely used in many sectors. However, the needs of addressing the use of a deterministic approach to configure common performance conditions (CPCs) and the assignment of the same importance to all the CPCs in a traditional CREAM method reveal a significant research gap to be fulfilled. This paper describes a modified CREAM methodology based on an Evidential Reasoning (ER) approach and a Decision Making Trial and Evaluation Laboratory (DEMATEL) technique for making human error probability quantification in CREAM rational. An illustrative case study associated with maritime operations is presented. The proposed method is validated by sensitivity analysis and the quantitative analysis result is verified through comparing the real data collected from Shanghai coastal waters. Its main contribution lies in that it for the first time addresses the data incompleteness in HEP, given that the previous relevant studies mainly focus on the fuzziness in data. The findings will provide useful insights for quantitative assessment of seafarers' errors to reduce maritime risks due to human errors

The hybrid method combined STPA and SLIM to assess the reliability of the human interaction system to the Emergency shutdown system of LNG ship-to-ship bunkering

By introducing autonomous or software-controlled systems, human operators are increasingly required to perform cognitive-intensive tasks in addition to existing labour-intensive tasks. As a result, it will be more difficult to identify human roles in future complex systems with traditional approaches such as hierarchical task analysis used in the conventional HRA. This paper proposes a novel systematic approach for a human reliability assessment to better understand human activities in complex systems. The proposed framework is a hybrid method combining the System Theoretic Process Analysis (STPA) and the Success Likelihood Index Method (SLIM) to assess the system reliability. The STPA is adopted to analyse the interaction relationship between different system components. The primary purpose of STPA is to find and analyse human activities that affect the risk in human-machine interaction systems. Then the identified human activities are evaluated and quantified by the SLIM as a probability of human error. The system reliability block diagram represents the derived human error probabilities to assess the entire system for a probabilistic risk assessment. Furthermore, the study proposed system alternations by comparing three different system configurations. Results demonstrate the importance of human performance in a complex system where humans, machines, and software interact

Application of a SPAR-H based framework to assess human reliability during emergency response drill for man overboard on ships

Emergency preparedness is of paramount importance in successful emergency responses at sea. Therefore, emergency drills are regularly conducted to maintain acceptable levels of emergency preparedness. However, it needs to be considered that emergency drill operations themselves include significant risks, and there is no evidence that these risks are appropriately considered when planning emergency drill operations. Human error is one of the main contributors of accidents during emergency drill procedures. The main question posed is how overall risk, including human errors, during an emergency drill can be correctly evaluated. This paper introduces a new hybrid approach based on the Standardised Plant Analysis Risk Human Reliability Analysis (SPAR-H) method with a fuzzy multiple attributive group decision-making method. The method provides a framework for evaluating specific scenarios associated with human errors and identifies contributors that affect human performance. Estimated human errors are utilised to assess human reliability using a new approach based on a system reliability block diagram. The rescue boat drill procedure for a man overboard is selected to illustrate the method. The findings of this research show each human error probability and its contributing factors per task. As a result, overall reliability of 6.06E-01 was obtained for rescue boat drill operation

Human reliability analysis: exploring the intellectual structure of a research field

Humans play a crucial role in modern socio-technical systems. Rooted in reliability engineering, the discipline of Human Reliability Analysis (HRA) has been broadly applied in a variety of domains in order to understand, manage and prevent the potential for human errors. This paper investigates the existing literature pertaining to HRA and aims to provide clarity in the research field by synthesizing the literature in a systematic way through systematic bibliometric analyses. The multi-method approach followed in this research combines factor analysis, multi-dimensional scaling, and bibliometric mapping to identify main HRA research areas. This document reviews over 1200 contributions, with the ultimate goal of identifying current research streams and outlining the potential for future research via a large-scale analysis of contributions indexed in Scopus database

Application of a CREAM based framework to assess human reliability in emergency response to engine room fires on ships

For a human reliability assessment in the maritime domain, the main question is how we correctly understand the human factors in the maritime situation in a practical manner. This paper introduces a new approach based on Cognitive Reliability and Error Analysis Method (CREAM). The key to the method is to provide a framework for evaluating specific scenarios associated with maritime human errors and for conducting an assessment of the context, in which human actions take place. The output of the context assessment is, then, to be applied for the procedure assessment as model inputs for reflection of the context effect. The proposed approach can be divided into two parts: processing context assessment and modelling human error quantification. Fuzzy multiple attributive group decision-making method, Bayesian networks and evidential reasoning are employed for enhancing the reliability of human error quantification. Fuzzy conclusion of the context assessment is utilised by the model input in CREAM basic method and weighting factors in CREAM extended method respectively for considering human failure probability which varies depending on external conditions. This paper is expected to contribute to the improvement of safety by identifying frequently occurred human errors during the maritime operating for minimising of human failures

Proceedings of the Second FAROS Public Workshop, 30th September 2014, Espoo, Finland

FAROS is an EC FP7 funded, three year project to develop an approach to incorporate human factors into Risk-Based Design of ships. The project consortium consists of 12 members including industry, academia and research institutes. The second FAROS Public Workshop was held in Dipoli Congress Centre in Otaniemi, Espoo, Finland, on the 30th of September 2014. The workshop included keynotes from industry, papers on risk models for aspects such as collision and grounding, fire and the human element, descriptions of parametric ship models and the overall approach being adopted in the FAROS project

Applying the cognitive reliability and error analysis method to reduce catheter associated urinary tract infections

Master of ScienceDepartment of Industrial & Manufacturing Systems EngineeringMalgorzata RysCatheter associated urinary tract infections (CAUTIs) are a source of concern in the healthcare industry because they occur more frequently than other healthcare associated infections and the rates of CAUTI have not improved in recent years. The use of urinary catheters is common among patients; between 15 and 25 percent of all hospital patients will use a urinary catheter at some point during their hospitalization (CDC, 2016). The prevalence of urinary catheters in hospitalized patients and high CAUTI occurrence rates led to the application of human factors engineering to develop a tool to help hospitals reduce CAUTI rates. Human reliability analysis techniques are methods used by human factors engineers to quantify the probability of human error in a system. A human error during a catheter insertion has the opportunity to introduce bacteria into the patient’s system and cause a CAUTI; therefore, human reliability analysis techniques can be applied to catheter insertions to determine the likelihood of a human error. A comparison of three human reliability analysis techniques led to the selection of the Cognitive Reliability and Error Analysis Method (CREAM). To predict a patient’s probability of developing a CAUTI, the human error probability found from CREAM is incorporated with several health factors that affect the patient’s risk of developing CAUTI. These health factors include gender, duration, diabetes, and a patient’s use of antibiotics, and were incorporated with the probability of human error using fuzzy logic. Membership functions were developed for each of the health factors and the probability of human error, and the centroid defuzzification method is used to find a crisp value for the probability of a patient developing CAUTI. Hospitals that implement this tool can choose risk levels for CAUTI that places the patient into one of three zones: green, yellow, or red. The placement into the zones depends on the probability of developing a CAUTI. The tool also provides specific best practice interventions for each of the zones