HUMAN FACTOR ANALYSIS OF CONTAINER VESSEL’S GROUNDING ACCIDENTS

2021 ◽  
Vol 159 (A1) ◽  
Author(s):  
U Yildirim ◽  
O Ugurlu ◽  
E Basar ◽  
E Yuksekyildiz
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Human Error ◽  
Human Performance ◽  
Human Factor ◽  
Study Data ◽  
Team Management ◽  
Contribution Rate ◽  
Physical Problems ◽  
Mental Problems

Investigation on maritime accidents is a very important tool in identifying human factor-related problems. This study examines the causes of accidents, in particular the reasons for the grounding of container ships. These are analysed and evaluation according to the contribution rate using the Monte Carlo simulation. The OpenFTA program is used to run the simulation. The study data are obtained from 46 accident reports from 1993 to 2011. The data were prepared by the International Maritime Organization (IMO) Global Integrated Shipping Information System (GISIS). The GISIS is one of the organizations that investigate reported accidents in an international framework and in national shipping companies. The Monte Carlo simulation determined a total of 23.96% human error mental problems, 26.04% physical problems, 38.58% voyage management errors, and 11.42% team management error causes. Consequently, 50% of the human error is attributable to human performance disorders, while 50% team failure has been found.

Human Factor Analysis of Container Vessel's Grounding Accidents

2017 ◽  
Vol Vol 159 (A1) ◽  
Author(s):  
U Yildirim ◽  
O Ugurlu ◽  
E Basar ◽  
E Yuksekyildiz
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Human Error ◽  
Human Performance ◽  
Human Factor ◽  
Study Data ◽  
Team Management ◽  
Contribution Rate ◽  
Physical Problems ◽  
Mental Problems

Investigation on maritime accidents is a very important tool in identifying human factor-related problems. This study examines the causes of accidents, in particular the reasons for the grounding of container ships. These are analysed and evaluation according to the contribution rate using the Monte Carlo simulation. The OpenFTA program is used to run the simulation. The study data are obtained from 46 accident reports from 1993 to 2011. The data were prepared by the International Maritime Organization (IMO) Global Integrated Shipping Information System (GISIS). The GISIS is one of the organizations that investigate reported accidents in an international framework and in national shipping companies. The Monte Carlo simulation determined a total of 23.96% human error mental problems, 26.04% physical problems, 38.58% voyage management errors, and 11.42% team management error causes. Consequently, 50% of the human error is attributable to human performance disorders, while 50% team failure has been found.

Based on Human Behavior Process of Human Error Defensive Management Research for NPP

2017 ◽  
Author(s):  
Shen Yang ◽  
Geng Bo ◽  
Li Dan
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Control Chart ◽  
Human Behavior ◽  
Nuclear Power ◽  
Human Error ◽  
Human Performance ◽  
Human Factor ◽  
Error Management ◽  
Root Cause

According to the research of nuclear power plant human error management, it is found that the traditional human error management are mainly based on the result of human behavior, the event as the point cut of management, there are some drawbacks. In this paper, based on the concept of the human performance management, establish the defensive human error management model, the innovation point is human behavior as the point cut, to reduce the human errors and accomplish a nip in the bud. Based on the model, on the one hand, combined with observation and coach card, to strengthen the human behavior standards expected while acquiring structured behavior data from the nuclear power plant production process; on the other hand, combined with root cause analysis method, obtained structured behavior data from the human factor event, thus forming a human behavior database that show the human performance state picture. According to the data of human behavior, by taking quantitative trending analysis method, the P control chart of observation item and the C control chart of human factor event is set up by Shewhart control chart, to achieve real-time monitoring of the process and result of behavior. At the same time, development Key Performance Indicators timely detection of the worsening trend of human behavior and organizational management. For the human behavior deviation and management issues, carry out the root cause analysis, to take appropriate corrective action or management improvement measures, so as to realize the defense of human error, reduce human factor event probability and improve the performance level of nuclear power plant.

scholarly journals Влияние человеческого фактора при создании авиационных двигателей

2021 ◽  
pp. 5-10
Author(s):  
Виктория Викторовна Кокотина ◽  
Лариса Анатольевна Лесная ◽  
Виталий Григорьевич Харченко
Keyword(s):  
Human Error ◽  
Human Performance ◽  
Human Factor ◽  
Causative Factor ◽  
Civil Aviation ◽  
Modern World ◽  
Destructive Testing ◽  
Effective Prevention ◽  
A Chain

Ensuring the safety of the civil aviation system is the main goal of the International Civil Aviation Organization (ICAO) activities and the "human factor" was define as a priority in the field of flight safety. Given the variety of factors potentially affecting human performance, it is not surprising, that human error has been recognized as a major causative factor in virtually all air crashes and accidents since the inception of aviation. The reliability and safety of flights are influenced by: the quality of preparation of aviation equipment for flight, the quality of manufacture, assembly, acceptance, and pre-flight tests, the quality of design of aircraft and engines. The quality of workmanship is confirmed by the execution of control at each stage of manufacture. In any activity, the "human factor" is manifested by mistakes, oversights, and omissions, or miscalculations that a person makes when doing his job under certain conditions. The theory of the occurrence and prevention of errors associated with human physiology and the environment were described by H. Heinrich's "domino theory". Human errors form sequences in which the first error causes a chain of subsequent ones, keeping one of the dominoes standing behind each other, it is possible to prevent the consequences of an accident in the form of material damage or an accident. Human physiological features such as vision can be one of the dominoes and lead to erroneous actions. In the modern world, non-destructive testing methods are relevant and the role of a defectoscopistꞌs in determining the nature of a defect is quite large. Regular monitoring of vision (prophylactic examination) allows you to identify potential vision problems with a specialist, which can lead to erroneous actions. Human factors research is fundamental to understanding the context in which normal, healthy, skilled, well-equipped and reasonably motivated personnel make mistakes, some of which are fatal and, if the causes of human error are correctly understood, it will be possible to develop more effective prevention strategies errors, their control, and safe elimination.

Analysis on High-Energy Physical Problems in Monte Carlo Simulation

2014 ◽  
Vol 577 ◽  
pp. 762-766
Author(s):  
Bao Guang Sun ◽  
Xiao Feng Wang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Energy Spectrum ◽  
High Energy ◽  
Extensive Air Shower ◽  
Air Shower ◽  
Secondary Particles ◽  
Physical Problems ◽  
Strip Pattern ◽  
Detector Simulation

This paper analyzes the data got in two Monte Carlo simulations, namely, extensive air shower simulation and detector simulation. Then, based on the data from experimental arrays, some physical problems have been analyzed and illustrated. Those problems include the distribution of energy spectrum of secondary particles, the distribution of zenith angle, of azimuths, of background noises, and that of strip pattern, as well as the atmospheric absorption.

Monte-Carlo Simulation Artefacts in Physical Modeling due to Pseudo-Random Generators

1991 ◽  
Vol 02 (01) ◽  
pp. 246-249 ◽  
Author(s):  
A.S. BERDNICOV ◽  
S.B. TURTIA
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Random Vector ◽  
Physical Modeling ◽  
Random Vectors ◽  
Special System ◽  
Physical Problems ◽  
Random Generators ◽  
Vector Algorithms

A set of vector algorithms for random vectors generation is tested by the authors. Only the most reliable algorithms are selected for testing. The special system of original tests is developed with the main criteria of the independence of random vector components and the equability of N-dimensional hypercube filling. Some algorithms are discovered to be bad for physical modeling, and for others recommendations for their use for different physical problems are given.

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

Kerntechnik ◽  
2021 ◽  
Vol 86 (6) ◽  
pp. 470-477
Author(s):  
M. Farcasiu ◽  
C. Constantinescu
Keyword(s):  
Reliability Analysis ◽  
Error Rate ◽  
Error Detection ◽  
Human Error ◽  
Human Performance ◽  
Human Factor ◽  
Human Reliability ◽  
Human Reliability Analysis ◽  
Human Factor Engineering ◽  
Rate Prediction

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.

scholarly journals Safer Systems: People Training or System Tuning?

2021 ◽  
Vol 11 (3) ◽  
pp. 990-998
Author(s):  
Erik Hollnagel
Keyword(s):  
Human Error ◽  
Human Performance ◽  
Human Factor ◽  
Effective Means ◽  
Safety Training ◽  
Technical System ◽  
Industrial Safety ◽  
System Component ◽  
Training Design ◽  
Specific Component

Safety is usually seen as a problem when it is absent rather than when it is present, where accidents, incidents, and the like represent a lack of safety rather than the presence of safety. To explain this lack of safety, one or more causes must be found. In the management of industrial safety, the human factor has traditionally been seen as a weak element; human error is often offered as the first, and sometimes the only cause of lack of safety and human factors have since the early days offered three principal solutions, namely training, design, and automation. Of these, training has considerable face value as an effective means to improve human performance. The drawback of safety training, however, is that it focuses on a single system component, the human, instead of on the system as a whole. Safety training further takes for granted that humans are a liability and focuses on overcoming the weakness of this specific component through simplistic models of what determines human performance. But humans may also be seen as an asset which changes the focus to strengthening how a complex socio-technical system functions. A socio-technical system comprises multiple functions that must be finely tuned in order to ensure expected and acceptable performance. Since systems cannot be made safer without developing effective ways of managing the conditions in which people work, system tuning offers an alternative solution to an old problem.

Human Factor Risk Assessment During Emergency Condition in Harsh Environment

2013 ◽  
Author(s):  
Mashrura Musharraf ◽  
Faisal Khan ◽  
Brian Veitch ◽  
Scott MacKinnon ◽  
Syed Imtiaz
Keyword(s):  
Risk Assessment ◽  
Human Factors ◽  
Human Error ◽  
Human Performance ◽  
Human Factor ◽  
Expert Judgment ◽  
Harsh Environment ◽  
Unrealistic Assumption ◽  
Error Probabilities ◽  
Human Error Probability

This paper presents a quantitative approach to human factors risk analysis during emergency conditions on an offshore petroleum facility located in a harsh environment. Due to the lack of human factors data for emergency conditions, most of the available human factors risk assessment methodologies are based on expert judgment techniques. Expert judgment is a valuable technique, however, it suffers from vagueness, subjectivity and incompleteness due to a lack of supporting empirical evidence. These weaknesses are often not accounted for in conventional human factors risk assessment. The available approaches also suffer from the unrealistic assumption of independence of the human performance shaping (HPS) factors and actions. The focus of this paper is to address the issue of handling uncertainty associated with expert judgments and to account for the dependency among the HPS factors and actions. These outcomes are achieved by integrating Bayesian Networks with Fuzzy and Evidence theories to estimate human error probabilities during different phases of an emergency. To test the applicability of the approach, results are compared with an analytical approach. The study demonstrates that the proposed approach is effective in assessing human error probability, which in turn improves reliability and auditability of human factors risk assessment.

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