Automatic Identification System (AIS): Data Reliability and Human Error Implications

2007 ◽  
Vol 60 (3) ◽  
pp. 373-389 ◽  
Author(s):  
Abbas Harati-Mokhtari ◽  
Alan Wall ◽  
Philip Brooks ◽  
Jin Wang
Keyword(s):  
Human Error ◽  
Human Factor ◽  
Automatic Identification ◽  
Identification System ◽  
Efficient Performance ◽  
Swiss Cheese Model ◽  
Potential Impact ◽  
Marine Navigation ◽  
Cheese Model ◽  
Different Levels

This paper examines the recent introduction of the AIS to the ship's bridge and its potential impact on the safety of marine navigation. Research has shown that 80 to 85% of all recorded maritime accidents are directly due to human error or associated with human error. Safety is an important element of marine navigation and many people at different levels are involved in its management. The safe and efficient performance of joint systems, is heavily dependent upon how functions are allocated between the human and the machine. This paper investigates different regulations, supervision for proper use, training, and management of AIS users. It uses previous research and three separate AIS studies to identify problems. The potential of the AIS to cause problems is analysed. The classic human factor “Swiss Cheese” Model of system failure has been modified for the AIS to investigate a possible accident trajectory. The paper then concludes with recommendations and suggestions for improvements and further work.

scholarly journals Preventing human error at an approved training organization using Dirty Dozen

2018 ◽  
Vol 7 (4.13) ◽  
pp. 71
Author(s):  
Abdul Ghani Abdul Samad ◽  
Muhd Khudri Johari ◽  
Sabri Omar
Keyword(s):  
Human Error ◽  
Human Factor ◽  
Kuala Lumpur ◽  
Safety Practices ◽  
Preventive Actions ◽  
Dirty Dozen ◽  
Swiss Cheese Model ◽  
The University ◽  
Cheese Model ◽  
Training Organization

Following a recently-submitted review on a few human factor identification models (interpretations of Professor Edwards’ SHELL Model, Boeing’s Maintenance Error Decision Aid (MEDA), Professor Reason’s Swiss Cheese Model, and Dupont’s Dirty Dozen), researchers have unanimously agreed on choosing the Dirty Dozen model for this quantitative study before its official implementation in hangars and workshops at Universiti Kuala Lumpur – Malaysian Institute of Aviation Technology (UniKL MIAT). This study measures the levels of awareness and effectiveness of UniKL MIAT’s current human factor safety practices. A specifically-tailored, comprehensive, Dirty Dozen checklist is produced and distributed as survey questionnaire to 120 UniKL MIAT’s students. Data from all 48 questions related to all 12 domains of Dirty Dozen are analyzed. The results shows that out of all 12 domains, six (Lack of communication, Lack of teamwork, Norm, Pressure, Lack of attention, Stress) are marked with “Agreed” and the other half (Complacency, Lack of knowledge, Lack of resources, Distraction, Lack of authority, Exhaustion) are marked as “Not Sure” in terms of awareness and effectiveness of their current human factor’s safety practices. These results will be reviewed by the top management of the university to take preventive actions and improvements for future human factor safety implementations. As Dirty Dozen is known to be the simplest technique to measure human error, it is significantly appropriate to be applied as this experiment’s variable, especially for students who are still studying and have no industrial working experiences.   

Gap Analysis of Accident Investigation Methodology in the Indian Mining Industry - an Application of Swiss Cheese Model and 5-Why Model

2015 ◽  
Vol 15 (1) ◽  
Author(s):  
Ashish Kumar Dash ◽  
Ram Madhab Bhattacharjee ◽  
Partha Sarathi Paul
Keyword(s):  
Human Error ◽  
Human Factor ◽  
Mining Industry ◽  
Organizational Factors ◽  
Task Environment ◽  
Critical Element ◽  
Swiss Cheese ◽  
Accident Investigation ◽  
Swiss Cheese Model ◽  
Cheese Model

Workplace accidents are investigated mainly for the purpose of identifying the causes that contributed to the occurrence of the accident and thereby providing recommendations to prevent recurrence of such accidents. The human factor has always been a critical element in the accident causation model applied in various industries. However, it is equally important to identify other parameters like task environment, task conditions, organizational culture and other organizational factors that influence human behavior in most of the cases. In this paper, an effort has been made to discuss some of the investigation models used for identification of root causes leading to an incident or accident. This paper highlighted the gaps in the investigation procedure in the Indian mining industry where too much focus is being given to human error and contravention of the health and safety statute application to mining activities. The authors emphasized the importance of using suitable investigation methodology for investigating into Indian mining accidents. A case study accident has been presented to highlight the necessity of using suitable accident investigation methodology like Swiss Cheese Model for identifying root causes of such accident. Keywords - Ecology, accident investigation, mining industry, human factor, task environment, organizational factors, investigation model, India

The ‘ALVA CAPE’ and the Automatic Identification System: The Use of VHF in Collision Avoidance at Sea

2002 ◽  
Vol 55 (3) ◽  
pp. 431-442 ◽  
Author(s):  
S. J. Harding
Keyword(s):  
Case Studies ◽  
Collision Avoidance ◽  
Samuel Johnson ◽  
Automatic Identification ◽  
Identification System ◽  
Controversial Issues ◽  
Automatic Identification System ◽  
Radio Technology ◽  
Marine Navigation

One of the most controversial issues relating to marine navigation is the efficacy of ships' crews using VHF radio technology for bridge-to-bridge communications to agree manoeuvres. Through a re-evaluation of historic case studies, this paper provides background on the development of applying VHF technology in collision avoidance and the legislation, national and international, underpinning the practice; a practice that has found little or no support from the legal establishment. Finally the consequential development of a policy to require specific VHF technology to be installed on ships to facilitate agreements in relation to collision avoidance manoeuvres will be reviewed, that is the Automatic Identification System (AIS).Integrity without knowledge is weak and useless, and knowledge without integrity is dangerous and dreadful. Samuel Johnson

Human factors in maintenance: a review

2016 ◽  
Vol 22 (3) ◽  
pp. 218-237 ◽  
Author(s):  
Mohammad Sheikhalishahi ◽  
Liliane Pintelon ◽  
Ali Azadeh
Keyword(s):  
Decision Making ◽  
Human Factors ◽  
Human Error ◽  
Human Performance ◽  
Decision Makers ◽  
Content Type ◽  
Swiss Cheese Model ◽  
Maintenance Decision ◽  
Cheese Model ◽  
Review Current

Purpose – The purpose of this paper is to review current literature analyzing human factors in maintenance, and areas in need of further research are suggested. Design/methodology/approach – The review applies a novel framework for systematically categorizing human factors in maintenance into three major categories: human error/reliability calculation, workplace design/macro-ergonomics and human resource management. The framework further incorporates two well-known human factor frameworks, i.e., the Swiss Cheese model and the ergonomic domains framework. Findings – Human factors in maintenance is a pressing problem. The framework yields important insights regarding the influence of human factors in maintenance decision making. By incorporating various approaches, a robust framework for analyzing human factors in maintenance is derived. Originality/value – The framework assists decision makers and maintenance practitioners to evaluate the influence of human factors from different perspectives, e.g. human error, macro-ergonomics, work planning and human performance. Moreover, the review addresses an important subject in maintenance decision making more so in view of few human error reviews in maintenance literature.

Marine Navigation Collision Preventing System

2014 ◽  
Vol 69 (7) ◽  
Author(s):  
Amirrudin Yaacob ◽  
M. Rashidi ◽  
Jaswar Koto
Keyword(s):  
Search And Rescue ◽  
Maritime Security ◽  
Automatic Identification ◽  
Identification System ◽  
World Community ◽  
Stopping Distance ◽  
Safety Distance ◽  
Entire World ◽  
Security Regulations ◽  
Marine Navigation

Navigation safety has become one of the important issues to the entire world community. Automatic Identification System (AIS) firstly has been used to comply with safety and security regulations, functioning as collision avoidance, vessel traffic services, maritime security, aids to navigation, search and rescue and accident investigation. This paper presents marine navigation collision preventing system between ships and ships using AIS. In the system, the raw data from AIS is crossed with database based MMSI number to find detail information then, location of each ship was plotted on to Google Map. The safety distance is assessed based actual and stopping distance. The system was tested in the Batam and Singapore Channel.   

scholarly journals DBSCAN OPTIMIZATION FOR IMPROVING MARINE TRAJECTORY CLUSTERING AND ANOMALY DETECTION

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 455-461
Author(s):  
X. Han ◽  
C. Armenakis ◽  
M. Jadidi
Keyword(s):  
Human Error ◽  
Spatial Clustering ◽  
Autonomous Systems ◽  
Computational Cost ◽  
Route Planning ◽  
Maritime Transportation ◽  
Automatic Identification ◽  
Identification System ◽  
Trajectory Clustering ◽  
Clustering Model

Abstract. Today maritime transportation represents 90% of international trade volume and there are more than 50,000 vessels sailing the ocean every day. Therefore, reducing maritime transportation security risks by systematically modelling and surveillance should be of high priority in the maritime domain. By statistics, majority of maritime accidents are caused by human error due to fatigue or misjudgment. Auto-vessels equipped with autonomous and semi-autonomous systems can reduce the reliance on human’s intervention, thus make maritime navigation safer. This paper presents a clustering method for route planning and trajectory anomalies detection, which are the essential part of auto-vessel system design and development. In this paper, we present the development of an enhanced density-based spatial clustering (DBSCAN) method that can be applied on historical or real-time Automatic Identification System (AIS) data, so that vessel routes can be modelled, and the trajectories’ anomalies can be detected. The proposed methodology is based on developing an optimized trajectory clustering approach in two stages. Firstly, to increase the attribute dimension of the vessel’s positioning data, therefore other characteristics such as velocity and direction are considered in the clustering process along with geospatial information. Secondly, the DBSCAN clustering model has been enhanced by introducing the Mahalanobis Distance metric considering the correlations of the position cluster points aiming to make the identification process more accurate as well as reducing the computational cost.

scholarly journals Comparison of four major industrial disasters from the perspective of human error factor

2020 ◽  
Vol 305 ◽  
pp. 00017
Author(s):  
Doru Costin Darabont ◽  
Daniel Onut Badea ◽  
Alina Trifu
Keyword(s):  
Human Factors ◽  
Human Error ◽  
Technological Advancement ◽  
Human Errors ◽  
Occupational Risks ◽  
Factors Analysis ◽  
Error Factor ◽  
Swiss Cheese Model ◽  
Component Failures ◽  
Cheese Model

This paper presents the preliminary findings of a project still in progress at INCDPM regarding” Knowledge transfer partnership and research development in the assessment and prevention of occupational risks which may conduct to disaster”. After studying the major industrial disasters of our times, it become clear that even with technological advancement, human error is still the major cause of accidents and incidents. Analysis of human error and their role in accidents is an important part of developing systematic methods for reliability in the industry and risk prediction. To obtain data for predictive analysis is necessary to analyse accidents and incidents to identify its causes in terms of component failures and human errors. Therefore, a proper understanding of human factors in the workplace is an important aspect in the prevention of accidents, and human factors should be considered in any program to prevent those that are caused by human error. The comparison between four major industrial disasters (Chernobyl, Bhopal, Deepwater Horizon, Alpha Piper) was made using Human Factors Analysis and Classification System (HFACS), a modified version of “Swiss Cheese” model that describes the levels at which active failures and latent failures/conditions may occur within complex operations.

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