Predictive Analysis Of Cognitive Errors Research Articles

A seafarers’ cognitive error mining model based on IDA and TRACEr and mitigation measures for ship collision accidents

Human factor is the main cause of maritime accidents. However, the in-depth study on the mechanism of cognitive errors in seafarers’ human error needs to be supplemented. This study empirically analyzes 116 collision cargo ships along the Yangtze River trunk line. To explore the relationship between seafarers’ task errors and the cognitive process of accidents, a seafarers’ cognitive error mining model is proposed, which integrates the Information, Decision, and Action (IDA) cognitive model into the Technique for the Retrospective and Predictive Analysis of Cognitive Errors (TRACEr). According to the approach of mining from the outside to the inside, firstly, the error mode in the cognitive stage of seafarer tasks are identified. Then the fine-grained main manifestation of the task errors is mined by K-means, and Apriori algorithm is employed to mine the hidden features between the external and internal error modes. The results show that there are four main problems of Yangtze River ship accidents, and seafarers have four to seven corresponding causal chains from psychological error mechanism to performance shaping factors for each major issue in the cognitive stage. On the basis of the results, this study summarizes the guiding principles of accident prevention and seafarer management measures to provide decision support for water traffic safety.

Modification of the TRACEr model as a tool foranalysingaccidents inthe Oil and Gas industry: The TRACEr-OGI.

Safetythe reversedside of an accidentis always anintegral partof every organisational set up. Hence, risk assessments andaccidents analysis in the oil and gas industriesis always inevitable. As one of the organizationswith highest number of accidentsreports,the oil and gas industry has beentrying most at times to adopt several established accidentsanalysing tools/models to address this aspect. Therefore, this paper critically analyses and adoptsone of the accident analysingmodels used in the aviation industry called the TRACEr (Techniques for the Retrospective and predictive Analysis of Cognitive Errors) model to fit into the oil and gas industrial accidentsanalysis. In achieving this, taxonomies of most benefit from the TRACEr model were adopted while making changes to some key areasto develop the new model called the TRACEr OGI. Accidents reports from variousoil and gas industriesretrieved fromthe international association of oil and gas producers (IOGP)database focussing on Russia and central Asiawere usedto test the reliability of the new TRACEr OGImodel. The new TRACEr OGI has seven levels of taxonomies asopposed to theeightlevelsfrom the main TRACErmodel were the central change been made at the Cognitive level of the operator context.The result of the analysis for reliability test shows that, there exist a reasonable percentage of correlation for the Human-Machine interface and the task errors in the oil and gas industryand hence the TRACEr OGI can be adopted for accident analysis inoil and gas industries.

Analysis of accidents through combination of CAST and TRACEr techniques: A case study

This article presents a methodology for accident analysis developed from the combination of CAST - Causal Analysis based on System Theory, derived from the STAMP (Systems-Theoretic Accident Model and Process) accident causation model and the human error analysis/investigation tool TRACEr (Technique for the Retrospective and Predictive Analysis of Cognitive Errors). This combination was proposed from the perceived need to structure one of the stages of the application of the CAST technique where unsafe control actions, or human errors, that occurred in the course of accidental events are analyzed, for which the application of the TRACEr technique proved extremely useful. From the application of the proposed combination to an accident that occurred in an oil production unit in operation on the coast of southeastern Brazil, it was possible to obtain a deeper understanding of the psychological phenomena that preceded one of the unsafe control actions, as well as the identification of performance shaping factors that contributed to its occurrence. These results demonstrated the conceptual cohesion and mutual complementarity of the associated techniques, which allowed a comprehensive understanding of the accidental event and, consequently, the elaboration of safety recommendations more appropriate to the findings of the analysis, better specified to the aspects of human factors involved.

Predictive Analysis of Cognitive Errors of Control Room Operators: a Case Study in a Petrochemical Industry

Predictive Analysis of Cognitive Errors of Control Room Operators: a Case Study in a Petrochemical Industry

Adaptation of Tracer—Technique for The Retrospective and Predictive Analysis of Cognitive Errors- for Analyzing Indonesian Train Accident Involving Train Dispatcher

Human factors have been as major factors that contribute to many accident in rail industry, including Indonesia. High train accident rate in Indonesia is underlining the importance to develop method in identifying and analyzing human error in rail system in particular for the second most involved in train accident that is train dispatcher. Purpose of this study is to adapt technique for the retrospective and predictive analysis of cognitive errors (TRACEr) in identifying human error in Indonesian train dispatcher. Task, sub-task, and cognitive domain analysis of Indonesian train dispatcher is identified through Standard Operation Procedure (SOP)’ review and direct observation involving four train dispatchers. Further, TRACEr taxonomy including task error, error taxonomies, information taxonomy of Indonesian train dispatcher is determined based on literature review and group discussion among authors and a station master. Performance shaping factors are observed through a questionnaire involving 26 train dispatchers. The adapted TRACEr is developed and the application of the adapted TRACEr in analyzing one train accident in Indonesia that is validated by Indonesian National Transportation Safety Committee reveals the usefulness of the adapted TRACEr. The result revealed that the adapted TRACEr is a valuable aid to analyze Indonesian train accident involving train dispatcher and can be used as a basis for further applications in train dispatcher in other countries with necessary adaptation.

A Technique for the Retrospective and Predictive Analysis of Cognitive Errors for the Oil and Gas Industry (TRACEr-OGI)

Human error remains a major cause of several accidents in the oil and gas (O&G) industry. While human error has been analysed in several industries and has been at the centre of many debates and commentaries, a detailed, systematic and comprehensive analysis of human error in the O&G industry has not yet been conducted. Hence, this report aims to use the Technique for Retrospective and Predictive Analysis of Cognitive Errors (TRACEr) to analyse historical accidents in the O&G industry. The study has reviewed 163 major and/or fatal O&G industry accidents that occurred between 2000 and 2014. The results obtained have shown that the predominant context for errors was internal communication, mostly influenced by factors of perception. Major accident events were crane accidents and falling objects, relating to the most dominant accident type: ‘Struck by’. The main actors in these events were drillers and operators. Generally, TRACEr proved very useful in identifying major task errors. However, the taxonomy was less useful in identifying both equipment errors and errors due to failures in safety critical control barriers and recovery measures. Therefore, a modified version of the tool named Technique for the Retrospective and Predictive Analysis of Cognitive Errors for the Oil and Gas Industry (TRACEr-OGI) was proposed and used. This modified analytical tool was consequently found to be more effective for accident analysis in the O&G industry.

Incorporation of human factors into ship collision risk models focusing on human centred design aspects

This paper presents an approach that more adequately incorporates human factor considerations into quantitative risk analysis of ship operation. The focus is on the collision accident category, which is one of the main risk contributors in ship operation. The approach is based on the development of a Bayesian Network (BN) model that integrates elements from the Technique for Retrospective and Predictive Analysis of Cognitive Errors (TRACEr) and focuses on the calculation of the collision accident probability due to human error. The model takes into account the human performance in normal, abnormal and critical operational conditions and implements specific tasks derived from the analysis of the task errors leading to the collision accident category. A sensitivity analysis is performed to identify the most important contributors to human performance and ship collision. Finally, the model developed is applied to assess the collision risk of a feeder operating in Dover strait using the collision probability estimated by the developed BN model and an Event tree model for calculation of human, economic and environmental risks.

Classification of human errors in grounding and collision accidents using the TRACEr taxonomy

This paper applies a Human Error Identification tool called Technique for the Retrospective and Predictive Analysis of Cognitive Errors to the analysis of ship accidents. Grounding and collision accidents investigation reports involving sixty-four vessels published by the UK’s Maritime Accident Investigation Branch, the Transportation Safety Board of Canada and the National Transportation Safety board of the United States of America are coded and analysed using the taxonomy of the Technique for the Retrospective and Predictive Analysis of Cognitive Errors. A total of two hundred and eighty-nine errors performed by the operators are coded. The results of the codification process are analysed with the objective of identifying the main task errors, cognitive domains and the technical equipment involved in grounding and collision accidents and the factors that affect the performance of the operators. This identification is a necessary step towards safety improvements resulting from dealing with the identified problems. A discussion on the use of the taxonomy of the Technique for the Retrospective and Predictive Analysis of Cognitive Errors is provided and it is proposed to combine it with some elements of the CASMET approach to accident investigation so as to improve the applicability of the methodology to the analysis of ship accidents.

Structure of Human Errors in Tasks of Operators Working in the Control Room of an Oil Refinery Unit

In the present research, human errors existed in tasks of operators working in two control rooms (Northern and Southern control rooms) in the Tehran oil refinery are identified and evaluated. Then, corrective strategies and actions are advised to decrease errors. At first, using hierarchical task analysis (HTA) method, four positions including shift controller, head operators, control room's operators, and outside operators are analyzed. Afterwards, human errors in the considered positions are identified and assessed using technique for retrospective and predictive analysis of cognitive errors (TRACEr). Results present 670 internal errors and 738 external errors in the Northern sector while 661 internal errors and 744 external errors in the Southern sector. In two sectors, this number of errors was identified for 27 major tasks and 108 minor tasks. Action errors are the most repeated errors among the internal errors while violation errors are the least. In addition, most of external errors are related to communication errors while the least errors are related to time and sequence errors. According to results of applying psychological error mechanism (PEM) in the considered positions, people's distraction is the main factor causing errors. Performance shaping factors (PFS) identifies lack of training as the major factor for errors. Using TRACEr, all external and internal errors, psychological error mechanism, and primary factors of errors in the control room can be detected.

Structure of human errors in tasks of operators working in the control room of an oil refinery unit

In the present research, human errors existed in tasks of operators working in two control rooms (Northern and Southern control rooms) in the Tehran oil refinery are identified and evaluated. Then, corrective strategies and actions are advised to decrease errors. At first, using hierarchical task analysis (HTA) method, four positions including shift controller, head operators, control room's operators, and outside operators are analyzed. Afterwards, human errors in the considered positions are identified and assessed using technique for retrospective and predictive analysis of cognitive errors (TRACEr). Results present 670 internal errors and 738 external errors in the Northern sector while 661 internal errors and 744 external errors in the Southern sector. In two sectors, this number of errors was identified for 27 major tasks and 108 minor tasks. Action errors are the most repeated errors among the internal errors while violation errors are the least. In addition, most of external errors are related to communication errors while the least errors are related to time and sequence errors. According to results of applying psychological error mechanism (PEM) in the considered positions, people's distraction is the main factor causing errors. Performance shaping factors (PFS) identifies lack of training as the major factor for errors. Using TRACEr, all external and internal errors, psychological error mechanism, and primary factors of errors in the control room can be detected.

Classification of errors contributing to rail incidents and accidents: A comparison of two human error identification techniques

Identifying the errors that frequently result in the occurrence of rail incidents and accidents can lead to the development of appropriate prevention and/or mitigation strategies. Nineteen rail safety investigation reports were reviewed and two error identification tools, the Human factors analysis and classification system (HFACS) and the Technique for the retrospective and predictive analysis of cognitive errors (TRACEr-rail version), used as the means of identifying and classifying train driver errors associated with rail accidents/incidents in Australia. We aimed to identify the similarities and differences between the techniques in their capacity to identify and classify errors and also to determine how consistently the tools are applied. The HFACS analysis indicated that slips of attention (i.e. ‘skilled based errors’) were the most common ‘unsafe acts’ committed by drivers. The TRACEr-rail analysis indicated that most ‘train driving errors’ were ‘violations’ while most ‘train stopping errors’ were ‘errors of perception’. Both tools identified the underlying factors with the largest impact on driver error to be decreased alertness and incorrect driver expectations/assumptions about upcoming information. Overall, both tools proved useful in categorising driver errors from existing investigation reports, however, each tool appeared to neglect some important and different factors associated with error occurrence. Both tools were found to possess only moderate inter-rater reliability. It is thus recommended that the tools be modified, or a new tool be developed, for complete and consistent error classification.

Analysis of Australian and UK Rail Incidents and Accidents using Retrospective TRACEr Lite for Train Drivers

Nineteen Australian safety investigation reports and 13 UK safety reports were reviewed and a human error identification technique (the Technique for the Retrospective and Predictive Analysis of Cognitive Errors: TRACEr-rail version) adopted as a means of identifying and classifying errors associated with rail incidents and accidents. In the Australian sample, it was revealed that most driver errors occurred when drivers were driving or stopping their train and that these errors consistently resulted in two consequences: speeding and signals passed at danger (SPADs). In the UK, errors occurred while drivers were performing a whole range of tasks, including driving, stopping, train assembly, and failed train operations. In Australia, most ‘train driving errors’ were ‘violations’ while most ‘train stopping errors’ were errors of ‘perception’. In the UK, a more varied range of errors was observed. Overall, the TRACEr-rail framework proved useful in categorizing driver errors from existing investigation reports and in highlighting recurring errors associated with incidents and accidents. However, the tool appeared to neglect some organizational factors contributing to driver error, and, preliminary inter-rater reliability testing revealed that some category descriptions may require further refinement. Future work will aim to develop a more Australian-friendly and reliable version of TRACEr for use in Australian rail.

Development and application of a human error identification tool for air traffic control

This paper outlines a human error identification (HEI) technique called TRACEr—technique for the retrospective and predictive analysis of cognitive errors in air traffic control (ATC). The paper firstly considers the need for an HEI tool in ATC, and key requirements for the technique are noted. The technique, which comprises a number of inter-related taxonomies, based around a simple cognitive framework, is then described. A study concerning a real-world application of TRACEr is outlined—the evaluation of several options for reduced separation minima in unregulated UK airspace. In this study, TRACEr was used predictively and retrospectively, looking forward to pre-empt potential problems and looking back to learn from experience. The paper concludes that TRACEr is a valuable aid to design, development and operations in UK ATC, and has indeed been used as a basis for further applications in ATC both in Europe and the USA.