Unsafe acts and unsafe outcomes in aircraft maintenance

Road safety studies using the Driver Behaviour Questionnaire (DBQ) have provided support for a three-way distinction between violations, skill-based errors and mistakes, and have indicated that a tendency to commit driving violations is associated with an increased risk of accident involvement. The aims of this study were to examine whether the three-way distinction of unsafe acts is applicable in the context of aircraft maintenance, and whether involvement in maintenance safety occurrences can be predicted on the basis of self-reported unsafe acts. A Maintenance Behaviour Questionnaire (MBQ) was developed to explore patterns of unsafe acts committed by aircraft maintenance mechanics. The MBQ was completed anonymously by over 1300 Australian aviation mechanics, who also provided information on their involvement in workplace accidents and incidents. Four factors were identified: routine violations, skill-based errors, mistakes and exceptional violations. Violations and mistakes were related significantly to the occurrence of incidents that jeopardized the quality of aircraft maintenance, but were not related to workplace injuries. Skill-based errors, while not related to work quality incidents, were related to workplace injuries. The results are consistent with the three-way typology of unsafe acts described by Reason et al. (1990) and with the DBQ research indicating an association between self-reported violations and accidents. The current findings suggest that interventions addressed at maintenance quality incidents should take into account the role of violations and mistakes, and the factors that promote them. In contrast, interventions directed at reducing workplace injury are likely to require a focus on skill-based errors.     

Unsafe acts and unsafe outcomes in aircraft maintenance

Road safety studies using the Driver Behaviour Questionnaire (DBQ) have provided support for a three-way distinction between violations, skill-based errors and mistakes, and have indicated that a tendency to commit driving violations is associated with an increased risk of accident involvement. The aims of this study were to examine whether the three-way distinction of unsafe acts is applicable in the context of aircraft maintenance, and whether involvement in maintenance safety occurrences can be predicted on the basis of self-reported unsafe acts. A Maintenance Behaviour Questionnaire (MBQ) was developed to explore patterns of unsafe acts committed by aircraft maintenance mechanics. The MBQ was completed anonymously by over 1300 Australian aviation mechanics, who also provided information on their involvement in workplace accidents and incidents. Four factors were identified: routine violations, skill-based errors, mistakes and exceptional violations. Violations and mistakes were related significantly to the occurrence of incidents that jeopardized the quality of aircraft maintenance, but were not related to workplace injuries. Skill-based errors, while not related to work quality incidents, were related to workplace injuries. The results are consistent with the three-way typology of unsafe acts described by Reason et al. (1990) and with the DBQ research indicating an association between self-reported violations and accidents. The current findings suggest that interventions addressed at maintenance quality incidents should take into account the role of violations and mistakes, and the factors that promote them. In contrast, interventions directed at reducing workplace injury are likely to require a focus on skill-based errors.     

Using Neural Networks to predict HFACS unsafe acts from the pre-conditions of unsafe acts

Abstract

Human Factors Analysis and Classification System (HFACS) is based upon Reason’s organizational model of human error which suggests that there is a ‘one to many’ mapping of condition tokens (HFACS level 2 psychological precursors) to unsafe act tokens (HFACS level 1 error and violations). Using accident data derived from 523 military aircraft accidents, the relationship between HFACS level 2 preconditions and level 1 unsafe acts was modelled using an artificial neural network (NN). This allowed an empirical model to be developed congruent with the underlying theory of HFACS. The NN solution produced an average overall classification rate of ca. 74% for all unsafe acts from information derived from their level 2 preconditions. However, the correct classification rate was superior for decision- and skill-based errors, than for perceptual errors and violations.

Practitioner Summary: A model to predict unsafe acts (HFACS level 1) from their preconditions (HFACS level 2) was developed from the analysis of 523 military aircraft accidents using an artificial NN. The results could correctly predict approximately 74% of errors.     

Application of HFACS and grounded theory for identifying risk factors of air traffic controllers’ unsafe acts

Unsafe acts of air traffic controllers (ATCers) are caused by various factors. Based on interview data and case reports, human factors analysis and classification system (HFACS) and the grounded theory were adopted to identify the risk factors of ATCers’ unsafe acts comprehensively. The interview data and the case data issued by the authority were first collected. Then, the above data were encoded to obtain the relevant concepts and categories based on the grounded theory, and the HFACS model is used to classify the concepts and categories. Finally, the relationship between the core category and the secondary category was sorted out in the way of storyline. The results show that the risk factors include environmental factors, organizational influences, unsafe supervision and controllers’ states, and the unsafe acts manifest as errors and violations. Among them, the controllers’ states are intermediate variable, and other factors indirectly affect the controllers’ unsafe acts. The first three risk factors with high frequency in unsafe incidents are technical environment, mental states and business ability. The three most common unsafe acts are giving the wrong order, insufficient situational awareness, and poor work order on-site. Through combining HFACS framework and grounded theory to analyze data, a more clear and comprehensive conceptual model of risk factors of ATCers’ unsafe acts can be obtained.     

Types and tokens in road accident causation

Accidents are preceded by long histories containing multitudes of events that constitute promising targets for preventive action. These antecedent events can be classified into at least four groups that occur in this order: failure types; psychological precursors; unsafe acts; and breakdown of defences. It is argued that events directly preceding an accident, such as breakdown of defences and unsafe acts, are only haphazard tokens of the more permanent weaknesses within a system, called failure types. Elimination of a type will therefore have much more impact than the elimination of one or a few tokens. It is also argued that there exist only a limited number of failure types, which are responsible for all accidents. However, in the specific area of road accidents, it is not known which types cause most of the problems. Therefore, their relative importance can only be guessed. We guessed that hardware problems and maintenance are unimportant types; that education and regulations are of moderate importance; and that incompatible goals, conditions promoting unsafe behaviour, and organizational inadequacy are the types that cause most of the accidents. The latter therefore constitute the most promising targets for accident prevention.     

Industrial accidents are produced by social relations of work: a sociological theory of industrial accidents

Industrial accidents are produced by social relations work. This sociological explanation of accidents differs from the hypotheses on which the majority of modern safety practices are based, which reduce accident causes to unsafe acts and unsafe conditions. Accidents are seen as produced at each of three levels of social relations of work (rewards, command and organisation), and also non-socially at the individual-member level. The resulting hypotheses were tested using data collected according to a semi-experimental design in seven plants in which shift (day/night), shift type (rotating/fixed), technological type and management styles were the factors controlled for. Because of the design, machines, materials and, in most cases, workers were the same across shifts and social relations varied. The sociological theory proved capable of explaining most of the variation in inter-shift differences in accident rates, and, when tested statistically, appeared to have greater explanatory power than competing hypotheses. It is concluded that accidents can be prevented by workers who exercise auto-control at all levels and by management which, in the absence of worker orientations favourable to auto-control, engages in safety management as defined sociologically. A practical consequence for ergonomics is that when plant, equipment and processes are to be modified, an attempt to understand their interaction with the social relations of work should be made. A theoretical consequence is that sociological insights should be incorporated into the perspective of the ergonomics discipline.     

The evolution of the HFACS method used in analysis of marine accidents: A review

The importance of accident investigations carried out in every field where operators play a vital role is increasingly recognised. Many researchers argue that understanding accident formation is the most important way to prevent future disasters. In this research, an analysis of the modified Human Factor Analysis and Classification System (HFACS) structures developed for use in the analysis of marine accidents was conducted. These structures include HFACS-PV (Passenger Vessels), HFACS-MA (Maritime Accidents), HFACS-Coll (Collisions), HFACS-SIBCI (ship collision accidents between assisted ships and icebreakers in ice-covered waters) and HFACS-Ground (Groundings). In this study, revisions in HFACS structures were examined. It was found that the accident factors were classified at different levels to facilitate the application of the original HFACS framework. The first of the remarkable differences among the basically developed methods is the level of external factors (first level), where the accident factors arising from national and international rules are classified. The second is the level of operational conditions (last level). It has been observed that the precondition for the unsafe acts level has been revised in all methods examined. This study will guide researchers in choosing an HFACS structure suitable for the area they will study, as well as revealing different aspects of the modified methods examined in marine accident analysis.     

A method for investigating human factor aspects of aircraft accidents and incidents

Approximately 70% of aircraft accidents and incidents have in the past been attributed to human error, and the importance of human error as a major contributory factor is now universally recognized. A satisfactory technique for the investigation of human error type accidents and incidents has not yet been standardized. This paper describes a comprehensive procedure which has been designed to assess human behaviour in instances of aircraft accidents and incidents. The aim of such a human factors investigation is to understand why the accident or incident occurred and so lead to a better understanding of human error, and as a result enhance flight safety.

The use of questionnaires and interviews to assess the subjective data available is discussed, and it is recommended that the most appropriate technique for the assessment of behavioural data is the use of an interview together with a check list. The importance of an early interview with all personnel associated with an accident or an incident by a human factors specialist trained in interviewing techniques is emphasized.

The check list described has been developed after reviewing the relevant literature and has been modified by personal experience derived from the investigation of Army aircraft accidents and incidents in which human factors have played an important part. The check list is based on a systems approach to understanding human error and includes such headings as ‘stress’ (including life events), ‘fatigue’, ‘arousal’ and ‘personality’. It is hoped that further improvements will be made to the check list to enable it to be used not only within the specific area of aircraft accidents and incidents, but also within the broader context of any accidents which may be attributed to human errors and as a result reduce the number of accidents and identify areas where further research may be useful.     

Computer vision and long short-term memory: Learning to predict unsafe behaviour in construction

Predicting unsafe behaviour in advance can enable remedial measures to be put in place to mitigate likely accidents on construction sites. Prevailing safety studies in construction tend to be retrospective and focus on examining the conditions that contribute to unsafe behaviour from a psychological perspective. While such studies are warranted, they can also not visually comprehend the dynamic and complex conditions that influence unsafe behaviour. In this paper, we aim to contribute to filling this void and, in doing so, combine computer vision with Long-Short Term Memory (LSTM) to predict unsafe behaviours from videos automatically. Our proposed approach for predicting unsafe behaviour is based on: (1) tracking people using a SiamMask; (2) predicting the trajectory of people using an improved Social-LSTM; and (3) predicting unsafe behaviour using Franklin's point inclusion polygon (PNPoly) algorithm. We use the Wuhan metro project as a case to evaluate our approach’s feasibility and effectiveness. Our adopted SiamMask method outperforms current techniques used for tracking people. Additionally, our improved Social-LSTM can achieve higher accuracy on trajectory prediction than other methods (e.g., Social-GAN). The research findings demonstrate that our developed computer vision approach can be used to accurately predict unsafe behaviour on construction sites.     

Human error and commercial aviation accidents: an analysis using the human factors analysis and classification system

OBJECTIVE: The aim of this study was to extend previous examinations of aviation accidents to include specific aircrew, environmental, supervisory, and organizational factors associated with two types of commercial aviation (air carrier and commuter/ on-demand) accidents using the Human Factors Analysis and Classification System (HFACS). BACKGROUND: HFACS is a theoretically based tool for investigating and analyzing human error associated with accidents and incidents. Previous research has shown that HFACS can be reliably used to identify human factors trends associated with military and general aviation accidents. METHOD: Using data obtained from both the National Transportation Safety Board and the Federal Aviation Administration, 6 pilot-raters classified aircrew, supervisory, organizational, and environmental causal factors associated with 1020 commercial aviation accidents that occurred over a 13-year period. RESULTS: The majority of accident causal factors were attributed to aircrew and the environment, with decidedly fewer associated with supervisory and organizational causes. Comparisons were made between HFACS causal categories and traditional situational variables such as visual conditions, injury severity, and regional differences. CONCLUSION: These data will provide support for the continuation, modification, and/or development of interventions aimed at commercial aviation safety. APPLICATION: HFACS provides a tool for assessing human factors associated with accidents and incidents.     

Hazard analysis in chemical complexes in Japan—especially those caused by human errors

Accidents al Japanese chemical industrial complexes are examined from the past to the present at the Chiba, Mizushima, Sakai-Senpoku, Yokkaichi, Kashima and Tokushima-Ohtake areas. Comparisons with the number of accidents caused by hard errors and soft errors were made; where the hard errors are defined as the faults of the machines and facilities and the soft errors as the human errors and the faults of the systems. The results may be summarized as follows: the ratio of the number of accidents caused by the hard errors to the soft errors was approximately 2:3 at all areas. Using the data obtained through a questionnaire on the potential accidents from about 200 workers engaged in the chemical industry, comparisons with the causes of the potential accidents and the actual accidents caused by human error were made and the results agreed approximately with the causes of the actual accidents.     

Associations between errors and contributing factors in aircraft maintenance

In recent years cognitive error models have provided insights into the unsafe acts that lead to many accidents in safety-critical environments. Most models of accident causation are based on the notion that human errors occur in the context of contributing factors. However, there is a lack of published information on possible links between specific errors and contributing factors. A total of 619 safety occurrences involving aircraft maintenance were reported using a self-completed questionnaire. Of these occurrences, 96% were related to the actions of maintenance personnel. The types of errors that were involved, and the contributing factors associated with those actions, were determined. Each type of error was associated with a particular set of contributing factors and with specific occurrence outcomes. Among the associations were links between memory lapses and fatigue and between rule violations and time pressure. Potential applications of this research include assisting with the design of accident prevention strategies, the estimation of human error probabilities, and the monitoring of organizational safety performance.     

Major and minor injuries at work: Are the circumstances similar or different?

A database containing accident records from a large petrochemical refining complex was employed to test the hypothesis that similar circumstances are associated with incidents that result in major and minor injuries. Results indicated a great deal of similarity for both major and minor accidents and across jobs. Several dimensions or characteristics of the accidents were examined including the kinds of activities employees were engaged in prior to the accident, the sequence of events preceding the injury, the nature of the injury sustained and the parts of the body that were injured. In addition to providing support for the notion that the conditions giving rise to major- and minor-injury accidents are similar, these findings extend previous work by demonstrating that such similarities hold across jobs.     

Workplace and organizational accident causation factors in the manufacturing industry

Labor inspectors investigate accidents to identify possible accident causes, initiate prosecution, and plan future accident prevention. The Method of Investigation for Labor Inspectors (MILI) was designed to help them to identify workplace and organizational factors in addition to immediate factors and legal breaches. The present study analyzes the impact of workplace (work design and provision of unsafe equipment) and organizational factors (training and employee involvement) on accident causation and validates MILI on real accident cases. Accident data from the manufacturing sector are analyzed with LISREL structural equation modeling. Results confirm the relationship between work design and training as well as between provision of unsafe equipment and employee involvement. The present study provides evidence that MILI is a structured accident investigation method allowing multiple accident causation factors to be revealed and that it could help all interested parts (not only labor inspectors, but companies as well) to thoroughly investigate occupational accidents. © 2009 Wiley Periodicals, Inc.     

矿井调度绞车运输事故原因分析及防范措施

从人为不安全因素、环境的不安全状态两个方面入手,详细分析了造成矿井调度绞车运输事故的原因,同时提出了加强入井人员的安全技术培训、调度绞车钢丝绳的检查和管理等有针对性的防范措施。     

Risk assessment of parking lot management based on occupational injuries data

This study is concerned with the characteristics of occupational injuries and sick leaves for the workers doing parking lot management. Possible incidents and sick leaves have been identified and analyzed for 470 occupational accidents in the parking lot management process. Management level of accidents for prioritizing prevention measures is induced for the combination of the accident types and parking lot management processes. Accidents occurring during the specific parking lot management process showed different characteristics, depending on the type of accident and agency of accident. Most critical accidents that required corrective actions for prevention were being struck by an object in the “guide” process and slipping in the “valet parking” process. And, possible incidents predicted also as high level of management were slips in the “fee collection” and “cleaning” processes and falls to lower level in the “operating lift” and “repair” processes. Also, traffic accidents in the “guide” process, overexertion and bodily reaction and posture in the “patrol” process were ranked ‘high. The findings of this study can be used to develop more effective accident prevention policies to reduce occupational accidents in parking lot management.     

面向化工企业事故的根原因关联分析

化工事故发生的根原因多是由人的不安全行为、机械或物的不安全状态等引发,其本质是企业管理上的缺陷。挖掘根原因间、根原因与事故间的关联关系是预防事故、提升企业安全管理水平的关键。由于事故调研根原因分析与安全管理指标体系存在稀疏关联现象,难以挖掘管理缺陷与事故演化间的关联关系。为此,本文通过协同过滤算法填补事故调研中缺失的评分数据;基于加权支持度计数的关联规则算法挖掘事故根原因间、根原因与事故属性间的强关联规则。实验结果表明,基于加权支持度的关联分析算法相比于现有的算法,能推荐更多危险程度高的企业潜在安全隐患及安全隐患与事故间的演化关联,从而能科学指导企业安全生产,实现面向生产过程的风险预警和事故预防。     

Identification of risk factors and countermeasures for slip, trip and fall accidents during the delivery of mail

Risk factors for slip, trip and fall accidents (STFA) during the delivery of mail were identified using a range of accident-centred and accident-independent methods. Key factors included slippery underfoot conditions, non-weather related environmental hazards (e.g., uneven paving, steps, inadequate lighting), poor slip resistance from footwear, unsafe working practices, management safety practices, and underlying organisational influences. Intervention measures were recommended that target STFA risks at three levels: slip resistance, exposure to hazardous conditions, and employee behaviour in the face of hazardous conditions. The use of a participative approach to intervention selection and design enabled allowance for the organisational context to be made.     

Injury severity assessment for underground coalmine workers

An injury severity model is proposed for assessment of injury incidents in industrial settings. A classification scheme for injury incidents considering interactions is also developed. The injury severity model considers injury potential in the form of unsafe conditions and analyzes its transfer to actual injury of varying severity. A case study was conducted in an underground coalmine of eastern India. An observed reduction in risk realization is explained through the model. Presence of interactions is found to be the most significant incident attribute of injury occurrences. The classification scheme and the results obtained from this study will help in improving accident/injury investigation reporting and devising preventive measures for reducing injury severity.