HFACS对商用航空事故分析DOT版

航空发表于 2010-5-15 08:36:46

HFACS对商用航空事故分析DOT版
 
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航空发表于 2010-5-15 08:37:00

Douglas A. Wiegmann University of Illinois at Urbana-Champaign Institute of Aviation Savoy, IL 61874 Scott A. Shappell FAA Civil Aeromedical Institute P.O. Box 25082 Oklahoma City, OK 73125 February 2001 Final Report This document is available to the public through the National Technical Information Service, Springfield, Virginia 22161. Office of Aviation Medicine Washington, D.C. 20591 A Human Error Analysis of Commercial Aviation Accidents Using the Human Factors Analysis and Classification System (HFACS) U.S. Department of Transpor tation Federal Aviation Administration DOT/FAA/AM-01/3 NOTICE This document is disseminated under the sponsorship of the U.S. Department of Transportation in the interest of information exchange. The United States Government assumes no liability for the contents thereof. Technical Report Documentation Page 1. Report No. 2. Government Accession No. 3. Recipient's Catalog No. DOT/FAA/AM-01/3 4. Title and Subtitle 5. Report Date A Human Error Analysis of Commercial Aviation Accidents February 2001 Using the Human Factors Analysis and Classification System (HFACS) 6. Performing Organization Code 7. Author(s) 8. Performing Organization Report No. Wiegmann, D.A.1, and Shappell, S.A.2 9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) 11. Contract or Grant No. 1University of Illinois at Urbana-Champaign, Institute of Aviation, Savoy, IL 61874 2FAA Civil Aeromedical Institute, P.O. Box 25082, Oklahoma City, OK 73125 99-G-006 12. Sponsoring Agency name and Address 13. Type of Report and Period Covered Office of Aviation Medicine Federal Aviation Administration 14. Sponsoring Agency Code 800 Independence Ave., S.W. Washington, DC 20591 15. Supplemental Notes Work was accomplished under task # AAM-A-00-HRR-520. 16. Abstract The Human Factors Analysis and Classification System (HFACS) is a general human error framework originally developed and tested within the U.S. military as a tool for investigating and analyzing the human causes of aviation accidents. Based upon Reason’s (1990) model of latent and active failures, HFACS addresses human error at all levels of the system, including the condition of aircrew and organizational factors. The purpose of the present study was to assess the utility of the HFACS framework as an error analysis and classification tool outside the military. Specifically, HFACS was applied to commercial aviation accident records maintained by the National Transportation Safety Board (NTSB). Using accidents that occurred between January 1990 and December 1996, it was demonstrated that HFACS reliably accommodated all human causal factors associated with the commercial accidents examined. In addition, the classification of data using HFACS highlighted several critical safety issues in need of intervention research. These results demonstrate that the HFACS framework can be a viable tool for use within the civil aviation arena. 17. Key Words 18. Distribution Statement Aviation, Human Error, Accident Investigation, Database Analysis, Commercial Aviation Document is available to the public through the National Technical Information Service, Springfield, Virginia 22161 19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22. Price Unclassified Unclassified 17 Form DOT F 1700.7 (8-72) Reproduction of completed page authorized i
ACKNOWLEDGMENTS The authors thank Frank Cristina and Anthony Pape for their assistance in gathering, organizing and analyzing the accident reports used in this study. iii
1 A HUMAN ERROR ANALYSIS OF COMMERCIAL AVIATION ACCIDENTS USING THE HUMAN FACTORS ANALYSIS AND CLASSIFICATION SYSTEM (HFACS) INTRODUCTION Humans, by their very nature, make mistakes; therefore, it should come as no surprise that human error has been implicated in a variety of occupational accidents, including 70% to 80% of those in civil and military aviation (O’Hare, Wiggins, Batt, & Morrison, 1994; Wiegmann and Shappell, 1999; Yacavone, 1993). In fact, while the number of aviation accidents attributable solely to mechanical failure has decreased markedly over the past 40 years, those attributable at least in part to human error have declined at a much slower rate (Shappell & Wiegmann, 1996). Given such findings, it would appear that interventions aimed at reducing the occurrence or consequences of human error have not been as effective as those directed at mechanical failures. Clearly, if accidents are to be reduced further, more emphasis must be placed on the genesis of human error as it relates to accident causation. The prevailing means of investigating human error in aviation accidents remains the analysis of accident and incident data. Unfortunately, most accident reporting systems are not designed around any theoretical framework of human error. Indeed, most accident reporting systems are designed and employed by engineers and front-line operators with only limited backgrounds in human factors. As a result, these systems have been useful for identifying engineering and mechanical failures but are relatively ineffective and narrow in scope where human error exists. Even when human factors are addressed, the terms and variables used are often ill-defined and archival databases are poorly organized. The end results are post-accident databases that typically are not conducive to a traditional human error analysis, making the identification of intervention strategies onerous (Wiegmann & Shappell, 1997). The Accident Investigation Process To further illustrate this point, let us examine the accident investigation and intervention process separately for the mechanical and human components of an accident. Consider first the occurrence of an aircraft system or mechanical failure that results in an accident or injury (Figure 1). A subsequent investigation takes place that includes the examination of objective and quantifiable information, such as that derived from the wreckage and flight data recorder, as well as that from the application of sophisticated analytical techniques like metallurgical tests and computer modeling. This kind of information is then used to determine the probable mechanical cause(s) of the accident and to identify safety recommendations. Upon completion of the investigation, this “objective” information is typically entered into a highlystructured and well-defined accident database. These data can then be periodically analyzed to determine system-wide safety issues and provide feedback to investigators, thereby improving investigative methods and techniques. In addition, the data are often used to guide organizations (e.g., the Federal Aviation Administration , National Aeronautics and Space Administration , Department of Defense , airplane manufacturers and airlines) in deciding which research or safety programs to sponsor. As a result, these needsbased, data-driven programs, in turn, have typically produced effective intervention strategies that either prevent mechanical failures from occurring altogether, or mitigate their consequences when they do happen. In either case, there has been a substantial reduction in the rate of accidents due to mechanical or systems failures. In stark contrast, Figure 2 illustrates the current human factors accident investigation and prevention process. This example begins with the occurrence of an aircrew error during flight operations that leads to an accident or incident. A human performance investigation then ensues to determine the nature and causes of such errors. However, unlike the tangible and quantifiable evidence surrounding mechanical failures, the evidence and causes of human error are generally qualitative and elusive. Furthermore, human factors investigative and analytical techniques are often less refined and sophisticated than those used to analyze mechanical and engineering concerns. As such, the determination of human factors causal to the accident is a tenuous practice at best; all of which makes the information entered in the accident database sparse and ill-defined. 2 As a result, when traditional data analyses are performed to determine common human factors problems across accidents, the interpretation of the findings and the subsequent identification of important safety issues are of limited practical use. To make matters worse, results from these analyses provide limited feedback to investigators and are of limited use to airlines and government agencies in determining the types of research or safety programs to sponsor. As such, many research programs tend to be intuitively-, or fad-driven, rather than data-driven, and typically produce intervention strategies that are only marginally effective at reducing the occurrence and consequence of human error. The overall rate of human-error related accidents, therefore, has remained relatively high and constant over the last several years (Shappell & Wiegmann, 1996). Addressing the Problem If the FAA and the aviation industry are to achieve their goal of significantly reducing the aviation accident rate over the next ten years, the primary causes of aviation accidents (i.e., human factors) must be addressed (ICAO, 1993). However, as illustrated in Figure 2, simply increasing the amount of money and resources spent on human factors research is not the solution. Indeed, a great deal of resources and efforts are currently being expended. Rather, the solution is to redirect safety efforts so that they address important human factors issues. However, this assumes that we know what the important human factors issues are. Therefore, before research efforts can be systematically refocused, a comprehensive analysis of existing databases needs to be conducted to determine those specific human factors responsible for aviation accidents and incidents. Furthermore, if these efforts are to be sustained, new investigative methods and techniques will need to be developed so that data gathered during human factors accident investigations can be improved and analysis of the underlying causes of human error facilitated. To accomplish this improvement, a general human error framework is needed around which new investigative methods can be designed and existing postaccident databases restructured. Previous attempts to do this have met with encouraging, yet limited, success (O’Hare, et al., 1994; Wiegmann & Shappell, 1997). This is primarily because performance failures are influenced by a Figure 1. General process of investigating and preventing aviation accidents involving mechanical or systems failures. Feedback Mechanical Failure - Catastrophic failures are infrequent events - When failures do occur, they are often less severe or hazardous due to effective intervention programs. Data-Driven Research Research Sponsors - FAA, DoD, NASA, & airplane manufacturers provide research funding. - Research programs are needsbased and data-driven. Interventions are therefore very effective. Accident Investigation - Highly sophisticated techniques and procedures - Information is objective and quantifiable - Effective at determining why the failure occurred Accident Database - Designed around traditional categories - Variables are welldefined and causally related - Organization and structure facilitate access and use Database Analysis - Traditional analyses are clearly outlined and readily performed. - Frequent analyses help identify common mechanical and engineering safety issues. Mitigation Prevention Effective Intervention and Prevention Programs 3 variety of human factors that are typically not addressed by traditional error frameworks. For instance, with few exceptions (e.g., Rasmussen, 1982), human error taxonomies do not consider the potential adverse mental and physiological condition of the individual (e.g., fatigue, illness, attitudes) when describing errors in the cockpit. Likewise, latent errors committed by officials within the management hierarchy such as line managers and supervisors are often not addressed, even though it is well known that these factors directly influence the condition and decisions of pilots (Reason, 1990). Therefore, if a comprehensive analysis of human error is to be conducted, a taxonomy that takes into account the multiple causes of human failure must be offered. Recently, the Human Factors Analysis and Classification System (HFACS) was developed to meet these needs (Shappell & Wiegmann, 1997a, 2000a, and in press). This system, which is based on Reason’s (1990) model of latent and active failures, was originally developed for the U.S. Navy and Marine Corps as an accident investigation and data analysis tool. Since its original development, however, HFACS has been employed by other military organizations (e.g., U.S. Army, Air Force, and Canadian Defense Force) as an adjunct to preexisting accident investigation and analysis systems. To date, the HFACS framework has been applied to more than 1,000 military aviation accidents, yielding objective, data-driven intervention strategies while enhancing both the quantity and quality of human factors information gathered during accident investigations (Shappell & Wiegmann, in press). Other organizations such as the FAA and NASA have explored the use of HFACS as a complement to preexisting systems within civil aviation in an attempt to capitalize on gains realized by the military (Ford, Jack, Crisp, & Sandusky, 1999). Still, few systematic efforts have examined whether HFACS is indeed a viable tool within the civil aviation arena, even though it can be argued that the similarities between military and civilian aviation outweigh their differences. The purpose of the present study was to empirically address this issue by applying the HFACS framework, as originally designed for the military, to the classification and analysis of civil aviation accident data. Before beginning, however, a brief overview of the HFACS system will be presented for those Feedback Human Error - Errors occur frequently and are the major cause of accidents. - Few safety programs are effective at preventing the occurrence or consequences of these errors. Research Sponsors - FAA, DoD, NASA, & Airlines provide funding for safety research programs. - Lack of good data leads to research programs based primarily on interests and intuitions. Interventions are therefore less effective. Fad-Driven Research Mitigation Prevention Ineffective Intervention and Prevention Programs Accident Investigation - Less sophisticated techniques and procedures - Information is qualitative and illusive - Focus on “what” happened but not “why” it happened Accident Database - Not designed around any particular human error framework - Variables often illdefined - Organization and structure difficult to understand Database Analysis - Traditional human factors analyses are onerous due to ill-defined variables and database structures. - Few analyses have been performed to identify underlying human factors safety issues. Figure 2. General process of investigating and preventing aviation accidents involving human error. 4 readers who may not be familiar with the framework (for a detailed description of HFACS, see Shappell and Wiegmann, 2000a and 2001). HFACS Drawing upon Reason’s (1990) concept of latent and active failures, HFACS describes human error at each of four levels of failure: 1) unsafe acts of operators (e.g., aircrew), 2) preconditions for unsafe acts, 3) unsafe supervision, and 4) organizational influences. A brief description of each causal category follows (Figure 3). Unsafe Acts of Operators The unsafe acts of operators (aircrew) can be loosely classified into one of two categories: errors and violations (Reason, 1990). While both are common within most settings, they differ markedly when the rules and regulation of an organization are considered. That is, errors can be described as those “legal” activities that fail to achieve their intended outcome, while violations are commonly defined as behavior that represents the willful disregard for the rules and regulations. It is within these two overarching categories that HFACS describes three types of errors (decision, skill-based, and perceptual) and two types of violations (routine and exceptional). Errors One of the more common error forms, decision errors, represents conscious, goal-intended behavior that proceeds as designed; yet, the plan proves inadequate or inappropriate for the situation. Often referred to as “honest mistakes,” these unsafe acts typically manifest as poorly executed procedures, improper choices, or simply the misinterpretation or misuse of relevant information. In contrast to decision errors, the second error form, skill-based errors, occurs with little or no conscious thought. Just as little thought goes into turning one’s steering wheel or shifting gears in an automobile, basic flight Figure 3. Overview of the Human Factors Analysis and Classification System (HFACS). Errors Perceptual Errors Skill-Based Errors UNSAFE ACTS Decision Errors Routine Exceptional Violations Inadequate Supervision Planned Inappropriate Operations Failed to Correct Problem Supervisory Violations UNSAFE SUPERVISION Substandard Conditions of Operators PRECONDITIONS FOR UNSAFE ACTS Adverse Physiological States Physical/ Mental Limitations Adverse Mental States Personal Readiness Crew Resource Mismanagement Substandard Practices of Operators Resource Management Organizational Climate Organizational Process ORGANIZATIONAL INFLUENCES 5 skills such as stick and rudder movements and visual scanning often occur without thinking. The difficulty with these highly practiced and seemingly automatic behaviors is that they are particularly susceptible to attention and/or memory failures. As a result, skill-based errors such as the breakdown in visual scan patterns, inadvertent activation/deactivation of switches, forgotten intentions, and omitted items in checklists often appear. Even the manner (or skill) with which one flies an aircraft (aggressive, tentative, or controlled) can affect safety. While, decision and skill-based errors have dominated most accident databases and therefore, have been included in most error frameworks, the third and final error form, perceptual errors, has received comparatively less attention. No less important, perceptual errors occur when sensory input is degraded, or “unusual,” as is often the case when flying at night, in the weather, or in other visually impoverished environments. Faced with acting on imperfect or less information, aircrew run the risk of misjudging distances, altitude, and decent rates, as well as a responding incorrectly to a variety of visual/vestibular illusions. Violations Although there are many ways to distinguish among types of violations, two distinct forms have been identified based on their etiology. The first, routine violations, tend to be habitual by nature and are often enabled by a system of supervision and management that tolerates such departures from the rules (Reason, 1990). Often referred to as “bending the rules,” the classic example is that of the individual who drives his/her automobile consistently 5-10 mph faster than allowed by law. While clearly against the law, the behavior is, in effect, sanctioned by local authorities (police) who often will not enforce the law until speeds in excess of 10 mph over the posted limit are observed. Exceptional violations, on the other hand, are isolated departures from authority, neither typical of the individual nor condoned by management. For example, while driving 65 in a 55 mph zone might be condoned by authorities, driving 105 mph in a 55 mph zone certainly would not. It is important to note, that while most exceptional violations are appalling, they are not considered “exceptional” because of their extreme nature. Rather, they are regarded as exceptional because they are neither typical of the individual nor condoned by authority. Preconditions for Unsafe Acts Simply focusing on unsafe acts, however, is like focusing on a patient’s symptoms without understanding the underlying disease state that caused it. As such, investigators must dig deeper into the preconditions for unsafe acts. Within HFACS, two major subdivisions are described: substandard conditions of operators and the substandard practices they commit. Substandard Conditions of the Operator Being prepared mentally is critical in nearly every endeavor; perhaps it is even more so in aviation. With this in mind, the first of three categories, adverse mental states, was created to account for those mental conditions that adversely affect performance. Principal among these are the loss of situational awareness, mental fatigue, circadian dysrhythmia, and pernicious attitudes such as overconfidence, complacency, and misplaced motivation that negatively impact decisions and contribute to unsafe acts. Equally important, however, are those adverse physiological states that preclude the safe conduct of flight. Particularly important to aviation are conditions such as spatial disorientation, visual illusions, hypoxia, illness, intoxication, and a whole host of pharmacological and medical abnormalities known to affect performance. For example, it is not surprising that, when aircrews become spatially disoriented and fail to rely on flight instrumentation, accidents can, and often do, occur. Physical and/or mental limitations of the operator, the third and final category of substandard condition, includes those instances when necessary sensory information is either unavailable, or if available, individuals simply do not have the aptitude, skill, or time to safely deal with it. For aviation, the former often includes not seeing other aircraft or obstacles due to the size and/or contrast of the object in the visual field. However, there are many times when a situation requires such rapid mental processing or reaction time that the time allotted to remedy the problem exceeds human limits (as is often the case during nap-of-the-earth flight). Nevertheless, even when favorable visual cues or an abundance of time is available, there are instances when an individual simply may not possess the necessary aptitude, physical ability, or proficiency to operate safely. 6 Substandard Practices of the Operator Often times, the substandard practices of aircrew will lead to the conditions and unsafe acts described above. For instance, the failure to ensure that all members of the crew are acting in a coordinated manner can lead to confusion (adverse mental state) and poor decisions in the cockpit. Crew resource mismanagement, as it is referred to here, includes the failures of both inter- and intra-cockpit communication, as well as communication with ATC and other ground personnel. This category also includes those instances when crewmembers do not work together as a team, or when individuals directly responsible for the conduct of operations fail to coordinate activities before, during, and after a flight. Equally important, however, individuals must ensure that they are adequately prepared for flight. Consequently, the category of personal readiness was created to account for those instances when rules such as disregarding crew rest requirements, violating alcohol restrictions, or self-medicating, are not adhered to. However, even behaviors that do not necessarily violate existing rules or regulations (e.g., running ten miles before piloting an aircraft or not observing good dietary practices) may reduce the operating capabilities of the individual and are, therefore, captured here. Unsafe Supervision Clearly, aircrews are responsible for their actions and, as such, must be held accountable. However, in many instances, they are the unwitting inheritors of latent failures attributable to those who supervise them (Reason, 1990). To account for these latent failures, the overarching category of unsafe supervision was created within which four categories (inadequate supervision, planned inappropriate operations, failed to correct known problems, and supervisory violations) are included. The first category, inadequate supervision, refers to failures within the supervisory chain of command, which was a direct result of some supervisory action or inaction. That is, at a minimum, supervisors must provide the opportunity for individuals to succeed. It is expected, therefore, that individuals will receive adequate training, professional guidance, oversight, and operational leadership, and that all will be managed appropriately. When this is not the case, aircrews are often isolated, as the risk associated with day-to-day operations invariably will increase. However, the risk associated with supervisory failures can come in many forms. Occasionally, for example, the operational tempo and/or schedule is planned such that individuals are put at unacceptable risk and, ultimately, performance is adversely affected. As such, the category of planned inappropriate operations was created to account for all aspects of improper or inappropriate crew scheduling and operational planning, which may focus on such issues as crew pairing, crew rest, and managing the risk associated with specific flights. The remaining two categories of unsafe supervision, the failure to correct known problems and supervisory violations, are similar, yet considered separately within HFACS. The failure to correct known problems refers to those instances when deficiencies among individuals, equipment, training, or other related safety areas are “known” to the supervisor, yet are allowed to continue uncorrected. For example, the failure to consistently correct or discipline inappropriate behavior certainly fosters an unsafe atmosphere but is not considered a violation if no specific rules or regulations were broken. Supervisory violations, on the other hand, are reserved for those instances when existing rules and regulations are willfully disregarded by supervisors when managing assets. For instance, permitting aircrew to operate an aircraft without current qualifications or license is a flagrant violation that invariably sets the stage for the tragic sequence of events that predictably follow. Organizational Influences Fallible decisions of upper-level management can directly affect supervisory practices, as well as the conditions and actions of operators. Unfortunately, these organizational influences often go unnoticed or unreported by even the best-intentioned accident investigators. Traditionally, these latent organizational failures generally revolve around three issues: 1) resource management, 2) organizational climate, and 3) operational processes. The first category, resource management, refers to the management, allocation, and maintenance of organizational resources, including human resource management (selection, training, staffing), monetary safety budgets, and equipment design (ergonomic specifications). In general, corporate decisions about how such resources should be managed center around two distinct objectives – the goal of safety and the goal of ontime, cost-effective operations. In times of prosperity, both objectives can be easily balanced and satisfied in full. However, there may also be times of fiscal austerity that demand some give and take between the two. Unfortunately, history tells us that safety is often the loser in such battles, as safety and training are often the first to be cut in organizations experiencing financial difficulties. 7 Organizational climate refers to a broad class of organizational variables that influence worker performance and is defined as the “situationally based consistencies in the organization’s treatment of individuals” (Jones, 1988). One telltale sign of an organization’s climate is its structure, as reflected in the chain-of-command, delegation of authority and responsibility, communication channels, and formal accountability for actions. Just like in the cockpit, communication and coordination are vital within an organization. However, an organization’s policies and culture are also good indicators of its climate. Consequently, when policies are ill-defined, adversarial, or conflicting, or when they are supplanted by unofficial rules and values, confusion abounds, and safety suffers within an organization. Finally, operational process refers to formal processes (operational tempo, time pressures, production quotas, incentive systems, schedules, etc.), procedures (performance standards, objectives, documentation, instructions about procedures, etc.), and oversight within the organization (organizational self-study, risk management, and the establishment and use of safety programs). Poor upper-level management and decisions concerning each of these organizational factors can also have a negative, albeit indirect, effect on operator performance and system safety. Summary The HFACS framework bridges the gap between theory and practice by providing safety professionals with a theoretically based tool for identifying and classifying the human causes of aviation accidents. Because the system focuses on both latent and active failures and their interrelationships, it facilitates the identification of the underlying causes of human error. To date, HFACS has been shown to be useful within the context of military aviation, as both a data analysis framework and an accident investigation tool. However, HFACS has yet to be applied systematically to the analysis and investigation of civil aviation accidents. The purpose of the present research project, therefore, was to assess the utility of the HFACS framework as an error analysis and classification tool within commercial aviation. The specific objectives of this study were three-fold. The first objective was to determine whether the HFACS framework, in its current form, would be comprehensive enough to accommodate all of the underlying human causal-factors associated with commercial aviation accidents, as contained in the accident databases maintained by the FAA and NTSB. In other words, could the framework capture all the relevant human error data or would a portion of the database be lost because it was unclassifiable? The second objective was to determine whether the process of reclassifying the human causal factors using HFACS was reliable. That is, would different users of the system agree on how causal factors should be coded using the framework? Finally, the third objective was to determine whether reclassifying the data using HFACS yield a benefit beyond what is already known about commercial aviation accident causation. Specifically, would HFACS highlight any heretofore unknown safety issues in need of further intervention research? METHOD Data A comprehensive review of all accidents involving Code of Federal Air Regulations (FAR) Parts 121 and 135 Scheduled Air Carriers between January 1990 and December 1996 was conducted using database records maintained by the NTSB and the FAA. Of particular interest to this study were those accidents attributable, at least in part, to the aircrew. Consequently, not included were accidents due solely to catastrophic failure, maintenance error, and unavoidable weather conditions such as turbulence and wind shear. Furthermore, only those accidents in which the investigation was completed, and the cause of the accident determined, were included in this analysis. One hundred nineteen accidents met these criteria, including 44 accidents involving FAR Part 121 operators and 75 accidents involving FAR Part 135 operators. HFACS Classification The 119 aircrew-related accidents yielded 319 causal factors for further analyses. Each of these NTSB causal factors was subsequently coded independently by both an aviation psychologist and a commercially-rated pilot using the HFACS framework. Only those causal factors identified by the NTSB were analyzed. That is, no new causal factors were created during the error-coding process. RESULTS HFACS Comprehensiveness All 319 (100%) of the human causal factors associated with aircrew-related accidents were accommodated using the HFACS framework. Instances of all but two HFACS categories (i.e., organizational climate and personal readiness) were observed as least once in the 8 accident database. Therefore, no new HFACS categories were needed to capture the existing causal factors, and no human factors data pertaining to the aircrew were left unclassified during the coding process. HFACS Reliability Disagreements among raters were noted during the coding process and ultimately resolved by discussion. Using the record of agreement and disagreement between the raters, the reliability of the HFACS system was assessed by calculating Cohen’s kappa — an index of agreement that has been corrected for chance. The obtained kappa value was .71, which generally reflects a “good” level of agreement according to criteria described by Fleiss (1981). HFACS Analyses Unsafe Acts Table 1 presents percentages of FAR Parts 121 and 135 aircrew-related accidents associated with each of the HFACS categories. An examination of the table reveals that at the unsafe acts level, skill-based errors were associated with the largest percentage of accidents. Approximately 60% of all aircrew-related accidents were associated with at least one skill-based error. This percentage was relatively similar for FAR Part 121 carriers (63.6%) and FAR Part 135 carriers (58.7%). Figure 4, panel A, illustrates that the proportion of accidents associated with skill-based errors has remained relatively unchanged over the seven-year period examined in the study. Notably, however, the lowest proportion of accidents associated with skill-based errors was observed in the last two years of the study (1995 and 1996). Among the remaining categories of unsafe acts, accidents associated with decision errors constituted the next highest proportion (i.e., roughly 29% of the accidents examined, Table 1). Again, this percentage was roughly equal across both FAR Part 121 (25.0%) and Part 135 (30.7%) accidents. With the exception of 1994, in which the percentage of aircrew-related accidents associated with decision errors reached a high of 60%, the proportion of accidents associated with decision errors remained relatively constant across the years of the study (Figure 4, panel B). Table 1. Percentage of Accidents Associated with each HFACS category. HFACS Category FAR Part 121 FAR Part 135 Total Organizational Influences Resource Management Organizational Climate Organizational Process 4.5 (2) 0.0 (0) 15.9 (7) 1.3 (1) 0.0 (0) 4.0 (3) 2.5 (3) 0.0 (0) 8.4 (10) Unsafe Supervision Inadequate Supervision Planned Inappropriate Operations Failed to Correct Known Problem Supervisory Violations 2.3 (1) 0.0 (0) 0.0 (0) 0.0 (0) 6.7 (5) 1.3 (1) 2.7 (2) 2.7 (2) 5.0 (6) 0.8 (1) 1.7 (2) 1.7 (2) Preconditions of Unsafe Acts Adverse Mental States Adverse Physiological Sates Physical/mental Limitations Crew-resource Mismanagement Personal Readiness Unsafe Acts Skill-based Errors Decision Errors Perceptual Errors Violations 13.6 (6) 4.5 (2) 2.3 (1) 40.9 (18) 0.0 (0) 63.6 (28) 25.0 (11) 20.5 (9) 25.0 (11) 13.3 (10) 0.0 (0) 16.0 (12) 22.7 (17) 0.0 (0) 58.7 (44) 30.7 (23) 10.7 (8) 28.0 (21) 13.4 (16) 1.7 (2) 10.9 (13) 29.4 (35) 0.0 (0) 60.5 (72) 28.6 (34) 14.3 (17) 26.9 (32) Note: Numbers in table are percentages of accidents that involved at least one instance of an HFACS category. Numbers in parentheses indicate accident frequencies. Because more than one causal factor is generally associated with each accident, the percentages in the table will not equal 100%. 9 Similar to accidents associated with decision errors, those attributable at least in part to violations of rules and regulations were associated with 26.9% of the accidents examined. Again, no appreciable difference was evident when comparing the relative percentages across FAR Parts 121 (25.0%) and 135 (28.0%). However, an examination of Figure 4, panel C, reveals that the relative proportion of accidents associated with violations increased appreciably from a low of 6% in 1990 to a high of 46% in 1996. Finally, the proportion of accidents associated with perceptual errors was relatively low. In fact, only 17 of the 119 accidents (14.3%) involved some form of perceptual error. While it appeared that the relative proportion of Part 121 accidents associated with perceptual errors was higher than Part 135 accidents, the low number of occurrences precluded any meaningful comparisons across either the type of operation or calendar year. Preconditions for Unsafe Acts Within the preconditions level, CRM failures were associated with the largest percentage of accidents. Approximately 29% of all aircrew-related accidents were associated with at least one CRM failure. A relatively larger percentage of FAR Part 121 aircrew-accidents involved CRM failures (40.9%) than did FAR Part 135 aircrew-related accidents (22.7%). However, the percentage of accidents associated with CRM failures remained relatively constant over the seven-year period for both FAR Part 121 and 135 carriers (Figure 4, panel d). The next largest percentage of accidents was associated with adverse mental states (13.4%), followed by physical/mental limitations (10.9%) and adverse physiological states (1.7%). There were no accidents associated with personal readiness issues. The percentage of accidents associated with physical/mental limitation was higher for FAR Part 135 carriers (16%) compared with FAR Part 121 carriers (2.3%), but accidents associated with adverse mental or adverse physiological states were relatively equal across carriers. Again, however, the low number of occurrences in each of these accident categories precluded any meaningful comparisons across calendar year. Supervisory and Organizational Factors Very few of the NTSB reports that implicated the aircrew as contributing to an accident also cited some form of supervisory or organizational failure (see Table 0 10 20 30 40 50 60 70 80 90 91 92 93 94 95 96 Percentage Year A. 0 10 20 30 40 50 60 70 80 90 91 92 93 94 95 96 Percentage Year D. 0 10 20 30 40 50 60 70 80 90 91 92 93 94 95 96 Percentage Year C. 0 10 20 30 40 50 60 70 80 90 91 92 93 94 95 96 Percentage Year B. Figure 4. Percentage of aircrew related accidents associated with skill-based errors (Panel A), decision errors (Panel B), violations (Panel C) and CRM failures (Panel D) across calendar years. Lines represent seven year averages. 10 1). Indeed, only 16% of all aircrew-related accidents involved some form of either supervisory or organizational involvement. Overall, however, a larger proportion of aircrew-related accidents involving FAR Part 135 carriers involved supervisory failures (9.3%) than did those accidents involving FAR Part 121 carriers (2.3%). In contrast, a larger proportion of aircrew-related accidents involving FAR Part 121 carriers involved organizational factors (20.5%) than did those accidents involving FAR Part 135 carriers (4.0%). DISCUSSION HFACS Comprehensiveness The HFACS framework was found to accommodate all 319 causal factors associated with the 119 accidents involving FAR Parts 121 and 135 scheduled carriers across the seven-year period examined. This finding suggests that the error categories within HFACS, originally developed for use in the military, are applicable within commercial aviation as well. Still, some of the error-factors within the HFACS framework were never observed in this commercial aviation accident database. For example, no instances of such factors as organizational climate or personal readiness were observed. In fact, very few instances of supervisory factors were evident at all in the data. One explanation for the scarcity of such factors could be that, contrary to Reason’s model of latent and active failures upon which HFACS is based, such supervisory and organizational factors simply do not play as large of a role in the etiology of commercial aviation accidents as once expected. Consequently, the HFACS framework may need to be pared down or simplified for use with commercial aviation. Another explanation, however, is that these factors do contribute to most accidents, yet they are rarely identified using existing accident investigation processes. Nevertheless, the results of this study indicate that the HFACS framework was able to capture all existing causal factors and no new error-categories or aircrew cause-factors were needed to analyze the commercial accident data. HFACS Reliability The HFACS system was found to produce an acceptable level of agreement among the investigators who participated in this study. Furthermore, even after this level of agreement between investigators was corrected for chance, the obtained reliability index was considered “good” by conventional standards. Still, this reliability index was somewhat lower than those observed in studies using military aviation accidents which, in some instances, have resulted in nearly complete agreement among investigators (Shappell & Wiegmann, 1997b). One possible explanation for this discrepancy is the difference in both the type and amount of information available to investigators across these studies. Unlike the present study, previous analysts using HFACS to analyze military accident data often had access to privileged and highly detailed information about the accidents, which presumably allowed for a better understanding of the underlying causal factors and, hence, produced higher levels of reliabilities. Another possibility is that the definitions and examples currently used to describe HFACS are too closely tied to military aviation and are therefore somewhat ambiguous to those within a commercial setting. Indeed, the reliability of the HFACS framework has been shown to improve within the commercial aviation domain when efforts are taken to provide examples and checklists that are more compatible with civil aviation accidents (Wiegmann, Shappell, Cristina & Pape, 2000). HFACS Analysis Given the large number of accident causal factors contained in the NTSB database, each accident appeared, at least on the surface, to be relatively unique. As such, commonalties or trends in specific error forms across accidents were not readily evident in the data. Still, the recoding of the data using HFACS did allow for similar error-forms and causal factors across accidents to be identified and the major human causes of accidents to be discovered. Specifically, the HFACS analysis revealed that the highest percentage of all aircrew-related accidents as associated with skill-based errors. Furthermore, this proportion was lowest during the last two years of this study, suggesting that accidents associated with skill-based errors may be on the decline. To some, the finding that skill-based errors were frequently observed among the commercial aviation accidents examined is not surprising given the dynamic nature and complexity of piloting commercial aircraft, particularly in the increasingly congested U.S. airspace. The question remains, however, as to the driving force behind the possible reduction in such errors. Explanations could include improved aircrew training practices or perhaps better selection procedures. Another possibility might be the recent transition within the regional commuter industry from turboprop to jet aircraft. Such aircraft are 11 generally more reliable and contain advanced automation to help off-load the attention and memory demands placed on pilots during flight. Unfortunately, the industry-wide intervention programs and other changes that were made during the 1990s were neither systematically applied nor targeted at preventing specific error types, such as skill-based errors. Consequently, it is impossible to determine whether all or only a few of these efforts are responsible for the apparent decline in skill-based errors. Nevertheless, given that an error analysis has now been conducted on the accident data, future invention programs can be strategically targeted at reducing skill-based errors. Furthermore, the effectiveness of such efforts can be objectively evaluated so that efforts can be either reinforced or revamped to improve safety. Additionally, intervention ideas can now also be shared across organizations that have performed similar HFACS analyses. One example is the U.S. Navy and Marine Corps, which have recently initiated a systematic intervention program for addressing their growing problem with accidents associated with skill-based errors in the fleet (Shappell & Wiegmann, 2000b). As a result, lessons learned in the military can now be communicated and shared with the commercial aviation industry, and vice versa. The observation that both CRM failures and decision errors are associated with a large percentage of aircrewrelated accidents is also not surprising, given that these findings parallel the results of similar HFACS and human error analyses of both military and civil aviation accidents (O’Hare et al., 1994; Wiegmann & Shappell, 1999). What is surprising, or at least somewhat disconcerting, is the observation that both the percentage and rate of aircrew-related accidents associated with both CRM and decision errors have remained relatively stable. Indeed, both the FAA and aviation industry have invested a great deal of resources into intervention strategies specifically targeted at improving CRM and aeronautical decision making (ADM), with apparently little overall effect. The modest impact that CRM and ADM programs have had on reducing accidents may be due to a variety of factors, including the general lack of systematic analyses of accidents associated with these problems. Consequently, most CRM and ADM training programs use single case studies to educate aircrew, rather then focus on the fundamental causes of these problems in the cockpit using a systematic analysis of the accident data. Another possible explanation for the general lack of CRM and ADM effectiveness is that many established training programs involve classroom exercises that are not followed up by simulator training that requires CRM and ADM principles to be applied. More recent programs, such as the Advanced Qualification Program (AQP), have been developed to take this next step of integrating ADM and CRM principles into the cockpit. Given that the current HFACS analyses has identified the accidents associated with these problems, at least across a seven-year period, more fine-grained analyses can be conducted to identify the specific problems areas in need of training. Furthermore, the effectiveness of the AQP program and other ADM training in reducing aircrew accidents associated with CRM failures and decision errors can be systematically tracked and evaluated. The percentage of aircrew-related accidents associated with violations (e.g., not following federal regulations or a company’s standard operating procedures) exhibited a slight increase across the years examined in this study. Some authors (e.g., Geller, 2000) have suggested that violations, such as taking short-cuts in procedures or breaking rules, are often induced by situational factors that reinforce unsafe acts while punishing safe actions. Not performing a thorough preflight inspection due to the pressure to achieve an on-time departure would be one example. However, according to Reason’s (1990) model of active and latent failures, such violation-inducing situations are often set up by supervisory and management policies and practices. Such theories suggest that the best strategy for reducing violations by aircrew is to enforce the rules and to hold both the aircrew and their supervisors/organizations accountable. Indeed, this strategy has been effective with the Navy and Marine Corps in reducing aviation mishaps associate with violations (Shappell, et al., 1999). Still, as mentioned earlier, very few of the commercial accident reports examined in this study cited supervisory or organizational factors as accident causes, suggesting that more often than not, aircrews were the only ones responsible for the violations. Again, more thorough accident investigations may need to be performed to identify possible supervisory and organizational issues associated with these events. Although pilots flying with FAR Part 135 scheduled carriers had fewer annual flight hours during the years covered in this study (NTSB, 2000), the overall number of accidents associated with most error types was generally higher for FAR Part 135 scheduled carriers, compared with FAR Part 121 scheduled carriers. This finding is likely due, at least in part, to the fact that most pilots 12 flying aircraft operating under FAR Part 135 are younger and much less experienced. Furthermore, such pilots often fly less sophisticated and reliable aircraft into areas that are less likely to be controlled by ATC. As a result, they may frequently find themselves in situations that exceed their training or abilities. Such a conclusion is supported by the findings presented here, since a larger percentage of FAR Part 135 aircrew-related accidents were associated with the physical/mental limitations of the pilot. However, a smaller percentage FAR Part 135 aircrew accidents were associated with CRM failures, possibly because some FAR Part 135 aircraft are singlepiloted, which simply reduces the opportunity for CRM failures. These differences between FAR Parts 121 and 135 schedule carriers may be less evident in future aviation accident data since the federal regulations were changed in 1997. Such changes require FAR Part 135 carriers operating aircraft that carry ten or more passengers to now operate under more stringent FAR Part 121 rules. Thus, the historical distinction in the database between FAR Part 135 and 121 operators has become somewhat blurred in the years extending beyond the current analysis. Therefore, future human-error analyses and comparisons across these different types of commercial operations will therefore need to consider these changes. SUMMARY AND CONCLUSIONS This investigation demonstrates that the HFACS framework, originally developed for and proven in the military, can be used to reliably identify the underlying human factors problems associated with commercial aviation accidents. Furthermore, the results of this study highlight critical areas of human factors in need of further safety research and provide the foundation upon which to build a larger civil aviation safety program. Ultimately, data analyses such as that presented here will provide valuable insight aimed at the reduction of aviation accidents through data-driven investment strategies and objective evaluation of intervention programs. The HFACS framework may also prove useful as a tool for guiding future accident investigations in the field and developing better accident databases, both of which would improve the overall quality and accessibility of human factors accident data. Still, the HFACS framework is not the only possible system upon which such programs might be developed. Indeed, there often appears to be as many human error frameworks as there are those interested in the topic (Senders & Moray, 1991). Indeed, as the need for better applied human error analysis methods has become more apparent, an increasing number of researchers have proposed other comprehensive frameworks similar to HFACS (e.g., O’Hare, in press). Nevertheless, HFACS is, to date, the only system that has been developed to meet a specific set of design criteria, including comprehensiveness, reliability, diagnosticity, and usability, all of which have contributed to the framework’s validity as an accident analysis tool (Shappell & Wiegmann, in press). Furthermore, HFACS has been shown to have utility as an erroranalysis tool in other aviation-related domains such as ATC (HFACS-ATC; Pounds, Scarborough, & Shappell, 2000) and aviation maintenance (HFACS-ME; Schmidt, Schmorrow, & Hardee, 1998), and is currently being evaluated within other complex systems such as medicine (currently referred to as HFACS-MD). Finally, it is important to remember that neither HFACS nor any other error-analysis tool can “fix” the problems once they have been identified. Such fixes can only be derived by those organizations, practitioners and human factors professionals who are dedicated to improving aviation safety. REFERENCES Bird, F. (1974). Management guide to loss control. Atlanta, GA: Institute Press. Fleiss, J. (1981). Statistical Methods for Rates and Proportions. New York: John Wiley. Ford, C., Jack, T., Crisp, V. & Sandusky, R. (1999). Aviation accident causal analysis. Advances in Aviation Safety Conference Proceedings, (P-343). Warrendale, PA: Society of Automotive Engineers Inc. Geller, E. (March, 2000). Behavioral safety analysis: A necessary precursor to corrective action. Professional Safety, 29-32. International Civil Aviation Organization (1993). Investigation of human factors in accidents and incidents (Human Factors Digest #7), Montreal: Canada. 13 Jones, A. (1988). Climate and measurement of consensus: A discussion of “organizational climate.” In S. Cole, R.Demaree & W. Curtis, (Eds.), Applications of Interactionist Psychology: Essays in Honor of Saul B. Sells (pp. 283-290). Hillsdale, NJ: Earlbaum. National Transportation Safety Board (2000). Aviation accident statistics. . Available: <A href="http://www.ntsb.gov/aviation/Stats.htm">www.ntsb.gov/aviation/Stats.htm</A> O’Hare, D. (in press). The Wheel of Misfortune. Ergonomics. O’Hare, D., Wiggins, M., Batt, R., and Morrison, D. (1994). Cognitive failure analysis for aircraft accident investigation. Ergonomics, 37, 1855-69. Pounds, J., Scarborough, A., & Shappell, S. (2000). A human factors analysis of Air Traffic Control operational errors (Abstract). Aviation, Space and Environmental Medicine, 71, pp. 329 Rasmussen, J. (1982). Human errors: A taxonomy for describing human malfunction in industrial installations. Journal of Occupational Accidents, 4, pp. 311-33. Reason, J. (1990). Human error. New York: Cambridge University Press. Schmidt, J., Schmorrow, D., & Hardee, M. (1998). A preliminary human factors analysis of Naval Aviation maintenance related mishaps. Proceedings of the 1998 Airframe/Engine Maintenance and Repair Conference (P329), Long Beach, CA. Senders, J., & Moray, N. (1991). Human error: Cause, prediction and reduction. Hillsdale, NJ: Earlbaum. Shappell, S., & Wiegmann, D. (1996). U. S. Naval Aviation mishaps 1977-92: Differences between single- and dual-piloted aircraft. Aviation, Space, and Environmental Medicine, 67, 65-9. Shappell, S. & Wiegmann D. (1997a). A human error approach to accident investigation: The taxonomy of unsafe operations. The International Journal of Aviation Psychology, 7, pp. 269-91. Shappell, S. & Wiegmann, D. (1997b). A reliability analysis of the Taxonomy of Unsafe Operations (Abstract). Aviation, Space, and Environmental Medicine, 69, pp. 620. Shappell, S. & Wiegmann, D. (2000a). The Human Factors Analysis and Classification System (HFACS). (Report Number DOT/FAA/AM-00/7). Washington DC: Federal Aviation Administration. Shappell, S. & Wiegmann, D. (2000b). Is proficiency eroding among U.S. Naval aircrews? A quantitative analysis using the Human Factors Analysis and Classification System (HFACS). Proceedings of the 44th meeting of the Human Factors and Ergonomics Society. Shappell, S. & Wiegmann, D. (2001). Applying Reason: The Human Factors Analysis and Classification System (HFACS). Human Factors and Aerospace Safety, 1, 59-86. Shappell, S., Wiegmann, D., Fraser, J., Gregory, G., Kinsey, P., & Squier, H (1999). Beyond mishap rates: A human factors analysis of U.S. Navy/ Marine Corps TACAIR and rotary wing mishaps using HFACS (Abstract). Aviation, Space, and Environmental Medicine, 70, pp. 416-7. Wiegmann, D. & Shappell, S. (1997). Human factors analysis of post-accident data: Applying theoretical taxonomies of human error. The International Journal of Aviation Psychology, 7, pp. 67-81. Wiegmann, D. & Shappell, S. (1999). Human error and crew resource management failures in Naval aviation mishaps: A review of U.S. Naval Safety Center data, 1990-96. Aviation, Space, and Environmental Medicine, 70, pp. 1147-51. Wiegmann, D., Shappell, S., Cristina, F. & Pape, A. (2000). A human factors analysis of aviation accident data: An empirical evaluation of the HFACS framework (Abstract). Aviation, Space and Environmental Medicine, 71, pp. 328. Yacavone, D. W. (1993). Mishap trends and cause factors in Naval aviation: A review of Naval Safety Center data, 1986-90. Aviation, Space and Environmental Medicine, 64, 392-5.

kevingi 发表于 2010-5-22 16:28:56

没有中文版的啊？英文的看起来太费劲了

Virgin 发表于 2010-5-28 23:39:47

看看，有用

mrmmx 发表于 2010-10-19 21:37:06

的点对点的点对点的点对点的点对点的的的

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航空论坛_航空翻译_民航英语翻译_飞行翻译's Archiver

HFACS对商用航空事故分析DOT版