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CU-Boulder ASEN 5519 - U.S. Military Unmanned Aerial Vehicle Mishaps

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U.S. Military Unmanned Aerial Vehicle Mishaps: Assessment of the Role of Human Factors Using HFACS Anthony P. Tvaryanas, MD, MPH, MTM Bill T. Thompson, MA Stefan H. Constable, PhD, MS 311th Performance Enhancement Directorate United States Air Force Corresponding Author: Anthony P. Tvaryanas, MD, MPH, MTM Phone: (210) 536-4446 Fax: (210) 536-3683 E-mail: [email protected] http://www.brooks.af.mil1 The rapid rise in unmanned aerial vehicle (UAV) employment has been accompanied by increased attention to their high mishap rates which are several orders of magnitude greater than manned aviation.17,18,19 Such high rates have negative implications for UAV affordability and mission availability.17,19 The Office of the Secretary of Defense’s UAV Reliability Study,17 the most comprehensive review of UAV mishaps to date, reported the proportion of human error-induced mishaps to be 17% but provided no further breakdown of human factors. Given the limited scope of the UAV Reliability Study human factors analysis, the literature was reviewed for other studies addressing the role of human factors in UAV mishaps. Five studies12,14,21,23,25 were identified which reported prevalences of human factors mishaps 2-3 times that reported in the UAV Reliability Study. While it was hoped a pooled analysis of these studies could provide an aggregate Department of Defense (DoD)-wide look at human factors in UAV mishaps, this was not possible because of the variety of human factors taxonomies employed. Thus, the purpose of this study is to provide a quantitative analysis of the role and patterns of active and latent human failures in UAV mishaps within the U.S. military services using a standardized human factors taxonomy. METHODS This study protocol was approved by the Brooks City-Base Institutional Review Board in accordance with 32 CFR 219 and AFI 40-402. The study design is a 10-year cross sectional quantitative analysis of UAV mishaps using the DoD Human Factors Analysis and Classification System (DoD HFACS)1 version 5.7 taxonomy with associated nanocodes. DoD HFACS is based on Weigmann and Shappell’s HFACS and the reader is referred to their work for a more detailed description of the taxonomy system.26,30 The inclusion criteria for this study were a U.S. Air Force, Army, or Navy/Marine UAV Class A, B, or C severity mishap occurring during fiscal2 years 1994-2003. Department of Defense Instruction 6055.7 definitions7 were utilized. Site visits were conducted to the respective safety centers for the U.S. Air Force, Army, and Navy/Marines to access all available records and databases pertaining to UAV mishaps. In total, 271 mishaps were identified. However, per OPNAVINST 3750.6R,8 the Navy specifically excludes “unmanned target drone aircraft” from the definition of UAVs in their aviation safety program. To reduce the heterogeneity of the data between the services, all mishap reports pertaining to unmanned target drones were censored from the study. This left 221 UAV mishaps which were submitted to further analyses. Two raters analyzed each mishap independently and classified all human causal factors using the DoD HFACS. After the raters made their initial classification of the human causal factors, the 2 independent ratings were compared. Where disagreement existed, the raters reconciled their differences and the consensus classification was included in the study database. No new casual factors were identified or mishaps reinvestigated. However, in cases where an inference could reasonably be made as to embedded human causal factors based on the mishap narrative, findings, or recommendations, codes were assigned accordingly. Statistical analyses were accomplished using Statistica’s (StatSoft, Tulsa, OK) log-linear analysis and Statistical Package for the Social Sciences’ (SPSS Inc, Chicago, IL) chi-square (χ2), Cramer’s V, Fisher’s Exact Test (FET), bivariate correlation, and binary logistic regression.22 RESULTS Of the 221 UAV Class A-C mishaps occurring during the period of fiscal years 1994-2003, 38 (17.2%) involved the RQ-1 Predator, 127 (57.5%) the RQ-2 Pioneer, 4 (1.8%) the RQ-4 Global Hawk, 25 (11.3%) the RQ-5 Hunter, 20 (9.0%) the RQ-7 Shadow, and 7 (3.2%) miscellaneous or unspecified UAVs. Excluding 18 mishaps solely caused by maintenance error3 Figure 1. To p level HFACS human caus al factors by military s erv ice as percentage of total mishaps01020304050607080OrganizationalInfluencesUnsafeSupervisionUnsafePreconditionsActsPercent (%)Air ForceArmy Navy/Marineswhich were not analyzed further, 133 (60.2%) mishaps involved human causal factors. The frequency distribution of human causal factors mishaps within the services differed significantly (χ22df = 15.974, P < 0.001) with 79.1% in the Air Force, 39.2% in the Army, and 62.2% in the Navy/Marines. Mechanical failure was present in 150 (67.9%) mishaps, although it was the sole causal factor in only 70 (31.7%) mishaps. In contrast, human causal factors were solely involved in 53 (24.0%) mishaps and 80 (36.2%) mishaps were attributed to the combination of mechanical and human causal factors (FET, P = 0.003). No cause was identified in 18 (8.1%) mishaps. The UAV mishap database was partitioned to distinguish between the services and human causal factors distributions in HFACS, the top-level results of which are summarized in figure 1. Since HFACS is a hierarchical model based on the premise latent failures at the levels of organizational influences, unsafe supervision, and unsafe preconditions predispose to active failures (e.g., acts), the dependent variable in this analysis was acts. Latent failures at the levels of organizational influences, unsafe supervision, and unsafe preconditions were the independent variables. Human causal factors mishaps were explored to verify the presence of independent variables was associated with the occurrence of an act. This was indeed the case for the independent variables unsafe supervision and unsafe preconditions. However, 47 (44.8%) human causal factors mishaps involving organizational influences did not have an associated act.4 The relationship of organizational influences and acts was further evaluated to explain the apparent deviation from the underlying assumptions of the HFACS model of error. Organizational influences is composed of 3 root categories, resource/acquisition management, organizational culture, and


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CU-Boulder ASEN 5519 - U.S. Military Unmanned Aerial Vehicle Mishaps

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