<p> Human error remains the highest symptomatic cause of system failure across multiple domains. Technology failure has seen a significant rate decrease through analyses and design changes, but human error causal factors have not experienced the same level of improvement. Designers and developers need improved methods to model the events leading up to and resulting in human error events. </p><p> This study examines the reported causal data of an integrated human complex system, multi-engine aircraft data, and explores methods of evaluating those data to identify the factors contributing to system failure including the human actor, technology and latent factors. The analysis methods demonstrated in this paper provide a means of assessing symptomatic failure data to provide quantitative information about the failure modes and related latent causal factors. National Transportation and Safety Board (NTSB) aviation accident data is coded using the Human Factors Analysis and Classification System (HFACS) to provide a framework for evaluating the mediating and moderating effects affecting the symptomatic and latent causal factors. We comparatively analyze these accident data for general aviation and air transport pilots to evaluate potential causal factor differences. The methodology explored leverages previous work in causal relationships to model the relationships between latent causal factors and symptomatic causal factors. </p><p> Two methods are employed to evaluate the data. Hierarchical multiple regression is used as derived from the Baron and Kenny method for statistical evaluation of moderator and mediator effects on human behavior. Multiple variable logistic regression is used to model the relationships between latent causal factors, symptomatic causal factors and accident severity. The usefulness of the framework and the possibilities for future research are discussed. </p><p> The findings first confirm the inverse relationship between pilot experience and accident rate to establish a baseline from which the quantifiable mediating and moderating effects of direct causal factors and latencies are demonstrated. Further analysis elucidates the association between the symptomatic human error initiating events and the latent failure causal factors. These data analyses are used to demonstrate the systemic relationships of the spatially and temporally disparate symptomatic and latent causal factors. </p><p> The relationship between symptomatic causal factor and latent causal factors are modeled using a causal loop and probability path diagrams. These methods identify the causal factors with the most impact to help guide users, managers and policy-makers as mitigation strategies are developed. The method is transportable to other systems based on the universality of the methods described. This research helps to enhance the ability to develop an objective systemic perspective to enable holistic solution sets. </p><p> The benefit to the systems engineering domain is the demonstration of methods that quantify and objectively link the symptomatic causal human error events initiating failures to the latent causal factors where mitigation methods can be applied. Using these methods, systems can be analyzed to better understand the disparate causal effects affecting system failure in complex socio-technical systems and actions that can be taken to mitigate the latent causal factors behind symptomatic events.</p><p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:10843526 |
| Date | 22 August 2018 |
| Creators | Erjavac, Anthony J. |
| Publisher | The George Washington University |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
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