Date of Award

Winter 2014

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Modeling Simul & Visual Engineering

Committee Director

John A. Sokolowski

Committee Member

Ivan K. Ash

Committee Member

Anthony P. Ciavarelli

Committee Member

C. Ariel Pinto

Committee Member

Robert R. Safford

Abstract

Associated risks in flying have resulted in injury or death to aircrew and passengers, and damage or destruction of the aircraft and its surroundings. Although the Naval Aviation's flight mishap rate declined over the past 60 years, the proportion of human error causal factors has stayed relatively constant at about 80%. Efforts to reduce human errors have focused attention on understanding the aircrew and maintenance actions occurring in complex systems.

One such tool has been the Naval Aviation squadrons' regular participation in survey questionnaires deigned to measure respondent ratings related to personal judgments or perceptions of organizational climate for meeting the extent to which a particular squadron achieved the High Reliability Organization (HRO) criteria of achieving safe and reliable operations and maintenance practices while working in hazardous environments. Specifically, the Maintenance Climate Assessment Survey (MCAS) is completed by squadron maintainers to enable leadership to assess their unit's aggregated responses against those from other squadrons.

Bayesian Network Modeling and Simulation provides a potential methodology to represent the relationships of MCAS results and mishap occurrences that can be used to derive and calculate probabilities of incurring a future mishap. Model development and simulation analysis was conducted to research a causal relationship through quantitative analysis of conditional probabilities based upon observed evidence of previously occurred mishaps. This application would enable Navy and Marine Corps aviation squadron leadership to identify organizational safety risks, apply focused proactive measures to mitigate related hazards characterized by the MCAS results, and reduce organizational susceptibility to future aircraft mishaps.

DOI

10.25777/99tt-9m41

ISBN

9781321561876

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