Risk assessment on the EA-6B aircraft utilizing Bayesian networks

ABSTRACT

Military weapon systems often remain in service longer than anticipated. Systems must continue to operate safety and effectively while maintaining mission readiness. Degraders to readiness, such as high failure items, excessive repair times, long logistics delays, and manpower shortfalls, must be anticipated in order to proactively reduce risk. We applied a Bayesian network to a field data set obtained from the U.S. military. Our approach yielded a predictive method with substantial benefits over reactive methods, and was able to predict failure of several important components, to include potential malfunction codes.

KEYWORDS:

• Bayesian analysis
• downtime
• military readiness
• proactive
• risk

About the authors

Marc Banghart is a reliability engineer focused on risk analysis in the automotive and aerospace sectors. He is a Certified Reliability Engineer with extensive experience in modelling and simulation and risk analysis. Marc holds a M.S. in Industrial and Systems Engineering from Mississippi State University and is currently pursuing his Ph.D.

Linkan Bian is an assistant professor in Industrial and Systems Engineering Department at MSU. He received his Ph.D. in Industrial and Systems Engineering from Georgia Institute of Technology in 2013. Dr. Bian research interests focus on the combination of advanced statistics and stochastic methods for system modelling, diagnosis, and prognosis. He is currently participating in a DoD project focusing on uncertainty quantification and process optimization in Additive Manufacturing processes.

Lesley Strawderman received her Ph.D. in Industrial Engineering from Pennsylvania State University in 2005. She joined the Industrial and Systems Engineering faculty of Mississippi State University in August 2006. Dr. Strawderman's research interests centers around human factors and ergonomics. She is particularly interested in the use of usability metrics in service quality improvement. Her research has also included topics such as health care process improvement, consumer product design, cognitive processing, safety, engineering education, and transportation systems.

Kari Babski-Reeves received her Ph.D., M.S., and B.S. degrees in Industrial Engineering from Mississippi State University in 2000, 1998, and 1995, respectively. She joined the Industrial and Systems Engineering Faculty in June 2006. Dr. Babski-Reeves' research efforts are focused in the areas of human factors, ergonomics, and safety. Specifically, her research interests include industrial ergonomics, work related musculoskeletal disorder prevention and control, occupational biomechanics, psychosocial risk factors, work physiology, occupational safety and health, total body fatigue, localized muscle fatigue, and thermography applications in ergonomics.