Learning Bayesian Network Structures to Augment Aircraft Diagnostic Reference Models
Abstract: Fault detection and isolation schemes are designed to detect the onset of adverse events during operations of complex systems, such as aircraft and industrial processes. The state-of-the the-art art fault diagnosis systems on aircraft combine an expert-created reference erence model of the associa ons between faults and symptoms, and a NaĂŻve Bayes reasoner reasoner.. For complex systems with many dependencies between components, the expert expert-generated generated reference models are often incomplete, which hinders timely and accurate fault di diagnosis. agnosis. Mining aircraft flight data is a promising approach to finding these missing relations between symptoms and data. However, mining algorithms generate a multitude of relations, and only a small subset of these relations may be useful for improving diagnoser performance. In this paper, we adopt a knowledge engineering approach that combines data mining methods with human expert input to update an existing reference model and improve the overall diagnostic performance. We discuss three case studies to demonstrate the effectiveness of this method.