Generating and exploiting bayesian networks for fault diagnosis in airplane engines

Abstract

Bayesian Networks has been proven to be successful tool for fault diagnosis. There are a variety of approaches for learning the structure of Bayesian Networks from data. This learning problem has been proven to be NP-hard hence none of the approaches are exact when no prior knowledge about the domain of the variables exists. Our approach is based on searching the best network by using particle swarm optimization (PSO) technique. PSO is inherently parallel, works for large domains and does not trap into local maxima. This paper is an application of this technique to a real world problem; fault diagnosis of an airplane engine for oil related variables. It is implemented by our improved software written in C/C++ by using MPI on Linux. Our implementation has the advantages of being general, robust and scalable. Moreover neither expert knowledge, nor node ordering is necessary prior to the optimization. The datasets are generated by preprocessing oil related sensor readings of airplane engines taken during the approach phase of flights. Using this datasets and our software, we constructed Bayesian Networks of the oil related variables in an airplane engine for diagnostics and predictive purposes.