Online fault diagnosis is critical for detecting the onset and hence the mitigation of adverse events that arise in complex systems, such as aircraft and industrial processes. A typical fault diagnosis system consists of: (1) a reference model that provides a mathematical representation for various diagnostic monitors that provide partial evidence towards active failure modes, and (2) a reasoning algorithm that combines set-covering and probabilistic computation to establish fault candidates and their rankings. However, for complex systems reference models are typically incomplete, and simplify- ing assumptions are made to make the reasoning algorithms tractable. Incompleteness in the reference models can take several forms, such as absence of discriminating evidence, and errors and incompleteness in the mapping between evidence and failure modes. Inaccuracies in the reasoning algorithm arise from the use of simplified noise models and independence assumptions about the evidence and the faults. Recently, data mining approaches have been proposed to help mitigate some of the problems with the reference models and reasoning schemes. This paper describes a Tree Augmented Na ̈ıve Bayesian Classifier (TAN) that forms the basis for systematically extending aircraft diagnosis reference models using flight data from systems operating with and without faults. The performance of the TAN models is investigated by comparing them against an expert supplied reference model. The results demonstrate that the generated TAN structures can be used by human experts to identify improvements to the reference model, by adding (1) new causal links that relate evidence to faults, and different pieces of evidence, and (2) updated thresholds and new monitors that facilitate the derivation of more precise evidence from the sensor data. A case study shows that this improves overall reasoner performance.
How to Cite
data driven methods, online faulr diagnosis, data mining methods, aircraft systems
Chickering, D. M., Heckerman, D., & Meek, C. (1997). A Bayesian approach to learning Bayesian networks with local structure. In In Proceedings of Thirteenth Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann.
Cohen, I., Goldszmidt, M., Kelly, T., Symons, J., & Chase, J. S. (2004). Correlating instrumentation data to system states: a building block for automated diagnosis and control. In Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6 (pp. 16–16). Berkeley, CA, USA: USENIX Association.
Dearden, R., & Clancy, D. (2001). Particle filters for real-time fault detection in planetary rovers. In Proc. of the 12th International Workshop on Principles of Diagnosis (p. 1-6).
Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian Network Classifiers. Machine Learning, 29, 131–163.
Grossman, D., & Domingos, P. (2004). Learning Bayesian network classifiers by maximizing conditional likelihood. In Proceedings of the twenty-first international conference on Machine learning (pp. 46–). New York, NY, USA: ACM.
Hall, M., Eibe, F., Holmes, G., Pfahringer, B., Reute- mann, P., & Witten, I. H. (2009). The WEKA Data Mining Software: An Update. SIGKDD Ex- plorations, 11(1), pp. 10-18.
Kruskal, J., Joseph B. (1956). On the Shortest Spanning Subtree of a Graph and the Traveling Salesman Problem. Proceedings of the American Mathematical Society, 7(1), pp. 48-50.
Lerner, U., Parr, R., Koller, D., & Biswas, G. (2000). Bayesian Fault Detection and Diagnosis in Dynamic Systems. In Proc. of the 17th Nat. Conf. on Artificial Intelligence (p. 531-537). San Mateo, CA, USA: AAAI Press.
Murphy, K. (2011). Bayesian Net Tool- Box @ONLINE. Available from http://code.google.com/p/bnt/
Pearl, J. (1988). Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann Publishers Inc.
Roychoudhury, I., Biswas, G., & Koutsoukos, X. (2008). Comprehensive diagnosis of continuous systems using dynamic Bayes nets. In Proc. of the 19th International Workshop on Principles of Diagnosis (p. 151-158).
Schwarz, G. (1978). Estimating the Dimension of a Model,. Annals of Statistics, 6.
Smyth, P. (1994). Hidden Markov models for fault detection in dynamic systems. Pattern Recognition, 27(1), pp. 149-164.
Spitzer, C. (2007). Honeywell Primus Epic Aircraft Diagnostic and Maintenance System. Digital Avionics Handbook(2), pp. 22-23.
Verma, V., Gordon, G., Simmons, R., & Thrun, S. (2004). Real-time fault diagnosis. IEEE Robotics and Automation Magazine, 11(2), pp. 56-66.
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