A Bayesian paradigm for aircraft operational capability assessment and improved fault diagnostics



Borja Sanz López Antonino Marco Siddiolo Partha Pratim Adhikari Matthias Buderath


In recent years, Bayesian networks have been drawing attention of the industrial and research community especially in the field of diagnostics for the reasoning capabilities they offer under conditions of uncertainty.
Given the system of interest, a Bayesian network represents a graphical model of the system itself, in which the different players are linked to each other through probabilistic and causal relations. If the model is queried with appropriate statistical techniques, the whole approach can present several advantages over other data analysis methods. Among the others: 1) the approach can provide outputs even if some entries to the model are missing, due to the above mentioned dependencies between the players of the system; 2) the approach represents an ideal environment to include prior knowledge during the building up of the model, given the causal and probabilistic semantics; 3) a Bayesian network provides the possibility to learn causal relationships and gives therefore the possibility to improve the domain knowledge.
Airbus Defence and Space has been working on improving the aircraft diagnostics capabilities at component, sub-system and system level in terms of fault detection and isolation. The focus has been also to develop means for reasoning about the remaining operational and functional capabilities of the aircraft.
The initial outcomes have been tested on a simulation platform featuring a Data Acquisition Processing Unit, various computing nodes, on which the different aircraft systems (like the fuel system, the hydraulic system, the actuation systems, etc…) run. The data communication architecture of the platform is based on OSA-CBM (Open System Architecture for Condition-Based Maintenance).
Initial objectives of the project are: 1) to demonstrate the feasibility of integration of the concept within the above described simulation framework; 2) to develop means to allow an easy and structured translation of the system engineer knowledge in terms of a Bayesian network with associated conditional probabilities; 3) to provide a modular architecture for the concept facilitating effective coordination between the development-departments and efficient development and maintenance of the software and 4) to prove the scalability of the concept (i.e. applicability to systems of different sizes and reasoning on different levels from component to system level).
The candidate systems selected for the proof of concept are the fuel and the hydraulic systems of a generic aircraft. The results obtained so far look promising with respect to the above mentioned objectives of the project.

How to Cite

López, B. S., Siddiolo, A. M., Adhikari, P. P., & Buderath, M. (2016). A Bayesian paradigm for aircraft operational capability assessment and improved fault diagnostics. PHM Society European Conference, 3(1). https://doi.org/10.36001/phme.2016.v3i1.1663
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fault isolation, High Level Reasoning, Bayesian Netowrk

Barua, A. & Khorasani, K. (2009). Hierarchical Fault Diagnosis in Satellites Formation Flight. Annual Conference of the Prognostics and Health Management Society.
Canaday, H. (2016). Predicting Failures – Using Big Data to solve the most expensive problems. Aviation Week & Space Technology, January 4-17, 2016, p137.
Cerquides, J. & López de Màntaras, R. (2003). Tractable Bayesian Learning of Tree Augmented Naïve Bayes Classifiers. TR-2003-04 CSIC.
Chavira, M. & Darwiche, A. (2005). Compiling Bayesian Networks with Local Structure. Mark Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI-2005).
Chavira, M. & Darwiche, A. (2007). Compiling Bayesian Networks Using Variable Elimination. Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI-2007).
Darwiche, A. Y. (1993). Argument Calculus and Networks. Uncertainty in Artificial Intelligence: Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence, The Catholic University of America, Washington, D.C., p420-427.
Das, B. (2008). Generating Conditional Probabilities for Bayesian Networks: Easing the Knowledge Acquisition Problem. Cornell University.
Davis, R. & Hamscher, W.C. (1988). Model-Based Reasoning: Troubleshooting. A.I. Memo No. 1059, Massachusetts Institute of Technology.
Davis, R. & King, J.J. (1984). The Origin of Rule-Based Systems in AI, reprinted as Ch. 2 of Rule-Based Expert Systems: Edited by Bruce G. Buchanan and Edward H. Shortliffe, Addison Wesley Publishing Company.
François, O.C.H. & Leray, P. (2006). Learning the Tree Augmented Naive Bayes Classifier from incomplete datasets. Third European Workshop on Probabilistic Graphical Models, Prague, Czech Republic.
Guo, H. & Hsu, W. (2002). A Survey of Algorithms for Real-Time Bayesian Network Inference. AAAI Technical Report WS-02-15.
Jong, C.G. & Leu, S.S. (2013). Bayesian-Network-Based Hydro-Power Fault Diagnosis System Development by Fault Tree Transformation. Journal of Marine Science and Technology, Vol. 21, No. 4, pp. 367-379 367. doi: 10.6119/JMST-012-0508-3.
King, D.E. (2009). Dlib-ml: A Machine Learning Toolkit. Journal of Machine Learning Research 10, pp. 1755-1758. Dlib C++ library v18.16. http://dlib.net/
Kokkonen, T., Koivusalo, H., Laine, H., Jolma, A. & Varis, O. (2005). A method for defining conditional probability tables with link strength parameters for a Bayesian network. MODSIM 2005 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, pp. 428-434. ISBN: 0-9758400-2-9.
Kolodner, J. (2014). Case-Based Reasoning. Morgan Kaufmann.
Löhr A. & Buderath, M. (2014). Evolving the Data Management Backbone: Binary OSA-CBM and Code Generation for OSA-EAI, European PHM Conference Nantes, France
Löhr A., Haines, C. & Buderath, M. (2012). Data Management Backbone for Embedded and PC-based Systems Using OSA-CBM and OSA-EAI, European PHM Conference Dresden, Germany
Mack, D.L.C., Biswas, G., Koutsoukos, X.D. & Mylaraswamy, D. (2011). Using Tree Augmented Naive Bayes Classifiers to Improve Engine Fault Models. Uncertainty in Artificial Intelligence: Bayesian Modeling Applications Workshop.
Mack, D.L.C., Biswas, G., Koutsoukos, X.D., Mylaraswamy, D. & Hadden, G. (2011). Deriving Bayesian Classifiers from Flight Data to Enhance Aircraft Diagnosis Models. Annual Conference of the Prognostics and Health Management Society.
Mengshoel, O.J., Chavira, M., Cascio, K., Poll, S., Darwiche, A. & Uckun, S. (2008). Efficient Probabilistic Diagnostics for Electrical Power Systems, NASA/TM-2008-214589 NASA Ames Research Center.
Namasivayam, V.K., Pathak, A. & Prasanna, V.K. (2006). Parallelizing Exact Inference in Bayesian Networks. 10th Annual Workshop, HPEC 2006 Proceedings
Ricks, B.W. & Mengshoel, O.J. (2009). Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study. Annual Conference of the Prognostics and Health Management Society.
Russell, S.J. & Norvig. P. (1995). Artificial Intelligence – A Modern Approach. Prentice-Hall Inc.
Schwabacher, M.A. (2005). A Survey of Data-Driven Prognostics. AIAA 2005-7002.
UCLA Automated Reasoning Group. 2002. Samiam: Sensitivity analysis, modeling, inference and more. SamIam 3.0, http://reasoning.cs.ucla.edu/samiam
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