A Decision System for Aircraft Faults Diagnosis Based on Classification Trees and PCA
Aircrafts are complex systems that require permanent and precise monitoring and troubleshooting. The automation of these tasks is thus of a high importance. This paper presents an intelligent decision system for faults diagnosis of aircrafts. The system relies on decision trees, being easier to interpret, quicker to learn than other data-driven methods, and able to work even with missing pieces of information. The used C4.5 algorithm automatically “learns” the best decision tree by performing a search through the set of possible trees according to the available training data. And Principal Component Analysis (PCA) is used to decrease the input data’s dimension. Compared to other methods, the proposed one is more advantageous and some presented evaluations demonstrate its abilities. High correct faults detection rates and low missed detection and false alarm rates are obtained. Such a decision system is highly useful for engineering consulting services, accumulating the knowledge for the operational rules of diagnosis, and the design of new aircrafts.
KeywordsFaults Diagnosis Aircrafts Decision System Decision Tree PCA
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- 1.Chen, W., Liu, X., He, C., Liu, Y.: Knowledge Base Design for Fault Diagnosis Expert System Based on Production Rule. In: 2009 Asia-Pacific Conference on Information Processing (2009) 978-0-7695-3699-6/09 IEEEGoogle Scholar
- 2.Chen, W., Liu, X., Fang, Y., Zhang, J.: Inference Engine Design of Expert System Based on Blackboard Model and Fault Tree. In: 2009 Asia-Pacific Conference on Information Processing (2009) 978-0-7695-3699-6/09 IEEEGoogle Scholar
- 3.Wei, L., Hua, W., Pu, H.: Neural Network Modeling of Aircraft Power Plant and Fault Diagnosis Method Using Time Frequency Analysis. In: 2009 Control and Decision Conference (2009) 978-1-4244-2723-9/09 IEEE Google Scholar
- 4.Li, B., Zhang, W.-G., Ning, D.-F., Yin, W.: Fault Prediction System Based on Neural Network Model. In: Innovative Computing, Information and Control Conference (2007) 0-7695-2882-1/07 IEEE Google Scholar
- 5.Schwabacher, M., Aguilar, R., Figueroa, F.: Using Decision Trees to Detect and Isolate Simulated Leaks in the J-2X Rocket Engine. IEEEAC 1408, Version 1 (2008)Google Scholar
- 6.Jamehbozorg, A., Mohammad Shahrtash, S.: A Decision-Tree-Based Method for Fault Classification in Single-Circuit. IEEE Transactions on Power Delivery 25(4) (October 2010)Google Scholar
- 7.Mitchell, T.M.: Machine Learning. McGraw-Hill Science/Engineering/Math. (1997)Google Scholar
- 8.Quinlan, Ross, J.: C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc. (1993)Google Scholar
- 9.Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Springer Series in Statistics. Springer, NY (2002)Google Scholar