Feature Selection and Categorization to Design Reliable Fault Detection Systems
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
In this work, we will develop a fault detection system which is identified as a classification task. The classes are the nominal or malfunctioning state. To develop a decision system it is important to select among the data collected by the supervision system, only those carrying relevant information related to the decision task. There are two objectives presented in this paper, the first one is to use data mining techniques to improve fault detection tasks. For this purpose, feature selection algorithms are applied before a classifier to select which measures are needed for a fault detection system. The second objective is to use STRASS (STrong Relevant Algorithm of Subset Selection), which gives a useful feature categorization: strong relevant features, weak relevant and/or redundant ones. This feature categorization permits to design reliable fault detection system. The algorithm is tested on real benchmarks in medical diagnosis and fault detection. Our results indicate that a small number of measures can accomplish and perform the classification task and shown our algorithm ability to detect the correlated features. Furthermore, the proposed feature selection and categorization permits to design reliable and efficient fault detection system.
How to Cite
##plugins.themes.bootstrap3.article.details##
fault detection, PHM sensors and detection methodologies, Data-driven and model-based prognostics
2. Barry, M. Wise and B. Neal. PARAFAC2 Part III. Application to Fault Detection and Diagnosis in Semiconductor Etch. Gallagher Eigenvector Research, Inc. Manson, WA USA. (1999).
3. Barna, G.G. Procedures for Implementing Sensor- Based Fault Detection and Classification (FDC) for Advanced Process Control (APC), SEMATECH Technical Transfer Document # 97013235A-XFR (1997).
4. A. L. Blum and P. Langley. Selection of relevant features and examples in machine learning, Artificial Intelligence 97(1-2), 1997. pp. 245-271.
5. Casillas, O. Cordón, M.J. del Jesus, F. Herrera. Genetic Feature Selection in a Fuzzy Rule-Based Classification System Learning Process. Information Sciences 136:1- 4 135-157 (2001)
6. Casimira, R., E. Boutleuxa, G. Clercb, A. Yahouib. The use of features selection and nearest neighbors rule for faults diagnostic in induction motors. Engineering Applications of Artificial Intelligence 19 169–177 (2006).
7. Dash, M., H. Liu, Hiroshi Motoda. Consistency Based Feature Selection. PAKDD: 98-109 (2000).
8. Fayyad, U. M., K. B. Irani (1993). Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. IJCAI: 1022-1029.
9. Guyon, I. and A. Elisseeff, An introduction to variable and feature selection, The Journal of Machine Learning Research, 3:1157-1182 (2003).
10. Hall, M. Correlation-based feature selection of discrete and numeric class machine learning. In Proceedings of the International Conference on Machine Learning, pages 359-366, San Francisco, CA. Morgan Kaufmann Publishers. (2000).
11. Jakulin, A., Ivan Bratko, Testing the significance of attribute interactions, Proceedings of the twenty-first international conference on Machine learning, p.52, July 04-08, Banff, Alberta, Canada. (2004).
12. John, G. H., R. Kohavi, and K. Pfleger. Irrelevant Features and the Subset Selection Problem. Proceedings of the Eleventh International Conference on Machine Learning . New Brunswick, NJ: Morgan Kaufmann, pp. 121-129. (1994).
13. Kohavi, R and G. H. John. Wrappers for feature subset selection. AIJ issue on relevance. (1995).
14. Kira, K. and L. Rendell. The feature selection problem: Traditional methods and a new algorithm. Proceedings of the Tenth National Conference on Artificial Intelligence (pp. 129{134). Menlo Park: AAAI Press/The MIT Press. (1992).
15. Kira, K., L. A. RENDELL (1992), A Practical Approach to Feature Selection, in Proc. of the Ninth International Workshop, ML, , pp. 249-255.
16. Kononenko, I., S.E. Hong. Attribute selection for modelling, Future Generation Computer Systems, 13, pp 181 – 195. (1997).
17. Langley, P. Selection of relevant features in machine learning, Proc of the AAAI, Fall Symposium on relevance, New Orleans pp 399 – 406. (1994)
18. Lanzi, P.L. Fast Feature Selection With Genetic Algorithms: A Filter Approach. IEEE International Conference on Evolutionary Computation. Indianapolis. Indianapolis 537-540. (1997).
19. Li, W., D. Li, J. Ni. Diagnosis of tapping process using spindle motor current. International Journal of Machine Tools & Manufacture 43 73–79 (2003).
20. Liu, H. et L. Yu. Toward Integrating Feature Selection Algorithms for Classification and Clustering, IEEE Trans on Knowledge and Data Engineering, VOL. 17, NO. 4. (2005).
21. Pudil, P., J. Navovicova, , J. Kittler, Floating search methods in feature selection. Pattern Recognition Letters 15, 1119–1125. (1994).
22. Senoussi, H. and Chebel-Morello. A New Contextual Based Feature Selection. IEEE International Joint Conference on Neural Networks, IJCNN 2008 and WCCI 2008 (IEEE World Congress on Computational Intelligence).Hong Kong June 1-6. (2008).
23. Torkola, K., S. Venkatesan. and H. Liu. Sensor Selection for Maneuver Classification. IEEE Intelligent TranspOltation Systems Conference Washington, D.C., USA, October 36. (2004)
24. Witten, I. H. and E. Frank, Data Mining—Practical Machine Learning Tools and Techniques with JAVA Implementations", Morgan Kaufmann, San Francisco, CA. (2000).
25. Yu, L.and H. Liu, Efficient Feature Selection via Analysis of Relevance and Redundancy. Journal of Machine Learning Research 5 1205–1224. (2004).
26. Zhao, Z. and H. Liu, Searching for Interacting Features, IJCAI2007. (2007).
27. Zio, E., P. Baraldi, D. Roverso. An extended classifiability index for feature selection in nuclear transients. Annals of Nuclear Energy. 32 1632–1649. (2005).
28. Sylvain Verron, Teodor Tiplica, Abdessamad Kobi. Fault detection and identification with a new feature selection based on mutual information. Journal of Process Control 18 (2008) 479–490.
29. J. Downs, E. Vogel, Plant-wide industrial process control problem, Computers and Chemical Engineering 17 (3) 245–255. (1993).
30. N. Ricker, Decentralized control of the tennessee eastman challenge process, Journal of Process Control 6 (4) 205–221. (1996).
31. Paljak, I. Kocsis, Z. Egel, D. Toth, and A. Pataricza, Sensor Selection for IT Infrastructure Monitoring, in Third International ICST Conference on Autonomic Computing and Communication Systems, 2009.
32. H. Peng, F. Long, Chris Ding, Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp1226- 1238, Aug. 2005.
33. Tyan C.,Wang,P.,Bahler,D. An application on intelligent control using neural network and fuzzy logic Neurocomputing 12(4): 345-363 (1996).
34. Widodo A,Yang, B application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors, Expert system with application, volume 33(1), pp 241-25. (2007).
35. Chebel Morello B., Michaut D, Baptiste P.(2001) A knowledge discovery process for a flexible manufacturing system. Proc. of the 8th IEEE, ETFA'2001, vol 1, pp.652-659, octobre, Antibes, Juan les Pins.
36. Riverol,C.,Carosi, C.. integration of fault diagnosis based on case based reasoning in brewing Sens.&Instrumen. Food Qual2:15-20 Springer.
37. Yang, B and Widodo, A. support Vector Machine for Machine Fault Diagnosis, journal of system design and dynamics vol 2 n°1 pp 12-23 (2008).
38. Sugumaran, V., Muralidharan, V. Ramachandran K.I. Feature selection using Decision Tree and classification through Proximal Support Vector Machine for fault diagnostics of roller bearing. Mechanical Systems and Signal Processing 21 930–942 (2007).
39. UCI Knowledge Discovery in Databases Archive: www.ics.uci.edu/~mlearn/MLRepository.html.
40. L. Chiang, M. Kotanchek, A. Kordon, Fault diagnosis based on fisher discriminant analysis and support vector machines, Computers and Chemical Engineering 28 (8) (2004) 1389–1401.
41. T. Jockenhövel, L. T. Biegler, and A. Wächter, Tennessee Eastman Plant-wide Industrial Process Challenge Problem. Complete Model. Computers and Chemical Engineering., 27, 1513-1531, 2003.
42. Mostafa Noruzi Nashalji, Mahdi Aliyari Shoorehdeli, Mohammad Teshnehlab. Fault Detection of the Tennessee Eastman Process Using Improved PCA and
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.