Evolving Fuzzy Classifier based on Clustering Algorithm and Drift Detection for Fault Diagnosis Applications
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Nowadays, in several areas, efficient fault diagnosis methods for complex machinery and equipments are required. Several fault diagnosis methods based on different theories and approaches have been proposed in the literature. In general, these methods use mathematical/statistical models, accumulated experience, or even process historical data to perform fault diagnosis. Although methods based on models or experience have shown to be effective, they have the disadvantage of requiring previous knowledge of the dynamic system in question. On the contrary, methods based on process historical data do not require a prior knowledge, they are based solely on data obtained directly from the dynamic system. The application of so-called “Evolving Intelligent Systems” to accomplish fault diagnosis from process data have been shown a promising approach. This paper proposes an evolving fuzzy classifier based on a new approach that combines a recursive clustering algorithm and a drift detection method and its application on dynamic systems fault diagnosis. The novel approach provides greater robustness to outliers and noise present in data from process sensors. The classifier is evaluated in fault diagnosis of an interacting tank system and the results are promising.
How to Cite
##plugins.themes.bootstrap3.article.details##
Diagnosis and fault isolation methods
Angelov, P., & Filev, D. (2003). Online Design of Takagi- Sugeno Models. In T. Bilgic ̧, B. D. Baets, & O. Kay- nak (Eds.), Fuzzy Sets and Systems — IFSA 2003 (Vol. 2715, p. 576-584). Springer Berlin Heidelberg. DOI: 10.1007/3-540-44967-1-69.
Angelov, P., Filev, D., & Kasabov, N. (2010). Evolving Intelligent Systems: Methodology and Applications. New York, USA: John Willey & Sons.
Braga, A. R., Jota, F. G., Polito, C. M., & Pena, R. T. (1995). Development of an interacting tank system for the study of advanced process control strategies. In Proceedings of the 38th Midwest Symposium on Circuits and Systems (Vol. 1, p. 441-444). DOI: 10.1109/MWS- CAS.1995.504471
Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern Classification. New York, USA: John Wiley & Sons.
Filev, D., Chinnam, R. B., Tseng, F., & Baruah, P. (2010). An Industrial Strength Novelty Detection Framework for Autonomous Equipment Monitoring and Diagnostics. IEEE Transactions on Industrial Informatics, 6(4), 767-779. DOI: 10.1109/TII.2010.2060732.
Filev, D., & Georgieva, O. (2010). An extended version of the Gustafson–Kessel algorithm for evolving data stream clustering. In P. Angelov, D. Filev, & N. Kasabov (Eds.), Evolving Intelligent Systems: Methodology and Applications (p. 273-300). New York, USA: John Wi- ley & Sons.
Gama, J., Medas, P., Castillo, G., & Rodrigues, P. (2004). Learning with drift detection. In A. L. C. Bazzan & S. Labidi (Eds.), Advances in Artificial Intelligence – SBIA 2004 (p. 286-295). Springer Berlin Heidelberg. doi: 10.1007/978-3-540-28645-5-29.
Gustafson, D. E., & Kessel, W. C. (1979). Fuzzy clustering with fuzzy covariance. In Proceedings of IEEE Conference on Decision and Control (p. 761-766).
Hastie, T., Tibshirani, R., & Friedman, J. (n.d.). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York, USA: Springer-Verlag.
Jain, A. K. (2010). Data clustering: 50 years beyond K- means. Pattern Recognition Letters, 31(8), 651–666. DOI: 10.1016/j.patrec.2009.09.011
Jang, J. S. R., Sun, C. T., & Mizutani, E. (1997). Neuro-Fuzzy and Soft Computing: a computational approach to learning and machine intelligence. Upper Saddle River, USA: Prentice-Hall, Inc.
Jardine, A. K. S., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510. DOI: 10.1016/j.ymssp.2005.09.012
Kasabov, N., & Song, Q. (2002). DENFIS: Dynamic Evolving Neural-Fuzzy Inference System and Its Application for Time Series Prediction. IEEE Transactions on Fuzzy Systems, 10(2), 144-154. DOI: 10.1109/91.995117
Kelly, P. M. (1994). An algorithm for merging hyper ellipsoidal clusters (Tech. Rep.). LA-UR-94-3306, Los Alamos National Laboratory, Los Alamos, NM.
Lemos, A., Caminhas, W., & Gomide, F. (2011). Multi- variable Gaussian Evolving Fuzzy Modeling System. IEEE Transactions on Fuzzy Systems, 19(1), 91-104. doi: 10.1109/TFUZZ.2010.2087381
Lemos, A., Caminhas, W., & Gomide, F. (2013). Adap- tive Fault Detection and Diagnosis Using an Evolving Fuzzy Classifier. Information Sciences, 220, 64-85. DOI: DOI: 10.1016/j.ins.2011.08.030
Leng, G., McGinnity, T. M., & Prasad, G. (2005). An Approach for On-Line Extraction of Fuzzy Rules Using a Self-Organising Fuzzy Neural Network. Fuzzy Sets & Systems, 150(2), 211–243. DOI: DOI: 10.1016/j.fss.2004.03.001
Lima, E., Hell, M., Gomide, F., & Ballini, R. (2010). Evolv- ing fuzzy modeling using participatory learning. In P. Angelov, D. Filev, & N. Kasabov (Eds.), Evolving Intelligent Systems: Methodology and Applications (p. 67-87). New York, USA: John Wiley & Sons.
Lughofer, E. (2008). FLEXFIS: A robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models. IEEE Transactions on Fuzzy Systems, 16(6), 1393-1410. doi: DOI: 10.1016/j.fss.2004.03.001
Lughofer, E., & Guardiola, C. (2008). Applying Evolving Fuzzy Models with Adaptive Local Error Bars to On-line Fault Detection. In Proceedings of 3rd International Workshop on Genetic and Evolving Fuzzy Systems - GEFS 2008 (p. 35-40). DOI: 10.1109/GEFS.2008.4484564.
Mitchell, T. M. (1997). Machine Learning. New York, USA: McGraw-Hill.
Rong, H. J., Sundararajan, N., Huang, G. B., & Saratchan- dran, P. (2006). Sequential Adaptive Fuzzy Inference System (SAFIS) for nonlinear system identification and prediction. Fuzzy Sets & Systems, 157(9), 1260-1275. DOI: 10.1016/j.fss.2005.12.011
Sebastia ̃o, R., & Gama, J. (2009). A Study on Change Detection Methods. In Proceedings of 4th Portuguese Conference on Artificial Intelligence (p. 353-364).
Soleimani-B., H., Lucas, C., & Araabi, B. N. (2010). Re- cursive Gath-Geva clustering as a basis for evolving neuro-fuzzy modeling. Evolving Systems, 1(1), 59-71. DOI: 10.1007/s12530-010-9006-x
Vachtsevanos, G., Lewis, F., Roeme, M., Hess, A., & Wu, B. (2006). Intelligent Fault Diagnosis and Prognosis for Engineering Systems. Hoboken, USA: John Wiley & Sons.
Venkatasubramanian, V. (2005). Prognostic and diagnostic monitoring of complex systems for product lifecycle management: Challenges and opportunities. Comput- ers and Chemical Engineering, 29(6), 1253-1263. DOI: 10.1016/j.compchemeng.2005.02.026.
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.