[email protected]http://www.flll.jku.at/staff/francisco Francisco Serdio NABIC 2014 – Porto, July 30,31 - August 1, 2014 Hybrid Genetic-Fuzzy Systems for Improved Performance in Residual-Based Fault Detection Francisco Serdio Fernández Department of Knowledge-Based Mathematical Systems Johannes Kepler University Linz, Austria
F. Serdio, A.-C. Zavoianu, E. Lughofer, K. Pichler, T. Buchegger and H. Efendic, Hybrid Genetic-Fuzzy Systems for Improved Performance in Residual-Based Fault Detection, World Congress on Natural and Biologically Inspired Computing, NaBIC 2014, Porto, Portugal, 2014, pp. 91-96.
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System models Allow to detect faults of small sizes Difficult or impossible for many systems
Limited for systems with simple equations
Expert systems Represent the expert knowledge
Fault Patterns Allow Pattern Recognition and Classification
approaches we know how a fault looks like
Expert Knowledge
J. Korbicz, J.M. Koscielny, Z. Kowalczuk and W. Cholewa (Eds.). Fault Diagnosis: Models, Artificial Intelligence, Applications. Springer-Verlag. Berlin Heidelberg. 2004.
J. Korbicz, J.M. Koscielny, Z. Kowalczuk and W. Cholewa (Eds.). Fault Diagnosis: Models, Artificial Intelligence, Applications. Springer-Verlag. Berlin Heidelberg. 2004.
FD with Residual-based approaches More information regarding Fault Detection in
F. Serdio, E. Lughofer, K. Pichler, T. Buchegger and H. Efendic, Fault Detection in Multisensor Networks based on Multivariate Time-series Models and Orthogonal Transformations. Information Fusion, (to appear), 2014.
F. Serdio, E. Lughofer, K. Pichler, T. Buchegger and H. Efendic, Residual-based Fault Detection using Soft Computing techniques for Condition Monitoring at Rolling Mills. Information Sciences, 259, pp. 304–330, 2014.
F. Serdio, E. Lughofer, K. Pichler, T. Buchegger and H. Efendic, Data-Driven Residual-Based Fault Detection for Condition Monitoring in Rolling Mills. Proceedings of the IFAC Conference on Manufacturing Modeling, Management and Control, MIM '2013, St. Petersburg, Russia, 2013, pp. 1546-1551. (Winner of MIM 2013 Best paper award)
F. Serdio, E. Lughofer, K. Pichler, T. Buchegger, M. Pichler and H. Efendic, Multivariate Fault Detection using Vector Autoregressive Moving Average and Orthogonal Transformation in the residual Space. Annual Conference of the Prognostics and Health Management Society, PHM 2013, New Orleans, LA, USA, 2013, pp. 548-555.
Training and Test 80% for training, 20% for validation
Trains the Fuzzy Systems of the individual Asses the quality of the Fuzzy System
Mean Squared Error (MSE) Uses training set The last generation uses the validation set
Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, 2nd edition, 2009.