Applying evolving fuzzy models with adaptive local error bars to on-line fault detection

2008 3rd International Workshop on Genetic and Evolving Fuzzy Systems, GEFS

Volumn , Issue , 2008, Pages 35-40

  Lughofer, Edwin ^a   Guardiola, Carlos ^b  

^a JOHANNES KEPLER UNIVERSITY LINZ   (Austria)

^b UNIVERSITAT POLITÈCNICA DE VALÈNCIA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ELECTRIC MEASURING INSTRUMENTS; ELECTRONIC EQUIPMENT TESTING; ERROR DETECTION; FUZZY LOGIC; FUZZY SYSTEMS; PHOTOMASKS;

CONFIDENCE REGIONS; DATA SAMPLES; DATA-DRIVEN; ENGINE TESTING; EVALUATION RESULTS; FUZZY MODELLING; HIGH-DIMENSIONAL; LOCAL ERRORS; MEASURED VALUES; MODEL OUTPUTS; NON-LINEAR; ON-LINE FAULT DETECTION; ON-LINE MEASUREMENT SYSTEMS; ON-LINE MEASUREMENTS; OPERATING CONDITIONS; STATISTICAL APPROACHES; SYSTEM STATES; SYSTEM VARIABLES; UNIVARIATE;

FAULT DETECTION;

EID: 50149110194     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/GEFS.2008.4484564     Document Type: Conference Paper

References (22)

1. J. Korbicz, J. Koscielny, Z. Kowalczuk, and W. Cholewa. Fault Diagnosis ?Models, Artificial Intelligence and Applications. Berlin Heidelberg: Springer Verlag, 2004.
2. L. Chiang, E. Russell, and R. Braatz, Fault Detection and Diagnosis in Industrial Systems. London, Great Britain: Springer Verlag London Berlin Heidelberg, 2001.
3. R. Duda, P. Hart, and D. Stork. Pattern Classification ?Second Edition. Southern Gate. Chichester, West Sussex PO 19 8SQ. England: Wiley-Interscience, 2000.
4. E. Lughofer, H. Efendic, L. D. Re, and E. Klement, "Filtering of dynamic measurements in intelligent sensors for fault detection based on data-driven models." in Proceedings IEEE CDC - IEEE CDC Conference, Maui, Hawaii, 2003, pp. 463-468.
5. J. Chen and R. Patton, Robust Model-Based Fault Diagnosis for Dynamic Systems. 101 Philip Drive, Assinippi Park, Norwell, Massachusetts 02061: Kluwer Academic Publishers, 1999.
6. S. Simani, C. Fantuzzi, and R. Patton. Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques. Berlin Heidelberg: Springer Verlag, 2002.
7. Y. Zheng, H. Fang, and H. Wang, "Takagi-Sugeno fuzzy-model-based fault detection for networked control systems with Markov delays," IEEE Transactions on Systems, Man and Cybernetics, Part B, vol. 36, no. 4, pp. 924-929, 2006.
8. X. Li, H. Li, X. Guan, and R. Du, "Fuzzy estimation of feed-cutting force from current measurement - a case study on tool wear monitoring," IEEE Trans. Systems, Man, and Cybernetics Part C: Applications and Reviews, vol. 34, no. 4, pp. 506-512, 2004.
9. X. Wang, U. Kruger, and B. Lennox, "Recursive partial least squares algorithms for monitoring complex industrial processes," Control-Engineering-Practice, vol. 11, no. 6, pp. 613-632, 2003.
10. P. Albertos and A. Sala, "Fault detection via continuous-time parameter estimation," in Proceedings of IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS. Helsinki Univ. Technol, Espoo, Finland, 1994. pp. 87-92.
11. P. Angelov, V. Giglio, C. Guardiola, E. Lughofer, and J. Luján, "An approach to model-based fault detection in industrial measurement systems with application to engine test benches," Measurement Science and Technology, vol. 17, pp. 1809-1818, 2006.
12. P. Angelov and D. Filev, "An approach to online identification of Takagi-Sugeno fuzzy models," IEEE Trans. on Systems, Man and Cybernetics, part B, vol. 34, no. 1, pp. 484-498, 2004.
13. N. K. Kasabov and Q. Song, "DENFIS: Dynamic evolving neural-fuzzy inference system and its application for time-series prediction," IEEE Trans. on Fuzzy Systems, vol. 10, no. 2, pp. 144-154, 2002.
14. E. Lughofer and E. Klement, "FLEXFIS: A variant for incremental learning of Takagi-Sugeno fuzzy systems," in Proceedings of FUZZ-IEEE 2005, Reno, Nevada, U.S.A., 2005, pp. 915-920.
15. L. Ljung, System Identification: Theory for the User. Upper Saddle River, New Jersey 07458: Prentice Hall PTR, Prentic Hall Inc., 1999.
16. L. Wang and J. Mendel, "Fuzzy basis functions, universal approximation and orthogonal least-squares learning," IEEE Trans. Neural Networks, vol. 3, no. 5, pp. 807-814, 1992.
17. E. Lughofer, E. Hüllermeier, and E. Klement, "Improving the interpretability of data-driven evolving fuzzy systems," in Proceedings of EUSFLAT 2005, Barcelona, Spain, 2005, pp. 28-33.
18. E. Lughofer, "Extensions of vector quantization for incremental clustering," Pattern Recognition, vol. 41, no. 3, pp. 995-1011, 2008.
19. _, "Process safety enhancements for data-driven evolving fuzzy models," in Proceedings of 2nd Symposium on Evolving Fuzzy Systems, Lake District, UK. 2006, pp. 42-48.
20. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York. Berlin, Heidelberg, Germany: Springer Verlag, 2001.
21. N. Draper and H. Smith, Applied Regression Analysis. Probability and Mathematical Statistics. New York: John Wiley & Sons, 1981.
22. W. Groißböck, E. Lughofer, and E. Klement, "A comparison of valuable selection methods with the main focus on orthogonalization," in Proceedings of SMPS Conference 2004, Oviedo, Spain, 2004.