Correcting Sensor Drift and Intermittency Faults With Data Fusion and Automated Learning

IEEE Systems Journal

Volumn 2, Issue 2, 2008, Pages 189-197

  Goebel, Kai ^a   Yan, Weizhong ^b  

^a NASA AMES RESEARCH CENTER   (United States)

^b GE GLOBAL RESEARCH   (United States)

Author keywords

Data fusion; drift fault; fuzzy fusion; intermittency; intermittent fault; sensor validation; soft fault

Indexed keywords

DETECTORS; FAULT DETECTION; FUZZY LOGIC; GENETIC ALGORITHMS; OPTIMIZATION; PARTICLE SWARM OPTIMIZATION (PSO); SENSORS;

AUTOMATED LEARNING; DETECTION ALGORITHMS; DETECTION LOGIC; EARLY DETECTION; FALSE POSITIVE (FP); INTERMITTENCY; MONITORING SYSTEMS; NORMAL BEHAVIOR; OPTIMIZATION APPROACH (OOA); PARTICLE SWARM OPTIMIZATION (PSO); REDUNDANT SENSORS; SENSOR DRIFTS; SENSOR VALIDATION; SENSORS FAULTS; STEP CHANGES; TRANSPORTATION INDUSTRIES;

SENSOR DATA FUSION;

EID: 47049103557     PISSN: 19328184     EISSN: 19379234     Source Type: Journal    
DOI: 10.1109/JSYST.2008.925262     Document Type: Article

References (32)

1. R. Isermann, “Process fault detection based on modeling and estimation methods,” Automatica, vol. 20, no. 4, pp. 387–404, 1984.
2. T. S. Sowers, L. M. Santi, and R. L. Bickford, “Performance evaluation of a data validation system,” presented at the 41st Joint Propulsion Conf., Tucson, AZ, Jul. 2005.
3. J. Nasi, A. Sorsa, and K. Leiviska, “Sensor validation and outlier detection using fuzzy limits,” presented at the 44th IEEE Conf. Dec. Control, Seville, Spain, Dec. 2005.
4. Y. J. Kim, W. H. Wood, and A. M. Agogino, “Signal validation for expert system development,” in Proc. 2nd Int. Forum Expert Syst. Comput. Simulations Energy Eng., Erlangen, Germany, Mar. 1992, pp. 9-5-1–9-5-6.
5. D. Gobbo, M. Napolitano, P. Famouri, and M. Innocenti, “Experimental application of extended Kalman filtering for sensor validation,” IEEE Trans. Control Syst. Technol., vol. 9, no. 2, pp. 376–380, Mar. 2001.
6. M. Napolitano, D. Windon, J. Casanova, M. Innocenti, and G. Silvestri, “Kalman filters and neural network schemes for sensor validation in flight control systems,” IEEE Trans. Control Syst. Technol., vol. 6, no. 5, pp. 596–611, Sep. 1998.
7. J. Ma, J. Q. Zhang, and Y. Yan, “Wavelet transform based sensor validation,” IEE Colloquium on Intelligent and Self-Validation Sensors, pp. 10/1–10/4, 1999.
8. J. C. Moller, J. S. Litt, and T. H. Guo, “Neural network-based sensor validation for turboshaft engines,” NASA TM-1998-208824, 1998.
9. X. Xu, J. W. Hines, and R. E. Uhrig, “Sensor validation and fault detection using neural networks,” presented at the Maintenance Reliab. Conf. (MARCON), Gatlinburg, TN, May 10–12, 1999.
10. M. Najafi, C. Gulp, and R. Langari, “Enhanced auto-associative neural networks for sensor diagnostics (E-AANN),” in Proc. IEEE Int. Conf. Fuzzy Syst., Jul. 2004, vol. 1, pp. 453–456.
11. G. Kerschen, P. D. Boe, J. Golinval, and K. Worden, “Sensor validation using principal component analysis,” Smart Mater. Structures, vol. 14, pp. 36–42, 2005.
12. P. H. Ibarguengoytia, L. E. Sucar, and S. Vadera, “Real time intelligent sensor validation,” IEEE Trans. Power Syst., vol. 16, no. 4, pp. 770–775, Nov. 2001.
13. S. J. Wellington, J. K. Atkinso, and R. P. Sion, “Sensor validation and fusion using the nadaraya-watson statistical estimator,” in Proc. 5th Int. Conf. Inf. Fusion, vol. 1, pp. 321–326.
14. T.Wei, Y. F. Huang, and P. Chen, “Sensor validation for flight control by particle filtering,” presented at the 13th Eur. Signal Process. Conf. (EUSIPCO), Antalya, Sep. 2005.
15. P. M. Frank, “Fault diagnosis in dynamic systems using analytical and knowledge based redundancy—A survey and some new results,” Automatica, vol. 26, pp. 459–70, 1990.
16. M. Tadeusiewicz, S. Halgas, and M. Korzybski, “An algorithm for softfault diagnosis of linear and nonlinear circuits. circuits and systems I: Fundamental theory and applications,” IEEE Trans. Circuits Syst. I: Reg. Papers, vol. 49, no. 11, pp. 1648–1653, Nov. 2002.
17. M. Catelani and A. Fort, “Soft fault detection and isolation in analog circuits: Some results and a comparison between a fuzzy approach and radial basis function networks,” IEEE Trans. Instrum. Meas., vol. 51, no. 2, pp. 196–202, Apr. 2002.
18. N. B. Hamida and B. Kaminska, “Multiple fault testing in analog circuits,” in Proc. 7th Int. Conf. VLSI Des., Jan. 1994, pp. 1–66, 1994.
19. V. M. Morgenstern, B. R. Upadhyaya, and M. Benedetti, “Signal anomaly detection using modified cusum method,” in Proc. 27th IEEE Conf. Dec. Control, 1988, vol. 3, pp. 2340–2341.
20. D. P. Malladi, “Ageneralized shiryayev sequential probability ratio test for change detection and isolation,” IEEE Trans. Autom. Control., vol. 44, pp. 1522–1534, 1999.
21. A. S. Willsky and H. L. Jones, “A generalized likelihood ratio approach to detection and estimation of jumps in linear systems,” IEEE Trans. Automat. Control., vol. 21, no. 1, pp. 108–112, Feb. 1976.
22. M. Sharifzadeh, F. Azmoodeh, and C. Shahabi, “Change detection in time series data using wavelet footprints,” Adv. Spatial Temporal Databases, vol. 3633, pp. 127–144, 2005.
23. R. S. Tsay, “Outliers, level shifts and variance changes in time series,” J. Forcasting, vol. 7, pp. 1–20, 1988.
24. N. D. Ramirez-Beltran and J. A. Montes, “Neural networks for on-line parameter change detection in time series model,” Comput. Ind. Eng., vol. 33, pp. 337–340, 1997.
25. R. Kozma, M. Kitamura, M. Sakuma, and Y. Yokoyam, “Anomaly detection by neural networks models and statistical timeseries analysis,” in Proc. Int. Joint Conf. Neural Netw., Orlando, FL, 1994, vol. 5, pp. 3207–3210.
26. K. Kumar and B. Wu, “Detection of change points in time series analysis with fuzzy statistics,” Int. J. Syst. Sci., vol. 32, no. 9, pp. 1185–1192, Sep. 2001.
27. X. Hu, N. Eklund, K. Goebel, and W. Cheetham, “Hybrid change detection for aircraft engine fault diagnostics,” in Proc. IEEE Aerosp. Conf., 2007, pp. 1–10.
28. K. Goebel and A. Agogino, “Fuzzy sensor fusion of gas turbine power plants,” Proc. SPIE, Sensor Fusion: Arch., Algorithms, Appl. III, vol. 3719, pp. 52–61, Apr. 1999.
29. K. Goebel and A. Agogino, “An architecture for fuzzy sensor validation and fusion for vehicle following in automated highways,” in Proc. 29th Int. Symp. Autom. Technol. Autom. (ISATA), Dedicated Conf. Fuzzy Syst./Soft Comput. Autom. Transport. Ind., Florence, Italy, 1996, pp. 203–209.
30. J. Kennedy, R. Eberhart, and Y. H. Shi, Swarm Intelligence. San Mateo, CA: Morgan Kaufmann, 2001.
31. K. Veeramachaneni, T. Peram, L. Ann Osadciw, and C. Mohan, “Optimization using particle swarms with near neighbor interactions,” in Lecture Notes in Computer Science. New York: Springer Verlag, 2003, vol. 2723.
32. J. H. Holland, Adaptation in Natural and Artificial Systems. Ann Arbor, MI: Univ. of Michigan Press, 1975.