A Data-level fusion model for developing composite health indices for degradation modeling and prognostic analysis

IEEE Transactions on Automation Science and Engineering

Volumn 10, Issue 3, 2013, Pages 652-664

  Liu, Kaibo ^a   Gebraeel, Nagi Z ^a   Shi, Jianjun ^a  

^a Georgia Institute of Technology   (United States)

Author keywords

Data level fusion; Degradation modeling; Prognostics; Residual life distributions

Indexed keywords

AIRCRAFT GAS TURBINE ENGINES; DATA-LEVEL FUSIONS; DECISION LEVEL FUSION; DEGRADATION MODEL; ENGINEERING SYSTEMS; PROGNOSTIC ANALYSIS; PROGNOSTICS; RESIDUAL LIFE DISTRIBUTION;

DATA PROCESSING; OCCUPATIONAL RISKS; SENSORS; SERVICE LIFE; SYSTEMS ENGINEERING;

SENSOR DATA FUSION;

EID: 84892606646     PISSN: 15455955     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASE.2013.2250282     Document Type: Article

References (28)

1. R. K. Mobley, An Introduction to Predictive Maintenance. Burlington, MA, USA: Elsevier Butterworth-Heinemann, 2002.
2. W. Q. Meeker and L. A. Escobar, Statistical Methods for Reliability Data. New York, NY, USA: Wiley, 1998.
3. W. Nelson, Accelerated Testing Statistical Models, Test Plans, and Data Analysis. New York, NY, USA: Wiley, 1990.
4. N. Gebraeel, "Sensory-updated residual life distributions for components with exponential degradation patterns," IEEE Trans. Autom. Sci. Eng., vol. 3, pp. 382-393, Oct. 2006.
5. D. L. Hall and J. Llinas, "An introduction to multisensor data fusion," Proc. IEEE, vol. 85, pp. 6-23, Jan. 1997.
6. A. J. Volponi, T. Brotherton, R. Luppold, and D. L. Simon, Development of an Information Fusion System for Engine Diagnostics and Health Management. Cleveland, OH, USA: NASA, Feb. 2004.
7. T. Heger and M. Pandit, "Optical wear assessment system for grinding tools," J. Electron. Imag., vol. 13, pp. 450-461, Jul. 2004.
8. D. Simon and D. L. Simon, "Aircraft turbofan engine health estimation using constrained Kalman filtering," ASME J. Eng. Gas Turbines Power, vol. 127, pp. 323-328, Apr. 2005.
9. T. Kobayashi and D. L. Simon, "Hybrid Kalman filter approach for aircraft engine in-flight diagnostics: Sensor fault detection case," J. Eng. Gas Turbines Power, vol. 129, no. 3, pp. 746-754, Jul. 2007.
10. K. Salahshoor, M. Mosallaei, and M. Bayat, "Centralized and decentralized process and sensor fault monitoring using data fusion based on adaptive extended Kalman filter algorithm," Measurement, vol. 41, pp. 1059-1076, Mar. 2008.
11. K. Goebel and P. Bonissone, "Prognostic information fusion for constant load systems," in Proc. 7th Annu. Conf. Inform. Fusion, 2005, vol. 2, pp. 1247-1255.
12. Q. Sun, "Sensor fusion for vehicle health monitoring and degradation detection," in Proc. 5th Int. Conf. Inform. Fusion, Annapolis, MD, USA, 2002, pp. 1422-1427.
13. A. K. S. Jardine, D. Lin, and D. Banjevic, "A review on machinery diagnostics and prognostics implementing condition-based maintenance," Mechanical Syst. Signal Process., vol. 20, no. 7, pp. 1483-1510, 2006.
14. C. Hu, B. D. Youn, and P. Wang, "Ensemble of data-driven prognostic algorithms with weight optimization and k-fold cross validation," in Proc. ASME 2010 Int. Design Eng. Tech. Conf. Comput. Inform. Engineering Conf., Montreal, Quebec, Canada, 2010, vol. 3, pp. 1023-1032.
15. C. S. Byington, R. C. Brewer, T. Meyer, and S. R. Amin, "Gearbox corrosion prediction via oil condition sensing and model fusion," in STLE 62nd Annu. Meeting and Exhibition, Philadelphia, PA, USA, May 2007, pp. 1-19.
16. A. Saxena, K. Goebel, D. Simon, and N. Eklund, "Damage propagation modeling for aircraft engine run-to-failure simulation," in Proc. 1st Int. Conf. Prognostics Health Manage., Denver, CO, USA, Oct. 2008, pp. 1-9.
17. S. Sarkar, X. Jin, and A. Ray, "Data-Driven fault detection in aircraft engines with noisy sensor measurements," ASME J. Eng. Gas Turbines Power, vol. 133, p. 081602, Aug. 2011.
18. D. Frederick, J. DeCastro, and J. Litt, "User's guide for the commercial modular aero-propulsion system simulation (C-MAPSS)," NASA/ARL, Tech. Manual TM2007-215026, 2007.
19. A. Saxena, J. Celaya, E. Balaban, K. Goebel, B. Saha, S. Saha, and M. Schwabacher, "Metrics for evaluating performance of prognostics techniques," in Int. Conf. Prognostics and Health Management (PHM08), Denver, CO, USA, Oct. 2008, pp. 1-17.
20. T. Hastie, R. Tibshirani, and J. H. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York, NY, USA: Springer-Verlag, 2003.
21. N. Gebraeel and M. Lawley, "A neural network degradation model for computing and updating residual life distributions," IEEE Trans. Autom. Sci. Eng., vol. 5, no. 1, pp. 154-163, Jan. 2008.
22. A. Demetriou and M. M. Polycarpou, "Incipient fault diagnosis of dynamical systems using online approximators," IEEE Trans. Autom. Control, vol. 43, no. 11, pp. 1612-1617, Nov. 1998.
23. M. J. Mousavi and K. L. Butler-Purry, "Detecting incipient faults via numerical modeling and statistical change detection," IEEE Trans. Power Del., vol. 25, pp. 1275-1283, July 2010.
24. P. M. Frank, "Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy - A survey and some new results," Automatica, vol. 26, no. 3, pp. 459-474, May 1990.
25. X. Zhang, M. M. Polycarpou, and T. Parisini, "A robust detection and isolation scheme for abrupt and incipient faults in nonlinear systems," IEEE Trans. Autom. Control, vol. 47, no. 4, pp. 576-593, Apr. 2002.
26. M. Y. Chow and S. O. Yee, "Methodology for on-line incipient fault detection in single-phase squirrel-cage inductionmotors using artificial neural networks," IEEE Trans. Energy Convers., vol. 6, pp. 536-545, Sep. 1991.
27. K. Holmström, A. O. Göran, and M. M. Edvall, User's Guide for TOMLAB. San Diego, CA, USA: Tomlab Optimization Inc., Feb. 2008.
28. N. Gebraeel, "Prognostics-based identification of the top-k units in a fleet," IEEE Trans. Autom. Sci. Eng., vol. 7, no. 1, pp. 37-48, Jan. 2010.