Intelligent particle filter and its application to fault detection of nonlinear system

IEEE Transactions on Industrial Electronics

Volumn 62, Issue 6, 2015, Pages 3852-3861

  Yin, Shen ^a,b   Zhu, Xiangping ^a  

^a BOHAI UNIVERSITY   (China)

^b HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Genetic algorithm (GA); Hidden state estimation; Intelligent particle filter (IPF); Particle filter (PF); Real time fault detection

Indexed keywords

GENETIC ALGORITHMS; MONTE CARLO METHODS; STATE ESTIMATION;

ESTIMATION RESULTS; GENETIC OPERATORS; HIDDEN STATE ESTIMATIONS; NON-GAUSSIAN SYSTEMS; NOVEL TECHNIQUES; PARTICLE FILTER; REAL TIME FAULT DETECTION; THREE TANK SYSTEM;

FAULT DETECTION;

EID: 84926526656     PISSN: 02780046     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIE.2015.2399396     Document Type: Article

References (38)

1. Z. Gao and S. X. Ding, "Actuator fault robust estimation and fault-tolerant control for a class of nonlinear descriptor systems", Automatica, vol. 43, no. 5, pp. 912-920, May 2007.
2. A. Abdullah and M. Zribi, "Sensor-fault-tolerant control for a class of linear parameter varying systems with practical examples", IEEE Trans. Ind. Electron., vol. 60, no. 11, pp. 5239-5251, Nov. 2013.
3. Z. Gao and D. W. Ho, "State/noise estimator for descriptor systems with application to sensor fault diagnosis", IEEE Trans. Signal Process., vol. 54, no. 4, pp. 1316-1326, Apr. 2006.
4. S. Chen, "Kalman filter for robot vision: A survey", IEEE Trans. Ind. Electron., vol. 59, no. 11, pp. 4409-4420, Nov. 2012.
5. S. S. Dias and M. G. Bruno, "Cooperative target tracking using decentralized particle filtering and RSS sensors", IEEE Trans. Signal Process., vol. 61, no. 14, pp. 3632-3646, Jul. 2013.
6. S. A. Hiremath, G. W. Van Der Heijden, F. K. Van Evert, A. Stein, and C. J. Ter Braak, "Laser range finder model for autonomous navigation of a robot in a maize field using a particle filter", Comput. Electron. Agric., vol. 100, pp. 41-50, Jan. 2014.
7. M. Atia, J. Georgy, M. Korenberg, and A. Noureldin, "Real-time implementation of mixture particle filter for 3D RISS/GPS integrated navigation solution", Electron. Lett., vol. 46, no. 15, pp. 1083-1084, Jul. 2010.
8. M. B. Loiola, R. R. Lopes, and J. M. T. Romano, "Modified Kalman filters for channel estimation in orthogonal space-time coded systems", IEEE Trans. Signal Process., vol. 60, no. 1, pp. 533-538, Jan. 2012.
9. S. Roshany-Yamchi et al., "Kalman filter-based distributed predictive control of large-scale multi-rate systems: Application to power networks", IEEE Trans. Control Syst. Technol., vol. 21, no. 1, pp. 27-39, Jan. 2013.
10. F. Gustafsson et al., "Particle filters for positioning, navigation, tracking", IEEE Trans. Signal Process., vol. 50, no. 2, pp. 425-437, Feb. 2002.
11. A. Shenoy, J. Prakash, V. Prasad, S. Shah, and K. McAuley, "Practical issues in state estimation using particle filters: Case studies with polymer reactors", J. Process Control, vol. 23, no. 2, pp. 120-131, Feb. 2013.
12. S. Das, A. Kale, and N. Vaswani, "Particle filter with a mode tracker for visual tracking across illumination changes", IEEE Trans. Image Process., vol. 21, no. 4, pp. 2340-2346, Apr. 2012.
13. M. S. Djogatović, M. J. Stanojević, and N. Mladenoviá, "A variable neighborhood search particle filter for bearings-only target tracking", Comput. Oper. Res., vol. 52, pp. 192-202, Dec. 2014.
14. N. J. Gordon, D. J. Salmond, and A. F. Smith, "Novel approach to nonlinear/non-Gaussian Bayesian state estimation", Proc. Inst. Elect. Eng.-Radar Signal Process., vol. 140, no. 2, pp. 107-113, Apr. 1993.
15. W. R. Gilks and C. Berzuini, "Following a moving target-Monte Carlo inference for dynamic Bayesian models", J. Roy. Stat. Soc. B, Stat. Methodol., vol. 63, no. 1, pp. 127-146, 2001.
16. M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, "A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking", IEEE Trans. Signal Process., vol. 50, no. 2, pp. 174-188, Feb. 2002.
17. T. Higuchi, "Monte Carlo filter using the genetic algorithm operators", J. Stat. Comput. Simul., vol. 59, no. 1, pp. 1-23, Aug. 1997.
18. N. M. Kwok, G. Fang, and W. Zhou, "Evolutionary particle filter: Re-sampling from the genetic algorithm perspective", in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., 2005, pp. 2935-2940.
19. S. M. K. Heris and H. Khaloozadeh, "Non-dominated sorting genetic filter a multi-objective evolutionary particle filter", in Proc. Iran. Conf. Intell. Syst., 2014, pp. 1-6.
20. A. Doucet, S. Godsill, and C. Andrieu, "On sequential Monte Carlo sampling methods for Bayesian filtering", Stat. Comput., vol. 10, no. 3, pp. 197-208, Jul. 2000.
21. R. Douc and O. Cappé, "Comparison of resampling schemes for particle filtering", in Proc. Intell. Symp. Image Signal Process. Anal., 2005, pp. 64-69.
22. L. Cen, Z. L. Yu, W. Ser, and W. Cen, "Linear aperiodic array synthesis using an improved genetic algorithm", IEEE Trans. Antennas Propag., vol. 60, no. 2, pp. 895-902, Feb. 2012.
23. Y. Yoon and Y.-H. Kim, "An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks", IEEE Trans. Cybern., vol. 43, no. 5, pp. 1473-1483, Oct. 2013.
24. V. Roberge, M. Tarbouchi, and G. Labonté, "Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning", IEEE Trans. Ind. Informat., vol. 9, no. 1, pp. 132-141, Feb. 2013.
25. D. Simon, Optimal State Estimation: Kalman, H Infinity, Nonlinear Approaches. Hoboken, NJ, USA: Wiley, 2006.
26. G. Wang, S. Yin, and O. Kaynak, "A LWPR based data-driven fault detection approach for nonlinear process monitoring", IEEE Trans. Ind. Informat., vol. 10, no. 4, pp. 2016-2023, Nov. 2014.
27. S. Mahadevan and S. L. Shah, "Fault detection and diagnosis in process data using one-class support vector machines", J. Process Control, vol. 19, no. 10, pp. 1627-1639, Dec. 2009.
28. S. Yin, X. Zhu, and O. Kaynak, "Improved PLS focused on key-performance-indicator-related fault diagnosis", IEEE Trans. Ind. Electron., vol. 62, no. 3, pp. 1651-1658, Mar. 2015.
29. R. Muradore and P. Fiorini, "A PLS-based statistical approach for fault detection and isolation of robotic manipulators", IEEE Trans. Ind. Electron., vol. 59, no. 8, pp. 3167-3175, Aug. 2012.
30. S. Yin and Z. Huang, "Performance monitoring for vehicle suspension system via fuzzy positivistic C-means clustering based on accelerometer measurements", IEEE/ASME Trans. Mechatronics, to be published.
31. S. Yin, G. Wang, and H. R. Karimi, "Data-driven design of robust fault detection system for wind turbines", Mechatronics, vol. 24, no. 4, pp. 298-306, Jun. 2014.
32. A. Soualhi, G. Clerc, and H. Razik, "Detection and diagnosis of faults in induction motor using an improved artificial ant clustering technique", IEEE Trans. Ind. Electron., vol. 60, no. 9, pp. 4053-4062, Sep. 2013.
33. B. Liu and Y. Xia, "Fault detection and compensation for linear systems over networks with random delays and clock asynchronism", IEEE Trans. Ind. Electron., vol. 58, no. 9, pp. 4396-4406, Sep. 2011.
34. S. X. Ding, P. Zhang, S. Yin, and E. L. Ding, "An integrated design framework of fault-tolerant wireless networked control systems for industrial automatic control applications", IEEE Trans. Ind. Informat., vol. 9, no. 1, pp. 462-471, Feb. 2013.
35. S. Yin, S. Ding, X. Xie, and H. Luo, "A review on basic data-driven approaches for industrial process monitoring", IEEE Trans. Ind. Electron., vol. 61, no. 11, pp. 6418-6428, Nov. 2014.
36. S. X. Ding, Model-Based Fault Diagnosis Techniques. Berlin, Germany: Springer-Verlag, 2008.
37. S. A. Arogeti, D. Wang, C. B. Low, and M. Yu, "Fault detection isolation and estimation in a vehicle steering system", IEEE Trans. Ind. Electron., vol. 59, no. 12, pp. 4810-4820, Dec. 2012.
38. V. Kadirkamanathan, P. Li, M. H. Jaward, and S. G. Fabri, "Particle filtering-based fault detection in non-linear stochastic systems", Int. J. Syst. Sci., vol. 33, no. 4, pp. 259-265, Jan. 2002.