Online fault detection of a mobile robot with a parallelized particle filter

Neurocomputing

Volumn 126, Issue , 2014, Pages 151-165

  Zajac, Michał ^a  

^a UNIVERSITY OF ZIELONA GÓRA   (Poland)

Author keywords

Fault detection; Mobile robots; Parallel computing; Particle filter

Indexed keywords

COMPUTATIONAL BURDEN; COMPUTER CLUSTERS; ESTIMATION ERRORS; ON-LINE FAULT DETECTION; PARALLEL-COMPUTING ENVIRONMENT; PARTICLE FILTER; PERFORMANCE BENEFITS; REAL-LIFE APPLICATIONS;

DISTRIBUTED COMPUTER SYSTEMS; MOBILE ROBOTS; MONTE CARLO METHODS; PARALLEL ARCHITECTURES; PARALLEL PROCESSING SYSTEMS; SIGNAL FILTERING AND PREDICTION; TARGET TRACKING;

FAULT DETECTION;

ALGORITHM; ARTICLE; COMPUTER SIMULATION; CONTROLLED STUDY; DISTRIBUTED RESAMPLING WITH NONPROPORTIONAL ALLOCATION ALGORITHM; EVALUATION; EXPERIMENTAL TEST; FALSE NEGATIVE RESULT; FALSE POSITIVE RESULT; FINITE REDRAW LOW IMPORTANCE MAXIMIZING PRIOR EDITING ALGORITHM; LOCAL EXCHANGE FINITE REDRAW LOW IMPORTANCE MAXIMIZING PARTICLE FILTER ALGORITHM; MATHEMATICAL COMPUTING; MATHEMATICAL PHENOMENA; NEGATIVE LOG LIKELIHOOD TEST; NODE DIVERGENCE INDEX; NONBIOLOGICAL MODEL; NORMAL DISTRIBUTION; ONLINE FAULT DETECTION SYSTEM; ONLINE MONITORING; ONLINE SYSTEM; PARALLELIZED PARTICLE FILTER; PRIORITY JOURNAL; RELIABILITY; ROBOTICS; ROOT MEAN SQUARE ERROR; SENSITIVITY ANALYSIS; SEQUENTIAL IMPORTANCE RESAMPLING ALGORITHM;

EID: 84887614201     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.11.049     Document Type: Article

References (48)

1. Korbicz J., Kościelny J., Kowalczuk Z., Cholewa W. Fault Diagnosis. Models, Artificial Intelligence, Applications 2004, Springer, Berlin.
2. Chen J., Patton R. Robust Model-Based Fault Diagnosis for Dynamic Systems 1999, Kluwer, Boston.
3. N. Gordon, D. Salmond, A. Smith, Novel approach to nonlinear/non-Gaussian Bayesian state estimation, in: IEE Proceedings-F, vol. 35, 1993, pp. 107-113.
4. Liu M., Zang S., Zhou D. Fast leak detection and location of gas pipelines based on an adaptive particle filter. Int. J. Appl. Math. Comput. Sci. 2005, 15(4):541-550.
5. Kadirkamanathan V., Li P., Jaward M., Fabri S. Particle filtering-based fault detection in non-linear stochastic systems. Int. J. Syst. Sci. 2002, 33:259-265.
6. Li P., Kadirkamanathan V. Fault detection and isolation in non-linear stochastic systems-a combined adaptive Monte Carlo filtering and likelihood ratio approach. Int. J. Control 2004, 77:1101-1114.
7. N. de Freitas, R. Dearden, F. Hutter, R. Morales-Menendez, J. Mutch, D. Poole, Diagnosis by a waiter and a mars explorer, in: IEEE Proceedings, 2004.
8. F. Hutter, R. Dearden, The Gaussian particle filter for diagnosis of non-linear systems, in: Proceedings of the 14th International Conference on Principles of Diagnosis, Washington, DC, USA, 2003.
9. Verma V., Gordon G., Simmons R., Thrun S. Particle filters for rover fault diagnosis. IEEE Robotics Autom. Mag. 2004, 11(1):56-66.
10. Zaja̧c M. Mobile robot self-diagnosis with a bank of adaptive particle filters. Lect. Notes Artif. Intell. 2011, 6943:168-179.
11. Plagemann C., Stachniss C., Burgard W. Efficient failure detection for mobile robots using mixed abstraction particle filters. Springer Tracts Adv. Robotics 2006, 22:93-107.
12. Teuliere V., Brun O. Parallelisation of the particle filtering technique and application to Doppler-bearing tracking of maneuvering sources. Parallel Comput. 2003, 29:1069-1090.
13. Bolić M., Djurić P., Hong S. Resampling algorithms and architectures for distributed particle filters. IEEE Trans. Signal Process. 2004, 53:2442-2450.
14. Bolić M., Djurić P., Hong S. Resampling algorithms for particle filters. a computational complexity perspective. EURASIP J. Appl. Signal Process. 2004, 2004:2267-2277.
15. M. Bolić, Architectures for Efficient Implementation of Particle Filters, Ph.D. Thesis, Stony Brook University, USA, 2005.
16. Míguez J. Analysis of parallelizable resampling algorithms for particle filtering. Signal Process. 2007, 87:3155-3174.
17. Míguez J., Bugallo M.F., Djurić P. A new class of particle filters for random dynamic systems with unknown statistics. EURASIP J. Appl. Signal Process. 2004, 2004:2278-2294.
18. Zenker S. Parallel particle filters for online identification of mechanistic mathematical models of physiology from monitoring data. performance and real-time scalability in simulation scenarios. J. Clin. Monit. Comput. 2010, 24:319-333.
19. Happe M., Lübbers E., Platzner M. A multithreaded framework for sequential Monte Carlo methods on CPU/FPGA platforms. Lect. Notes Comput. Sci. 2009, 5453:380-385.
20. Hendeby G., Karlsson R., Gustafsson F. Particle filtering. the need for speed. EURASIP J. Adv. Signal Process. 2010, 2010:1-9.
21. M. Chao, C. Chu, C. Chao, A. Wu, Efficient parallelized particle filter design on CUDA, in: Proceedings of the 2010 IEEE Workshop on Signal Processing Systems, San Francisco, CA, USA, 2010.
22. Z. Cai, Z. Duan, J. Cai, X. Zou, J. Yu, A multiple particle filters method for fault diagnosis of mobile robot dead-reckoning, in: IEEERSJ International Conference on Intelligent Robots and Systems, IEEE, 2005, pp. 481-486.
23. Duan Z., Fu M., Cai Z., Yu J. An adaptive particle filter for mobile robot fault diagnosis. J. Central South Univ. Technol. 2006, 13(6):689-693.
24. Duan Z., Cai Z., Yu J. An adaptive particle filter for soft fault compensation of mobile robots. Sci. China Ser. F Inf. Sci. 2008, 51(12):2033-2046.
25. Li P., Kadirkamanathan V. Particle filtering based likelihood ratio approach to fault diagnosis in nonlinear stochastic systems. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 2001, 31:337-343.
26. C. Andrieu, A. Doucet, S. Singh, V. Tadić, Particle methods for change detection, system identification, and control, in: IEEE Proceedings, vol. 92, 2004, pp. 423-438.
27. Zaja̧c M., Uciñski D. Adaptacyjny filtr czasteczkowy w detekcji i lokalizacji uszkodzeń robota mobilnego, Prace Naukowe Politechniki Warszawskiej. Elektronika 2010, 175:635-644.
28. Thrun S., Burgard W., Fox D. Probabilistic Robotics 2005, The MIT Press, Cambridge, MA.
29. Arulampalam M., Maskell S., Gordon N., Clapp T. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Process. 2002, 50(2):174-188.
30. Doucet A., Godsill S., Andrieu C. On sequential Monte Carlo sampling methods for Bayesian filtering. Stat. Comput. 2000, 10:197-208.
31. O. Straka, M. Šimandl, A survey of sample size adaptation techniques for particle filters, in: Proceedings of 15th IFAC Symposium on System Identification, vol. 15, 2009.
32. O. Straka, M. Šimandl, Particle filter adaptation based on efficient sample size, in: Proceedings of 14th IFAC Symposium on System Identification, 2006, pp. 991-996.
33. Basseville M., Nikiforov I. Detection of Abrupt Changes. Theory and Application 1993, Prentice-Hall, Engelwood Cliffs.
34. Verdier G., Hilgert N., Vila J. Adaptive threshold computation for cusum-type procedures in change detection and isolation problems. Comput. Stat. Data Anal. 2008, 52:4161-4174.
35. A. Ozdemir, P. Seiler, G. Balas, Wind turbine fault detection using counter-based residual thresholding, in: Preprints of the 18th IFAC Word Congress, Milano, Italy, 2011, pp. 8289-8294.
36. Nozari H., Shoorehedeli M., Simani S., Bandaki H. Model-based robust fault detection and isolation of an industrial gas turbine prototype using soft computing techniques. Neurocomputing 2012, 91:29-47.
37. Patan K. Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes, Lecture Notes in Control and Information Sciences 2008, Springer, Berlin.
38. Puig V., Quevedo J. Passive robust fault detection using parity equations. Math. Comput. Simulation 2002, 60:193-207.
39. Korbicz J., Kowal M. Neuro-fuzzy networks and their application to fault detection of dynamical systems. Eng. Appl. Artif. Intell. 2007, 20:609-617.
40. Zhang X., Polycarpou M., Parisini T. Design and analysis of a fault isolation scheme for a class of uncertain nonlinear systems. Annu. Rev. Control 2008, 32:107-121.
41. Shi Z., Gu F., Lennox B., Ball A. The development of an adaptive threshold for model fault detection of a nonlinear electro-hydraulic system. Control Eng. Pract. 2005, 13:1357-1367.
42. Frank P., Ding X. Frequency domain approach to optimally robust residual generation and evaluation for model-based fault diagnosis. Automatica 1994, 30(5):789-804.
43. Athamena B., Abbassi H., Kambhampati C. On-line fault diagnosis technique of the magnetic levitation vehicle system. Courr. Savoir 2001, 1:49-56.
44. Alkaya A., Eker I. Variance sensitive adaptive threshold-based PCA method for fault detection with experimental application. ISA Trans. 2011, 50:287-302.
45. Frank P., Ding X. Survey of robust residual generation and evaluation methods in observer-based fault detection systems. J. Process Control 1997, 7(6):403-424.
46. Patton R. Robustness in model-based fault diagnosis. the 1995 situation. Annu. Rev. Control 1997, 21:103-123.
47. Martinelli A. The odometry error of a mobile robot with a synchronous drive system. IEEE Trans. Robotics Autom. 2002, 18:399-405.
48. http://www.ros.org.