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A Design of Finite Memory Residual Generation Filter for Sensor Fault Detection

   | 26 apr 2017
INFORMAZIONI SU QUESTO ARTICOLO

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[1] Venkatasubramanian, V., Rengaswamy, R., Yin, K., Kavuri, S.N. (2003). A review of process fault detection and diagnosis - Part I: Quantitative modelbased methods. Computers and Chemical Engineering, 27 (3), 293-311.10.1016/S0098-1354(02)00160-6Search in Google Scholar

[2] Angeli, C., Chatzinikolaou, A. (2004). On-line fault detection techniques for technical systems: A survey. International Journal of Computer Science & Applications, 1 (1), 12-30.Search in Google Scholar

[3] Hwang, I., Kim, S., Kim, Y., Seah, C. (2010). A survey of fault detection, isolation, and reconfiguration methods. IEEE Transactions on Control Systems Technology, 18 (3), 636-653.10.1109/TCST.2009.2026285Search in Google Scholar

[4] Kobayashi, T., Simon, D.L. (2005). Enhanced bank of Kalman filters developed and demonstrated for inflight aircraft engine sensor fault diagnostics. Research and Technology, NASA Glenn Research Center at Lewis Field 2005-213419, 25-26.Search in Google Scholar

[5] Wang, Y., Zheng, Y. (2005). Kalman filter based fault diagnosis of networked control system with white noise. Journal of Control Theory and Application, 3 (1), 55-59.10.1007/s11768-005-0061-ySearch in Google Scholar

[6] Tudoroiu, N., Khorasani, K. (2007). Satellite fault diagnosis using a bank of interacting Kalman filters. IEEE Transactions on Aerospace and Electronic Systems, 43 (4), 1334-1350.10.1109/TAES.2007.4441743Search in Google Scholar

[7] Xue, W., Guo, Y., Zhang, X. (2008). Application of a bank of Kalman filters and a robust Kalman filter for aircraft engine sensor/actuator fault diagnosis. International Journal of Innovative Computing, Information and Control, 4 (12), 3161-3168.Search in Google Scholar

[8] Tudoroiu, N. (2011). Real time embedded Kalman filter estimators for fault detection in a satellite’s dynamics. International Journal of Computer Science & Applications, 8 (1), 83-109.Search in Google Scholar

[9] Villez, K., Srinivasanb, B., Rengaswamyb, R., Narasimhanc, S., Venkatasubramaniana, V. (2011). Kalman-based strategies for fault detection and identification (FDI): Extensions and critical evaluation for a buffer tank system. Computers and Chemical Engineering, 35 (5), 806-816.10.1016/j.compchemeng.2011.01.045Search in Google Scholar

[10] Bruckstein, A.M., Kailath, T. (1985). Recursive limited memory filtering and scattering theory. IEEE Transactions on Information Theory, 31 (3), 440-443.10.1109/TIT.1985.1057031Search in Google Scholar

[11] Kim, P.S. (2010). An alternative FIR filter for state estimation in discrete-time systems. Digital Signal Processing, 20 (3), 935-943.10.1016/j.dsp.2009.10.033Search in Google Scholar

[12] Kim, P.S. (2013). A computationally efficient fixedlag smoother using recent finite measurements. Measurement, 46 (1), 846-850.Search in Google Scholar

[13] Zhao, S., Shmaliy, Y.S., Huang, B., Liu, F. (2015). Minimum variance unbiased FIR filter for discrete time-variant systems. Automatica, 53 (2), 355-361.10.1016/j.automatica.2015.01.022Search in Google Scholar

[14] Pak, J., Ahn, C., Shmaliy, Y., Lim, M. (2015). Improving reliability of particle filter-based localization in wireless sensor networks via hybrid particle/FIR filtering. IEEE Transactions on Industrial Informatics, 11 (9), 1-10.10.1109/TII.2015.2462771Search in Google Scholar

[15] Kim, P.S., Lee, E.H., Jang, M.S., Kang, S.Y. (2017). A finite memory structure filtering for indoor positioning in wireless sensor networks with measurement delay. International Journal of Distributed Sensor Networks, 13 (1), 1-8.10.1177/1550147716685419Search in Google Scholar

[16] Kwon, W.H., Kim, P.S., Han, S.H. (2002). A receding horizon unbiased FIR filter for discrete-time state space models. Automatica, 38 (3), 545-551.10.1016/S0005-1098(01)00242-4Search in Google Scholar

[17] Zhao, S., Shmaliy, Y.S., Liu, F. (2015). Fast Kalman- Like optimal unbiased FIR filtering with applications. IEEE Transactions on Signal Processing, 64 (9), 2284-2297.Search in Google Scholar

[18] Oppenheim, A., Schafer, R. (1989). Discrete-Time Signal Processing. Prentice Hall.Search in Google Scholar

eISSN:
1335-8871
Lingua:
Inglese
Frequenza di pubblicazione:
6 volte all'anno
Argomenti della rivista:
Engineering, Electrical Engineering, Control Engineering, Metrology and Testing