1. bookVolume 17 (2017): Issue 2 (April 2017)
Journal Details
License
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English
access type Open Access

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

Published Online: 26 Apr 2017
Volume & Issue: Volume 17 (2017) - Issue 2 (April 2017)
Page range: 76 - 82
Received: 25 Oct 2016
Accepted: 14 Mar 2017
Journal Details
License
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English
Abstract

In the current paper, a residual generation filter with finite memory structure is proposed for sensor fault detection. The proposed finite memory residual generation filter provides the residual by real-time filtering of fault vector using only the most recent finite measurements and inputs on the window. It is shown that the residual given by the proposed residual generation filter provides the exact fault for noisefree systems. The proposed residual generation filter is specified to the digital filter structure for the amenability to hardware implementation. Finally, to illustrate the capability of the proposed residual generation filter, extensive simulations are performed for the discretized DC motor system with two types of sensor faults, incipient soft bias-type fault and abrupt bias-type fault. In particular, according to diverse noise levels and windows lengths, meaningful simulation results are given for the abrupt bias-type fault.

Keywords

[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

Recommended articles from Trend MD

Plan your remote conference with Sciendo