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

Open access

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.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • [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.

  • [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.

  • [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.

  • [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.

  • [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.

  • [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.

  • [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.

  • [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.

  • [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] Bruckstein A.M. Kailath T. (1985). Recursive limited memory filtering and scattering theory. IEEE Transactions on Information Theory 31 (3) 440-443.

  • [11] Kim P.S. (2010). An alternative FIR filter for state estimation in discrete-time systems. Digital Signal Processing 20 (3) 935-943.

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

  • [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.

  • [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.

  • [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.

  • [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.

  • [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.

  • [18] Oppenheim A. Schafer R. (1989). Discrete-Time Signal Processing. Prentice Hall.

Search
Journal information
Impact Factor

IMPACT FACTOR 2018: 1.122
5-year IMPACT FACTOR: 1.157

CiteScore 2018: 1.39

SCImago Journal Rank (SJR) 2018: 0.325
Source Normalized Impact per Paper (SNIP) 2018: 0.881

Cited By
Metrics
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 161 66 3
PDF Downloads 77 43 1