A Fault Detection Algorithm using Multiple Residual Generation Filters

Authors

  • Pyung Soo Kim System Software Solution Lab., Dept. of Electronic Engineering, Korea Polytechnic University, 237 Sangidaehak-ro, Siheung-si, Gyeonggi-do, 429-793, Korea.

Keywords:

Fault Detection, Residual Generation, Estimation Filter, Kalman Filter,

Abstract

This paper proposes a fault detection algorithm based on multiple residual generation filters for discrete-time systems. Residuals are generated from estimation errors between the reference filter and multiple residual generation filters. These filters utilize only finite observation on the most recent window. The reference filter gives optimal state estimates based on all sensors. One the other hand, one of multiple residual generation filters can give the sub-optimal state estimates which can be independent of faulty sensor. Then, the fault detection rule is developed to indicate presence of fault by checking the agreement of multiple residuals. Multiple test variables for the detection rule are defined using the chi-squared distribution with one degree of freedom. Via numerical simulations for the aircraft engine system, the proposed algorithm is verified.

References

Venkatasubramanian, R. Rengaswamy, K. Yin, and S. N. Kavuri, A review of process fault detection and diagnosis - Part I: Quantitative model-based methods,” Computers and Chemical Engineering, 27(3) (2003), 293–311.

C. Angeli and A. Chatzinikolaou, On-line fault detection techniques for technical systems: A survey, International Journal of Computer Science & Applications, 1(1) (2004), 12–30.

Hwang, S. Kim, Y. Kim, and C.E. Seah, A survey of fault detection, isolation, and reconfiguration methods, IEEE Trans. Control Systems Technology, 18(3) (2010), 636–653.

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

Y. Wang and Y. Zheng, Kalman filter based fault diagnosis of networked control system with white noise, Journal of Control Theory and Application, 3(1) (2005), 55–59.

N. Tudoroiu and K. Khorasani, Satellite fault diagnosis using a bank of interacting Kalman filters, IEEE Trans. Aerosp. Electron. Syst., 43(4) (2007), 1334–1350.

W. Xue, Y. Guo, and X. Zhang, 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) (2008), 3161–3168.

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

K. Villez, B. Srinivasanb, R. Rengaswamyb, S. Narasimhanc, and V. Venkatasubramaniana, Kalman-based strategies for fault detection and identification (FDI): Extensions and critical evaluation for a buffer tank system, Computers and Chemical Engineering, 35(5) (2011), 806–816.

M. Bruckstein and T. Kailath, Recursive limited memory filtering and scattering theory, IEEE Trans. Inform. Theory, 31(3) (1985), 440–443.

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

Y. S. Shmaliy, Linear optimal FIR estimation of discrete time-invariant state-space models, IEEE Trans. on Signal Processing, 58(6) (2010), 3086–3096.

S. Zhao, Y. S. Shmaliy, B. Huang, and F. Liu, Minimum variance unbiased FIR filter for discrete time-variant systems, Automatica, 53(2) (2015), 355–361.

P. S. Kim, A computationally efficient fixed-lag smoother using recent finite measurements, Measurement, 46(1) (2013), 846–850.

P. S. Kim, E. H. Lee, M. S. Jang, An estimation filtering for packet loss probability using finite memory structure strategy, Lecture Notes in Electrical Engineering, 373 (2015), 301–307.

P. S. Kim, E. H. Lee, M. S. Jang, and K. S. Song, Bank of finite memory filters for fault detection and adjusting detection latency, in Proc. 2nd International Conference on Mechanical, Automotive and Materials Engineering, (2014), 811–81.

Downloads

Published

2017-06-01

How to Cite

Soo Kim, P. (2017). A Fault Detection Algorithm using Multiple Residual Generation Filters. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2-5), 33–36. Retrieved from https://jtec.utem.edu.my/jtec/article/view/2388

Issue

Vol. 9 No. 2-5: Circuit and System Advancement in Modern World IV

Section

Articles

License

TRANSFER OF COPYRIGHT AGREEMENT

The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):

  1. All proprietary right other than copyright, such as patent rights
  2. The right to make further copies of all or part of the published article for my use in classroom teaching
  3. The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
  4. The right to make copies of the published work for internal distribution within the institution that employs me

I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.