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Design of Observer-Based Fault Detection Structure for Unknown Systems using Input–Output Measurements: Practical Application to BLDC Drive

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Języki publikacji
EN
Abstrakty
EN
Industrial systems serve us in all areas of life. Faults may result in economic loss and wasting energy. Detecting the onset of faults, and determining their location are important engineering tasks. An important class of fault detection (FD) and diagnosis methods utilizes the mathematical model of the monitored system. But, the parameters required for mathematical modelling are limited or unavailable for the most real industrial engineering applications. Observer-based FD is one of the main approaches to FD and identification. At the same time, the traditional observer’s gain calculation required system model parameters. So, this article presents the design of a novel observer for FD purposes using the input–output measurements of the system with unknown parameters. This proposed observer’s design considers observer’s gain tuning, regardless of the mathematical representation of the plant. This the new feature that distinction our observer will facilitate the implementation of FD systems for many unknown parameters industrial systems. The effectiveness of the proposed observer is verified by experimental application to BLDC motor and compared with classical Luenberger observer. The experimental and comparison results prove feasibility and effectiveness of the proposed observer for FD purposes.
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Rocznik
Strony
215--224
Opis fizyczny
Bibliogr. 34 poz., rys., tab.
Twórcy
  • Faculty of Engineering, Mechatronics Division, Mechanical Engineering Department, Helwan University, Cairo, Egypt
  • Faculty of Engineering, Mechatronics Division, Mechanical Engineering Department, Helwan University, Cairo, Egypt
  • Faculty of Engineering, Mechatronics Division, Mechanical Engineering Department, Helwan University, Cairo, Egypt
Bibliografia
  • Alkaya, A. and Eker, I. (2014). Luenberger Observer-Based Sensor Fault Detection: Online Application to DC Motor. Turkish Journal of Electrical Engineering & Computer Sciences, 22(2), pp. 363–370.
  • Casdagli, M. (1992). A dynamical systems approach to modeling input-output systems. In: Santa Fe Institute Studies in the Sciences of Complexity-Proceedings, Vol. 12, pp. 265–265. Addison-Wesley Publishing Co.
  • Chen, W., and Saif, M. (2007). Observer-Based Strategies for Actuator Fault Detection, Isolation, and Estimation for Certain Class of Uncertain Nonlinear Systems. IET Control Theory & Applications, 1(6), pp. 1672–1680.
  • Doan, P. T., Bui, T. L., Kim, H. K., and Kim, S. B. (2013, June). Sliding-mode observer design for sensorless vector control of AC induction motor. In: 2013 9th Asian Control Conference (ASCC), Istanbul, Turkey, 23–26 June 2013, IEEE, pp. 1–5.
  • Eissa, M. A., Ahmed, M. S., Darwish, R. R., and Bassiuny, A. M. (2015a, December). Model-based sensor fault detection to brushless dc motor using Luenberger observer. In: 2015 7th International Conference on Modelling, Identification and Control (ICMIC), Sousse, Tunisia, 18–20 December 2015, IEEE, pp. 1–6.
  • Eissa, M. A., Ahmed, M. S., Darwish, R. R., and Bassiuny, A. M. (2015b, December). Improved fuzzy luenberger observer-based fault detection for BLDC motor. In: 2015 Tenth International Conference on Computer Engineering & Systems (ICCES), Cairo, Egypt, 23–24 December 2015, IEEE, pp. 167–174.
  • Ellis, G. (2002). Observers in Control Systems: A Practical Guide. Elsevier.
  • Frank, P. M., and Ding, X. (1997). Survey of Robust Residual Generation and Evaluation Methods in Observer-Based Fault Detection Systems. Journal of Process Control, 7(6), pp. 403–424.
  • Gadsden, S. A., Song, Y., and Habibi, S. R. (2013). Novel Model-Based Estimators for the Purposes of Fault Detection and Diagnosis. IEEE/ASME Transactions on Mechatronics, 18(4), pp. 1237–1249.
  • Hakiki, K., Mazari, B., Liazid, A., and Djaber, S. (2006). Fault Reconstruction Using Sliding Mode Observers. American Journal of Applied Sciences, 3(1), pp. 1669–1674.
  • Heredia, G., and Ollero, A. (2009, April). Sensor fault detection in small autonomous helicopters using observer/Kalman filter identification. In: 2009 IEEE International Conference on Mechatronics, Malaga, Spain, 14–17 April 2009, IEEE, pp. 1–6.
  • Ibaraki, S., Suryanarayanan, S., and Tomizuka, M. (2005). Design of Luenberger state observers using Fixed-Structure H/sub/spl infin//Optimization and its Application to Fault Detection in Lane-Keeping Control of Automated Vehicles. IEEE/ ASME Transactions on Mechatronics, 10(1), pp. 34–42.
  • Isermann, R. (2006). Fault-Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance. Berlin: Springer Science & Business Media.
  • Khalid, H. M., Khoukhi, A., and Al-Sunni, F. M. (2011, March). Fault detection and classification using Kalman filter and genetic neuro-fuzzy systems. In: Proceedings of the Annual Meeting of the North American Fuzzy Information Processing Society, El Paso, TX, USA, 18–20 March 2011, IEEE, pp. 18–20.
  • Ko, J. S. (1998). Asymptotically Stable Adaptive Load Torque Observer for Precision Position Control of BLDC Motor. IEE Proceedings-Electric Power Applications, 145(4), pp. 383–386.
  • Li, X. J., and Yang, G. H. (2012). Dynamic Observer-Based Robust Control and Fault Detection for Linear Systems. IET Control Theory & Applications, 6(17), pp. 2657–2666.
  • Liu, L., and Collins, E. (2006). Robust Fault Detection and Diagnosis for Permanent Magnet Synchronous Motors. The Florida State University.
  • Luenberger, D. (1971). An introduction to Observers. IEEE Transactions on Automatic Control, 16(6), pp. 596–602.
  • Nandam, P. K., and Sen, P. C. (1988, July). Sliding mode speed control of a self controlled synchronous motor (SCSM) using an adaptive state observer. In: Third International Conference on Power Electronics and Variable-Speed Drives, London, UK, 13–15 July 1988, IET, pp. 315–318.
  • Padmakumar, S., Agarwal, V., and Roy, K. (2009). A comparative study into observer based fault detection and diagnosis in DC motors: Part-I. World Academy of Science, Engineering and Technology, 51.
  • Poor, H. V. (2013). An Introduction to Signal Detection and Estimation. New York: Springer Science & Business Media.
  • Ruderman, M., and Iwasaki, M. (2014, July). Sensorless control of motor velocity in two-mass actuator systems with load sensing using extended state observer. In: 2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Besançon, France, 8–11 July 2014, IEEE, pp. 360–365.
  • Simani, S., Fantuzzi, C., and Patton, R. J. (2013). Model-Based Fault Diagnosis in Dynamic Systems Using Identification Techniques. New York: Springer Science & Business Media.
  • Sobhani, M. H., and Poshtan, J. (2012). Fault Detection and Isolation Using Unknown Input Observers with Structured Residual Generation. International Journal of Instrumentation and Control Systems, 2(2), pp. 1–12.
  • Szabat, K., and Serkies, P. J. (2009). Design and analysis of the Luenberger observers for three-inertia system. Prace Naukowe Instytutu Maszyn, Napędów i Pomiarów Elektrycznych Politechniki Wrocławskiej, Studia i Materiały, 29, pp. 329–340.
  • Szabat, K., Tran-Van, T., and Kamiński, M. (2015). A modified fuzzy Luenberger observer for a two-mass drive system. IEEE Transactions on Industrial Informatics, 11(2), pp. 531–539.
  • Tan, C. P., and Edwards, C. (2002). Sliding Mode Observers for Detection and Reconstruction of Sensor Faults. Automatica, 38(10), pp. 1815–1821.
  • Tarantino, R., Szigeti, F., and Colina-Morles, E. (2000). Generalized Luenberger Observer-Based Fault-Detection Filter Design: An Industrial Application. Control Engineering Practice, 8(6), pp. 665–671.
  • Vinodh Kumar, E., and Jerome, J. (2013). Sensor Fault Detection in Dc Servo System Using Unknown Input Observer with Structured Residual Generation. Journal of Electrical Engineering, 13, pp. 225–229.
  • Vinodh Kumar, E., Jovitha, J., and Ayyappan, S. (2013). Comparison of Four State Observer Design Algorithms for MIMO System. Archives of Control Sciences, 23(2), pp. 243–256.
  • Weiss, L., Sanderson, A., and Neuman, C. (1987). Dynamic Sensor-Based Control of Robots with Visual Feedback. IEEE Journal on Robotics and Automation, 3(5), pp. 404–417.
  • Yan, X. G., and Edwards, C. (2008). Robust Sliding Mode Observer-Based Actuator Fault Detection and Isolation for a Class of Nonlinear Systems. International Journal of Systems Science, 39(4), pp. 349–359.
  • Yang, S. K., and Liu, T. S. (1999). State Estimation for Predictive Maintenance Using Kalman Filter. Reliability Engineering & System Safety, 66(1), pp. 29–39.
  • Yi, J., Huang, Z., Liu, W., Yang, Y., Zhang, X., and Liu, J. (2014, May). Actuator Fault Detection Based on Robust Adaptive Observer for CCBII Braking System. In: The 26th Chinese Control and Decision Conference (2014 CCDC), Changsha, China, 31 May–2 June 2014, IEEE, pp. 2841–2846.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-31ea8f49-2079-4afc-939e-e534f78d0ad9
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