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تشخیص و جداسازی عیب مبتنی بر فیلتر کالمن مقاوم در سیستمهای زمان گسسته با نامعینی تصادفی و کران‌دار | ||
| نشریه مهندسی مکانیک امیرکبیر | ||
| مقاله 4، دوره 53، شماره 6 (Special Issue)، شهریور 1400، صفحه 3825-3840 اصل مقاله (1.1 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/mej.2021.18775.6887 | ||
| نویسندگان | ||
| امیرحسین براتی1؛ مهدی رحمانی* 2 | ||
| 1گروه مهندسی برق-کنترل، دانشکده فنی و مهندسی، دانشگاه بین المللی امام خمینی، قزوین، ایران | ||
| 2گروه مهندسی برق-کنترل، دانشکده فنی و مهندسی، دانشگاه بین المللی امام خمینی | ||
| چکیده | ||
| این مقاله به مسأله تشخیص و جداسازی عیب در سیستمهای گسسته متغیر با زمان، با داشتن نامعینی تصادفی، کران‌دار و وجود نویز در سیستم و حس‌گر می‌پردازد. عیوب می‌تواند به‌طور همزمان یا به طور متوالی رخ دهند، از این‌رو فیلتر طراحی‌شده توانایی تشخیص و جداسازی این عیوب را، با توجه به چالش‌های ایجادشده به علت وجود نامعینی و اثرات نویز داراست. در حل مساله تشخیص عیب، به طراحی و ارائه فیلتر تشخیص و جداساز عیب مقاوم مبتنی بر فیلتر کالمن مقاوم پرداخته خواهد شد. به همین منظور، آستانه‌ای به‌صورت متغیر با زمان براساس حد بالای کوواریانس مانده‌ها تعریف می‌شود. این حد آستانه به تشخیص بهتر کمک می‌کند و از اخطار اشتباه در تشخیص عیوب جلوگیری می‌کند. در طراحی تشخیص‌گر عیب به تعداد خروجی‌های سیستم تشخیص‌گر طراحی می‌شود ، هم‌چنین برای طراحی جداساز عیب با بررسی مانده‌های سیستم شروطی به دست می‌آید که با اعمال این شروط در طراحی، به یک جداساز عیب مقاوم دست می‌یابد. در انتها با استفاده از سه مثال کارایی و عملکرد روش پیشنهادی نشان داده خواهد شد. در مثال اول عملکرد روش پیشنهادی در حضور عدم قطعیت و نویز نشان داده می‌شود و در مثال‌های دوم و سوم عملکرد و کارایی روش خود را با روش‌های دیگر مقایسه و برتری این روش در حضور نامعینی نشان داده خواهد شد. | ||
| کلیدواژه‌ها | ||
| تشخیص عیب؛ جداسازی عیب؛ فیلتر کالمن مقاوم؛ سیستم زمان-گسسته؛ نامعینی | ||
| عنوان مقاله [English] | ||
| Fault detection and isolation based on robust Kalman filter for discrete-time systems with stochastic and norm-bounded uncertainties | ||
| نویسندگان [English] | ||
| Amir Hossein Barati1؛ Mehdi Rahmani2 | ||
| 1Department of Electrical Engineering-Control, Faculty of Technical and Engineering, Imam Khomeini International University, Qazvin, Iran | ||
| 2Department of Electrical Engineering (control), Faculty of Engineering and Technology, Imam Khomeini International University | ||
| چکیده [English] | ||
| This paper deals with the problem of fault detection and isolation for discrete time-varying systems with stochastic and bounded uncertainties, and in presence of noises in the plant and sensors. Faults can occur simultaneously or sequentially, so the designed filter has the ability to detect and isolate these faults, and handle the challenges posed by uncertainty and the effects of noises. In solving the problem of fault diagnosis, fault detection and isolation filter based on the robust Kalman filter are presented. For this purpose, a time-varying threshold is defined based on the upper bound of covariance of the residuals. This threshold helps in better performance and prevents misdiagnosis. In the design of the fault detector, due to the number of outputs, fault detectors are designed. Moreover, by examining the residuals of the system, some conditions are obtained, which, by applying these conditions, a robust fault isolator is achieved. Finally, using three examples, the efficiency and performance of the proposed method are shown. In the first example, the performance of the proposed method is studied in the presence of uncertainty and noise, and in the second and third examples, the performance of the method is compared with other methods and the superiority of the proposed approach in the presence of uncertainties is shown. | ||
| کلیدواژه‌ها [English] | ||
| Fault detection, Fault isolation, Robust Kalman filter, Discrete-time system, Uncertainty | ||
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| مراجع | ||
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[1] M. shahab, M. Moavenian, Fault diagnosis based on model and dynamic behavior of vehicle suspension system, Amirkabir Journal of Mechanical Engineering, 52(1) (2020) 27-42. (in persian) [2] M. Kordestani, M. Saif, M.E. Orchard, R. Razavi-Far, K. Khorasani, Failure prognosis and applications—A survey of recent literature, to appear, IEEE transactions on reliability, (2019). [3] H.H. Alhelou, Fault detection and isolation in power systems using unknown input observer, in: Advanced condition monitoring and fault diagnosis of electric machines, IGI global, 2019, pp. 38-58. [4] B. Zhang, H. Du, J. Lam, N. Zhang, W. Li, A novel observer design for simultaneous estimation of vehicle steering angle and sideslip angle, IEEE Transactions on Industrial Electronics, 63(7) (2016) 4357-4366. [5] M.S. Phatak, N. Viswanadham, Actuator fault detection and isolation in linear systems, (1988). [6] J. CHEN, H. ZHANG, Robust detection of faulty actuators via unknown input observers, International Journal of Systems Science, 22(10) (1991) 1829-1839. [7] K. Watanabe, D. Himmelblau, Instrument fault detection in systems with uncertainties, International Journal of Systems Science, 13(2) (1982) 137-158. [8] Y. Wan, E. Harinath, R.D. Braatz, Probabilistic robust parity relation for fault detection using polynomial chaos, IFAC-PapersOnLine, 50(1) (2017) 1019-1024. [9] Y. Wu, Y. Li, M. Li, Z. Wang, D. Wang, Fault Diagnosis of Linear Discrete Time-Varying System with Multiplicative Noise Based on Parity Space Method, in: 2018 IEEE 27th International Symposium on Industrial Electronics (ISIE), IEEE, 2018, pp. 957-962. [10] J. Gertler, D. Singer, A New Structural Framework for Parity Equation-based Failure Detection, Automatica, 26(2) (1990) 381-388. [11] M. Zhou, M. Rodrigues, Y. Shen, D. Theilliol, H_/H∞ fault detection observer design for a polytopic LPV system using the relative degree, International Journal of Applied Mathematics and Computer Science, 28(1) (2018) 83-95. [12] Y. Li, H.R. Karimi, Q. Zhang, D. Zhao, Y. Li, Fault detection for linear discrete time-varying systems subject to random sensor delay: A Riccati equation approach, IEEE Transactions on Circuits and Systems I: Regular Papers, 65(5) (2017) 1707-1716. [13] Y. Li, H.R. Karimi, C.K. Ahn, Y. Xu, D. Zhao, Optimal residual generation for fault detection in linear discrete time-varying systems with uncertain observations, Journal of the Franklin Institute, 355(7) (2018) 3330-3353. [14] J. Chen, J.R. Patton, Standard H∞ filtering formulation of robust fault detection, IFAC Proceedings Volumes, 33(11) (2000) 261-266. [15] T. Li, L. Wu, X. Wei, Robust fault detection filter design for uncertain LTI systems based on new bounded real lemma, International Journal of Control, Automation and Systems, 7(4) (2009) 644-650. [16] J. Liu, J.L. Wang, G.-H. Yang, An LMI approach to minimum sensitivity analysis with application to fault detection, Automatica, 41(11) (2005) 1995-2004. [17] X. Wei, M. Verhaegen, Robust fault detection observer for LTI systems with additive uncertainties, IFAC Proceedings Volumes, 42(8) (2009) 756-761. [18] V. Venkatasubramanian, R. Rengaswamy, K. Yin, S.N. Kavuri, A review of process fault detection and diagnosis: Part I: Quantitative model-based methods, Computers & chemical engineering, 27(3) (2003) 293-311. [19] R. Isermann, Process fault detection based on modeling and estimation methods—A survey, automatica, 20(4) (1984) 387-404. [20] I. Hwang, S. Kim, Y. Kim, C.E. Seah, A survey of fault detection, isolation, and reconfiguration methods, IEEE transactions on control systems technology, 18(3) (2009) 636-653. [21] R.K. Mehra, J. Peschon, An innovations approach to fault detection and diagnosis in dynamic systems, Automatica, 7(5) (1971) 637-640. [22] J.-Y. Keller, Fault isolation filter design for linear stochastic systems, Automatica, 35(10) (1999) 1701-1706. [23] B. Friedland, Treatment of bias in recursive filtering, IEEE Transactions on Automatic Control, 14(4) (1969) 359-367. [24] A. Alouani, P. Xia, T. Rice, W. Blair, On the optimality of two-stage state estimation in the presence of random bias, IEEE Transactions on Automatic Control, 38(8) (1993) 1279-1283. [25] J.-Y. Keller, M. Darouach, Optimal two-stage Kalman filter in the presence of random bias, Automatica, 33(9) (1997) 1745-1748. [26] C.-S. Hsieh, F.-C. Chen, Optimal solution of the two-stage Kalman estimator, IEEE Transactions on automatic control, 44(1) (1999) 194-199. [27] C.-S. Hsieh, Robust two-stage Kalman filters for systems with unknown inputs, IEEE Transactions on Automatic Control, 45(12) (2000) 2374-2378. [28] A. Qiu, S. Shen, J. Zhang, Optimal intermittent fault diagnosis for discrete-time systems, in: 2016 35th Chinese Control Conference (CCC), IEEE, 2016, pp. 6814-6819. [29] M.A. Moradi, H. Bolandi, M. Abedi, Federated Extended Kalman Filter for Sensor Fault Detection and Isolation, Journal of Iranian Association of Electrical and Electronics Engineers, 13(4) (2017) 71-79. (in persian) [30] F. Bagheri, H. Khaloozaded, K. Abbaszadeh, Stator fault detection in induction machines by parameter estimation, using adaptive kalman filter, in: 2007 Mediterranean Conference on Control & Automation, IEEE, 2007, pp. 1-6. [31] Z. Hashemi, A. Rahideh, Rotor Electrical Fault Detection of Wind Turbine Induction Generators Using an Unscented Kalman Filter, Iranian Journal of Science and Technology, Transactions of Electrical Engineering, (2019) 1-10. [32] M. Abolhasani, M. Rahmani, Robust Kalman filtering for discrete-time time-varying systems with stochastic and norm-bounded uncertainties, Journal of Dynamic Systems, Measurement, and Control, 140(3) (2018). [33] L. Xie, Y.C. Soh, C.E. De Souza, Robust Kalman filtering for uncertain discrete-time systems, IEEE Transactions on automatic control, 39(6) (1994) 1310-1314. [34] J. Zarei, M. Tabatabaei, R. Razavi-Far, M. Saif, Fractional order unknown input filter design for fault detection of discrete linear systems, in: IECON 2017-43rd Annual Conference of the IEEE Industrial Electronics Society, IEEE, 2017, pp. 4333-4338. | ||
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