Simultaneous Fault Diagnosis of Broken Rotor Bar and Speed Sensor for Traction Motor in High-speed Train
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摘要: 为提升高速列车牵引系统的稳定性和可靠性, 针对其牵引电机提出一种基于未知输入观测器的转子断条和速度传感器故障联合诊断方法. 首先, 通过非奇异坐标变换, 将牵引电机系统解耦为两个分别只包含转子断条故障和速度传感器故障的子系统, 实现转子断条故障与速度传感器故障的解耦, 并进一步利用一阶低通滤波器将含速度传感器故障的子系统转化为增广系统. 其次, 对含转子断条故障的子系统和速度传感器故障增广系统分别设计未知输入区间观测器和未知输入滑模观测器. 在此基础上, 采用未知输入区间观测器上界和下界构建转子断条故障诊断的检测变量和自适应阈值, 利用未知输入滑模观测器的等效输出控制原理实现速度传感器故障估计. 最后, 通过仿真和TDCS-FIB平台实验验证了所提方法的有效性和鲁棒性.Abstract: In order to improve the stability and reliability of the traction system of high-speed train, this paper proposes a simultaneous diagnosis method for broken rotor bar fault and speed sensor fault of traction motor based on the unknown input observer. Firstly, through non singular coordinate transformation, the traction motor system is decoupled into two subsystems that only contain broken rotor bar fault and speed sensor fault, respectively, so as to realize the decoupling of broken rotor bar fault and speed sensor fault, and the subsystem containing speed sensor fault is further transformed into augmented system by using first-order low-pass filter. Then, the unknown input interval observer and the unknown input sliding mode observer are designed for the subsystem with broken rotor bar fault and the speed sensor fault augmentation system, respectively. On this basis, the upper and lower bounds of the unknown input interval observer are used to construct the detection variables and adaptive thresholds for broken rotor bar fault diagnosis, and the speed sensor fault estimation is realized by using the equivalent output control principle of the unknown input sliding mode observer. Finally, the effectiveness and robustness of the proposed method are verified by simulation and TDCS-FIB platform experiments.
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表 1 牵引电机主要参数
Table 1 Main parameters of traction motor
参数 符号 数值 极对数 $ {n_p} $ 3 定子电感 $ {L_s} $ $ 0.3410 \ {\rm{ H}} $ 定子电阻 $ {R_s} $ $0.0087 \; {{ \Omega } }$ 转子电感 $ {L_r} $ $ 0.0355 \ {\rm{ H}} $ 转子电阻 $ {R_r} $ $0.0087 \; {{ \Omega } }$ 磁链 $ {\psi _f} $ $ 0.1840 \ {\rm{ Wb}} $ 转动惯量 $ J $ $0.8620 \; {\rm{ kg} } {\cdot} {\rm{ m^2} }$ 直流侧电压 $ {U_{dc}} $ $ 311 \ {\rm{ V}} $ -
[1] 周东华, 纪洪泉, 何潇. 高速列车信息控制系统的故障诊断技术. 自动化学报, 2018, 44(7): 1153-1164Zhou Dong-Hua, Ji Hong-Quan, He Xiao. Fault diagnosis technology for high-speed train information control system. Acta Automatica Sinica, 2018, 44(7): 1153-1164 [2] 杨超, 彭涛, 阳春华, 陈志文, 桂卫华. 高速列车牵引传动系统故障测试与验证仿真平台研究. 自动化学报, 2019, 45(12): 2218-2232Yang Chao, Peng Tao, Yang Chun-Hua, Chen Zhi-Wen, Gui Wei-Hua. Research on a simulation platform for fault testing and verification of high-speed train traction drive systems. Acta Automatica Sinica, 2019, 45(12): 2218-2232 [3] 姜斌, 吴云凯, 陆宁云, 冒泽慧. 高速列车牵引系统故障诊断与预测技术综述. 控制与决策, 2018, 33(05): 841-855Jiang Bin, Wu Yun-Kai, Lu Ning-Yun, Mao Ze-Hui. Review of fault diagnosis and prediction technology of high-speed train traction system. Control and Decision, 2018, 33(05): 841-855 [4] Manohar M, Das S. Current sensor fault-tolerant control for direct torque control of induction motor drive using flux-linkage observer. IEEE Transactions on Industrial Informatics, 2017, 13(6): 2824-2833 doi: 10.1109/TII.2017.2714675 [5] Comanescu M. Design and implementation of a highly robust sensorless sliding mode observer for the flux magnitude of the induction motor. IEEE Transactions on Energy Conversion, 2016, 31(2): 649-657 doi: 10.1109/TEC.2016.2516951 [6] De Angelo C H, Bossio G R, Giaccone S J, MI Valla, GO Garcia. Online model-based stator-fault detection and identification in induction motors. IEEE Transactions on Industrial Electronics, 2009, 56(11): 4671-4680 doi: 10.1109/TIE.2009.2012468 [7] Panagiotou P A, Arvanitakis I, Lophitis N, J Antonino-Daviu, KN Gyftakis. A new approach for broken rotor bar detection in induction motors using frequency extraction in stray flux signals. IEEE Transactions on Industry Applications, 2019, 55(4): 3501-3511 doi: 10.1109/TIA.2019.2905803 [8] Iglesias-Martínez M E, de Córdoba P F, Antonino-Daviu J A, Conejero CA. Detection of nonadjacent rotor faults in induction motors via spectral subtraction and autocorrelation of stray flux signals. IEEE Transactions on Industry Applications, 2019, 55(5): 4585-4594 doi: 10.1109/TIA.2019.2917861 [9] Soleimani Y, Cruz S M A, Haghjoo F. Broken rotor bar detection in induction motors based on air-gap rotational magnetic field measurement. IEEE Transactions on Instrumentation and Measurement, 2018, 68(8): 2916-2925 [10] Yang Xiao-Yue, Yang Chun-Hua, Yang Chao, Peng Tao, Chen Zhi-Wen, Wu Zhi-Liang. Transient fault diagnosis for traction control system based on optimal fractional-order method. ISA transactions, 2020, 102: 365-375 doi: 10.1016/j.isatra.2020.03.006 [11] Li B, Zhang P L, Wang Z J, Mi S S, Lui D S. A weighted multi-scale morphological gradient filter for rolling element bearing fault detection. ISA transactions, 2011, 50(4): 599-608 [12] 李睿彧, 刘飞, 梁霖, 罗爱玲, 徐光华. 基于参数优化变分模态分解的交流变频电机转子断条故障识别方法. 电工技术学报, 2021, 36(18): 3922-3933Li Rui-Yu, Liu Fei, Liang Lin, Luo Ai-Ling, Xu Guang-Hua. A fault identification method for broken bars of AC variable frequency motor rotor based on parameter optimization variational modal decomposition. Transactions of China Electrotechnical Society, 2021, 36(18): 3922-3933 [13] Pablo M, Otero M, Schallschmidt T, GR Bossio, R Leidhold. Active broken rotor bar diagnosis in induction motor drives. IEEE Transactions on Industrial Electronics, 2020, 68(8): 7556-7566 [14] Morales-Perez C, Rangel-Magdaleno J, Peregrina-Barreto H, JP Amezquita-Sanchez, M Valtierra-Rodriguez. Incipient broken rotor bar detection in induction motors using vibration signals and the orthogonal matching pursuit algorithm. IEEE Transactions on Instrumentation and Measurement, 2018, 67(9): 2058-2068 doi: 10.1109/TIM.2018.2813820 [15] Chen Hong-Tian, Jiang Bin, Liu Ning-Yun. A newly robust fault detection and diagnosis method for high-speed trains. IEEE Transactions on Intelligent Transportation Systems, 2018, 20(6): 2198-2208 [16] Hajary A, Kianinezhad R, Seifossadat S G, SS Mortazavi, A Saffarian. Detection and localization of open-phase fault in three-phase induction motor drives using second order rotational park transformation. IEEE Transactions on Power Electronics, 2019, 34(11): 11241-11252 doi: 10.1109/TPEL.2019.2901598 [17] Nguyen V, Wang Dan-Wei, Seshadrinath J, A Ukil, MS Krishna, S Nadarajan. A method for incipient interturn fault detection and severity estimation of induction motors under inherent asymmetry and voltage imbalance. IEEE Transactions on Transportation Electrification, 2017, 3(3): 703-715 doi: 10.1109/TTE.2017.2726351 [18] Tian Yang, Zhang Ke, Jiang Bin, Yang Xing-Gang. Interval observer and unknown input observer-based sensor fault estimation for high-speed railway traction motor. Journal of the Franklin Institute, 2020, 357(2): 1137-1154 doi: 10.1016/j.jfranklin.2019.11.062 [19] Li Shan-Zhi, Wang Hao-Ping, Tian Yang, Abdel Aitouch, John-Klein. Direct power control of DFIG wind turbine systems based on an intelligent proportional-integral sliding mode control. ISA transactions, 2016, 64: 431-439 doi: 10.1016/j.isatra.2016.06.003 [20] 陶宏伟, 彭涛, 杨超, 陈志文, 桂卫华. 高速列车牵引整流器多类故障联合诊断方法. 自动化学报, 2019, 45(12): 2294-2302Tao Hong-Wei, Peng Tao, Yang Chao, Chen Zhi-Wen, Gui Wei-Hua. Joint diagnosis method for multi-type faults of high-speed train traction rectifiers. Acta Automatica Sinica, 2019, 45(12): 2294-2302 [21] Zhang Kang-Kang, Jiang inB, Yan Xing-Gang, Mao Ze-Hui. Incipient fault detection for traction motors of high-speed railways using an interval sliding mode observer. IEEE Transactions on Intelligent Transportation Systems, 2018, 20(7): 2703-2714 [22] Wu Yun-Kai, Jiang Bin, Lu Ning-Yun. A descriptor system approach for estimation of incipient faults with application to high-speed railway traction devices. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, 49(10): 2108-2118 [23] Wu Yun-Kai, Jiang Bin, Wang Yu-Long. Incipient winding fault detection and diagnosis for squirrel-cage induction motors equipped on CRH trains. ISA transactions, 2020, 99: 488-495 doi: 10.1016/j.isatra.2019.09.020 [24] Raoufi R, Marquez H J, Zinober A S I. H∞ sliding mode observers for uncertain nonlinear Lipschitz systems with fault estimation synthesis. International Journal of Robust and Nonlinear Control, 2010, 20(16): 1785-1801 doi: 10.1002/rnc.1545 [25] Ziyabari S, Shoorehdeli M A. Robust fault diagnosis scheme in a class of nonlinear system based on UIO and fuzzy residual. International Journal of Control, Automation and Systems, 2017, 15(3): 1145-1154 doi: 10.1007/s12555-016-0145-0 [26] Guzinski J, Diguet M, Krzeminski Z, A Lewicki, H Abu-Rub. Application of speed and load torque observers in high-speed train drive for diagnostic purposes. IEEE transactions on Industrial Electronics, 2008, 56(1): 248-256 [27] Wang Hui-Ming, Ge Xing-Lai, Liu Yang-Chao. Second-order sliding-mode MRAS observer-based sensorless vector control of linear induction motor drives for medium-low speed maglev applications. IEEE Transactions on Industrial Electronics, 2018, 65(12): 9938-9952 doi: 10.1109/TIE.2018.2818664 [28] Sahin I, Keysan O. Model predictive controller utilized as an observer for inter-turn short circuit detection in induction motors. IEEE transactions on Energy Conversion, 2021, 36(2): 1449-1458 doi: 10.1109/TEC.2020.3048071 [29] 文传博, 邓露, 吴兰. 基于滑模观测器和广义观测器的故障估计方法. 自动化学报, 2018, 44(9): 1698-1705Wen Chuan-Bo, Deng Lu, Wu Lan Fault estimation method based on sliding mode observer and generalized observer Acta Automatica Sinica, 2018, 44 (9): 1698-1705 [30] Efimov D, Rassi T, Zolghadri A. Control of nonlinear and LPV systems: Interval observer-based framework. IEEE Transactions on Automatic Control, 2013, 58(3): 773-778 doi: 10.1109/TAC.2013.2241476 [31] Yang Chun-Hua, Yang Chao, Peng Tao, Yang Xiao-Yue, Gui Wei-Hua. A fault-injection strategy for traction drive control systems. IEEE Transactions on Industrial Electronics, 2017, 64(7): 5719-5727 doi: 10.1109/TIE.2017.2674610 [32] Abd-el-Malek M B, Abdelsalam A K, Hassan O E. Novel approach using Hilbert Transform for multiple broken rotor bars fault location detection for three phase induction motor. ISA transactions, 2018, 80: 439-457 doi: 10.1016/j.isatra.2018.07.020 [33] Gou Bin, Yan Xu, Yang Xia, Gary Wilson Liu Shu-Yong. An intelligent time-adaptive data-driven method for sensor fault diagnosis in induction motor drive system. IEEE Transactions on Industrial Electronics, 2018, 66(12): 9817-9827 [34] 杨泽斌, 许婷, 孙晓东, 贾晶荆, 朱熀秋. 基于BPNN的无轴承异步电机传感器故障诊断及容错控制.中国电机工程学报, 2022, 42(11): 4218-4226Yang Ze-Bin, Xu Ting, Sun Xiao-Dong, Jia Jing-Jing, Zhu Xi-Qiu. Sensor Fault Diagnosis and Fault-Tolerant Control of a Bearingless Induction Motor Based on BPNN. Proceedings of the CSEE, 2022, 42(11): 4218-4226