| Citation: | SUN You-gang, ZHANG Zeng, GAO Ding-gang, XU Jun-qi, MENG Chuan-shu. Fault detection and recovery method of suspension control system based on SPRT and hybrid intelligent decision-making[J]. Journal of Traffic and Transportation Engineering, 2026, 26(9): 12-25. doi: 10.19818/j.cnki.1671-1637.2026.249
|
A full-state fault detection algorithm incorporating hybrid decision-making and reset logic was proposed to address the problem that the probes of maglev train air gap sensors are prone to failure in special operating conditions including strong magnetic fields, vibration, and rail joint crossing. By adopting fault identification, signal switching, and fault-tolerant strategies, system stability in special operating conditions was ensured. Firstly, the suspension control system based on a joint structure was taken as the research object to carry out system dynamics modeling, and the coupling relationship and stability between the left and right electromagnets were analyzed. Secondly, a new method capable of rapidly identifying faults and implementing timely fault tolerance was proposed by integrating a full-state feedback control algorithm based on a signal selection strategy, sequential probability ratio test (SPRT), and residual chi-square detection. Simulation results show that in cases where some sensor probes fail, the initial fault detection time is delayed by 0.02~0.2 s after fault occurrence. The missed detection rate during the fault duration is approximately 5%. For sensor probe faults of varying severity degrees, the proposed fault-tolerant control strategy reduces the average fluctuation of the left electromagnet to 0.27 mm and the right electromagnet to 0.13 mm. The SPRT algorithm advances the response time to fault termination by about 0.5 s, enabling rapid detection and recovery. In rail gap crossing conditions, faults can be identified instantly, and the fluctuation of the suspension gap is significantly suppressed by approximately 89%~91%. The proposed method significantly enhances the robustness and fault tolerance of the suspension control system against gap sensor probe failures, ensuring the dynamic stability and ride comfort of maglev trains under long-term service.
| [1] |
XIONG Jia-yang, SHEN Zhi-yun, CHI Mao-ru, et al. Review on high-speed maglev train technology[J]. Journal of Traffic and Transportation Engineering, 2025, 25(2): 1-23. doi: 10.19818/j.cnki.1671-1637.2025.02.001
|
| [2] |
YANG Biao, MEI Zi, LONG Zhi-qiang. Online anomaly detection method integrating LSTM and MGD for suspension system of maglev trains[J]. Journal of Traffic and Transportation Engineering, 2023, 23(6): 216-231. doi: 10.19818/j.cnki.1671-1637.2023.06.014
|
| [3] |
SUN Y G, LI F X, WANG Y, et al. A survey of coupled vibration characteristics and control methods in maglev transportation: Challenges, methods and progress[J]. Journal of Railway Science and Technology, 2025, 1(2): 1-27.
|
| [4] |
SUN Y G, LI F X, LIN G B, et al. Adaptive fault-tolerant control of high-speed maglev train suspension system with partial actuator failure: Design and experiments[J]. Journal of Zhejiang University—Science A, 2023, 24(3): 272-283. doi: 10.1631/jzus.A2200189
|
| [5] |
NAJAFI A, MOBAYEN S, ROUHANI S H, et al. Robust adaptive fault-tolerant control for MAGLEV train systems: A nonsingular finite-time approach[J]. IEEE Transactions on Transportation Electrification, 2025, 11(1): 3680-3690. doi: 10.1109/TTE.2024.3445322
|
| [6] |
ZHAI M D, LONG Z Q, LI X L. Fault-tolerant control of magnetic levitation system based on state observer in high speed maglev train[J]. IEEE Access, 2019, 7: 31624-31633. doi: 10.1109/ACCESS.2019.2898108
|
| [7] |
JIN L, LONG Z Q, ZENG J W, Research on fault-tolerant control problem for suspension system of medium speed maglev train[C]//IEEE. The 29th Chinese Control and Decision Conference. New York: IEEE, 2017: 2993-2998.
|
| [8] |
MICHAIL K, ZOLOTAS A C, GOODALL R M, et al. Optimised configuration of sensors for fault tolerant control of an electro-magnetic suspension system[J]. International Journal of Systems Science, 2012, 43(10): 1785-1804. doi: 10.1080/00207721.2011.598959
|
| [9] |
MICHAIL K, ZOLOTAS A C, GOODALL R M. Optimised sensor selection for control and fault tolerance of electromagnetic suspension systems: A robust loop shaping approach[J]. ISA Transactions, 2014, 53(1): 97-109. doi: 10.1016/j.isatra.2013.08.006
|
| [10] |
NAZARI R, YETENDJE A, SERON M M. Fault-tolerant control of a magnetic levitation system using virtual-sensor-based reconfiguration[C]//IEEE. 18th Mediterranean Conference on Control and Automation. New York: IEEE, 2010: 1067-1072.
|
| [11] |
YETENDJE A, SERON M M, DE DONA J A. Multisensor fusion fault-tolerant control of a magnetic levitation system[C]//IEEE. 18th Mediterranean Conference on Control and Automation. New York: IEEE, 2010: 1055-1060.
|
| [12] |
WANG Z Q, LONG Z Q, LI X L. Fault tolerant control for joint structure in PEMS high speed maglev train[J]. Asian Journal of Control, 2021, 23(1): 486-498. doi: 10.1002/asjc.2320
|
| [13] |
LI J H, DING K, SUN J M, et al. A practical control strategy for magnetic levitation system against two probes failures[C]//IEEE. 2020 39th Chinese Control Conference. New York: IEEE, 2020: 5577-5583.
|
| [14] |
SUN You-gang, HUANG Zhi-chuang, LIN Guo-bin, et al. Fault-tolerant control for levitation systems of high-speed maglev train based on diversified basis neural networks[J]. Journal of Traffic and Transportation Engineering, 2025, 25(2): 61-74. doi: 10.19818/j.cnki.1671-1637.2025.02.004
|
| [15] |
LI Y, HE G, LI J. Nonlinear robust observer-based fault detection for networked suspension control system of maglev train[J]. Mathematical Problems in Engineering, 2013, 2013: 713560.
|
| [16] |
LIU J H, DAI C H, SUN S, et al. Fault-tolerant control for electromagnetic suspension system of maglev train: Comprehensive research and design[C]//IEEE. 13th Data Driven Control and Learning Systems Conference. New York: IEEE, 2024: 1734-1739.
|
| [17] |
GAO Chao, ZHAO Xiu-bin, PANG Chun-lei, et al. An interacted algorithm based on residual Chi-square and SPRT methods to test abrupt faults[J]. Fire Control & Command Control, 2020, 45(4): 148-152, 157.
|
| [18] |
YANG B, LIU F, XUE L, et al. Fault-tolerant SINS/Doppler radar/odometer integrated navigation method based on two-stage fault detection structure[J]. Entropy, 2023, 25(10): 1412. doi: 10.3390/e25101412
|
| [19] |
WU X J, SU Z, LI L, et al. Improved adaptive federated kalman filtering for INS/GNSS/VNS integrated navigation algorithm[J]. Applied Sciences, 2023, 13(9): 5790. doi: 10.3390/app13095790
|
| [20] |
ZHOU Jun, ZHANG Peng. Hard fault isolation of GPS navigation system based on gray prediction for agricultural robot[J]. Transactions of the Chinese Society for Agricultural Machinery, 2010, 41(12): 165-168, 177.
|
| [21] |
SUN Ke-cheng, ZENG Qing-hua, WANG Shou-yi, et al. Pseudorange fault detection and adaptive isolation method based on factor graph navigation[J]. Journal of Chinese Inertial Technology, 2022, 30(1): 65-73.
|
| [22] |
LIU J Z. Fault diagnosis based on strong tracking SRUKF and residual Chisquare test[J]. Recent Patents on Engineering, 2018, 12(1): 64-72. doi: 10.2174/1872212111666170329102749
|
| [23] |
HAO A-ming, LI Xiao-long, LIU Heng-kun. Research on levitation joining-structure controller of high-speed maglev train[J]. Journal of the China Railway Society, 2014, 36(5): 39-45.
|
| [24] |
LIAO Hai-jun. Research on time-sharing detecting method of low speed maglev train gap sensor[D]. Chengdu: Southwest Jiaotong University, 2018: 17-42.
|
| [25] |
YAO Wen-hua. Application of improved LQR controller in maglev train suspension control[J]. Railway Signalling & Communication Engineering, 2025, 22(7): 10-17.
|
Website copyright Journal of Traffic and Transportation Engineering Editorial Department陕ICP备05001904号-1
Address ：Editorial Department of Journal of Traffic and Transportation Engineering, Chang'an University, Middle Section of South 2nd Ring Road, Xi'an, China（710064） Tel：029-82334388 Email：jygc@chd.edu.cn
All visit:Today's visit:
Supported by:
Beijing Renhe Information Technology Co. Ltd
DownLoad:
