Fault detection and recovery method of suspension control system based on SPRT and hybrid intelligent decision-making
-
摘要:
磁浮列车间隙传感器在强磁、振动及过轨缝等特殊工况下探头易故障，为此，本文构建了一种含混合决策与复位逻辑的全状态故障检测算法，通过故障识别、信号切换与容错策略，保障特殊工况下的系统稳定性。本文以基于搭接结构的悬浮控制系统为研究对象，开展了系统动力学建模，并分析了左右电磁铁的耦合关系及稳定性；将基于信号选择策略的全状态反馈控制算法、序贯概率比检验以及残差卡方检测算法进行融合，构建了一套能够快速识别故障并及时容错的新方法。仿真结果表明：在传感器部分探头失效的情况下，故障首次检测时间晚于故障发生后的0.02~0.2 s；故障持续期间的漏检率约为5%，针对不同严重程度的传感器探头故障，采用所提方法后，左侧电磁铁平均波动值为0.27 mm，右侧为0.13 mm；序贯概率比检验算法对故障结束的响应时间提前约0.5 s，实现了快速检测与恢复；在过轨缝工况下能瞬时识别故障，并将悬浮间隙波动值大幅抑制89%~91%。所提方法显著增强了悬浮控制系统对间隙传感器探头故障的鲁棒性与容错能力，保障磁浮列车长期服役下的动态稳定性及乘坐舒适性。
Abstract: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.
-
图 3 探头1、2故障情况下探头1、2健康情况统计
Figure 3. Health statistics of probes 1 and 2 under probes 1 & 2 fault
图 4 探头1故障情况下信号选择策略容错情况
Figure 4. Fault tolerance situation of the signal selection strategy under probe 1 fault
图 5 探头1、2故障情况下信号选择策略容错情况
Figure 5. Fault tolerance situation of the signal selection strategy under probes 1 & 2 fault
图 6 探头1、2缓变故障情况下探头1、2健康情况统计
Figure 6. Health statistics of probes 1 and 2 for slow drift faults on probes 1 & 2
图 7 探头1、2缓变故障情况下信号选择策略容错情况
Figure 7. Fault tolerance scenario of the signal selection strategy for slow drift faults on probes 1 & 2
图 9 探头1、2故障情况下SPRT检测情况
Figure 9. SPRT detection results under the fault of probes 1 & 2
图 11 探头1、2故障情况下残差卡方故障检测
Figure 11. Fault detection using residual chi-square test under the fault of probes 1 & 2
图 12 探头1~3故障情况下残差卡方故障检测
Figure 12. Fault detection using residual chi-square test under the fault of probes 1~3
图 14 过轨缝工况下探头1故障时探头1健康状态统计
Figure 14. Health statistics of probe 1 under probe 1 fault condition in rail joint crossing operation
图 15 过轨缝工况下探头1故障时信号选择策略容错情况
Figure 15. Fault tolerance scenario of the signal selection strategy under probe 1 fault condition in rail joint crossing operation
图 16 过轨缝工况下探头1、2故障时探头1、2健康情况统计
Figure 16. Health statistics of probes 1 and 2 under probes 1 & 2 fault condition in rail joint crossing operation
图 17 过轨缝工况下探头1、2故障时信号选择策略容错情况
Figure 17. Fault tolerance scenario of the signal selection strategy under probes 1 & 2 fault condition in rail joint crossing operation
物理参数 取值 电磁铁面积/m2 $ {A}_{\mathrm{e}}=0.115 $ 电阻/Ω $ {R}_{\mathrm{e}}=3.84 $ 悬浮框质量/kg $ {M}_{\mathrm{h}}=450 $ 正常悬浮间隙/mm $ {z}_{10}={z}_{20}=12 $ 电流环系数 $ {k}_{\mathrm{c}1}=40,{k}_{\mathrm{c}2}=1 $ 匝数 $ {N}_{\mathrm{e}}=270 $ 电磁铁质量/kg $ {M}_{1}={M}_{2}=300 $ 车体施加给悬浮框的干扰力/kN $ {M}_{\mathrm{c}}g=24.5 $ 静态工作电流/A $ {i}_{10}={i}_{20}=30.8 $ 金属橡胶弹簧刚度/（kN·mm-1） $ {K}_{1}={K}_{2}=20 $ 
下载: 导出CSV
表 2 阈值参数敏感性仿真分析结果
Table 2. Results of threshold parameter sensitivity simulation analysis
参数 测试范围 对性能的主要影响 选取值 鲁棒性区间 $ \alpha $ 0.001~0.1 $ \alpha \ge 0.05 $时出现虚警
$ \alpha < 0.05 $时无虚警0.01 0.005~0.02 $ \beta $ 0.000 1~1 $ \beta < 0.001 $时恢复延迟长
$ \beta > 0.1 $时系统失效0.01 0.005~0.05 $ \theta $ 1 000~15 000 $ \theta < 3\ 500 $时提前复位
$ \theta > 12\ 000 $时复位延迟7 000 3 500~12 000
下载: 导出CSV
表 3 信号选择策略的仿真结果
Table 3. Simulation results of the signal selection strategy
故障情况 探头1故障 探头1、2故障 探头1、2、3故障 漏检率/% 3.38（探头1） 3.38（探头1）
6.98（探头2）6.98（探头1）
6.98（探头2）
10.58（探头3）首次检测时间/s 3.02 波动值/mm 左侧电磁铁：0.14
右侧电磁铁：0.06左侧电磁铁：0.25
右侧电磁铁：0.12左侧电磁铁：0.41
右侧电磁铁：0.20
下载: 导出CSV
表 4 序贯概率比检验的仿真结果
Table 4. Simulation results of the sequential probability ratio test
故障情况 探头1故障 探头1、2故障 探头1、2、3故障 漏检率/% 4.6 3.8 3.6 故障恢复时反应时长/s 0.6 首次检测时间/s 3.23 3.19 3.18
下载: 导出CSV
表 5 残差卡方检测的仿真结果
Table 5. Simulation results of the residual chi-square test
故障情况 探头1故障 探头1、2故障 探头1、2、3故障 漏检率/% 3.2 2 0 首次检测时间/s 3.02 3.01 0（反应延迟0.01）
下载: 导出CSV
-
[1] 熊嘉阳, 沈志云, 池茂儒, 等. 高速磁悬浮列车技术综述[J]. 交通运输工程学报, 2025, 25(2): 1-23. doi: 10.19818/j.cnki.1671-1637.2025.02.001 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] 杨彪, 梅子, 龙志强. 磁浮列车悬浮系统LSTM与MGD融合的在线异常检测方法[J]. 交通运输工程学报, 2023, 23(6): 216-231. doi: 10.19818/j.cnki.1671-1637.2023.06.014 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] 孙友刚, 黄志创, 林国斌, 等. 高速磁浮车辆悬浮系统多元基神经网络容错控制[J]. 交通运输工程学报, 2025, 25(2): 61-74. doi: 10.19818/j.cnki.1671-1637.2025.02.004 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] 高超, 赵修斌, 庞春雷, 等. 一种残差$ {\chi }^{2} $与改进SPRT互辅的故障检测方法[J]. 火力与指挥控制, 2020, 45(4): 148-152, 157.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] 周俊, 张鹏. 基于灰色预测的农业机器人GPS导航系统硬故障隔离[J]. 农业机械学报, 2010, 41(12): 165-168, 177.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] 孙克诚, 曾庆化, 王守一, 等. 基于因子图导航的伪距故障检测与自适应隔离方法[J]. 中国惯性技术学报, 2022, 30(1): 65-73.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] 郝阿明, 李晓龙, 刘恒坤. 高速磁浮列车悬浮搭接结构控制研究[J]. 铁道学报, 2014, 36(5): 39-45.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] 廖海军. 中低速磁浮列车间隙传感器三探头分时检测方法研究[D]. 成都: 西南交通大学, 2018: 17-42.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] 姚文华. 改进型LQR控制器在磁悬浮列车悬浮控制中的应用[J]. 铁路通信信号工程技术, 2025, 22(7): 10-17.YAO Wen-hua. Application of improved LQR controller in maglev train suspension control[J]. Railway Signalling & Communication Engineering, 2025, 22(7): 10-17. -
计量
- 文章访问数: 15
- HTML全文浏览量: 13
- PDF下载量: 6
- 被引次数: 0
