中低速磁浮列车传感器故障检测
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摘要
磁悬浮控制系统是整个磁浮交通系统的核心,悬浮传感器是悬浮控制系统的状态感知来源,悬浮传感器所采集数据的正确性直接影响悬浮的稳定性。因此,及时准确地检测出传感器故障,提高数据采集系统的可靠性,对于提高整车运行的安全性具有重要意义。本文以工程应用为背景,在对比分析各种故障检测方法的基础上,选择采用基于信号处理的方法进行传感器故障检测,分别应用Hilbert-Huang变换和自适应滤波的方法对不同故障条件下的信号进行了仿真分析和实验,主要做了以下具体工作:
首先,在HHT方法定义的基础上,通过对传感器数据的分析以及和傅立叶变换、小波变换的对比说明了HHT方法在分析非平稳信号中的优越性。同时,对方法自身存在的不足作了改进和补充。在此基础上,对实际采集的传感器信号添加不同干扰,分别模拟乘性故障、加性故障和缓变故障,应用改进的HHT方法对各种故障条件下的信号进行了经验模态分解(EMD),对所得固有模态函数(IMFs)的幅值谱采用了滑动平均的方法提取其中的故障特征,针对不同故障信号的幅频特征及剩余信号的状态提出了对应的故障检测方法。对于HHT方法在传感器故障检测中的不足以及不适用的情况作了解释说明和补充。
其次,在悬浮控制模型的基础上,分析了位置传感器探头输出之间的关系,在传感器输出特性相同的情况下提出用自适应滤波的方法提取位置传感器探头输出差值之间的相关系数,通过相关系数的变化情况判断故障发生的时刻、位置及类型,并通过仿真和单转向架实验说明了方法的有效性。
最后,本文总结了全文的研究成果及不足之处,对故障检测技术在中低速磁浮列车传感器故障检测中未来的发展方向提出了展望。
Suspension control system is the kernel of the maglev vehicle system, and the sensors measure states of the suspension control system. Validity of the data from the sensors affects the stability of suspension directly. Thus, for improving the reliability of the data-gathering system and the security of maglev train, a timely and exact fault detect of the sensors is very important. Comparing kinds of fault detect methods, the signal-processing-based method was chosen to carry out. Separately, using the method of Hilbert-Huang transform(HHT, shortly) and self-adaptive filtering, signals under different fault conditions were simulated and analyzed. The main works of this paper are listed below.
Firstly, based on the definition of HHT, the superiority of HHT in analyzing non-smooth signals was showed in applying the method to the sensor data-analyzing and comparing with Fourier transform and wavelet transform, and the deficiencies of the method itself were improved. On this ground, different disturbances were added into the normal signals to simulate the multiplication fault, the addition fault and the slow-changed fault, and then the improved HHT was applied to the simulant fault signals. After the empirical mode decomposition(EMD, shortly) of the signals and Hilbert transform of the intrinsic mode function(IMF, shortly) gained from the former step, fault characteristics were extracted from the amplitude spectrum and frequency spectrum, and the corresponded fault detection basing on the amplitude-frequency characteristics of the IMFs were presented. The deficiencies of HHT in fault detection of the sensors were also explained and supplemented.
Secondly, based on the maglev control model, relations in the outputs of position sensors were analyzed, and the point of view which correlation coefficients among the differences of the outputs can be gained using self-adaptive filtering was put forward. The occurring time, location and form of the fault can be judged by the altering of the correlation coefficients. The results of simulations and experiments on the bogie of maglev train well proved the effectiveness of the method.
Finally, the achievements and insufficiency of this paper were summarized, and the future of sensor fault detection in maglev train was prospected.
首先,在HHT方法定义的基础上,通过对传感器数据的分析以及和傅立叶变换、小波变换的对比说明了HHT方法在分析非平稳信号中的优越性。同时,对方法自身存在的不足作了改进和补充。在此基础上,对实际采集的传感器信号添加不同干扰,分别模拟乘性故障、加性故障和缓变故障,应用改进的HHT方法对各种故障条件下的信号进行了经验模态分解(EMD),对所得固有模态函数(IMFs)的幅值谱采用了滑动平均的方法提取其中的故障特征,针对不同故障信号的幅频特征及剩余信号的状态提出了对应的故障检测方法。对于HHT方法在传感器故障检测中的不足以及不适用的情况作了解释说明和补充。
其次,在悬浮控制模型的基础上,分析了位置传感器探头输出之间的关系,在传感器输出特性相同的情况下提出用自适应滤波的方法提取位置传感器探头输出差值之间的相关系数,通过相关系数的变化情况判断故障发生的时刻、位置及类型,并通过仿真和单转向架实验说明了方法的有效性。
最后,本文总结了全文的研究成果及不足之处,对故障检测技术在中低速磁浮列车传感器故障检测中未来的发展方向提出了展望。
Suspension control system is the kernel of the maglev vehicle system, and the sensors measure states of the suspension control system. Validity of the data from the sensors affects the stability of suspension directly. Thus, for improving the reliability of the data-gathering system and the security of maglev train, a timely and exact fault detect of the sensors is very important. Comparing kinds of fault detect methods, the signal-processing-based method was chosen to carry out. Separately, using the method of Hilbert-Huang transform(HHT, shortly) and self-adaptive filtering, signals under different fault conditions were simulated and analyzed. The main works of this paper are listed below.
Firstly, based on the definition of HHT, the superiority of HHT in analyzing non-smooth signals was showed in applying the method to the sensor data-analyzing and comparing with Fourier transform and wavelet transform, and the deficiencies of the method itself were improved. On this ground, different disturbances were added into the normal signals to simulate the multiplication fault, the addition fault and the slow-changed fault, and then the improved HHT was applied to the simulant fault signals. After the empirical mode decomposition(EMD, shortly) of the signals and Hilbert transform of the intrinsic mode function(IMF, shortly) gained from the former step, fault characteristics were extracted from the amplitude spectrum and frequency spectrum, and the corresponded fault detection basing on the amplitude-frequency characteristics of the IMFs were presented. The deficiencies of HHT in fault detection of the sensors were also explained and supplemented.
Secondly, based on the maglev control model, relations in the outputs of position sensors were analyzed, and the point of view which correlation coefficients among the differences of the outputs can be gained using self-adaptive filtering was put forward. The occurring time, location and form of the fault can be judged by the altering of the correlation coefficients. The results of simulations and experiments on the bogie of maglev train well proved the effectiveness of the method.
Finally, the achievements and insufficiency of this paper were summarized, and the future of sensor fault detection in maglev train was prospected.
引文
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[3]张锟,李杰,常文森. EMS型磁浮列车模块的运动耦合研究[J].机车电传动, 2006, 28(3): 22-26.
[4] Weiheng Zhu, Dao-Yi Zhu. Future Maglev in China and Beyond [A]. Maglev’2004 Proceedings [C], 2004, Vol. 1:256-268.
[5] Willsky A S. A Survey of Design Methods for Failure Detection in Dynamic Systems[J]. Automatica, 1976, 12: 601-611.
[6]周东华,王桂增.故障诊断技术综述[J].化工自动化及仪表, 1998, 25(1): 58-62
[7] Frank P.M. Fault Diagnosis in Dynamic Systems Using Analytical and Knowledge-based Redundancy—A Survey and Some NewResults[J]. Automatica, 1990, 26(3): 459-474
[8]叶银忠,潘日芳,蒋慰孙.动态系统的故障检测与诊断方法[J].信息与控制, 1986, 15(6):27-34.
[9] Gertler J J. Fault Detection and Diagnosis in Engineering Systems[M]. Marcel Dekker, NewYork, 1998.
[10]闻新,张洪钺,周露.控制系统的故障诊断和容错控制[M].北京:机械工业出版社, 1998.
[11] S.Dash, V.Venkatasubramnian. Challenges in the Industrial Application of Fault Diagnosis Systems[J]. Computers and Chemical Engineering, 2000, 24: 785-791.
[12] Patton R J, Chen J. Review of Parity Space Approaches to Fault Diagnosis for Aerospace System[J]. Journal of Guidance, Control and Dynamics, 1994, 17(2): 278-285.
[13]吴今培.智能故障诊断技术的发展和展望[J].震动、测试与诊断, 1999, 19(2): 80-147.
[14]刑琰,吴宏鑫,王晓磊,李智斌.航天器故障诊断与容错控制技术综述[J].宇航学报, 2003, 24(3): 221-226.
[15]邱浩,王道波,张焕春.控制系统的故障诊断方法综述[J].航天控制, 2004, 22(2): 53-60.
[16]李渭华,萧德云,方崇智.一种基于自适应滑动窗格性滤波算法的故障检测器[J].自动化学报, 1996, 22(2): 251-253.
[17] Jiang J., Jia F. A Robust Fault Diagnosis Scheme Based on Signal Modal Estimation[J]. International Journal of Control, 1995, 62(2): 461-475.
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