智能车辆的惯性传感器故障诊断研究
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摘要
随着自动化技术在各行各业的应用,其系统越来越复杂,传感器作为主要信息获取装置,为系统可靠安全稳定工作提供了保证。然而如果传感器出现性能降低、故障、甚至失效,那么将给系统监测、控制等带来严重影响,有可能造成无法估量的损失。所以传感器的故障诊断就显得尤为重要。
本课题来源于国家自然科学基金重大专项“高速公路车辆智能驾驶中的关键科学问题研究”。作为该项目研究的一部分,本文主要针对惯性传感器和组合导航系统的故障以及异常检测与诊断问题,从数字信号处理、故障诊断方法、以及故障预测方法三个方面进行研究。首先通过对传感器采集的数据进行数字信号处理,降低噪声,减少不确定性影响,提高采集数据的精度；然后通过采用不同精度传感器的冗余方式,结合软件冗余和硬件冗余两种方法的优点进行故障诊断；最后通过故障预测3技术来提高故障诊断的实时性,以将故障防范于未然。
本文的主要创新点是：
1)针对基于分段方式的多尺度卡尔曼滤波计算量大、延时长的问题,提出基于无抽取Haar算法的实时卡尔曼滤波方法。该方法采用简单的加减、移位运算在t时刻完成多尺度变换,然后在各个尺度进行小波阈值去噪和卡尔曼滤波。为了验证该方法的有效性,在自主改装的智能车上对低精度加速度传感器进行了实验,结果表明,通过无抽取Haar算法的小波重构完成信号处理,减少了重复运算,提高了算法实时性。该方法能有效提高传感器的性能,在不能准确估计状态转移误差情况下,该方法的去噪性能优于卡尔曼滤波。
2)提出了不同精度的冗余传感器故障诊断方法。该方法采用动态模型不确定性影响最小化而故障影响最大化的原则,对低精度传感器数据进行预处理,轮流使用一个传感器作为输入,另一个作为输出建立卡尔曼滤波方程组,并通过所得新息进行故障诊断。实验表明,所提出方法能有效抑制低精度传感器的噪声干扰,降低硬件成本以及系统建模复杂性,在传感器故障诊断的工程应用中具有较好的实用性。同时针对智能车辆行驶过程中,背景噪声变化很大,多精度冗余传感器故障难以诊断的问题,提出了基于小波噪声估计的二次卡尔曼滤波故障诊断方法。通过实验分析了该方法故障检测率与噪声强度的关系,结果表明,该方法提高了故障诊断的准确性,具有较好的鲁棒性。
3)提出了基于后验概率分布的自适应粒子滤波器方法。该方法采用先验知识设定似然概率置信区间,通过置信范围内粒子分布逼近真实状态分布的后验估计来自适应调整粒子集大小,既保证算法精度,又提高了计算效率。然后将粒子滤波器用于故障诊断与故障预测,并提出了一种基于多模态RBPF粒子滤波器的故障预测算法。该算法通过k步迭代后产生k组加权粒子来估计系统的状态,以此计算故障状态的分布情况和故障发生的概率,并通过故障概率的变化率来预测故障类型以及故障发生时刻。通过在移动机器人航迹推算系统上进行实验,验证了该方法的有效性和可行性。
With the application of automation technology in all walks of life, the automation systems have become more and more complex. As the main information acquirement device, the sensors guarantee a reliable, secure and stable work. However, if the sensors have low performance, failure, and even become useless, it will have serious effect on system monitor and control, and cause incalculable damage. Thus, the sensor fault diagnosis is very important.
This dissertation is supported by the National Natural Science Foundation of China under Grant90820302, Research on the Key Scientific Problems of Intelligent Vehicle Driving on the Highway. As one part of the project, the dissertation focuses on anomaly detection and fault diagnosis of inertial sensors and navigation systems in unmanned vehicle. Three aspects are researched, including digital signal process, fault diagnosis and fault prediction methods. Firstly, the original sensor data is processed by using digital signal process methods to remove noise, reduce uncertainties and improve the data accuracy. Then, the sensor fault diagnosed by using different precision sensors in redundancy way, which combines the advantages of software redundancy method and hardware redundancy method; Finally, the fault prediction technology based on particle filter is researched to improve the real-time of the fault diagnosis.
Main contributions of the dissertation are shown as following:
1) A real-time Kalman filter based on the non-decimated Haar algorithm is proposed to avoid the problem of large computation and long delays in existing multi-scale Kalman filter. The simple addition, subtraction, and shift operations are used to complete multi-scale transformation at time t, and the signal is reconstructed after de-noising by wavelets soft-threshold and Kalman filter in each scale. To verify the validity of this method, the experiments with low-precision acceleration sensor for intelligent vehicle are carried out. The experimental results show that the repeated computation is reduced and the speed of the algorithm is improved by using the non-decimated Haar algorithm. The method can effectively improve the sensor performance. Meanwhile, the de-noising performance of this method is superior to Kalman filter when the status can not be estimated correctly.
2) A fault diagnosis method with different precision redundant sensors is proposed in this thesis. The method uses the principle of minimizing uncertainties of the dynamic model and maximizing the impact of fault to preprocess the low-precision sensor data, taking turns to use a sensor data as input, and the other sensor as output to establish the Kalman filter equations, and the fault diagnosis can be made by the obtained new information. The experiments show that, the proposed method can not only effectively suppresses the noise in low-precision sensors, but also can reduce costs and complexity of system modeling. Meanwhile, in the moving process of the intelligent vehicle, the background noise changes badly, which causes the diagnosis for multiple-precision sensor failure more difficult. Thus, the wavelet-based noise estimation of the two stage Kalman filter fault diagnosis method is proposed. The relationship between the fault detection rate and noise intensity is also discussed, and the result shows that the method improves the accuracy of fault diagnosis and has relatively good robustness.
3) An adaptive particle filter based on posterior distribution is proposed. The prior knowledge is used to set the confidence interval of likelihood, and the number of particles is adjusted by the posterior estimation in the confidence interval. Then the particle filter is used in fault diagnosis and fault prediction. An algorithm of fault prediction base on particle filter is proposed. After k-step iterative, the algorithm uses k groups particles to estimate the system status and get the fault status distribution and the probability of failure, then the change rate of the failure probability is obtained to predict the fault type and the time of the failure. The experimental results of the mobile robot dead-reckoning system demonstrate the effectiveness and feasibility of the method.
本课题来源于国家自然科学基金重大专项“高速公路车辆智能驾驶中的关键科学问题研究”。作为该项目研究的一部分,本文主要针对惯性传感器和组合导航系统的故障以及异常检测与诊断问题,从数字信号处理、故障诊断方法、以及故障预测方法三个方面进行研究。首先通过对传感器采集的数据进行数字信号处理,降低噪声,减少不确定性影响,提高采集数据的精度；然后通过采用不同精度传感器的冗余方式,结合软件冗余和硬件冗余两种方法的优点进行故障诊断；最后通过故障预测3技术来提高故障诊断的实时性,以将故障防范于未然。
本文的主要创新点是：
1)针对基于分段方式的多尺度卡尔曼滤波计算量大、延时长的问题,提出基于无抽取Haar算法的实时卡尔曼滤波方法。该方法采用简单的加减、移位运算在t时刻完成多尺度变换,然后在各个尺度进行小波阈值去噪和卡尔曼滤波。为了验证该方法的有效性,在自主改装的智能车上对低精度加速度传感器进行了实验,结果表明,通过无抽取Haar算法的小波重构完成信号处理,减少了重复运算,提高了算法实时性。该方法能有效提高传感器的性能,在不能准确估计状态转移误差情况下,该方法的去噪性能优于卡尔曼滤波。
2)提出了不同精度的冗余传感器故障诊断方法。该方法采用动态模型不确定性影响最小化而故障影响最大化的原则,对低精度传感器数据进行预处理,轮流使用一个传感器作为输入,另一个作为输出建立卡尔曼滤波方程组,并通过所得新息进行故障诊断。实验表明,所提出方法能有效抑制低精度传感器的噪声干扰,降低硬件成本以及系统建模复杂性,在传感器故障诊断的工程应用中具有较好的实用性。同时针对智能车辆行驶过程中,背景噪声变化很大,多精度冗余传感器故障难以诊断的问题,提出了基于小波噪声估计的二次卡尔曼滤波故障诊断方法。通过实验分析了该方法故障检测率与噪声强度的关系,结果表明,该方法提高了故障诊断的准确性,具有较好的鲁棒性。
3)提出了基于后验概率分布的自适应粒子滤波器方法。该方法采用先验知识设定似然概率置信区间,通过置信范围内粒子分布逼近真实状态分布的后验估计来自适应调整粒子集大小,既保证算法精度,又提高了计算效率。然后将粒子滤波器用于故障诊断与故障预测,并提出了一种基于多模态RBPF粒子滤波器的故障预测算法。该算法通过k步迭代后产生k组加权粒子来估计系统的状态,以此计算故障状态的分布情况和故障发生的概率,并通过故障概率的变化率来预测故障类型以及故障发生时刻。通过在移动机器人航迹推算系统上进行实验,验证了该方法的有效性和可行性。
With the application of automation technology in all walks of life, the automation systems have become more and more complex. As the main information acquirement device, the sensors guarantee a reliable, secure and stable work. However, if the sensors have low performance, failure, and even become useless, it will have serious effect on system monitor and control, and cause incalculable damage. Thus, the sensor fault diagnosis is very important.
This dissertation is supported by the National Natural Science Foundation of China under Grant90820302, Research on the Key Scientific Problems of Intelligent Vehicle Driving on the Highway. As one part of the project, the dissertation focuses on anomaly detection and fault diagnosis of inertial sensors and navigation systems in unmanned vehicle. Three aspects are researched, including digital signal process, fault diagnosis and fault prediction methods. Firstly, the original sensor data is processed by using digital signal process methods to remove noise, reduce uncertainties and improve the data accuracy. Then, the sensor fault diagnosed by using different precision sensors in redundancy way, which combines the advantages of software redundancy method and hardware redundancy method; Finally, the fault prediction technology based on particle filter is researched to improve the real-time of the fault diagnosis.
Main contributions of the dissertation are shown as following:
1) A real-time Kalman filter based on the non-decimated Haar algorithm is proposed to avoid the problem of large computation and long delays in existing multi-scale Kalman filter. The simple addition, subtraction, and shift operations are used to complete multi-scale transformation at time t, and the signal is reconstructed after de-noising by wavelets soft-threshold and Kalman filter in each scale. To verify the validity of this method, the experiments with low-precision acceleration sensor for intelligent vehicle are carried out. The experimental results show that the repeated computation is reduced and the speed of the algorithm is improved by using the non-decimated Haar algorithm. The method can effectively improve the sensor performance. Meanwhile, the de-noising performance of this method is superior to Kalman filter when the status can not be estimated correctly.
2) A fault diagnosis method with different precision redundant sensors is proposed in this thesis. The method uses the principle of minimizing uncertainties of the dynamic model and maximizing the impact of fault to preprocess the low-precision sensor data, taking turns to use a sensor data as input, and the other sensor as output to establish the Kalman filter equations, and the fault diagnosis can be made by the obtained new information. The experiments show that, the proposed method can not only effectively suppresses the noise in low-precision sensors, but also can reduce costs and complexity of system modeling. Meanwhile, in the moving process of the intelligent vehicle, the background noise changes badly, which causes the diagnosis for multiple-precision sensor failure more difficult. Thus, the wavelet-based noise estimation of the two stage Kalman filter fault diagnosis method is proposed. The relationship between the fault detection rate and noise intensity is also discussed, and the result shows that the method improves the accuracy of fault diagnosis and has relatively good robustness.
3) An adaptive particle filter based on posterior distribution is proposed. The prior knowledge is used to set the confidence interval of likelihood, and the number of particles is adjusted by the posterior estimation in the confidence interval. Then the particle filter is used in fault diagnosis and fault prediction. An algorithm of fault prediction base on particle filter is proposed. After k-step iterative, the algorithm uses k groups particles to estimate the system status and get the fault status distribution and the probability of failure, then the change rate of the failure probability is obtained to predict the fault type and the time of the failure. The experimental results of the mobile robot dead-reckoning system demonstrate the effectiveness and feasibility of the method.
引文
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