Fault tolerant nonlinear control with applications to an automated transit bus

This paper presents an integrated structure for a passive and active fault tolerant control (FTC) design approach in the framework of a robust nonlinear control technique called Dynamic Surface Control (DSC). As motivated by the automated vehicle application, we consider two categories of possible faults: pre-specified (a priori) and non-specified faults. It is first shown that DSC can be considered as a passive FTC approach in the sense that it gives simultaneous robust stability to a set of nonlinear systems even in the presence of model uncertainties and the pre-specified faults. Then, the non-specified fault is classified depending on the fault’s impact on the closed-loop system and isolatability from a fault detection and diagnosis (FDD) system. If a fault is both intolerable and isolatable, an active FTC approach is taken which includes FDD and controller reconfiguration. More specifically, trajectory reconfiguration is considered to accommodate the actuator fault, i.e., to compensate for the performance loss due to the fault within the framework of a switched hierarchical structure. Finally, the integrated structure for the longitudinal control of an automated transit bus is designed through the proposed method. Simulation results of the fault tolerant controller are shown for both single and multiple multiplicative faults. This controller was implemented on the California PATH transit buses in a demonstration of automated public transportation technology in San Diego, California in August of 2003.     

A signal-based fault detection and classification method for heavy haul wagons

This paper proposes a signal-based fault detection and isolation (FDI) system for heavy haul wagons considering the special requirements of low cost and robustness. The sensor network of the proposed system consists of just two accelerometers mounted on the front left and rear right of the carbody. Seven fault indicators (FIs) are proposed based on the cross-correlation analyses of the sensor-collected acceleration signals. Bolster spring fault conditions are focused on in this paper, including two different levels (small faults and moderate faults) and two locations (faults in the left and right bolster springs of the first bogie). A fully detailed dynamic model of a typical 40t axle load heavy haul wagon is developed to evaluate the deterioration of dynamic behaviour under proposed fault conditions and demonstrate the detectability of the proposed FDI method. Even though the fault conditions considered in this paper did not deteriorate the wagon dynamic behaviour dramatically, the proposed FIs show great sensitivity to the bolster spring faults. The most effective and efficient FIs are chosen for fault detection and classification. Analysis results indicate that it is possible to detect changes in bolster stiffness of ±25% and identify the fault location.     

Bolster spring fault detection strategy for heavy haul wagons

An on-board health monitoring system is proposed for heavy haul wagons in this paper including a signal-based fault detection and isolation (FDI) method and an on-line fault diagnose strategy. Such a system, to be feasible on freight wagons, must be sufficiently cheap and robust, hence the design assumes the constraint of using only two accelerometers mounted on the front left and right rear part of each carbody in a heavy haul train. This paper focuses on the detection of bolster spring fault conditions. The problem is made more complex by the modes of failure which might be expected in bolster spring nests. Types of spring failure are firstly identified and discussed covering situations of broken (shortening springs) and softening (individual spring loss from a nest or cross-section loss through corrosion). The effects of these faults and their detectability were investigated using simulations on straight and curved track and using a fully detailed model of a typical 40?t axle-load heavy haul wagon. The simulation results were then examined and compared using cross-correlation analysis and an FDI system was proposed. The FDI system introduced five possible fault indicators. Initial results indicated that it was possible to detect changes in bolster stiffness of ±25%. An on-line fault diagnoses strategy is proposed for bolster spring faults which only requires data from wagon monitoring during travel around sharp curves to detect and the occurrence of confirm faults. The functionality envisaged needs only a ‘once per trip’ monitoring site, such as a sharper curve, and is aimed at condition monitoring rather than the provision of alarms or comprehensive monitoring of all events.     

基于粗糙集与支持向量机的推土机故障诊断

提出了一种基于粗糙集理论和支持向量机算法的推土机发动机故障诊断方法。首先利用粗糙集理论对故障诊断决策系统进行约简,消除样本噪声和冗余,然后在此基础上设计支持向量机多分类器,进行故障检测分类。这样,可以有效减小SVM训练的数据,加快多分类器的处理速度。实验结果显示它能提高故障诊断的准确性和效率。     

Design of Optimal Four-Wheel Steering System

Optimal design of the four wheel steering (4WS) system of the ground vehicle is studied. 4WS vehicles with the optimal control scheme are considered first. General formulation of the optimal control law is developed based on the linear quadratic regulator theory. The vehicle speed function (VSF) based 4WS vehicle with a simple feedback controller is considered as a special case of the optimal system. Two new designs of the VSF 4WS system are proposed and their performances are compared with the optimal 4WS systems and the existing VSF 4WS system. The first system is designed for the maximum stability while the second system is designed to emulate the response of the optimal 4WS vehicle. Advantages of the new VSF designs are discussed.     

基于模糊神经网络的发电机调节器故障诊断研究

电子调节器起着调节发电量和保护汽车用电器的作用。本文分析了汽车电子调节器的技术原理,并针对车用爪极发电机调节器电路参数的特点,提出了用智能诊断方法——模糊神经网络对其进行诊断,组建了一个基于CAN总线的发电机电子调节器模糊神经网络故障诊断系统,实验证明了该方法的有效性。     

基于电压频域特征和异常系数的动力电池故障诊断方法

动力电池系统是电动汽车(EV)的关键部件和主要故障源,因而提高动力电池故障诊断的效率和准确率显得尤为重要。基于此提出一种基于快速傅里叶变换(FFT)和异常系数评估(ACE)的动力电池电压不一致性故障诊断方法。针对6辆发生故障或热失控事故的电动汽车和1辆电压一致性良好的电动汽车,基于其在新能源汽车国家监管平台的全生命周期运行数据,经过电压数据的数据清洗、数据变换等大数据预处理后,利用FFT技术时频变换,提取频域中的幅值作为故障诊断的特征参数;然后,引进基于Z分数理论的异常系数对故障程度进行定量评估,以实现故障单体的检测和定位;此外,针对存在多个故障单体的情况,基于单体异常率的计算,实现单体故障程度的判定和排序;在此基础上,详细分析电压数据长度及采样间隔、FFT采样点数对模型的影响;最后,与基于熵和Z分数的电压故障诊断方法进行比较。研究结果表明:在上述研究条件下,该诊断方法对于电压一致性良好的车辆未产生误报警,且可以有效地检测出事故车辆动力电池系统存在的电压不一致性故障;相比之下,模型平均计算准确率提高了3.25%,模型平均耗时仅为熵值模型的0.55%;验证了该方法故障单体定位更精准、数据适用性更好及计算速度更快的优点。该研究成果能有效实现动力电池电压不一致性故障诊断,具有较高的工程应用价值。     

专用短程通信辅助的车辆卫星定位故障检测方法

为了确保卫星定位性能满足特定协作式智能交通应用需求,提高车辆定位系统的故障容错能力,针对车辆卫星定位的自主故障检测与性能优化问题,提出基于专用短程通信辅助的卫星定位故障检测方法,充分利用专用短程通信设备的测距率观测信息,实现故障检测对不同类型卫星可视条件的有效适应。基于专用短程通信多普勒观测特性,构建基于载波频偏的车间测距率观测模型;设计卫星定位与专用短程通信组合观测与解算框架;基于容积卡尔曼滤波提出适于非线性观测特征的故障检测、识别与排除算法,并叠加量测噪声方差矩阵动态调整策略,对故障检测性能进行优化;基于实测试验检验车间测距率的观测性能,并运用实车轨迹对多车协同运行及定位采集过程进行仿真,检验所提出方法的故障检测性能。研究结果表明：提出的方法有效解决了常规接收机自主完好性监测算法受卫星可视条件限制的问题,所引入的量测噪声方差矩阵调整策略提升了故障检测及故障排除性能的稳定性,在给定仿真场景中,常规卫星观测条件下阶跃故障、斜坡故障排除率相对常规方法最高可分别提升52%、18%,受限观测条件下不同水平2类故障的排除率最高分别可达100%、89%,边界观测条件下不同水平2类故障的检测率最高分别可达100%、96%。研究结果对于充分发挥车-车协同模式的核心优势、保障车辆定位性能具有重要价值。     

丰田凯美瑞发动机点火系统原理与故障排除实例

针对广州丰田凯美瑞轿车一例特殊故障——发动机高速运行之后抖动、动力下降,着重分析其发动机点火系统的工作原理,并对故障现象作逐步的诊断和分析,最终排除故障。     

Design of Optimal Four-Wheel Steering System

SUMMARY

Optimal design of the four wheel steering (4WS) system of the ground vehicle is studied. 4WS vehicles with the optimal control scheme are considered first. General formulation of the optimal control law is developed based on the linear quadratic regulator theory. The vehicle speed function (VSF) based 4WS vehicle with a simple feedback controller is considered as a special case of the optimal system. Two new designs of the VSF 4WS system are proposed and their performances are compared with the optimal 4WS systems and the existing VSF 4WS system. The first system is designed for the maximum stability while the second system is designed to emulate the response of the optimal 4WS vehicle. Advantages of the new VSF designs are discussed.     

基于多尺度核独立元分析与核极限学习机的柴油机故障诊断

为提高柴油机故障诊断速度和精度,提出了基于改进多尺度核独立元分析与量子粒子群优化核极限学习机的故障诊断方法。首先利用固有时间尺度分解对缸盖振动信号进行多尺度时频分解,并根据故障敏感度参数筛选有效分量以实现振动冲击特征增强;然后利用核独立元分析消除有效分量间的频带混叠,分离故障敏感频带,并提取各频带的AR模型参数、多尺度模糊熵和标准化能量矩构造联合故障特征向量;最后建立基于量子粒子群优化的核极限学习分类器实现柴油机故障诊断。试验结果表明,该方法有效增强了缸盖振动信号中的故障敏感特征,提高了柴油机故障诊断速度和精度,故障分类准确率达到98.45%。     

峭度分析法在发动机噪声信号故障特征提取中的应用

将峭度分析法引入发动机噪声信号故障特征提取中。基于连续小波变换对信号时间-尺度特征的细致刻画功能,提出了噪声信号中瞬时特性的峭度敏感特征参数计算方法,对不同转速不同工况下的发动机噪声信号进行了基于尺度-峭度的特征提取。研究结果表明,峭度结合连续小波变换能够很好地提取发动机噪声信号的故障特征。利用连续小波变换的尺度-峭度分析,能够有效提取噪声信号不同转速不同工况下的故障特征,为发动机的状态监测与故障诊断提供了重要的理论和现实依据。     

An Active Pantograph with Shaped Frequency Response Employing Linear Output Feedback Control

In pantographs used for current collection on high speed electric trains it is desirable to minimise the fluctuations in the contact force between the collector head and the catenary. A simple two-mass linear model is employed for the pantograph and the design of the proposed control system is based on the input admittance at low frequencies. Frequency shaping is incorporated in the performance index, and a simple dynamic controller is employed to achieve optimality in an equivalent transformed system, while minimising the number of feedback quantities to be measured. A significant reduction in the average contact force appears possible.     

故障树分析法在汽油发动机电控系统故障诊断中的应用

发动机电控系统是由传感器、执行器和控制单元三部分组成的,发动机正常运转过程是以控制系统为基础,通过不同信号的采集、处理和传输来实现发动机进气、喷油、点火等环节的有序进行。一旦发生故障,则症状的界限模糊。通常情况下系统故障发生在某个部件的具体位置,其他位置表现的状态正常,因此当发生局部故障时,最好是查清故障的具体位置然后作出相应的故障处理策略,不然更换整体部件虽然也排除了故障但是造成了很大程度的资源浪费。本文具体的阐述了故障树分析法在汽车发动机电控系统故障诊断过程中的应用研究,并通过实际的案例分析证明了故障树分析法的实用性和有效性。在未来故障诊断的相关研究中可以不断深化新型诊断技术的研究和开发。     

Fault detection and isolation for a full-scale railway vehicle suspension with multiple Kalman filters

Reliability and dependability in complex mechanical systems can be improved by fault detection and isolation (FDI) methods. These techniques are key elements for maintenance on demand, which could decrease service cost and time significantly. This paper addresses FDI for a railway vehicle: the mechanical model is described as a multibody system, which is excited randomly due to track irregularities. Various parameters, like masses, spring- and damper-characteristics, influence the dynamics of the vehicle. Often, the exact values of the parameters are unknown and might even change over time. Some of these changes are considered critical with respect to the operation of the system and they require immediate maintenance. The aim of this work is to detect faults in the suspension system of the vehicle. A Kalman filter is used in order to estimate the states. To detect and isolate faults the detection error is minimised with multiple Kalman filters. A full-scale train model with nonlinear wheel/rail contact serves as an example for the described techniques. Numerical results for different test cases are presented. The analysis shows that for the given system it is possible not only to detect a failure of the suspension system from the system's dynamic response, but also to distinguish clearly between different possible causes for the changes in the dynamical behaviour.     

Degree of fault isolability and active fault diagnosis for redundantly actuated vehicle system

This paper proposes á degree of fault isolability concept and active fault diagnosis method for redundantly actuated vehicle systems. Fault isolability is a structural property related to system dynamics and composition of actuators and sensors. Existing research on testing fault isolability has involved checking whether the system is isolable, i.e., binary in nature. A continuous value rather than a binary metric is needed to evaluate how isolable a given system fault is based on a specific measurement set. After fault components are isolated, the fault type and magnitude are estimated by analyzing residual vectors. In a redundantly actuated system, the number of controls/actuators is greater than the system mobility. Thus, the control input distribution to achieve a given control objective is not unique. In the case of a fault, the active fault diagnosis system adjusts the control input distribution to diagnose the fault. Thus, much more system information can be identified by additional excitation through a redundantly actuated system, which improves the fault diagnosis performance. Simulation results of a four-wheel independently driven and steered vehicle model validated the proposed degree of fault isolability and the effectiveness of the proposed active fault diagnosis method.     

An Active Pantograph with Shaped Frequency Response Employing Linear Output Feedback Control

SUMMARY

In pantographs used for current collection on high speed electric trains it is desirable to minimise the fluctuations in the contact force between the collector head and the catenary. A simple two-mass linear model is employed for the pantograph and the design of the proposed control system is based on the input admittance at low frequencies. Frequency shaping is incorporated in the performance index, and a simple dynamic controller is employed to achieve optimality in an equivalent transformed system, while minimising the number of feedback quantities to be measured. A significant reduction in the average contact force appears possible.     

基于LS-SVM模型的无级变速器夹紧力控制系统故障诊断

在不增加硬件成本和过大的运算量的前提下,研究了基于模型的无级变速器夹紧力控制系统故障诊断方法.提出了根据测得的信号以及全局和部分元件诊断模型的输出结果来完成故障隔离与定位的诊断策略;针对系统的非线性特征,采用最小二乘支持向量机建立反映其输入输出关系的非线性预测模型.测试结果表明所建立的故障自诊断系统具有较高的精度和反应速度,可以粗略地实现故障的隔离和定位.     

High Speed Railway: Collecting Pantographs Active Control and Overhead Lines Diagnostic Solutions

The catenary-pantograph system and its dynamic behaviour play a decisive role for high speed trains from the power collecting point of view. The decisive criterion for assessing the contact quality is to reduce the contact force variability as far as possible. In this paper active pantograph elements are introduced in order to improve the system performance. The control strategy is based on Extended Kalman Filter technique, used to get a contact force estimation available for control feedback. The same estimation procedure based on the Extended Kalman Filter is also used to obtain some information useful to overhead line diagnostic purposes.     

Application of observer-based fault detection in vehicle roll control

This paper describes how observer-based techniques for intelligent fault detection were applied to monitoring an active suspension control system in an experimental articulated heavy vehicle. The aim was to define a practical method for detecting faults, taking into account the nonlinearities of the vehicle. The experimental vehicle was divided conceptually into subsystems, namely the passive dynamics of the trailer, the dynamics of the hydraulic actuators, and the expected response of the closed-loop system. A linear dynamic model was designed for each subsystem. A fault detection observer was then designed for each dynamic model. The observer feedback gains were chosen to optimise estimation by the observer residual of specified errors on the output measurements. The observer residuals were then normalised and combined logically to provide a fault diagnosis. The performance of the fault detection scheme is demonstrated in the case of sensor faults and changes in the operation of the active control system.