Model-based fault detection and isolation in automotive yaw moment control system

This paper presents a model-based fault detection and isolation technique for automotive yaw moment control system. For this purpose, a novel fault detection and isolation algorithm for a class of actuator-plant systems is proposed. Compared with the existing fault detection and isolation techniques that can only isolate a target fault or require multiple observers to isolate multiple faults, a unique strength of the proposed algorithm is its ability to isolate faults at the component level solely based on the residuals generated by a single observer. The validity of the proposed algorithm, applied to automotive yaw moment control system, is investigated via a simulation study based on a realistic vehicle dynamics model. The results suggest that the proposed algorithm can isolate the component subject to fault while effectively handling two perennial nuisances: sensitivity to disturbances and false alarms due to uncertainties.     

A comparative study on fault detection methods of rail vehicle suspension systems based on acceleration measurements

Reliability of the railway vehicle suspension system is of critical importance to the safety of the vehicle. On-line health condition monitoring for the suspension system of rail vehicles offers a number of benefits such as preventing further deterioration of vehicle performance, enhancing vehicle safety, increasing operational reliability and availability, and reducing maintenance costs. It is desirable to timely detect the fault and monitor the performance degradation of vehicle suspension systems. In this paper, a comparative study on fault detection methods of urban rail vehicle suspension systems is considered. A novel sensor configuration is proposed where the underlying vehicle system is equipped with only acceleration sensors in the four corners of the carbody, the leading and trailing bogie, respectively. A mathematical model is developed for the considered vehicle suspension system. Both model-based and data-driven approaches are studied for the suspension fault detection problem. The robust observer, the Kalman filter combined with the generalised likelihood ratio test method, the dynamical principle components analysis and the canonical variate analysis approaches are applied to the fault detection problem. The simulation is carried out by means of the professional multi-body simulation tool, SIMPACK. In addition, the advantages and disadvantages of these methods are compared. The simulation results show that the data-driven methods outperform the model-based methods.     

An efficient recursive least square-based condition monitoring approach for a rail vehicle suspension system

A model-based condition monitoring strategy for the railway vehicle suspension is proposed in this paper. This approach is based on recursive least square (RLS) algorithm focusing on the deterministic ‘input–output’ model. RLS has Kalman filtering feature and is able to identify the unknown parameters from a noisy dynamic system by memorising the correlation properties of variables. The identification of suspension parameter is achieved by machine learning of the relationship between excitation and response in a vehicle dynamic system. A fault detection method for the vertical primary suspension is illustrated as an instance of this condition monitoring scheme. Simulation results from the rail vehicle dynamics software ‘ADTreS’ are utilised as ‘virtual measurements’ considering a trailer car of Italian ETR500 high-speed train. The field test data from an E464 locomotive are also employed to validate the feasibility of this strategy for the real application. Results of the parameter identification performed indicate that estimated suspension parameters are consistent or approximate with the reference values. These results provide the supporting evidence that this fault diagnosis technique is capable of paving the way for the future vehicle condition monitoring system.     

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.     

Parallel model based fault detection algorithm for electronic parking brake system

This paper describes a parallel model-based fault detection algorithm for an electronic parking brake (EPB) system, which consists of an electronic control unit with built-in current sensor and braking force sensor. For the EPB system to supply sufficient parking force to a vehicle, the parking force sensor is of utmost importance. If a fault occurs in this sensor, sufficient parking force may not be supplied, thereby seriously threatening the safety of the vehicle. Thus, a fault detection method is required for the parking force sensor of the EPB system to improve the safety of vehicles. For this purpose, a highly reliable fault detection method is needed to detect abnormal fault signals, which cannot be detected by the existing on-line sensor monitoring fault detection methods. This paper proposes a novel parallel model-based fault detection algorithm for the EPB system, which compares the physical sensor data with the mathematical model, the fuzzy model, and the neural network model at the same time. In order to reduce false alarms, the magnitude of thresholds and the operation counts are changed adaptively. When the proposed parallel model-based fault detection algorithm detects severe failures of the force sensor, it warns the driver in advance to prevent accidents due to the failures. The proposed algorithm is verified by hardware-in-theloop simulations in various situations.     

总线技术在汽车ASM尾气检测系统中的应用

将CAN总线技术应用于汽车ASM工况法的尾气检测系统中,构建了系统框架,选配了硬件设备,制定了系统协议,研究了智能协议转换器的配置方法。根据CAN总线的特点和稳态加载工况测量方法,进行了系统软件设计。实验结果表明：将CAN总线技术应用于汽车ASM尾气检测系统中,能够保证测试数据传输准确,减少外界干扰并提升检测效率。     

纯电动商用车绝缘故障分析

电动汽车通过高压电能进行整车驱动及附件控制,为保证整车安全性,高压系统均设置有绝缘检测及断电保护功能。文章针对纯电动车辆最受关注的绝缘故障进行原理及故障分析,在设计、装配、使用等环节进行详细原因及排查方案介绍,用于指导市场故障车辆的排查及后期设计方案的优化。     

Hierarchical Scheme of Vehicle Detection and Tracking in Nighttime Urban Environment

In this paper, we propose a novel hierarchical scheme for detection and tracking of vehicles using a vehicle-mounted camera in nighttime under urban environment, where a vehicle can be represented by a pair of taillights and various types of lights are commonplace. The proposed scheme, therefore, mainly focuses on devising robust detection and pairing of taillights in spite of their inherent diversity and continuous transformation in appearance. Thus the appearance symmetry, which many conventional methods rely on, for paring is not guaranteed to be available all the times. Each of the three layers in the scheme is devised to identify a vehicle from individual lights and clutters detected in a hierarchical manner. Robust detection of a pair of taillights, which can be regarded as a vehicle, is sought by successive groupings of the components in a layer and checking not only the intra-layer but the inter-layer relations between them. A structural Kalman filter is employed to maintain the temporal consistency in the motion of the components and their relations as well. Exploiting such relational information increases accuracy in tracking of individual components by reducing effects from fluctuation in positions and shapes, and eventually compensating possible failures in detection of them. As a result, the proposed scheme achieves enhancement in detection and tracking of vehicles in nighttime as proven by experiments on videos including crowded urban traffic scenes.     

Bootstrap法在车辆故障检测中的研究及应用

介绍Bootstrap方法的原理并将Bootstrap方法应用于检测车辆中存在的故障,通过计算不同故障下的车辆样本之间均匀Kullback-Lcible距离的均值以及在给定置信度下的置信区间等特征值来反映被测车辆的故障种类。给出一种新的随机数产生器用在Bcotstrap法重采样中产生新的随机序列。以车辆卜的驱动桥为检测对象对该算法进行了验证,结果表明该算法能准确检测出驱动桥所存在的故障。     

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。     

线控转向系统故障检测及容错控制协同设计

为解决线控转向系统故障可能导致车辆失控的问题,提出一种故障检测及容错控制协同设计方法。首先,建立了包含线控转向系统加性故障的车辆动力学模型;其次,联合车辆动力学模型及故障检测/容错控制器,建立跟踪误差闭环控制系统;然后,求解满足闭环系统H∞性能的线性矩阵不等式,得到故障检测/容错控制器参数;最后,基于dSPACE Full-Size HIL进行硬件在环仿真测试。结果表明,该方法可快速检测出转向系统故障,同时实现了车辆的容错控制。     

基于小波免疫系统的车辆总成故障检测

匹配算法。根据小波分析和人工免疫系统的原理，提出了一种基于小波变换和免疫系统的故障诊断系统。针对小波分析的特点，将其用来对非稳定信号进行分析，获取信号特征向量作为原始数据，利用匹配算法对原始数据进行己-非己分析。将此系统应用到车辆总成故障诊断中，取得了良好的效果。     

A modified integral sliding mode control to lateral stabilisation of 4-wheel independent drive electric vehicles

This paper presents a novel sliding mode controller (SMC) and its application in the lateral stability control of a 4-wheel independent drive electric vehicle. The structure of the SMC is modified and online-tuned to ensure vehicle system stability, and to track the desired vehicle motion references when an in-wheel motor fault happens. The proposed controller is faster, more accurate, more robust, and with smaller chattering than common SMCs chatter. The effectiveness of the introduced approach is investigated through conducted simulations in the CARSIM and MATLAB software environments.     

Discerning the operational state of a vehicle’s distributed electronic systems from vehicle network traffic for use as a fault detection and diagnosis tool

This paper suggests a novel approach to finding faults in a vehicle’s electronic systems by monitoring the network traffic directly and generating statistical traits. The nature of the data in a CAN network is considered, and a case for the use of statistical analysis presented. Statistical traits extracted from the temporal behavior of network messages are investigated as a metric for fault detection. It is shown how this trait information can be extracted from network data, and how this information could be used for fault detection of an unknown fault on a CAN network. It is then demonstrated that combining multiple types of trait data can be used to correctly identify a fault once detected.     

Vehicle detection system design based on stereo vision sensors

Vehicle detection is a crucial issue for driver assistance system as well as for autonomous vehicle guidance function and it has to be performed with high reliability to avoid any potential collision. The vision-based vehicle detection systems are regarded promising for this purpose because they require little infrastructure on a highway. However, the feasibility of these systems in passenger car requires accurate and robust sensing performance. In this paper, a vehicle detection system using stereo vision sensors is developed. This system utilizes feature extraction, epipoplar constraint and feature matching in order to robustly detect the initial corresponding pairs. The proposed system can detect a leading vehicle in front and can estimate its position parameters such as the distance and heading angle. After the initial detection, the system executes the tracking algorithm for the vehicles in the lane. The proposed vehicle detection system is implemented on a passenger car and its performances are verified experimentally.     

汽车ACC系统纵向控制六模式切换策略仿真研究

为了增强现有六模式汽车自适应巡航(ACC)系统全工况下的适应性,文中综合考虑了2车相对速度、相对距离和本车速度等参数对ACC系统控制策略的影响,提出了1种六模式ACC系统控制模式的划分方法,并定量地确定了控制模式划分的边界条件.为了使ACC系统能够根据车辆行驶工况做出合理的响应,分别设计了各控制模式的加速度算法.将模式划分方法及控制策略建立相应的Simulink模型,考虑到PreScan具有场景建立便利性和可视化等优点,采用PreScan仿真场景并通过CarSim车辆动态模型,对所设计的六模式ACC系统进行了仿真试验.仿真结果表明:提出的六模式ACC系统,在全工况特别是前车切入等复杂工况下,较现有的六模式ACC系统表现出更好的适应性.      

Low cost design of parallel parking assist system based on an ultrasonic sensor

This paper presents a low cost design and implementation of a parallel parking assist system (PPAS) based on ultrasonic sensors. Generally, a PPAS requires several types of sensors, such as an ultrasonic sensor, camera sensor, radar sensor and laser sensor for parking space detection. However, our proposed PPAS only requires two ultrasonic sensors on the front and lateral sides for parking space detection. Moreover, a steering angle sensor and wheel speed sensor installed in the vehicle are used to obtain vehicle position information for localization in ultrasonic range data. The hardware architecture of the PPAS based on an electronic control unit (ECU) module, sensor modules and a human machine interface (HMI) module was proposed. Moreover, the software architecture of the PPAS is based on system initialization, scheduling, recognition and a control algorithm. In particular, a novel sensor algorithm was proposed to minimize the vehicle corner error of the ultrasonic sensor. A prototype of the PPAS based on the proposed architecture was constructed. The experimental results demonstrate that the implemented prototype is robust and successfully performs parking space detection and automatic steering control. Finally, the low cost design and implementation of the PPAS was possible due to the cheap ultrasonic sensors, simple hardware design and low computational complexity of the proposed algorithm.     

Slip regulation for anti-lock braking systems using multiple surface sliding controller combined with inertial delay control

In this paper, a multiple surface sliding controller is designed for an anti-lock braking system to maintain the slip ratio at a desired level. Various types of uncertainties coming from unknown road surface conditions, the variations in normal force and the mass of the vehicle are estimated using an uncertainty estimation technique called the inertial delay control and then the estimate is used in the design of the multiple surface sliding controller. The proposed scheme does not require the bounds of uncertainties. The ultimate boundedness of the overall system is proved. The proposed scheme is validated by simulation under various scenarios of road friction, road gradient and vehicle loading followed by experimentation on a laboratory anti-lock braking set-up for different friction conditions.     

带随机网络时滞补偿的测功机动态负载模拟

本文研究了控制网络传输时滞影响下测功机加载控制算法。首先,针对一款前驱电动汽车,建立车辆与台架动力学动态耦合模型和服从均匀分布的随机网络诱导延时数学模型。其次,为改善测功机网络控制系统负载模拟性能,提出了一种基于预测控制结构的随机网络诱导延时补偿算法;接着通过系统增广将随机系统跟踪控制问题转化为鲁棒镇定问题,并分析系统H∞性能,通过非线性优化问题得到系统控制增益。最后,进行了防抱死制动控制台架测试的仿真,结果表明:提出的方法可大幅提升测功机负载模拟精度。     

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.