Fault Diagnostics for GPS-based Lateral Vehicle Control

Summary This paper develops a fault diagnostic system to monitor the health of the lateral motion sensors on an instrumented highway vehicle. The fault diagnostic system utilizes observer design with the observer gains chosen so as to ensure that each sensor failure causes estimation errors to grow in an unique direction. The performance of the fault diagnostic system is verified through extensive experimental results obtained from an instrumented truck called the “Safetruck”. The fault diagnostic system is able to monitor the health of a GPS system, a gyroscope and an accelerometer on the Safetruck. It can correctly detect a failure in any one of the three sensors and accurately identify the source of the failure. A GPS-based geographic database containing information on road coordinates, curvature and bank angles plays a key role in ensuring accurate experimental performance of the observers.     

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.     

传感器故障检测与隔离算法研究

传感器数据采集和分析是桥梁健康系统对桥梁状态评估的基础。由于传感器采集的数据格式复杂、信息量大,如不能有效的对传感器故障进行自动检测和隔离,将影响评估的准确性,产生错误预警信息。本文提出一种基于几何后非线性 ICA(Geometric Post Nonlinear ICA,gp ICA)的传感器故障检测与隔离算法。该算法通过引入几何后非线性(PNL)混合模型,将非线性数据线性化,再利用快速独立元分析(FastICA)对故障进行检测。通过计算监测数据对监控统计量的贡献度,基于贡献度分析法实现对故障传感器的隔离。利用MATLAB软件进行数值模拟,实现了模拟故障传感器的检测和隔离。该算法相比传统的线性ICA故障检测具有更高的故障检测率,更适用于桥梁健康监测系统的故障检测与隔离。。     

Sliding-mode observer for urea-selective catalytic reduction (SCR) mid-catalyst ammonia concentration estimation

This paper presents an observer design for SCR mid-catalyst ammonia concentration estimation using tailpipe NO_x and ammonia sensors. Urea-SCR has been popularly used by Diesel engine powered vehicles to reduce NO_x emissions in recent years. It utilizes ammonia, converted from urea injected at upstream of the catalyst, as the reductant to catalytically convert NO_x emissions to nitrogen. To simultaneously achieve high SCR NO_x conversion efficiency and low tailpipe ammonia slip, it is desirable to control the ammonia storage distribution along the SCR catalyst. Such a control method, however, requires a mid-catalyst ammonia sensor. The observer developed in this paper can replace such a mid-catalyst ammonia sensor and be used for SCR catalyst ammonia distribution control as well as serves for fault diagnosis purpose of the mid-catalyst ammonia sensor. The stability of the observer was shown based on the sliding mode approach and analyzed by simulations. Experimental validation of the observer was also conducted based on a medium-duty Diesel engine two-catalyst SCR system setup with emission sensors.     

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.     

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。     

Lateral load transfer and normal forces estimation for vehicle safety: experimental test

Knowledge of vehicle dynamics data is important for vehicle control systems that aim to enhance vehicle handling and passenger safety. This study introduces observers that estimate lateral load transfer and wheel–ground contact normal forces, commonly known as vertical forces. The proposed method is based on the dynamic response of a vehicle instrumented with cheap and currently available standard sensors. The estimation process is separated into three blocks: the first block serves to identify the vehicle’s mass, the second block contains a linear observer whose main role is to estimate the roll angle and the one-side lateral transfer load, while in the third block we compare linear and nonlinear models for the estimation of four wheel vertical forces. The different observers are based on a prediction/estimation filter. The performance of this concept is tested and compared with real experimental data acquired using the INRETS-MA (Institut National de Recherche sur les Transports et leur Sécurité – Département Mécanismes d’Accidents) Laboratory car. Experimental results demonstrate the ability of this approach to provide accurate estimation, thus showing its potential as a practical low-cost solution for calculating normal forces.     

Radar Health Monitoring for Highway Vehicle Applications

Monitoring the health of the radar sensor on a highway vehicle poses a special challenge. This is because the radar measures the distance to other independent vehicles on the highway and the motion of these other vehicles may be completely unknown to the fault detection system. Traditional observer-based approaches to fault diagnostic system design cannot be used. A number of new approaches are therefore explored in this paper in an attempt to create a reliable fault detection system for the radar. These include: (a) Use of inter-vehicle communication; (b) Use of a geographic database of pre-identified roadside radar targets; (c) Detection of abrupt failures using fuzzy logic and a knowledge of vehicle acceleration abilities; (d) Use of a redundant sensor that is inexpensive but of poor quality. The performance of each of these approaches is evaluated. Experimental results indicate that a combination of approaches (c) and (d) would provide the most reliable method for radar health monitoring. This combination would work effectively even in the absence of inter-vehicle communication in a realistic highway environment.     

On fault isolation for rail vehicle suspension systems

Reliability of the railway vehicle suspension system is of critical importance to the safety of the vehicle. It is very desirable to monitor the health condition and the performance degradation of the suspension system online, which offers the important information of the suspension system and is critically important for the condition-based maintenance rather than scheduled maintenance in the future. Advanced fault diagnosis method is one of the most effective means for the health monitoring of the suspension system. In this paper, taking the lateral suspension system as an examcple, the fault isolation issue for different component faults occurring in the suspension system is concerned. The sensor configuration for obtaining the vehicle state information and the mathematical model for the lateral suspension system are presented. Four fault features in the time domain and three fault features in the frequency domain are used for each sensor signal. Three different methods, Dempster–Shafer (D–S) evidence theory, Fisher discrimination analysis (FDA) and support vector machine (SVM) techniques are applied to the fault isolation problem. Simulation study is carried out by means of the professional multi-body simulation tool, SIMPACK. The simulation results show that these methods can isolate the considered component faults effectively with a high accuracy. The D–S evidence-based fault isolation approach outperforms the other two methods.     

Estimation of Tire-Road Friction Using Observer Based Identifiers

This paper presents methods for identifying the tire-road friction coefficient. The proposed methods are: an observer-based least square method and an observer/filtered-regressor-based method. These methods were designed assuming that some of the states are not available since physical parameter identification methods developed assuming that the system states are available are not attractive from a practical point of view. The observer is used to estimate signals which are difficult or expensive to measure. Using the estimated states of the system and the filtered-regressor, the parameter estimates are obtained. The proposed methods are evaluated on an eight state nonlinear vehicle/transmission simulation model with a Bakker-Pacejka's formula tire model. Vehicle tests have been performed on dry and wet roads to verify the performance of the methods. It has been shown through simulations and vehicle tests how the RPM sensors can be used with observer based identification methods to estimate the tire-road friction from measurements of engine rpm, transmission output speed and wheel speed. The proposed methods will be useful in the implementation and adaptation of vehicle collision warning/avoidance algorithm since the tire-road friction can be estimated only using the RPM sensors which are currently being used in production vehicles.     

丰田汽车巡航控制过程与故障诊断

丰田系列汽车巡航控制系统由传感器、控制开关、巡航控制电子控制器(CCSECU)和电力驱动式执行机构等组成。本文主要分析该系列汽车巡航控制系统的控制过程与故障诊断方法,并给出故障代码表。     

Nonlinear sliding mode observer for exhaust manifold pressure estimation in a light-duty diesel engine

In this paper, a sliding mode observer is proposed to estimate exhaust pressure for a diesel engine equipped with variable geometry turbocharger (VGT) and exhaust gas recirculation (EGR) systems. Since the exhaust pressure directly affects generation of the VGT power and the EGR rate in the cylinder, the exhaust pressure information is important for precise control of the VGT and EGR systems. In order to estimate the exhaust pressure accurately, a dynamic model of intake and exhaust pressure was derived. Furthermore, the mass flow rate and temperature of the air system in the diesel engines were modeled by consideration of physical phenomena and the thermodynamic law. Based on the developed models, a nonlinear sliding mode observer was designed to estimate the exhaust pressure. Convergence of the proposed observer was verified by the Lyapunov stability criterion. The proposed observer was implemented on a real-time embedded system and validated with the engine experiments. The experimental results show that the observer estimates the exhaust pressure accurately in both steady and transient engine operating conditions. Moreover, as a case study, the estimation results of the proposed observer could be applied for detecting a fault of the EGR system. The fault of the EGR system was detected precisely using the estimation result and the limited sensor information in mass-produced engines.     

Diagnosis Methods for Electronic Controlled Vehicles

The development of automotive systems shows an increasing number of sensors, actuators and microelectronic controllers, partially for active driver assistance. However, electronic and electrical components have quite different failure behavior and in general lower reliability than mechanical components. On the other side microcomputers can also be used for fault detection and diagnosis. The contribution therefore shows how model-based fault detection and diagnosis methods together with few available measurements can be applied for automobiles. After an introduction, a short summary is given for fault detection and diagnosis methods, especially for model-based methods. Then some research results are shown, like the fault detection and diagnosis of an electromechanical throttle actuator, a suspension system and the lateral behavior of a passenger car. Finally, methods for fault-tolerant sensors and actuators are discussed which are required for drive-by-wire systems.     

Diagnosis Methods for Electronic Controlled Vehicles

The development of automotive systems shows an increasing number of sensors, actuators and microelectronic controllers, partially for active driver assistance. However, electronic and electrical components have quite different failure behavior and in general lower reliability than mechanical components. On the other side microcomputers can also be used for fault detection and diagnosis. The contribution therefore shows how model-based fault detection and diagnosis methods together with few available measurements can be applied for automobiles. After an introduction, a short summary is given for fault detection and diagnosis methods, especially for model-based methods. Then some research results are shown, like the fault detection and diagnosis of an electromechanical throttle actuator, a suspension system and the lateral behavior of a passenger car. Finally, methods for fault-tolerant sensors and actuators are discussed which are required for drive-by-wire systems.     

欧IV柴油机SCR系统添蓝控制器的研发

介绍了欧IV柴油机SCR系统添蓝控制器及配套上位机软件的设计和功能.添蓝控制器通过采集CAN总线及各传感器信号,根据柴油机的工况变化和催化器的温度变化,按照内部写定的稳态喷射策略和动态修正策略,准确地控制定量给料单元向排气管中喷射添蓝;添蓝控制器具备故障诊断功能,实时对整个SCR系统进行故障监测与诊断.上位机软件通过CAN-USB模块与添蓝控制器相连,共同完成系统标定、实时监测、故障诊断的功能.     

Pedaling torque sensor-less power assist control of an electric bike via model-based impedance control

This paper proposes a method to assist human force acting on electric bike without using costly torque sensors via a model-based impedance control technique. In general, electric bikes are classified into two categories, i.e., pedelec electric bikes and throttle electric bikes. We focus on the system called a pedelec electric bike. It assists human pedaling force using the pedaling information, e.g., pedaling force or speed. To obtain the human’s pedaling information in real-time, it needs physical sensors such as a torque sensor and a velocity sensor. But, these sensors are expensive and weak against external loads. Also, since these sensors are fixed directly to the forced component in a bike system, there are the risks of damage. For these reasons, sensor-less control methods based on a disturbance observer have been studied so far. In this paper, we have proposed a pedaling torque sensor-less power assist method and have applied it to the experimental pedelec electric bike. A power assist control algorithm, designed by employing an impedance model, consists of a PI-type feedback controller, an inverse model-based feedforward controller, and a pedaling torque observer. Finally, we performed experiments and confirmed the effectiveness of a proposed power assist control method.     

High Performance ACC System Based on Sensor Fusion with Distance Sensor, Image Processing Unit, and Navigation System

Curve sensors used in first generation “Adaptive Cruise Control” systems (ACC) are based on steering angle sensors, lateral accelerometers or yaw rate sensors. The disadvantage of these curve sensors is that they do not have any preview characteristics. This leads in many driving situations to misinterpretations by the ACC system, e.g. wrong path assignments of vehicles ahead because of non-constant curve radii particularly in the beginning and ending of curves. The consequence is that the ACC car brakes due to vehicles in adjacent lanes or it ignores relevant obstacles. In the following a second generation ACC system will be presented whose curve sensor is realized by a real time image processing system with the support of a GPS-based navigation system. This multi-sensor fusion system is now suitable for collision avoidance and stop and go applications.     

汽车电控系统故障检测与诊断新方法

系统地论述了检测滤波器，双阶段检测滤波器，自适应非线性观测器，基于非线性等价方程的残差生成器等故障检测与诊断方法的原理以及在车辆电控系统中的具体应用，并提出了汽车电控系统故障检测与诊断面临的问题和发展方向。     

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.     

Nonlinear Robust Control of Torque Converter Clutch Slip System for Passenger Vehicles Using Advanced Torque Estimation Algorithms

In this paper, a torque-estimation-based robust controller for passenger car torque converter clutch slip system is presented. The proposed robust controller uses only the measurements available from inexpensive sensors that are installed in current passenger vehicles for torque estimation and feedback control. A conventional full state observer along with a neural-network-based open-loop hydraulic actuator observer is designed to estimate the unknown driving load, and a neural-network-based turbine torque estimator considering the temperature of oil circulating the torque converter is developed for improved turbine torque estimation accuracy. The stability of the internal dynamics is also investigated, and the performance and robustness of the robust controller is validated by simulation studies.