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.     

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.     

Fault-tolerant control of heavy-haul trains

The fault-tolerant control (FTC) of heavy-haul trains is discussed on the basis of the speed regulation proposed in previous works. The fault modes of trains are assumed and the corresponding fault detection and isolation (FDI) are studied. The FDI of sensor faults is based on a geometric approach for residual generators. The FDI of a braking system is based on the observation of the steady-state speed. From the difference of the steady-state speeds between the fault system and the faultless system, one can get fault information. Simulation tests were conducted on the suitability of the FDIs and the redesigned speed regulators. It is shown that the proposed FTC does not explicitly worsen the performance of the speed regulator in the case of a faultless system, while it obviously improves the performance of the speed regulator in the case of a faulty 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。。     

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。     

A review of dynamics modelling of friction wedge suspensions

Three-piece bogies with friction wedge suspensions are the most widely used bogies in heavy haul trains. Fiction wedge suspensions play a key role in these wagon systems. This article reviews current techniques in dynamic modelling of friction wedge suspension with various motivations: to improve dynamic models of friction wedge suspensions so as to improve general wagon dynamics simulations; to seek better friction wedge suspension models for wagon stability assessments in complex train systems; to improve the modelling of other friction devices, such as friction draft gear. Relevant theories and friction wedge suspension models developed by using commercial simulation packages and in-house simulation packages are reviewed.     

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.     

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.     

某型电站自动监控系统故障剖析及排除

分析了某型电站自动监控系统电路工作原理,剖析了故障发生的原因,介绍了故障排除方法,提出预防此类故障发生的措施和判断故障的方法。     

Three-dimensional derailment analysis of crashed freight trains

In this paper, the collision-induced derailment of freight trains was investigated. The collision between two identical freight trains occurring on a curved path rather than along a straight line was investigated. This is because from the point of view of safety against derailment this collision scenario is thought to be more critical than the scenarios defined in the European standard EN 15227. In this work, one of the trains is stationary and the other moving train collides at 36 km/h. Two kinds of container wagons were simulated. One is the two-axle freight wagon Kls 442. Another is the freight wagon Rmms 662 with two Y25 bogies. Simulation results demonstrate that in terms of safety against derailment the bogie wagon Rmms 662 was found to have better behaviour than the two-axle wagon Kls 442. In addition, this study points out that there are many contributory factors to the responses of freight wagons during a collision, such as curve radius, distance between bogie pivots and loading mass. The derailment phenomenon is less likely to occur, when freight trains collide on the curve with a larger radius. Besides that the characteristics of freight wagons with large axle loads, low centre of gravity of car body and appropriate static strength are favourable for the collided wagons in reducing the risk of derailment.     

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.     

Fault detection method for electric parking brake (EPB) systems with sensorless estimation using current ripples

A fault detection method with parity equations is proposed in this paper. Due to its low cost implementation, the velocity of the motor is not measurable in electric parking brake (EPB) systems. Therefore, residuals are not reliable when estimating the motor velocity with a low-resolution encoder. In this paper, we propose a fault detection method with sensorless estimation using current ripples that estimates the position and velocity of the motor by detecting periodical oscillations of the armature current caused by rotor slots. In addition, this method can estimate the position and velocity of the motor with less computational effort than a state observer. Moreover, the method is less sensitive to motor parameters than model-based estimation methods. The effectiveness of this method is validated with experimental data, and the simulation results show that various faults have their own residual patterns. Therefore, we can detect the presence of faults by monitoring the residual signals.     

Dynamics of railway wagons subjected to braking torques on defective tracks

It is well known that track defects cause profound effects to the dynamics of railway wagons; normally such problems are examined for cases of wagons running at a constant speed. Brake/traction torques affect the speed profile due to the wheel–rail contact characteristics but most of the wagon–track interaction models do not explicitly consider them in simulation. The authors have recently published a model for the dynamics of wagons subject to braking/traction torques on a perfect track by explicitly considering the pitch degree of freedom for wheelsets. The model is extended for cases of lateral and vertical track geometry defects and worn railhead and wheel profiles. This paper presents the results of the analyses carried out using the model extended to the dynamics of wagons containing less ideal wheel profiles running on tracks with geometry defects and worn rails.     

Analysis of the nonlinear dynamics of a 2–axle freight wagon in curves

This paper deals with the study of the nonlinear dynamic behaviour of 2-axle freight wagons in curves, considering the case of one single wagon (neglecting inter-car coupling forces) and of multiple wagons interacting through the buffers and the couplers. A multi-body model of a single wagon and of a three-car assembly is introduced, paying particular attention to the nonlinear and nonsmooth modelling of the suspensions and of the inter-car coupling elements. Using this model, a numerical analysis of the steady-state solution reached after the negotiation of curve transition is presented and bifurcations are identified for some particular values of the curve radius. For the single car case, it is shown that depending on the curve radius and the vehicle speed the carbody may experience severe periodic oscillations at speeds lying in the operating range of the vehicle. For the car-assembly case, it is shown that the coupling forces exchanged by the wagons significantly affect their dynamics in a curve, reducing the amplitude of vibration.     

Fault detection and diagnosis of the electromechanical brake based on observer and parity space

In the future, the conventional hydraulic brake system in automobiles will be removed and replaced by an electrically operated brake system called brake-by-wire. The brake-by-wire units, such as the EMB (Electro-Mechanical Brake), provide better braking performance by directly controlling the brake motor and are environmentally friendly because they do not use hydraulic fluid. For implementation of the EMB systems, reliable and robust fault detection and diagnosis methods become increasingly important. In this study, a sensor fault diagnosis method is proposed with parity space and observer approaches to detect faults in the motor current sensor, speed (or position) sensor and clamping force sensor. The proposed method is verified through a closed-loop simulation using Matlab/Simulink, and the simulation result is compared with the HILS bench test results.     

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。     

Fault detection and isolation for a nonlinear railway vehicle suspension with a Hybrid Extended Kalman filter

Fault detection is considered to be one way to improve system reliability and dependability for railway vehicles. The secondary lateral and anti-yaw dampers are the most critical parts in railway suspension systems. So far, the dampers have been modelled as linear components in the fault detection and isolation observer design. In this work, a Hybrid Extended Kalman filter is used to capture the nonlinear characteristics of the dampers. In order to detect and isolate faults, a nonlinear residual generator is developed, which can distinguish clearly between different types of faults. A lateral half train model serves as an example for the proposed technique. The results show that failures in the nonlinear suspension system can be detected and isolated accurately.     

洞庭湖大桥结构状态在线监测系统

桥梁结构状态在线监测系统是目前桥梁工程领域的研究热点,其目的是为结构损伤诊断、可靠性评估和维护提供科学的依据和指导。在分析了传统人工检测的不足的基础上,以洞庭湖大桥为例,从适用性和经济性角度,讨论了适于桥梁状态在线监测的监测项目和测量手段,并分析了监测系统结构、网络拓扑结构和通讯技术等关键问题,同时针对现有桥梁结构损伤诊断和评估的不足,提出建立因特网远程监测与分析平台,将异地专家与现场有机联系起来。     

Effect of arc surfaces friction coefficient on coupler stability in heavy haul locomotives: simulation and experiment

The heavy haul coupler/buffer system equipped with arc surfaces on the coupler tail and the follower is widely applied to connect the locomotives and wagons. As one of the most important parameters, arc surfaces friction coefficient plays a key role in coupler instability, which threatens the safety of trains. To investigate the effect of arc surfaces friction coefficient on coupler stability, a simulation model adopting the latest modelling methods is established and field tests employing the locomotives equipped with different friction coefficients are conducted. The results show that the friction coefficient of arc surfaces can affect the coupler yaw angle remarkably. Increasing the friction coefficient can improve the coupler stability. However, under severe compressive force condition, the increased friction coefficient can be reduced quickly, which calls for further attention.     

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。