Suspension parameter estimation in the frequency domain using a matrix inversion approach

The dynamic lumped parameter models used to optimise the ride and handling of a vehicle require base values of the suspension parameters. These parameters are generally experimentally identified. The accuracy of identified parameters can depend on the measurement noise and the validity of the model used. The existing publications on suspension parameter identification are generally based on the time domain and use a limited degree of freedom. Further, the data used are either from a simulated ‘experiment’ or from a laboratory test on an idealised quarter or a half-car model. In this paper, a method is developed in the frequency domain which effectively accounts for the measurement noise. Additional dynamic constraining equations are incorporated and the proposed formulation results in a matrix inversion approach. The nonlinearities in damping are estimated, however, using a time-domain approach. Full-scale 4-post rig test data of a vehicle are used. The variations in the results are discussed using the modal resonant behaviour. Further, a method is implemented to show how the results can be improved when the matrix inverted is ill-conditioned. The case study shows a good agreement between the estimates based on the proposed frequency-domain approach and measurable physical parameters.     

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.     

Hybrid modelling and damping collaborative optimisation of Five-suspensions for coupling driver-seat-cab system

For the complex structure and vibration characteristics of coupling driver-seat-cab system of trucks, there is no damping optimisation theory for its suspensions at present, which seriously restricts the improvement of vehicle ride comfort. Thus, in this paper, the seat suspension was regarded as ‘the fifth suspension’ of cab, the ‘Five-suspensions’ for this system was proposed. Based on this, using the mechanism modelling method, a 4 degree-of-freedom coupling driver-seat-cab system model was presented; then, by the tested cab suspensions excitation and seat acceleration response, its parameters identification mathematical model was established. Based on this, taking optimal ride comfort as target, its damping collaborative optimisation mathematical model was built. Combining the tested signals and a simulation model with the mathematical models of parameters identification and damping collaborative optimisation, a complete flow of hybrid modelling and damping collaborative optimisation of Five-suspensions was presented. With a practical example of seat and cab system, the damping parameters were optimised and validated by simulation and bench test. The results show that the model and method proposed are correct and reliable, providing a valuable reference for the design of seat suspension and cab suspensions.     

A new parameter identification methodology for a modal analysis test of a rail vehicle

The validation of vehicle mathematical models is a key part of the virtual acceptance process since it is essential to ensure a precise representation of the reality. The model validation procedure should include validation of stationary but also dynamic tests. However, parameter identification from on-track tests is a challenging task due to the non-controlled excitation and the great variability of the test results. Thus, an alternative solution by means of a vehicle modal analysis is proposed, developing a parameter identification methodology for dynamic vehicle model parameters. This methodology calculates estimated values of the vehicle model parameters that have an influence on the excited vehicle vibration modes. Moreover, a new criterion for taking into account the effect of the measurement uncertainties on the selection process of the vehicle parameters is developed. Finally, experimental results show that not only estimations of the suspension stiffness parameters can be obtained, but damping values and structural frequencies from the vehicle bodies can also be estimated.     

Condition monitoring of rail vehicle suspensions based on changes in system dynamic interactions

A novel scheme for the fault detection and condition monitoring of vehicle suspensions is presented in this study. The new technique exploits the dynamic interactions between different vehicle modes caused by component failures in the system, leading to a simple but effective solution. Compared with many model-based fault detection techniques, the proposed technique does not require complex mathematical models of the system and it overcomes potential difficulties associated with nonlinearities and parameter variations in the system. The use of inexpensive inertial sensors and ease of tuning make the practical implementation of the proposed scheme straightforward. A conventional railway vehicle is used in the study to illustrate the basic ideas as well as the effectiveness of the novel fault detection method, although the general principle is applicable to other systems.     

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.     

基于转速辨识的EPS阻尼控制策略的研究

将基于递推最小二乘算法的转速在线辨识方法引入电动助力转向系统中,以估计助力电动机的转速。利用估计转速结合车速,设计了相应的阻尼特性,提出了阻尼控制策略,并对转速辨识和阻尼特性进行仿真。结果表明,利用递推算法进行转速辨识成本低,可靠性、鲁棒性高;提出的阻尼控制策略有效可行。     

轮毂电机驱动车辆驱动力控制研究

轮毂电机驱动车辆各轮转矩精确可控且响应迅速的特点适用于越野工况，但越野路面起伏不一且附着条件多变，因此，开发基于越野工况辨识的车辆驱动力控制策略，对提升轮毂电机驱动车辆的纵向行驶稳定性具有重要意义。基于动力学模型分析路面附着与路面几何特征，确定可用于越野工况辨识的车辆特征参数集；针对车轮悬空垂向载荷估计失真现象，且由于地面垂向力的实际变化导致车辆垂向载荷分配比例的改变，修正了垂向载荷的计算；利用各特征参数的差异与越野工况的映射关系判定工况属性，采用模糊识别法界定4种地形工况；驱动力控制上层考虑工况与驾驶员影响因素，通过越野工况辨识结果决策驱动利用系数，作为前馈期望转矩调节权重；中层通过四轮垂向载荷得到转矩分配系数，设计驱动力分配算法；下层针对车辆在越野工况下出现车轮滑转与悬空状态，对车轮进行动态转矩补偿。仿真测试与实车验证表明，越野工况辨识结果与预期相符，驱动力控制策略综合优化了车辆稳定性和动力性。     

Improved calibration of simulation models in railway dynamics: application of a parameter identification process to the multi-body model of a TGV train

This paper aims at estimating the vehicle suspension parameters of a TGV (Train à Grande Vitesse) train from measurement data. A better knowledge of these parameters is required for virtual certification or condition monitoring applications. The estimation of the parameter values is performed by minimising a misfit function describing the distance between the measured and the simulated vehicle response. Due to the unsteady excitation from the real track irregularities and nonlinear effects in the vehicle behaviour, the misfit function is defined in the time domain using a least squares estimation. Then an optimisation algorithm is applied in order to find the best parameter values within the defined constraints. The complexity of the solution surface with many local minima requires the use of global optimisation methods. The results show that the model can be improved by this approach providing a response of the simulation model closer to the measurements.     

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.     

工程车辆非线性橡胶悬架动力学建模与优化

以AD250铰接式自卸车的非线性变刚度橡胶悬架为研究对象,应用模态综合法和多柔体系统动力学理论,并通过整车试验建立了整车刚柔耦合动力学模型。以行驶平顺性和操纵稳定性为优化目标,采用序列二次规划法,对不同载荷、不同等级路面和不同车速下的悬架特性参数进行优化,得到了不同工况下的最优悬架特性参数。通过最小二乘法拟合得到了橡胶悬架刚度参数的理想非线性特性曲线。仿真结果表明,优化后的橡胶悬架系统能使车辆保持良好的行驶平顺性。     

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.     

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.     

Identification of the technical state of suspension elements in railway systems

The running safety and passenger comfort levels in a vehicle are tightly related to the technical state of the suspension elements. The technical state of the suspension depends of the service life time as its components become old and wear out. In this paper, a study on the dynamic behaviour of a railway vehicle is established in relation to the damping elements in one of its suspension stages. An experimental measurement model is developed, obtaining a set of useful signals for the identification of the dynamic parameters of the vehicle and developing a test through the application of the operational modal analysis technique, using least-squares complex exponential method as a basis to validate the numerical model of the multi-body system. Then, the study focuses on developing numerical simulations for the identification of the technical state of the dampers by the registration of dynamic variables under commercial service conditions and on estimating the state of the suspension elements.     

汽油机进气系统传感器故障诊断和应急管理策略研究

利用Q480电控汽油机的相关参数和试验数据,建立了进气管空气动态模型;结合信号特征比较法和进气管空气动态模型设计出适合于电控汽油机进气系统传感器的故障诊断策略和故障应急管理策略;最后,将自主研发的带有进气系统传感器故障诊断和故障应急管理策略的控制单元安装到整车上进行试验,试验结果证明了故障诊断策略的正确性和故障应急策略的可行性。     

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。     

Tractor–semitrailer model for vehicles carrying liquids

This article presents a model for solving solid–fluid interactions in vehicles carrying liquids. A tractor–semitrailer model is developed by incorporating suspension systems and tire dynamics. Owing to the solid–fluid interaction, equations of motion for the vehicle system are coupled. To simplify the complicated solution procedure, the coupled equations are solved separately using two different codes. Each code is analyzed separately; but as the parameters of the two codes depend on each other, the codes must be connected at the end of each time step. To determine the dynamic behavior of the system, different braking moments are applied. As the braking moments increase, braking time decreases. However, it turns out that increasing the braking moment to more than a certain level produces no significant results. It is also shown that vehicles carrying fluids need a greater amount of braking moments in comparison to vehicles carrying solids during braking. In addition, as the level of the fluid inside the tanker increases, from one-third to two-third of the tanker’s volume, the sloshing forces applied to the tanker’s walls increase. It was also concluded that the strategy used in this article to solve for the solid–fluid interaction by incorporating vehicle dynamic effects represents an effective method for determining the dynamic behavior of vehicles carrying fluids in other critical maneuvers.     

Robustness analysis of bogie suspension components Pareto optimised values

Bogie suspension system of high speed trains can significantly affect vehicle performance. Multiobjective optimisation problems are often formulated and solved to find the Pareto optimised values of the suspension components and improve cost efficiency in railway operations from different perspectives. Uncertainties in the design parameters of suspension system can negatively influence the dynamics behaviour of railway vehicles. In this regard, robustness analysis of a bogie dynamics response with respect to uncertainties in the suspension design parameters is considered. A one-car railway vehicle model with 50 degrees of freedom and wear/comfort Pareto optimised values of bogie suspension components is chosen for the analysis. Longitudinal and lateral primary stiffnesses, longitudinal and vertical secondary stiffnesses, as well as yaw damping are considered as five design parameters. The effects of parameter uncertainties on wear, ride comfort, track shift force, stability, and risk of derailment are studied by varying the design parameters around their respective Pareto optimised values according to a lognormal distribution with different coefficient of variations (COVs). The robustness analysis is carried out based on the maximum entropy concept. The multiplicative dimensional reduction method is utilised to simplify the calculation of fractional moments and improve the computational efficiency. The results showed that the dynamics response of the vehicle with wear/comfort Pareto optimised values of bogie suspension is robust against uncertainties in the design parameters and the probability of failure is small for parameter uncertainties with COV up to 0.1.     

Feedback brake distribution control for minimum pitch

The distribution of brake forces between front and rear axles of a vehicle is typically specified such that the same level of brake force coefficient is imposed at both front and rear wheels. This condition is known as ‘ideal’ distribution and it is required to deliver the maximum vehicle deceleration and minimum braking distance. For subcritical braking conditions, the deceleration demand may be delivered by different distributions between front and rear braking forces. In this research we show how to obtain the optimal distribution which minimises the pitch angle of a vehicle and hence enhances driver subjective feel during braking. A vehicle model including suspension geometry features is adopted. The problem of the minimum pitch brake distribution for a varying deceleration level demand is solved by means of a model predictive control (MPC) technique. To address the problem of the undesirable pitch rebound caused by a full-stop of the vehicle, a second controller is designed and implemented independently from the braking distribution in use. An extended Kalman filter is designed for state estimation and implemented in a high fidelity environment together with the MPC strategy. The proposed solution is compared with the reference ‘ideal’ distribution as well as another previous feed-forward solution.     

Prediction of ride quality of a Maglev vehicle using a full vehicle multi-body dynamic model

In magnetically levitated (Maglev) transportation systems, especially in electromagnetic suspension system (EMS) type Maglev systems, highly accurate prediction of ride quality is very important in order to reasonably relax guideway construction tolerances or constraints and stiffness while meeting the specification for ride comfort, thereby reducing guideway construction and maintenance costs. A full vehicle multi-body dynamic model is proposed, to facilitate a rigorous ride quality prediction of an EMS-type Maglev vehicle. Using the more realistic dynamic model proposed in this paper, the effects of guideway deflection limits, surface roughness, and levitation control system parameters on ride quality are studied numerically. The results obtained from the simulation studies are then used to facilitate a discussion of the trade-off between guideway smoothness and vehicle suspension. It can be expected that these studies could suggest cost-effective specifications for guideway construction tolerances and stiffness and EMS.