A comparative study on fault detection methods of rail vehicle suspension systems based on acceleration measurements (original) (raw)
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Condition Monitoring and Fault Detection of Railway Vehicle Suspension using Multiple-Model Approach
IFAC Proceedings Volumes
This paper demonstrates the possibility to detect suspension failures of railway vehicles using a multiple-model approach from on-board measurement data. The railway vehicle model used includes the lateral and yaw motions of the wheelsets and bogie, and the lateral motion of the vehicle body, with sensors measuring the lateral acceleration and yaw rate of the bogie, and lateral acceleration of the body. The detection algorithm is formulated based on the Interacting Multiple-Model (IMM) algorithm. The IMM method has been applied for detecting faults in vehicle suspension systems in a simulation study. The mode probabilities and states of vehicle suspension systems are estimated based on a Kalman Filter (KF). This algorithm is evaluated in simulation examples. Simulation results indicate that the algorithm effectively detects on-board faults of railway vehicle suspension systems.
Journal of Mechanical Science and Technology, 2015
The design of a vibration based Fault Detection and Isolation (FDI) unit that may tackle the combined problem of fault detection, isolation (or identification) and magnitude estimation (collectively known as fault diagnosis), in railway vehicle suspensions is presented. The unit is initially "trained" in a baseline phase based on data obtained from a simplified physics-based model of a railway vehicle suspension. Fault diagnosis is subsequently achieved in an inspection phase through a single, properly preselected, pair of vibration signals acquired from the vehicle, and a recently introduced data-based method, referred to as the Functional Model Based Method (FMBM), without resorting on the physics-based model of the baseline phase. The method's cornerstone is the novel class of stochastic ARX-type models which are capable of accurately representing a system in a faulty state for its continuum of fault magnitudes. Fault diagnosis feasibility in a railway vehicle suspension is demonstrated via Monte Carlo simulations using different types and magnitudes of faults in the physics-based model and generating vibration signals corresponding to the healthy and faulty suspension. Two vibration signals are used by the diagnosis unit: the track velocity profile and the vehicle body acceleration above the trailing airspring. Fault diagnosis based on the FMBM is effective in a compact and unified statistical framework accounting for experimental and modelling uncertainty through appropriate interval estimates and hypothesis testing procedures. The unit is shown to exhibit high sensitivity and accurate estimation of even very small fault magnitudes, to detect and isolate unknown faults for which it has not been trained, and to be robust to high measurement noise, car body mass variations, and varying track irregularity.
2015
This paper presents a model-based strategy for condition monitoring of suspensions in a railway bogie. This approach is based on recursive least-square (RLS) algorithm focusing on the ‘Input-output’ model. RLS is able to identify the unknown parameters from a noisy input-output system by memorizing the correlation properties. The identification of the suspension parameter is achieved by establishing the relationship between the excitation and response of a bogie. A fault detection method for vertical primary suspensions of one bogie is illustrated as an example of this scheme. Numerical simulation results from the rail vehicle dynamics software ‘ADTreS’ are utilized as ‘virtual measurements’, considering a trailed car of Italian ETR500 high-speed train. The test data from an E464 locomotive are also employed to validate the feasibility of this strategy for the real situation. Results of the parameter identification performed indicate that estimated suspension parameters are consiste...
A linear Kalman filtering scheme for estimation of secondary vertical suspension of railway vehicles
Model based filtering is fast becoming a key instrument for maintenance and condition monitoring of railway vehicles. This study presents the use of linear Kalman filtering scheme to identify vertical secondary suspension of a railway vehicle by using the vertical vibrations of a vehicle due to vertical track irregularities. As well as the use of linear Kalman filtering scheme, a weighted least squares estimation is used to identify vertical secondary spring coefficient as a parameter by using residuals of the filter. In this investigation, a 7 degree of freedom dynamic model of ERRI B176 benchmark vehicle is considered. Unlike previous studies, to the authors' knowledge, this research provides the simplest estimation scheme for identification of secondary vertical spring parameter and can be used to achieve a cost-effective condition based maintenance for railway vehicles.
Fault Isolation for Urban Rail Vehicle Suspension Systems
Proceedings of the 19th IFAC World Congress, 2014
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 for rail vehicle suspension systems online, which offers the important information of the suspension system and it 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 rail suspension systems. In this paper, taking the lateral suspension system as an example, the fault isolation issue for different component faults occurring in the suspension system is concerned. The sensor configuration for obtaining the state information for fault diagnosis and the mathematical model for the lateral suspension system are presented. 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, respectively. 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 proposed methods provide an effective alternative for the health monitoring of rail vehicle suspension systems.
Modern techniques for condition monitoring of railway vehicle dynamics
Journal of Physics: Conference Series, 2012
A modern railway system relies on sophisticated monitoring systems for maintenance and renewal activities. Some of the existing conditions monitoring techniques perform fault detection using advanced filtering, system identification and signal analysis methods. These theoretical approaches do not require complex mathematical models of the system and can overcome potential difficulties associated with nonlinearities and parameter variations in the system. Practical applications of condition monitoring tools use sensors which are mounted either on the track or rolling stock. For instance, monitoring wheelset dynamics could be done through the use of track-mounted sensors, while vehicle-based sensors are preferred for monitoring the train infrastructure. This paper attempts to collate and critically appraise the modern techniques used for condition monitoring of railway vehicle dynamics by analysing the advantages and shortcomings of these methods.
Fault detection of rail vehicle suspension system based on CPCA
2013 Conference on Control and Fault-Tolerant Systems (SysTol), 2013
The suspension system plays a crucial role of the rail vehicles. The fault detection of the suspension system is an effective way to ensure the security of the safe, stable operation of rail vehicles. This paper concerns the fault detection issue of rail vehicle suspension systems with the consensus principle components analysis (CPCA). The signal information used in the fault detection is obtained from the SIMPACK and MATLAB co-simulation environment. In this paper, two typical primary spring and damper fault with coefficient reduction of 5% and 25% are detected successfully using CPCA. Compared with DPCA, the simulation results show that the CPCA method can detect smaller fault with faster response speed.
Fault estimation methods for semi-active suspension systems
2015 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), 2015
Semi-Active (SA) suspension systems aim to improve the stability and comfort of vehicles. Although they offer better performance than passive suspensions, the actuators such as magneto-rheological dampers are more susceptible to failure. Oil leakage is the most common fault, and its effect is a reduction of the damping force. The estimation of suspension faults can be used with a Fault Tolerant Control system to prevent handling and comfort deterioration. However, fault estimation schemes introduce additional challenges due to the damper non-linear dynamics and the strong influence of the disturbances (i.e the road profile). One of the first obstacles for appropriate damper fault detection is the modeling of the fault, which has been shown to be of multiplicative nature. However, many of the most widespread fault detection schemes consider additive faults due to mathematical convenience. Two complementary model-based fault estimation schemes for semi-active dampers are proposed: an observer-based approach, which is intended to estimate additive faults; and a parameter identification approach, which is intended to estimate multiplicative faults. The performance of these schemes is validated and compared through simulations using a pickup truck model. Early results shows that a parameter identification approach is more accurate in fault estimation, whereas an observer-based approach is less sensible to parametric uncertainty.
Fault Detection and Optimal Sensor Location in Vehicle Suspensions
Journal of Vibration and Control, 2003
A statistical system identification methodology is applied for performing parametric identification and fault detection studies in nonlinear vehicle systems. The vehicle nonlinearities arise due to the function of the suspension dampers, which assume a different damping coefficient in tension than in compression. The suspension springs may also possess piecewise linear characteristics. These lead to models with parameter discontinuities. Emphasis is put on investigating issues of unidentifiability arising in the system identification of nonlinear systems and the importance of sensor configuration and excitation characteristics in the reliable estimation of the model parameters. A methodology is proposed for designing the optimal sensor configuration (number and location of sensors) so that the corresponding measured data are most informative about the condition of the vehicle. The effects of excitation characteristics on the quality of the measured data are systematically explored. ...
Detection of Fault in Actively Controlled Railway Vehicle
It was the recent study when the traditional passive suspension used to stabilize the sets of wheel for the railway but it created the obstacle of the effect of rolling for the wheels. The design dispute between the stability and the rolling performance can crack by active components instead of the straight passive suspension which has been shown theoretically that the use of active control can mitigate wear of the wheels and its tracking forces. The study of active control in the place of traditional components that substituted by the actuators, sensors and data processor may increase the problems of stability which is a serious case, results in the derailment. Additionally, the cost of the actuators is expensive and it takes more space than sensors and needs an electronic control unit in the active control system for railway vehicles. The Kalman Bucy filter is used to generate the values for detection and isolation of faults in the railway wheelsets.