Wheel impact load detectors are widespread railway systems used for measuring the wheel–rail contact force. They usually measure the rail strain and convert it to force in order to detect high impact forces and corresponding detrimental wheels. The measured strain signal can also be used to identify the defect type and its severity. The strain sensors have a limited effective zone that leads to partial observation from the wheels. Therefore, wheel impact load detectors exploit multiple sensors to collect samples from different portions of the wheels. The discrete measurement by multiple sensors provides the magnitude of the force; however, it does not provide the much richer variation pattern of the contact force signal. Therefore, this paper proposes a fusion method to associate the collected samples to their positions over the wheel circumferential coordinate. This process reconstructs an informative signal from the discrete samples collected by multiple sensors. To validate the proposed method, the multiple sensors have been simulated by an ad hoc multibody dynamic software (VI-Rail), and the outputs have been fed to the fusion model. The reconstructed signal represents the contact force and consequently the wheel defect. The obtained results demonstrate considerable similarity between the contact force and the reconstructed defect signal that can be used for further defect identification.@en

A wheel impact load detector is used to assess the condition of a railway wheel by measuring the dynamic forces generated by defects. This system normally measures the impact force at multiple points by exploiting multiple sensors to collect samples from different portions of the wheel circumference. The outputs of the sensors are used to estimate the dynamic force as the main indicator for detecting the presence of the defect. This method fails to identify the defect type and its severity. Recently, a data fusion method has been developed to reconstruct the wheel defect signal from the wheel–rail contact signals measured by multiple wayside sensors. The reconstructed defect signal can be influenced by different parameters such as train velocity, axle load, number of sensors, and wheel diameter. This paper aims to carry out a parametric study to investigate the influence of these parameters. For this purpose, VI-Rail is used to simulate the wheel–rail interaction and provide the required data. Then, the developed fusion method is exploited to reconstruct the defect signal from the simulated data. This study provides a detailed insight into the effects of the influential parameters by investigating the variation of the reconstructed defect signals.@en

Nowadays, various railway components are checked by numerous sensors, in workshops and in-service. Railway wheels are critical components, which their health status directly and indirectly are monitored. The Wheel Impact Load Detector (WILD) is a widespread commercial monitoring system, which exploits one or more strain sensors to detect defective wheels using wheel-rail contact force measurement. The objective of the research described in this paper is to use the WILD to measure the wheel diameter. This trait enhances the WILD ability to monitor more features of the wheel condition using the same system. Each wheel has its unique condition along its circumference, which causes a unique pattern of contact force, replicated in every revolution. By having the wheel diameter, the measured impacts can be mapped to the corresponding position over the wheel circumference. To achieve this purpose, a new configuration of strain sensors was proposed and the required algorithm for data filtration and processing was developed. To evaluate this method a set of simulation was carried out and the effect of different parameters such as sensor number, filter threshold, defect size and sensor noise were investigated. As the main result, the capability of the WILD for wheel diameter monitoring was proved.@en

A periodic out-of-round wheel generates a range of excitation frequencies that may approach to the fundamental resonance of the track and vehicle. Detecting these defects by current condition monitoring systems is challenging. This paper focuses on the polygonal defect detection. VI-Rail is used to model the wheel-rail interaction to generate required data. A fusion algorithm reconstructs the defect signals in the space domain. The comparisons between the reconstructed defect signals and the wheel defects present a great potential for defect type identification. The results show that different polygonal defects can be correctly recognized and the dominant harmonics can be successfully decomposed.@en

A periodic out-of-round wheel generates a range of excitation frequencies that may approach to the fundamental resonance of the track and vehicle. Detecting these defects by current condition monitoring systems is challenging. This paper focuses on the polygonal defect detection. VI-Rail is used to model the wheel-rail interaction to generate required data. A fusion algorithm reconstructs the defect signals in the space domain. The comparisons between the reconstructed defect signals and the wheel defects present a great potential for defect type identification. The results show that different polygonal defects can be correctly recognized and the dominant harmonics can be successfully decomposed.@en

Condition monitoring systems are commonly exploited to assess the health status of equipment. A fundamental part of any condition monitoring system is data acquisition. Meaningfully estimating the current condition and predicting the future behaviour of the equipment strongly depend on the characteristic of the data measurement stage. Nowadays, condition monitoring has wide applications in the railway industry, and various monitoring approaches have been proposed for the inspection of wheel and rail conditions. In-service condition monitoring of wheels provides the real-time data required for maintenance planning, while in-workshop inspection is normally done at fixed intervals carried out periodically. In-service data acquisition can be divided into on-board and wayside measurements. In this paper, on the basis of these classifications, the existing data acquisition techniques for the monitoring of railway wheel condition are reviewed, and the state-of-the-art methods and required research are discussed.@en

Wheels are critical components of trains, and their conditions should be therefore monitored.Wheel defects change the wheel-rail contact and cause high impact forces thatare damaging for tracks and trains. Wheel defects can also cause unexpected failuresthat reduce the availability and reliability of the railway system. Several monitoring systemshave been developed to detect and identify the wheel defects. Wheel Impact LoadDetector (WILD) is commonly used to estimate the wheel condition by measuring thewheel-rail contact force.WILDs normally measure the contact force by multiple sensors in different locationsto sample fromdifferent portions of the wheel circumference. The variation in the forcesmeasured by the multiple sensors presents the condition of the wheel. Force ratio anddynamic force are two main indicators using for detecting the defective wheels. Forceratio is the division of the peak force by the average force and the dynamic force is thesubtraction of the peak force and the average force. Force ratio and dynamic force areinfluenced by axle load, and train velocity. In addition, these criteria fail to identify thedefect types. Furthermore, these methods are not useful for monitoring the minor defects.@en

Wheel defects are detrimental for railway train and track components and should be detected and identified as early as possible. Wheel Impact Load Detector (WILD) is a commercial condition monitoring system used for detecting the defective wheels. This system usually measures the rail strain at different points by multiple sensors. WILD converts the measured strains to the force and uses the peak force, dynamic force, and ratio of the peak force to the static force to estimate the condition of the in-service wheels. These methods are useful for detecting the severe defects contributing to the contact force to the extent that exceed a predetermined threshold. Therefore, in the prior research a fusion method has been developed to reconstruct a new informative pattern from the data collected by the multiple sensors. The reconstructed pattern provides a comprehensive description of the wheel condition. This paper validates the fusion method using a set of lab tests to investigate the applicability of the proposed method. For this purpose, a test rig has been built consisting of a circular rail, a rotating arm, and a wheel. Six strain sensors have been installed under the rail in the symmetric locations over the rail circle with 60 degree intervals. The fusion method used to reconstruct a signal from the bending strain signals measured by the multiple sensors. Different wheel defects including the flat and out-of-round wheels have been tested and the results validated the fusion method by providing informative patterns.@en

Wheel Impact Load Detectors are common devices that measure the rail response made by the wheel-rail contact to estimate the condition of the in-service wheels. The data collected by the multiple sensors can be fused to reconstruct a wheel-rail contact pattern over the circumferential coordinate that provides some description of the wheel condition. Moving of a defective wheel with different velocities and axle loads influence the wheel-rail interaction and the pattern reconstructed. As a result, there is a range of variation in the pattern reconstructed for each defect. Therefore, this paper aims to tackle this challenge and to classify the railway wheel defects using pattern recognition tools. Due to the lack of real data, ADAMS/Rail is used to model the wheel-rail contact and to simulate the data collected by the sensors. Then, based on the fusion algorithm, the condition state signals are reconstructed for different wheel defects with different velocities, and axle loads. Then a dataset based on these patterns is generated that is used for training, and testing the classifiers. In this paper, the magnitude of the signal is directly used as the features. The results of the classification show that the wheel defects including a minor defect can be correctly classified with zero error.@en

Wheel Impact Load Detectors are common devices that measure the rail response made by the wheel-rail contact to estimate the condition of the in-service wheels. The data collected by the multiple sensors can be fused to reconstruct a wheel-rail contact pattern over the circumferential coordinate that provides some description of the wheel condition. Moving of a defective wheel with different velocities and axle loads influence the wheel-rail interaction and the pattern reconstructed. As a result, there is a range of variation in the pattern reconstructed for each defect. Therefore, this paper aims to tackle this challenge and to classify the railway wheel defects using pattern recognition tools. Due to the lack of real data, ADAMS/Rail is used to model the wheel-rail contact and to simulate the data collected by the sensors. Then, based on the fusion algorithm, the condition state signals are reconstructed for different wheel defects with different velocities, and axle loads. Then a dataset based on these patterns is generated that is used for training, and testing the classifiers. In this paper, the magnitude of the signal is directly used as the features. The results of the classification show that the wheel defects including a minor defect can be correctly classified with zero error.@en