Train Wheelset Research Articles

Research on the Simulation of Wheelset Response Characteristic Identification of Railway Fastener Loosening

Rail fastener is a crucial component equipment to ensure the safe operation of the train, and it is very paramount to detect the loose state of the fastener. In this paper, the vertical vibration acceleration signal of wheelset is taken as the research object, and the loose state of fastener is identified by separating and calculating the key IMF energy entropy. Firstly, based on the finite element theory and the principle of multibody dynamics, the rigid-flexible coupling simulation model of vehicle track is established. Then, the vertical vibration acceleration signals of the wheelset under the speed of 200 km/h are obtained by setting the different loosening degrees of the fastener. Finally, we use optimized HHT to process signals, and the orthogonal empirical mode decomposition method (OEMD) is proposed to optimize the orthogonality of the intrinsic mode function, to eliminate the IMF component having poor correlation with the original signal; the Hilbert time spectrum and information entropy theory are combined to calculate the energy entropy of the key IMF, and the HHT energy entropy evaluation algorithm of the vertical acceleration response signal of the train wheelset is proposed. The simulation results show that the HHT energy entropy of 100% fastener looseness is less than 25%, 50%, and 75%, decreasing trend. The algorithm can recognize the looseness of track fastener through the experiment under different working conditions.

High-speed train wheel set bearing fault diagnosis and prognostics: A new prognostic model based on extendable useful life

Diagnosis and prognostics of rolling element bearings have been widely studied in recent years, but very few researches were dealing with high-speed train wheel set bearings (HSTWSB). Most prognostics and health management (PHM) models are generally based on obtaining the remaining useful life (RUL) of concerned bearings. Since it is difficult to quantify and to monitor bearing status from vibration signal and there is no clear definition what is the end of bearing service life, determine RUL is not realistic in industrial practice. In order to achieve reliable fault diagnosis and prognosis for HSTWSB, it is of great importance and necessity to conduct a thorough research under realistic or close to reality operation conditions. Therefore, in this paper two types of techniques, i.e. vibration and acoustic emission, have been particularly studied. Different from many previous PHM studies which seek seeking bearing’s RUL by establishing physics model or artificial neural network model, a new hybrid model based on extendable useful life (EUL) under continuous monitoring and bearing status classification is proposed. Statistical properties of typical time domain features extracted from vibration and acoustic emission are studied. Correlations of these parameters with bearing status are reviewed and feasible parameters are evaluated for bearing status quantification. By driving an electric multiple unit (EMU) speed up to 350 km/h, a test device close to real running environment was introduced. A batch of bearings with different level of nature defects instead of artifacts were particularly selected as database samples of this paper. Test procedure was designed to allow fault diagnosis to be verified under low, medium and high speeds and the corresponding database and knowledgebase of bearing status assessment are established. Defect geometries were quantified with 3D laser scanning technology so that it provides intuitive references for evaluating effectiveness of signal processing approaches with respective to bearing damage status. Instead of calculating how much RUL left by physics model or neural network model, the proposed approach determines if the useful life can be extended from one grade level to another or to next overhaul under continuous monitoring. The proposed model establishes an initial database and knowledgebase for HSTWSB monitoring. This model can be dynamically enhanced with involvement of AI technology and accumulation of tested bearing database in the future.

Feasibility study on wheelset fatigue damage with NOFRFs-KL divergence detection method in SIMO

Train wheelset is an important part of the train. After long-time and high load working, fatigue damage will appear. Then the micro-cracks will form and expand into macro cracks, which may cause a serious accident. Therefore, it is necessary to study the fatigue damage detection methods for train wheelset. Fatigue damage will cause nonlinear effects. The Nonlinear Output Frequency Response Functions (NOFRFs) is an effective tool to characterize the nonlinearity of the system. In this paper, based on the concept of Kull Back-Leibler (KL) divergence and NOFRFs, a detection method denoted as NOFRFs-KL (NKL) is proposed to study the feasibility of comprehensively evaluate the fatigue damage of wheelset. The train wheelset is considered as a single input multiple output (SIMO) nonlinear system while hammering on one point in the hammering test. And the estimated NOFRFs of multiple positions in the SIMO system are used to evaluate the fatigue damage at each output position. To verify the effectiveness of the NKL, some hammering tests are carried out. The NOFRFs of the wheelsets with micro-cracks, repaired wheelsets are calculated. The NOFRFs of the wheelsets with different served time are calculated. And then the corresponding NKL values are obtained. The results show that the fatigue damage in the wheelsets can be identified by the NKL index, and the NKL index can be proportional to the service time of the wheelset.

A Fault Diagnosis Method of Train Wheelset Rolling Bearing Combined with Improved LMD and FK

Trackside acoustic signals contain intense noise and nonstationary features even after Doppler distortion correction. Information on bearing defects in these signals is either weak or heavily attenuated. Thus, an improved compound interpolation envelope local mean decomposition (ICIE LMD) method combined with a fast kurtogram (FK) is proposed for wheelset bearings. In this methodology, cubic Hermite interpolation and cubic spline interpolation are employed to find the envelope of the extremal points in the ICIE LMD algorithm to improve accuracy and decrease the computing time of the decomposed signal component. An FK is sensitive to the impact signal and extracts the fault impact features efficiently. In the application, the proposed method uses ICIE LMD to decompose the multicomponent signal into several specific single product function (PF) components. The kurtosis index of the PF is calculated to select the component which contains the most fault information. Then, the selected component of PF is filtered by FK. Finally, the squared envelope spectrum is used to obtain the fault frequency and identify the fault location. The advantages of the ICIE LMD method are verified by simulation analysis. In the application, the results show that the proposed method efficiently extracts the fault features and enhances the target characteristics of the sound signals from a trackside microphone array. Furthermore, the influence of rotating frequency on locating the fault is reduced.

Data-driven approach to study the polygonization of high-speed railway train wheel-sets using field data of China’s HSR train

Environmental factors, like seasonality, have been proved to exert significant impact on reliability of China high-speed rail train wheels in this article. Most studies on polygonization of train wheels are based on physical models, mathematical models or simulation systems. Normally, characteristics and mechanisms of wheel polygonization are studied under ideal conditions without considering the impact of the environment. However, in practical use, there are many irregular wear wheels and irregular wear cannot be explained by theoretical models with assumptions of ideal conditions. We look at two possible factors in polygonization: seasonality and proximity to engines. Our analysis of field data shows the environmental factor has more impact on wheel polygonization than the mechanical factor. Based on the Bayesian models, the mean time to failure of the wheels under different operation conditions is conducted. A case study of China’s HSR train wheels is conducted to confirm the finding. The case study shows the degree of polygonal wear is much more severe in summer than other seasons. The finding may give a totally new research perspective on polygonization of train wheels. We use Bayesian analysis because this method is useful for small and incomplete data sets. We propose three Bayesian data-driven models.

Visual analytics towards axle health of high-speed train based on large-scale scatter image

Axle is an important part of high-speed train. The axle is the key component connecting the train wheelset, which has a great impact on the train safety. The health monitoring of axles is very important for the safe and smooth operation of trains. The axle health detection is a complex process of multi-factor coupling, which faces the problems of health model construction. From the perspective of big data visual analysis, this paper helps people find the information behind the big data of high-speed railway axle monitoring, and makes a prediction and analysis of the health status of high-speed railway axle operation. Starting from the present situation of scatter plot presentation of multi-dimensional data visual analysis, this paper proposes a visual analysis and processing method for high-speed train axle health monitoring, aiming at the problems of intensive rendering, visual mutation and trend prediction when drawing large data scatter plot. Firstly, a new method of the axle data fusion model is proposed in this paper, which can effectively clean the axle health monitoring data and construct the data acquisition and expression mode of axle temperature of high-speed train. Then, visualization of axle data and prediction of axle health trend provide a new analysis model for axle health monitoring. In addition, the visual analysis method of scatter density map data can eliminate the dependence of the original complex mechanical model, and can be used to analyze different working conditions and axle types. Compared with the existing axle health monitoring methods, this method has high accuracy and practicability.

Flow-induced Noise Behaviour around High-speed Train Wheelsets

Flow-induced Noise Behaviour around High-speed Train Wheelsets

An Adaptive Weighted Multiscale Convolutional Neural Network for Rotating Machinery Fault Diagnosis Under Variable Operating Conditions

Extracting robust fault sensitive features of vibration signals remains a challenge for rotating machinery fault diagnosis under variable operating conditions. Most existing fault diagnosis methods based on the convolutional neural network (CNN) can only extract single-scale features, which not only loss fault sensitive information on other scales, but also suffer from the domain shift problem. In this work, a novel end-to-end deep learning network named adaptive weighted multiscale convolutional neural network (AWMSCNN) is proposed to adaptively extract robust and discriminative multiscale fusion features from raw vibration signals. The AWMSCNN consists of three main components: the denoising layer, the adaptive weighted multiscale convolutional (AWMSC) block, and the multiscale feature fusion layer. The AWMSC block can learn rich and complementary features on multiple scales in parallel. Then, an adaptive weight vector is introduced to modulate multiscale features to emphasize fault sensitive features and suppress features that are sensitive to operating conditions. The train wheelset bearing dataset and the bearing dataset provided by Case Western Reserve University (CWRU) are used to verify the superiority of the proposed model over the basic CNN and other multiscale CNN models. The experiment results show that the proposed model has strong fault discriminative ability and domain adaptive ability against variable operating conditions.

Experimental investigation by Laser Ultrasonics for train wheelset flaw detection

The transport safety is a key factor in the rail field and to ensure it the monitoring of train components structural integrity must be performed regularly and following strict regulations. Consequently, the development of Non-Destructive Testing (NDT) procedures is a topic of great interest and impact, it aiming at the early identification of cracks and inhomogeneities on train axle and wheels which propagation can causes faults and accidents undermining the passenger and crew safety. Ultrasound based nondestructive testing is currently applied in ordinary maintenance procedures, although they are applied to axle and wheels dismounted from the train wheelset. In fact, the technique requires ultrasonic probes to be in contact with the object under test and therefore the fretting surface between wheel and axle, where fatigue phenomenon induces cracks enucleation and propagation, cannot be monitored. The possibility of applying NDT systems to the whole wheelset, without dismounting it in its components, will allow drastically reducing the inspection time and increasing the inspection frequency. The present paper proposes a feasibility study for the development of a Laser-Ultrasonic Testing (LUT) procedure to improve performances of train wheelset ultrasonic inspection. The method exploits one or an array of air-coupled ultrasonic probes, which detects ultrasonic waves generated by a high-energy pulsed laser. Thanks to the non-contact nature of both the response measurement sensor and the excitation system, the experimental set-up is extremely flexible and it allows extremely speeding up the inspection time.

An investigation on wheel/rail impact dynamics with a three-dimensional flat model

ABSTRACTWheel flat is one kind of railway train wheelset defects. It has great influence on wheel/rail dynamics and damages. In most of the presented studies, wheel/rail impact velocity or rolling radius variation of the wheel because of flat spot was taken into account to study the wheel/rail impact dynamics. In this paper, a three-dimensional wheel flat model considering the length, width and depth of the flat spot is established. Including the wheel rotation and wheel/rail contact geometry, a high-speed vehicle–rail coupling system dynamics model is developed to investigate the effect of the wheel flat on the wheel/rail dynamics. With time integration method of the models, the impact dynamics of the wheel/rail system with three types of flat width and five kinds of flat length are obtained. The results show that the width, the length of the wheel flat and the width/length ratio have a great influence on the wheel/rail impact dynamics. The wheel/rail impact dynamics of the flat with large width is more severe than with small width as the flat length is fixed. When a flat spot occurs, the permissible length of the wheel defect, needless to action, is 25 mm in maximum. The speed safety domain with three kinds of flat width/length ratio of a vehicle is obtained according to the wheel/rail vertical force limitation.

Joint Prediction of Remaining Useful Life and Failure Type of Train Wheelsets: Multitask Learning Approach

The failures of train wheels account for disruptions of train operations and even a large portion of train derailments. Remaining useful life (RUL) of a wheelset measures the how soon the next failure will arrive, and the failure type reveals how severe the failure will be. RUL prediction is a regression task, whereas failure type is a classification task. In this paper, we propose a multi-task learning approach to jointly accomplish these two tasks by using a common input space to achieve more desirable results. We develop a convex optimization formulation to integrate both least square loss and the negative maximum likelihood of logistic regression, and model the joint sparsity as the L2/L1 norm of the model parameters to couple feature selection across tasks. The experiment results show that our method outperforms the single task learning method by 3% in prediction accuracy.

Development of a morphological convolution operator for bearing fault detection

This paper presents a novel signal processing scheme, namely morphological convolution operator (MCO) lifted morphological undecimated wavelet (MUDW), for rolling element bearing fault detection. In this scheme, a MCO is first designed to fully utilize the advantage of the closing & opening gradient operator and the closing–opening & opening–closing gradient operator for feature extraction as well as the merit of excellent denoising characteristics of the convolution operator. The MCO is then introduced into MUDW for the purpose of improving the fault detection ability of the reported MUDWs. Experimental vibration signals collected from a train wheelset test rig and the bearing data center of Case Western Reserve University are employed to evaluate the effectiveness of the proposed MCO lifted MUDW on fault detection of rolling element bearings. The results show that the proposed approach has a superior performance in extracting fault features of defective rolling element bearings. In addition, comparisons are performed between two reported MUDWs and the proposed MCO lifted MUDW. The MCO lifted MUDW outperforms both of them in detection of outer race faults and inner race faults of rolling element bearings.

Flexible global calibration of multiple cameras with nonoverlapping fields of view using circular targets.

Global calibration of multicamera systems is a difficult problem. The key is to find an appropriate calibration target. This paper proposes a flexible method to construct a global calibration target with circular targets. Any object that is to be measured by the system can be used as a calibration target with the help of a hand-held scanner. The calibration method does not need a special calibration target or multiple shots, which makes it flexible in applications. Circular targets pasted on the calibration target are used as features that are captured by cameras. The particle swarm optimization method is employed to correct the eccentricity error in the projection of circular targets. The eccentricity correction method does not need any prior knowledge except the cameras' intrinsic parameters. A synthetic data experiment was performed to validate the eccentricity correction method, and a physical experiment in a train wheelset inner-side distance measurement system was performed to validate the global calibration method. The measurement accuracy of the system was better than 0.1 mm, and the eccentricity correction method improved the three-dimensional reconstruction accuracy by about 0.1 mm.

Flexible method of refraction correction in vision measurement systems with multiple glass ports.

Many vision measurement systems, especially in outdoor engineering, usually utilize glass ports to protect sensors against environmental influences. The refraction caused by glass ports lead to measurement errors in traditional single viewpoint model. Most existing methods only deal with the refraction that happens once, and the glass ports are required to be perpendicular to cameras or the orientations of glass ports are obtained by auxiliary equipment. This paper proposes a corrected 3D reconstruction model based on refraction geometry, which can be used for any number of glass ports with any orientations. The orientation of each glass port is obtained only using refracted and unrefracted images of the same scene, which doesn't need any auxiliary equipment. A series of validation experiments are performed. An existing image rectification method is used to make a comparison. The proposed method is also employed in a train wheelset profile measurement application, which proves that the method is effective in actual applications.

Fault Detection of High-Speed Train Wheelset Bearing Based on Impulse-Envelope Manifold

A novel fault detection method employing the impulse-envelope manifold is proposed in this paper which is based on the combination of convolution sparse representation (CSR) and Hilbert transform manifold learning. The impulses with different sparse characteristics are extracted by the CSR with different penalty parameters. The impulse-envelope space is constructed through Hilbert transform on the extracted impulses. The manifold based on impulse-envelope space (impulse-envelope manifold) is executed to learn the low-dimensionality intrinsic envelope of vibration signals for fault detection. The analyzed results based on simulations, experimental tests, and practical applications show that (1) the impulse-envelope manifold with both isometric mapping (Isomap) and locally linear coordination (LLC) can be successfully used to extract the intrinsic envelope of the impulses where local tangent space analysis (LTSA) fails to perform and (2) the impulse-envelope manifold with Isomap outperforms those with LLC in terms of strengthening envelopes and the number of extracted harmonics. The proposed impulse-envelope manifold with Isomap is superior in extracting the intrinsic envelope, strengthening the amplitude of intrinsic envelope spectra, and enlarging the harmonic number of fault-characteristic frequency. The proposed technique is highly suitable for extracting intrinsic envelopes for bearing fault detection.

Flow Simulation and Aerodynamic Noise Prediction for a High-Speed Train Wheelset

Aerodynamic noise becomes significant for high-speed trains and its prediction in an industrial context is hard to achieve. The aerodynamic and aeroacoustic behaviour of the flow past a high-speed train wheelset, one of the main components of a bogie, are investigated at a scale 1:10 using a two-stage hybrid method of computational fluid dynamics and acoustic analogy. The near-field unsteady flow is obtained by solving the Navier-Stokes equations numerically through delayed detached-eddy simulations and the results are fed to predict the far-field noise signals using the Ffowcs Williams-Hawkings acoustic analogy. Far-field sound radiated from the scaled model is also measured in a low noise open-jet anechoic wind tunnel. Good agreement is achieved between numerical and experimental results for the dominant frequency of tonal noise and the shape of the spectra. Results show that turbulent flow past the wheelset is characterized by three-dimensional streamwise and spanwise vortices with various scales and orientations. Vortex shedding and flow separation around the wheelset are the key factors for the aerodynamic noise generation. It is found that the radiated tonal noise corresponds to the dominant frequencies of the oscillating lift and drag forces from the wheelset. The directivity of the noise radiated exhibits a typical dipole pattern. As the inflow velocity increases, the shedding frequency scales with the freestream velocity and the axle diameter to yield a Strouhal number of 0.18 while the noise levels increase noticeably. For the current wheelset case investigated without considering the ground effect, the inclusion of wheelset rotation increases the radiated noise levels slightly with similar directivity.

Experimental and numerical modal analyses of high-speed train wheelsets

The frequency responses of the wheelsets of a high-speed train are believed to have an important influence on wheel/rail dynamics. This paper is focused on an experimental modal analysis (EMA) and numerical modal analysis (NMA) of a motor wheelset and a trailer wheelset with a hollow-drilled shaft that are both utilized on high-speed trains. The EMA investigation of high-speed train wheelsets is achieved by using a hammer-impact excitation method and a varied-time-base (VTB) technique that has a high identification precision. The NMA is performed using the finite element method to model equivalent spring unit connections among jointed components. The results obtained in the NMA studies are in excellent agreement with those obtained from the EMA studies. Furthermore, NMA is also performed for the case of pre-stressed wheelsets; this is achieved by considering their stress states under a static axle load and a gyroscopic force mimicking operating conditions. Compared with free wheelsets, the results for the preloaded wheelsets have slightly higher eigenfrequencies. In addition, the results obtained in the NMA show the influence of the flexibility of the wheels on the total structural flexibility of the wheelset. Finally, it is expected that the VTB technique can be generalized for use in examining the wheelsets on general rolling-stock.

Control and Communication Techniques of High-Speed Railway Train Wheel-Set Running-In Test Based on LabVIEW

The production, assembly and repair qualities of high-speed railway train wheel-set directly relates to traffic safety. It is very import to build wheel-set running-in test bench and develop high efficiency control and communication module. Running-in test methods of high-speed railway wheel-set, the communication techniques between computer and Frequency Inverter and serial port communication based on LabVIEW are introduced. The control and communication module is set up by using the Frequency Inverter, motor, serial communication card, computer and other devices. The control and communication software based on LabVIEW is developed. Working conditions are simulated effectively. The automatic testing is realized. The mutual interference of reading and writing commands to Frequency Inverter because of using one port is solved. All kinds of control and communication functions are achieved. The applications show that the design of control and communication module is reasonable, the testing process is flexible and controllable, the control and communication is accurate and efficient, the analyzed results are reliable.

Computer Vision Based Method and System for Online Measurement of Geometric Parameters of Train Wheel Sets

Train wheel sets must be periodically inspected for possible or actual premature failures and it is very significant to record the wear history for the full life of utilization of wheel sets. This means that an online measuring system could be of great benefit to overall process control. An online non-contact method for measuring a wheel set’s geometric parameters based on the opto-electronic measuring technique is presented in this paper. A charge coupled device (CCD) camera with a selected optical lens and a frame grabber was used to capture the image of the light profile of the wheel set illuminated by a linear laser. The analogue signals of the image were transformed into corresponding digital grey level values. The ‘mapping function method’ is used to transform an image pixel coordinate to a space coordinate. The images of wheel sets were captured when the train passed through the measuring system. The rim inside thickness and flange thickness were measured and analyzed. The spatial resolution of the whole image capturing system is about 0.33 mm. Theoretic and experimental results show that the online measurement system based on computer vision can meet wheel set measurement requirements.

An Approach to the Characteristic Spectrum Analysis for Train Wheelset Fault Diagnosis

In the view of the fact that the information of the fault characteristic of the train wheelset is submerged into the background noises and the conventional spectral analysis method has its own deficiency due to the fuzzy spectrum value created by the load variation and rotation speed fluctuation, the paper proposes a method of the characteristic spectrum analysis of the on-line fault diagnosis of the train wheelset. And the correspondent system is developed to meet the practical application. In the paper, the issues such as the signal sampling, signal processing and the inhabitation of the characteristic spectrum leakage besides the principle of the new method. The application indicates the method is reliable and effective since it can describe the fault characteristic more exactly than any other conventional ones.