Wheel Condition Research Articles

Refurbishing ANN with the aid of adaptive crow search optimisation for effectively diagnosing railway wheel condition

The research intends to diagnosis the railway wheel condition with the aid of artificial neural network (ANN). In diagnosing, ANN has been proven its convenience over manual computation in various applications. The research utilises optimisation techniques for identifying appropriate hidden layers and their associated neurons to enhance the performance of ANN techniques. This configuration process includes optimisation techniques like evolutionary algorithm (EA), genetic algorithm (GA), particle swarm optimisation (PSO), and crow search optimisation (CSO). Also, this research includes modified and improved conventional strategy in CSO, which urge incorporating novel strategy called adaptive crow search optimisation (ACSO) to enhance the performance. The proposed strategy unveils proficient performance of 99.2% accuracy, which is 1.7% greater than the conventional ANN model and an average of 0.9% greater than other contest techniques consider for configuration. The credibility of the ANN model gets increased while employs the optimisation techniques in diagnosing the railway wheel condition.

Evaluation of the influential parameters contributing to the reconstruction of railway wheel defect signals

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.

Wayside Detection of Wheel Minor Defects in High-Speed Trains by a Bayesian Blind Source Separation Method.

For high-speed trains, out-of-roundness (OOR)/defects on wheel tread with small radius deviation may suffice to give rise to severe damage on both vehicle components and track structure when they run at high speeds. It is thus highly desirable to detect the defects in a timely manner and then conduct wheel re-profiling for the defective wheels. This paper presents a wayside fiber Bragg grating (FBG)-based wheel condition monitoring system which can detect wheel tread defects online during train passage. A defect identification algorithm is developed to identify potential wheel defects with the monitoring data of rail strain response collected by the devised system. In view that minor wheel defects can only generate anomalies with low amplitude compared with the wheel load effect, advanced signal processing methods are needed to extract the defect-sensitive feature from the monitoring data. This paper explores a Bayesian blind source separation (BSS) method to decompose the rail response signal and to obtain the component that contains defect-sensitive features. After that, the potential defects are identified by analyzing anomalies in the time history based on the Chauvenet’s criterion. To verify the proposed defect detection method, a blind test is conducted using a new train equipped with defective wheels. The results show that all the defects are identified and they concur well with offline wheel radius deviation measurement results. Minor defects with a radius deviation of only 0.06 mm are successfully detected.

Voluntary Wheel Running Has Beneficial Effects in a Rat Model of CKD-Mineral Bone Disorder (CKD-MBD).

Reduced bone and muscle health in individuals with CKD contributes to their higher rates of morbidity and mortality. We tested the hypothesis that voluntary wheel running would improve musculoskeletal health in a CKD rat model. Rats with spontaneous progressive cystic kidney disease (Cy/+ IU) and normal littermates (NL) were given access to a voluntary running wheel or standard cage conditions for 10 weeks starting at 25 weeks of age when the rats with kidney disease had reached stage 2-3 of CKD. We then measured the effects of wheel running on serum biochemistry, tissue weight, voluntary grip strength, maximal aerobic capacity (VO2max), body composition and bone micro-CT and mechanics. Wheel running improved serum biochemistry with decreased creatinine, phosphorous, and parathyroid hormone in the rats with CKD. It improved muscle strength, increased time-to-fatigue (for VO2max), reduced cortical porosity and improved bone microarchitecture. The CKD rats with voluntary wheel access also had reduced kidney cystic weight and reduced left ventricular mass index. Voluntary wheel running resulted in multiple beneficial systemic effects in rats with CKD and improved their physical function. Studies examining exercise interventions in patients with CKD are warranted.

Abrasive wear characteristics of 40Mn2 steel in simulated condition of sprocket wheel in crawler bulldozer walking system

The abrasive wear characteristics of 40Mn2 steel be studied in this paper. The rubber wheel abrasive wear tester be used for simulating the sprocket wheel working-conditions. The relationship between worn surface residual stress and wear rate was studied. Wear surface morphology and mechanism worn surface micro-structures are also shown in results. In addition, there are XRD pattern of the experimental abrasives, to better reveal the abrasive wear mechanisms. The results indicates that the larger the applied load and sliding speed is, the greater the wear rate will be. The content of hard phase in abrasive determine the wear rate. The main wear mechanisms of soil and coal in working-conditions are scratching and spalling, respectively. Spalling, chipping, scratching and pitting are the main wear mechanisms in sand working-condition. There are same trend of the wear rate and the worn surface residual stress with the wear time increasing. Intergranular carbides can decrease the wear resistance to some extent.

ABS using Fuzzy Logic in MATLAB and Its Hardware Implementation

This paper proposes a cheap and effective method to interface the concept of FUZZY LOGIC and (Antilock Braking System) ABS system used in cars and bikes for preventing the condition of wheel locking and wheel slipping and if wheel slipping occurs then how much of intensity of brake should be applied to keep the vehicle in control of the user. This decision is taken by Fuzzy System which makes decision based on user inputs namely Obstacle, Brake force and Slip ratio. The Fuzzy system gives an Pulse Width Modulation output based on three intensity levels of brake will be applied such as High Brake , Medium Brake and No Brake. Hardware Implementation consists of an MCU which is interfaced with an LCD and DC Series motor which displays intensity of brake applied and motor represents the motion of the actual wheel of a vehicle respectively.

Tribological properties under the grinding wheel and workpiece interface by using graphene nanofluid lubricant

In nanofluid minimum quantity lubrication (NMQL) grinding of titanium (Ti) alloy, existing nanoparticles cannot solve the technical bottleneck of high surface integrity. Therefore, graphene (GR) nanoparticles, which have excellent lubrication performance, were applied in NMQL. The tribological properties of GR nanofluid on wheel–workpiece interface were studied by friction and wear test. In the experiment, 0.5–3 nm thick GR nanoparticles were used to prepare 3% vol. palm oil-based nanofluid. Ball-disc experiment under grinding conditions was carried out on the friction and wear tester. Grinding balls with SiC abrasive grains (to simulate the grinding wheel) and Ti-6Al-4V disc (to simulate the workpiece) were used. Load force was set for simulation of pressure boundary condition of the grinding wheel–workpiece interface. Stratiform nanoparticles (MoS2, MoO3, and HBN) were used as the comparison group. Results demonstrated that GR nanofluid achieved smaller friction coefficient (0.295), error bars (0.0029), and area of scratches (182,940 μm2). GR nanoparticles with small gravity and large specific surface area improved the viscosity of nanofluid and consequently the lubrication performance. The plane hexagonal honeycomb structure determines the strong lubrication stability and abrasive resistance of the GR nanoparticles. The scanning electron microscope (SEM) and energy dispersive spectrometer (EDS) images of the scratch surface also verified the above conclusions.

Implementation of a non-Hertzian contact model for railway dynamic application

The development of wheel–rail contact models is an active topic of railway research with the dual objective of improving the accuracy of multibody simulations and reducing its computational effort. This paper extends the online Hertzian contact model, proposed by Pombo et al. (Veh. Syst. Dyn. 45: 165–189, 2007) to propose a non-Hertzian contact model. The new methodology presented here includes the following steps: (i) search of the points of contact; (ii) identification of the undeformed distance function; (iii) evaluation of the contact patch; (iv) calculation of the normal and tangential contact forces; (v) application of the contact forces in the multibody vehicle model. Among several contact models available in the literature, this non-Hertzian contact approach uses the Kik–Piotrowski model for the normal contact force, while the tangential forces are obtained from the interpolation of the available Kalker Book of Tables for non-Hertzian (KBTNH) contact. With the purpose to demonstrate the proper implementation and selection of parameters that define this new model, several contact analysis and dynamic simulations are performed in which the wheel S1002 and the rail UIC50 are considered. First, the contact analyses that determine the contact condition of different wheel–rail interactions serve to assess the accuracy of the Hertzian and non-Hertzian models with respect to the software of reference CONTACT. Second, the Hertzian and non-Hertzian models are utilised to perform dynamic simulations of a wheelset, a bogie and a vehicle running in tangent and curved tracks. In short, this work provides, not only a complete description of the implementation of a non-Hertzian contact model in a multibody code, but also suggests for the proper selection of the parameters that promote better accuracy and optimal computational efficiency.

Numerical Simulation and Life Prediction of Steel Ball Unfolding Wheel Wear

Wear is the main failure form of unfolding wheel for bearing steel ball. When the wear increase to a certain extent, the function of the wheel is ineffective. According to the unfolding wheel structure and unfolding principle, the critical failure condition of the unfolding wheel is established. Based on the classical Archard wear model and combined with the finite element method, a discrete Archard wear model is proposed, and a prediction model of the wear life of the unfolding wheel is established. Using Ansys software to simulate the wear of the unfolding wheel, the relationship equation between the wear depth and the wear times is obtained, and the wear life prediction of the unfolding wheel is carried out.

An intelligent monitoring system of grinding wheel wear based on two-stage feature selection and Long Short-Term Memory network

Grinding wheel condition is considered as the key factor affecting grinding performance, and therefore, accurate monitoring of wheel wear is necessary to prevent the deterioration of part quality. An intelligent wheel wear monitoring system is introduced in this article to realize processing of grinding signal, extraction of signal features, selection of optimal feature subset, and prediction of wheel wear. Physical information generated during the grinding of C-250 maraging steel is collected by a dynamometer, accelerometer, and acoustic emission sensor, and a large quantity of features in time domain and frequency domain are extracted from the processed grinding signals. To reduce feature redundancy and increase relevancy of feature to wheel wear, a two-stage feature selection approach combining filter and wrapper framework is proposed. The filter preselects individual features by minimum Redundancy Maximum Relevance method, while the wrapper evaluates different feature subsets by the model performance. A deep learning network structure named Long Short-Term Memory network is adopted to develop the wheel wear monitoring model and is compared with a conventional machine learning algorithm, Random Forest. The results have shown that the two-stage feature selection method is able to provide the globally optimal feature subset for the model. Long Short-Term Memory model achieves an R2 of 0.994 and a root-mean-square error of 0.240 with four features, while Random Forest model obtains an R2 of 0.980 and a root-mean-square error of 0.463 with seven features, which indicates that Long Short-Term Memory model is capable of predicting wheel wear more accurately even with less features.

Analysis of chip shape distribution using image processing technology to estimate wearing condition of gear grinding wheel

The chip shape distribution changes due to grinding wheel wear in surface and cylindrical grinding. However, in gear grinding, the relationship between wear and distribution has not been clarified. To investigate the relationship, in this study, we collected chips from repeated gear grindings. For rough grinding, wherein the cutting quantity is large, the gear surface became rougher with each repetition. Analysis of images of individual chips revealed unexpected change in the chip shape distribution even when the number of grinding processes increased. The surface property of work pieces was determined more dominant by finishing grinding than rough grinding.

РОЗРОБКА СТРУКТУРНОЇ СХЕМИ ІНФОРМАЦІЙНО-ВИМІРЮВАЛЬНОГО КОМЛЕКСУ ДЛЯ КОНТРОЛЮ ГЕОМЕТРИЧНИХ ПАРАМЕТРІВ ШЕСТЕРНІ З УРАХУВАННЯМ ПОХИБКИ

To control the entire set of gear gear parameters, it is necessary to use a number of measuring devices and complexes simultaneously. Many of them are outdated, non-automated, have no access to computer tools and do not provide the current level of accuracy, informativeness and speed. Therefore, the article proposes a block diagram of the information-measuring and diagnostic channel, by means of which the technical condition of the toothed wheel is evaluated at the average integral speed of wear of the cutting tool. Error analysis for real operating conditions at the stage of previous studies, when the list of components of the main error is not known and their quantitative assessment is not carried out. Objects of research in this work are gear transmission with an engraving profile. To date, the metrological control of gear wheel parameters is based on a large number of measuring equipment. All of them have a limited range of measuring parameters, different accuracy and range of measured values. The reference base in the field of control of the gear draw gear is considered, which requires its creation on the basis of a detailed analysis of the new principles of measurement, the need to justify their accuracy, expansion of the range and range of measured parameters. Manufacturing of new modifications of domestic measuring devices on the basis of new management principles and equipment of leading foreign manufacturers in the field of control of evolvent. The complex of works on creation of a system for ensuring the unity of the geometry of the geometric parameters of gear gears is considered. This requires revision, systematization and development of methods and means of metrological support. The mathematical description of the involute curve of the toothed wheel by the method of triangulation of multi-directional regions and description of the geometric model of the evolvent is proposed. The results were analyzed and recommendations on the application of the description method to the cylindrical wheel with an engraving profile were given.

Investigation into the dynamics of wheel spray released from a rotating tyre of a simplified vehicle model

Accurate prediction of vehicle soiling is an important step towards being able to understand and reduce this problem. Previous work has shown that eddy resolving CFD methods are capable of predicting the soiling pattern on the surface of automotive geometries when a known spray source is used. Here the influence of the wheel, ground and spray boundary conditions on a simulation of rear soiling of a generic SUV are investigated. The inclusion of rotating wheels led to a greater vertical distribution of the soiling pattern whereas the moving ground plane led to increased lateral distribution. The total soiling rate with moving wheels and ground, as well as with the offset between wheel and ground removed, was approximately 50% higher than the experimental conditions. When spray was released from around the rotating wheel it was found that a large majority of the parcels which ended up on the base originated from close to the contact patch, indicating that this is the most important region for the tyre spray model. A model based on a measured distribution of droplet sizes from downstream of the wheel gave good agreement with previous experimental work for the spray topology around the contact patch.

Proposed rail pad sensor for wheel-rail contact force monitoring

A non-intrusive method is proposed in this study with details showing the steps to convert rail pad into a load sensor for train wheel damage detection applications. The fabrication process is simple, requiring only the addition of a thin PVDF-based multilayered sensing device (approximately 0.7 mm) beneath the rail pad. An alternating-strip configuration is adopted in the multilayered sensing device, comprising active strips for sensing the vertical forces acting on the rail pad and dummy strips for resisting the potential lateral forces together with the baseplate shoulder. To validate the performance of the proposed rail pad sensor, both laboratory and field tests have been conducted. The tests results highlight the feasibility and potential of the rail pad sensor for train wheel condition monitoring. Unlike conventional strain gauge-based wheel load monitoring system, the rail pad sensor does not have blind zone problem.

A pneumatic sensor for grinding wheel condition monitoring

This paper presents the proof-of-concept of a pneumatic sensor for monitoring the performance of grinding wheels. The system concurrently considers the static and dynamic components of the back pressure signal, which refer to the position and topography of the functional wheel surface, respectively. Auxiliary experiments and numerical simulations are presented to demonstrate that the dynamic back pressure variations do indeed correspond to wheel topographic features. The performance of the sensor is evaluated with respect to wheel dressing, progressive wheel wear and grinding burn. The monitoring capability of the sensor is confirmed to be not compromised by the application of a flood coolant.

Evaluation of grinding wheel loading phenomena by using acoustic emission signals

In the industrial manufacturing field, machining is a major process. Machining operations involve grinding, drilling, milling, turning, pressing, molding, and so on. Among these operations, grinding is the most precise and complicated process. The surface condition of the grinding wheel plays an important role in grinding performance, and the identification of grinding wheel loading phenomena during the grinding process is critical. Accordingly, this present study describes a measurement method based on the acoustic emission (AE) technique to characterize the loading phenomena of a Si2O3 grinding wheel for the grinding mass production process. The proposed measurement method combines the process-integrated measurement of AE signals, offline digital image processing, and surface roughness measurement of the ground workpieces for the evaluation of grinding wheel loading phenomena. The experimental results show that the proposed measurement method provides a quantitative index from the AE signals to evaluate the grinding wheel loading phenomena online for the grinding mass production process, and this quantitative index is determined via some experiments in advance in the same grinding environment to help the monitoring and controlling of the grinding process.

Experimental Validation of Multi-Sensor Data Fusion Model for Railway Wheel Defect Identification

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.

Towards a real-time pneumatic tire performance prediction using an advanced tire-ice interface model

Icy road conditions and tire operational parameters play a vital role in determining the overall performance of a vehicle. This study builds on prior work in the researchers’ group. The Advanced Tire-Ice Interface Model (ATIIM) simulates the temperature rise in the contact patch based on the measured pressure distribution and the thermal properties of the tread compound and of the ice surface. It has the capability to simulate the height of the thin water film created from the melted ice, to predict the tractive performance, and to estimate the viscous friction due to the water layer and the influence of braking operations, including the locked wheel condition. The experimental investigation included measuring the bulk temperature distribution in the contact patch to validate the temperature rise simulations of the ATIIM. As shown by the simulations and the test data, a rise in temperature was observed from the leading edge to the trailing edge of the contact patch. As the wheel load increases, the difference in temperature rise increases, as also reflected in the experimental study. When the temperature difference was significant, a thin water film was observed that resulted in a reduction of friction, which was simulated using the ATIIM.

An Analysis of Electroplated cBN Grinding Wheel Wear and Conditioning during Creep Feed Grinding of Aeronautical Alloys

Cubic boron nitride (cBN), in addition to diamond, is one of the two superabrasives most commonly used for grinding hard materials such as ceramics or difficult-to-cut metal alloys such as nickel-based aeronautical alloys. In the manufacturing process of turbine parts, electroplated cBN wheels are commonly used under creep feed grinding (CFG) conditions for enhancing productivity. This type of wheel is used because of its chemical stability and high thermal conductivity in comparison with diamond, as it maintains its shape longer. However, these wheels only have one abrasive layer, for which wear may lead to vibration and thermal problems. The effect of wear can be partially solved through conditioning the wheel surface. Silicon carbide (SiC) stick conditioning is commonly used in the industry due to its simplicity and good results. Nevertheless, little work has been done on the understanding of this conditioning process for electroplated cBN wheels in terms of wheel topography and later wheel performance during CFG. This work is focused, firstly, on detecting the main wear type and proposing a manner for its measurement and, secondly, on analyzing the effect of the conditioning process in terms of topographical changes and power consumption during grinding before and after conditioning.

Wheel tread defect detection for high-speed trains using FBG-based online monitoring techniques

The problem of wheel tread defects has become a major challenge for the health management of high-speed rail as a wheel defect with small radius deviation may suffice to give rise to severe damage on both the train bogie components and the track structure when a train runs at high speeds. It is thus highly desirable to detect the defects soon after their occurrences and then conduct wheel turning for the defective wheelsets. Online wheel condition monitoring using wheel impact load detector (WILD) can be an effective solution, since it can assess the wheel condition and detect potential defects during train passage. This study aims to develop an FBG-based track-side wheel condition monitoring method for the detection of wheel tread defects. The track-side sensing system uses two FBG strain gauge arrays mounted on the rail foot, measuring the dynamic strains of the paired rails excited by passing wheelsets. Each FBG array has a length of about 3 m, slightly longer than the wheel circumference to ensure a full coverage for the detection of any potential defect on the tread. A defect detection algorithm is developed for using the online-monitored rail responses to identify the potential wheel tread defects. This algorithm consists of three steps: 1) strain data pre-processing by using a data smoothing technique to remove the trends; 2) diagnosis of novel responses by outlier analysis for the normalized data; and 3) local defect identification by a refined analysis on the novel responses extracted in Step 2. To verify the proposed method, a field test was conducted using a test train incorporating defective wheels. The train ran at different speeds on an instrumented track with the purpose of wheel condition monitoring. By using the proposed method to process the monitoring data, all the defects were identified and the results agreed well with those from the static inspection of the wheelsets in the depot. A comparison is also drawn for the detection accuracy under different running speeds of the test train, and the results show that the proposed method can achieve a satisfactory accuracy in wheel defect detection when the train runs at a speed higher than 30 kph. Some minor defects with a depth of 0.05 mm~0.06 mm are also successfully detected.