Wheel Condition Research Articles

Numerical thermo-mechanical analysis of a railway train wheel

Abstract In this paper, the variation of thermal stresses, strains and deformations created by combined thermal and mechanical loads is investigated in block braked railway wheels. Thermo-mechanical analyses of the railway wheel were performed by using finite element (FE) for the numerical analysis. Before performing FE analyses, input parameters, e.g. heat flow, heat conduction, heat convection, applied loads and boundary conditions were transferred from railway wheel conditions in reality. The thermal analysis of wheel structure in the analysis calculation was evaluated experimentally, and validated with numerical result used in current model. In this purpose, ANSYS Workbench module package was used to simulate the distribution of stress and temperature field over the wheel. Combined thermal and mechanically induced damage surfaces of the railway wheel in reality were compared with numerical results in ANSYS. It was concluded from the results that 3D simulations in this wheel model showed a significant effect of thermal loading rather than mechanical loading on wheel braking tread, and combination of thermal and mechanical loading caused the detrimental effect on railway wheel structures those used in this model.

Online prediction of grinding wheel condition and surface roughness for the fused silica ceramic composite material based on the monitored power signal

The fused silica ceramic matrix composite (SiO2f/SiO2) is characterized as one of the most promising materials in radar radomes and wave-transparent antenna windows. However, the ceramic matrix surface is rather rough, which reduces their service life in severe environments. Although precision grinding is used to improve surface quality, it gets difficult to precisely achieve the desired surface requirement due to the SiO2f/SiO2 material hardness, brittleness, and complicated fiber structure, as well as the uncertain wheel conditions. Especially the wheel condition and its influence on surface quality are unable to quantify directly. With the fast development of IoT monitoring and cyber-physical system, online prediction using process physics signals gains more interest. Power monitoring is a convenient way to obtain valuable grinding information at a relatively lower cost, making it more popular in the industry. However, the links between grinding parameters, power signal, and wheel conditions, surface quality is unclear. A series of grinding experiments of the SiO2f/SiO2 workpiece has been designed to demonstrate how the wheel conditions are indicated using the warm-cold density chart of specific grinding energy. A novel exponential polynomial model of surface roughness associated with wheel conditions indicated by the cutting power signal has been built. Compared with the conventional prediction model, REs center, MSE, and Pearson correlation coefficient are improved significantly from −0.0213, 0.029669, and 0.8695 to 0.0022, 0.022478, and 0.8710, respectively.

Anomaly detection of train wheels utilizing short-time Fourier transform and unsupervised learning algorithms

Anomaly detection of train wheels helps railway operators to find wheel defects and save cost by enabling condition-based maintenance. Existing approaches focus on applications on freight trains and use supervised data-driven methods which need substantial volume of fault data. This is often unavailable in passenger railways that normally provide reliable services. In this regard, this paper reports an unsupervised data-driven approach for anomaly detection of passenger train wheels. Particularly, short-time Fourier transform (STFT) is used to extract time–frequency features from the vibration signal collected with a pair of fiber Bragg grating (FBG) sensors during normal operating hours. Then, four common unsupervised learning algorithms, namely, non-negative matrix factorization (NMF), one-class support vector machine (OC-SVM), multilayer perceptron autoencoder (MLP-AE), and convolutional neural network autoencoder (CNN-AE), are used to derive five health indexes for monitoring the health condition of the train wheels. The proposed workflow is tested against a dataset complied with respect to the wheel turning record. Our results show that the health indexes obtained from the proposed workflow lead to improved correlation with the condition of train wheels compared with existing health metric. In addition, comparison of the four learning algorithms indicates that NMF and MLP-AE outperformed OC-SVM and CNN-AE.

Diagnosis of the Pneumatic Wheel Condition Based on Vibration Analysis of the Sprung Mass in the Vehicle Self-Diagnostics System.

This paper presents a method for the multi-criteria classification of data in terms of identifying pneumatic wheel imbalance on the basis of vehicle body vibrations in normal operation conditions. The paper uses an expert system based on search graphs that apply source features of objects and distances from points in the space of classified objects (the metric used). Rules generated for data obtained from tests performed under stationary and road conditions using a chassis dynamometer were used to develop the expert system. The recorded linear acceleration signals of the vehicle body were analyzed in the frequency domain for which the power spectral density was determined. The power field values for selected harmonics of the spectrum consistent with the angular velocity of the wheel were adopted for further analysis. In the developed expert system, the Kamada-Kawai model was used to arrange the nodes of the decision tree graph. Based on the developed database containing learning and testing data for each vehicle speed and wheel balance condition, the probability of the wheel imbalance condition was determined. As a result of the analysis, it was determined that the highest probability of identifying wheel imbalance equal to almost 100% was obtained in the vehicle speed range of 50 km/h to 70 km/h. This is known as the pre-resonance range in relation to the eigenfrequency of the wheel vibrations. As the vehicle speed increases, the accuracy of the data classification for identifying wheel imbalance in relation to the learning data decreases to 50% for the speed of 90 km/h.

Die structure optimization study for magnesium alloy wheel formed by backward extrusion

The current magnesium (Mg) alloy wheel formed by backward extrusion (BE) often has upper rim cracking and coarse microstructure at the spokes to reduce the yield, and a reasonable die structure design is an important factor to improve the yield of the wheel, therefore, this poses new challenges on the die structure of Mg alloy wheels. The paper selected four parameters for the three positions of the wheel for optimizing the die structure by analyzing the forming characteristics and defects of Mg alloy wheels. Based on the actual extrusion conditions of the Mg alloy wheel, a numerical model of Mg alloy wheels formed by BE was established, and the effect of a single parameter on the physical field of the forming process was analyzed by numerical simulation in turn, and then the four parameters of the die structure were progressively combined and optimized to determine the better three positions of the Mg alloy wheel die, while an experimental trial was conducted to verify the accuracy of the results of this study. The results show that increasing the spoke thickness, decreasing the spoke inclination angle, increasing the punch fillet radius and increasing the inner fillet radius of the upper rim can effectively improve the metal flow and make the billet filling more smoothly. The optimized die structure can make the temperature at the wheel bottom lower and evenly distributed, the stress concentration at the window is relieved, the filling at the upper rim is complete, and the macroscopic tearing phenomenon disappears.

Technological Aspects of Variation in Process Characteristics and Tool Condition in Grinding Process Diagnostics

The article presents the technological aspects of the diagnostics of grinding processes. The main features of the grinding process and their importance in diagnostic issues are discussed. Selected issues of research and assessment of the condition of the active surface of grinding wheels are presented. The authors pointed out that the parameters used to assess the topography of the ground surfaces do not have sufficient possibilities to differentiate the surface condition of the grinding wheels. This publication draws attention to the possibility of using new dedicated parameters to assess the properties of the grinding wheel surface. These parameters have a high ability to differentiate changes occurring as a result of the abrasion of grain vertices, their chipping or loading of the grinding wheel surface. The methodology of assessing the processes of abrasive grain wear and changes in the shape and dimensions of the grinding wheel, taking into account the probabilistic features of the grinding process, was formulated. The directions for the development of abrasive tools are presented, pointing to hybrid tools with a multi-phase structure, modified by additions of abrasive aggregates. A new research direction has also been formulated on the use of additive technology to produce specialised abrasive tools, including those with built-in process sensors.

Influence of grinding wheel conditioning on the grindability of Ti-6Al-4V alloy

Influence of grinding wheel conditioning on the grindability of Ti-6Al-4V alloy

Study on the Interaction between Wheel Polygon and Rail Corrugation in High-Speed Railways.

The wheel polygonization and rail corrugation are typical wheel-rail periodic wear problems, which seriously affect the safe operation of high-speed railways. In the present paper, the interaction between the wheel polygon and the rail corrugation in the long-slope section of high-speed railways is mainly studied based on theory of friction coupling vibration. Firstly, the simulation model of the wheel-rail contact model is established, as well as the polygonal wear of the wheel and the corrugated wear of the rail. Then, the stability analyses of the wheel-rail system with periodic wear are studied, in which the four working conditions of smooth rail-smooth wheel, polygonal wheel-smooth rail, smooth wheel-corrugated rail and polygonal wheel-corrugated rail are compared. Finally, the competition mechanisms between the wheel polygon and rail corrugation under different parameters are discussed, including the wheel-rail friction coefficient and the depth of periodic wear of the wheel-rail system. The numerical results show that both the periodic wear of the wheel and rail with certain relevance will increase the friction coupling vibration of the wheel-rail system, which may aggravate the subsequent relevant wheel polygonal and rail corrugation wear. With the increase of the friction coefficient between wheel and rail, as well as the depth of the wheel polygon and rail corrugation, the vibration trend of the friction coupling vibration of the wheel-rail system increases gradually. Moreover, the proportion of the wheel polygon's influence on the friction coupling vibration of the wheel-rail system is greater than that of rail corrugation.

Shuffle algebras for quivers and wheel conditions

Abstract We show that the shuffle algebra associated to a doubled quiver (determined by 3-variable wheel conditions) is generated by elements of minimal degree. Together with results of Varagnolo–Vasserot and Yu Zhao, this implies that the aforementioned shuffle algebra is isomorphic to the localized 𝐾-theoretic Hall algebra associated to the quiver by Grojnowski, Schiffmann–Vasserot and Yang–Zhao. With small modifications, our theorems also hold under certain specializations of the equivariant parameters, which will allow us in joint work with Sala and Schiffmann to give a generators-and-relations description of the Hall algebra of any curve over a finite field (which is a shuffle algebra due to Kapranov–Schiffmann–Vasserot). When the quiver has no edge loops or multiple edges, we show that the shuffle algebra, localized 𝐾-theoretic Hall algebra, and the positive half of the corresponding quantum loop group are all isomorphic; we also obtain the non-degeneracy of the Hopf pairing on the latter quantum loop group.

Wear-related topography changes for electroplated cBN grinding wheels and their effect on thermo-mechanical load

During the grinding process, a large proportion of the cutting energy is converted into heat. Since not all the heat can dissipate into the cooling lubricant, the material structure of the workpiece can suffer from thermally-induced damage, which has a negative effect on component quality. The generation and distribution of heat is influenced by the change of the grinding wheel topography due to wear. A means of wear analysis for grinding wheel topographies has been developed to describe and explain the wear condition of the grinding wheel and is presented in this paper. For this purpose, creep feed grinding investigations were conducted on bearing steel with electroplated cBN grinding wheels with two different grain specifications. Static and kinematic topography parameters describing the topography changes of the grinding wheels due to wear and their influence on the thermo-mechanical load were investigated in the tests. The static parameters such as the Abbott-Firestone curve or the wear volume make it possible to quanitfy the wear mechanisms. By determining the kinematic topography parameters such as engagement areas or angles, the change in engagement conditions could be derived, which is important for determining the heat flows into the workpiece.

Reducing short- and long-term cocaine craving with voluntary exercise in male rats.

In a previous study in female rats, voluntary wheel running attenuated incubation of cocaine craving after 30 but not 3days (Zlebnik and Carroll Zlebnik and Carroll, Psychopharmacology 232:3507-3413, 2015). The present study in male rats, using the same procedure, showed that wheel running reduced incubated craving after both 30 and 3days of abstinence. Male rats self-administered i.v. cocaine (0.4mg/kg) during 6-h sessions for 10days. They were then moved from the operant chamber to a home cage with an attached running wheel or stationary wheel, for 6h daily for a 3- or 30-day period when cocaine craving was hypothesized to incubate. Rats were then returned to the operant chamber for a 30-min test of cocaine seeking, or "craving," indicated by responses on the former "drug" lever was formerly associated with drug stimulus lights and responses (vs. no drug stimuli), and lever responding was compared to responses on the "inactive" that was illuminated and counted lever pressing. Mean wheel revolutions were similar across the 3- and 30-day incubation groups, when both groups of rats were given access to wheel running vs. access to a stationary wheel in controls. Subsequently, when rats were tested in the operant chamber for "relapse" responding (drug-lever responding) on the lever formerly associated with drug access, cocaine craving was reduced by recent running wheel access (vs. stationary wheel access) in both the 3- and 30-day wheel exposure groups. Voluntary, self-initiated, and self-sustained physical exercise reduced cocaine craving after short- (3days) and long-term (30days) abstinence periods in male rats that previously self-administered cocaine. This was contrasted with reduction of cocaine seeking in females after 30-day, but not 3-day, incubation periods under the wheel running vs. stationary wheel conditions in a previous study (Zlebnik and Carroll Zlebnik and Carroll, Psychopharmacology 232:3507-3413, 2015). These initial findings suggest males may be more sensitive to incubated craving for cocaine than females.

Kinematic Characteristics of Vertical Runner and Numerical Simulation Analysis of Rock Breaking

In order to solve the problems of PDC teeth easy to collapse and fail in hard, hard and soft interlaced formations and gravel bearing formations, low mechanical speed, and difficult orientation caused by high torque response of pdc bits in directional drilling, this paper proposes a new type of composite bit with vertical wheels on the blade. Through theoretical analysis, the rotating conditions of the vertical wheel are obtained, that is, the contact angle of the cutting teeth of the vertical wheel must be greater than the center angle of its two adjacent teeth. At the same time, the cutting tracks generated by the previous vertical wheel unit experiments are analyzed, and the causes of different cutting tracks are obtained. Finally, the rock breaking of the PDC teeth under the pre damage of the vertical wheel is numerically simulated and analyzed, and it is concluded that the force on the PDC teeth is closely related to the relative height between the cutting teeth of the vertical wheel, It increases with the increase of relative height. When the relative height is 2mm, the tangential force on the PDC tooth is 4.78 times that when the relative height is 0mm, and the axial force is 2.47 times that when the relative height is 0mm. At the same time, the relationship between the force on the PDC teeth and the normal deflection angle and the rake angle of the vertical roller is not obvious.

Strength analysis and optimization of three-bridge rigid frame for mining dump truck using finite element stress linearization method

A finite element stress linearization method is introduced into the structural verification of the three-bridge rigid frame, a newly designed frame for the wide-body mining dump truck. This method successfully captures the potential local failure around the structural-discontinuity zone. The results indicate that the suspension supports experience high-level stress under six operating conditions and the most dangerous location of the frame always lies at the weld bead between the suspension support and frame. The stress intensities of the original frame under six operating conditions are all smaller than the allowable value, manifesting that the strength of the original frame can meet the strength requirements. The local modifications greatly reduce the local stress and thus improve the reliability of the frame, manifested by the reduced stress level and stress intensity along dangerous paths. The primary plus secondary stress intensity along the most dangerous path decreases by 16.69% under the condition of free-hanging left front wheel, and by 59.91% under the condition of free-hanging right rear wheel. The present work provides a sound method to evaluate the local failure of the frame for the mining dump truck containing many structural-discontinuity zones, and the result is helpful for the continuous improvement of the frame for the wide-body mining dump truck and provides a guideline for designing a new frame.

Probabilistic review of wheel profiles based on hollow tread in the U.S. heavy haul rail network

Wheel profile is one of the most critical factors that governs the dynamic interaction between railcar wheels and the rail. This paper quantifies and analyzes 15,000 wheel profiles that were randomly selected from a dataset obtained from wayside wheel profile measurement systems from railcars that are in unrestricted interchange in the North American Class I railroad network. Mean dimensions for each of the four critical wheel tread parameters were as follows; 30.5 mm (1.202 in.) for flange height, 35.4 mm (1.395 in.) for flange thickness, 0.483 mm (0.019 in.) for hollow tread, and 37.8 mm (1.488 in.) for rim thickness. Further evaluation focused on the magnitude of hollow tread, one of the important factors that determines wheel rail contact location, contact patch size, and dynamic interaction of the railcar and track. Correlative analysis revealed that hollow tread is linearly related to flange height, flange thickness, and rim thickness. Given this relationship, we categorized wheel profile data into characteristic bins of wheel profiles, and a new classification system based on hollow tread was proposed that should be considered when designing track infrastructure components (e.g., turnout frogs). Combinations of parameters were derived and organized into five wheel-profile classification ranges, based on “most likely” to “least likely” profiles that might be encountered. For the five classifications (Type A to Type E), a range of hollow tread from 0 mm to 5 mm (interval of 1 mm) and corresponding mean values for the three other parameters were applied. This probabilistic analysis and classification of wheel profiles into categories enables better understanding of current North American wheel conditions. Additionally, these data and the proposed analysis method can be leveraged to further optimize wheel profiles and track components, identify relationships with rates of rail surface defects and track degradation, and prioritize maintenance.

Changes in the Concentration of Phthalic Acid Esters in the Plastic Materials of Vehicles During the Usage

The presence of dibutyl phthalate (DBP) and di-2-ethylhexyl phthalate (DEHP) was monitored in 16 automotive plastic samples (steering wheel, dashboard, seats, headrests, and air conditioning control). The plastic samples were obtained from a crashed vehicle that had been in operation for only one month. Phthalates were determined by HPLC. The first analysis was performed immediately after the sample preparation, the second one after 3 months of storage, the third one after 6 months of storage, the fourth one after 9 months of storage, and the fifth one after 15 months of storage. The average DBP concentrations ranged from 1.3 to 197.1 μg g–1 of plastic at the beginning, 1.7to 161.5 μg g–1 of plastic in the 3rd month, 0.5 to 78.9 μg g–1 of plastic in the 6th month, 2.3 to 123.3 μg g–1 of plastic in the 9th month, and 14.0 to 98.6 μg g–1 of plastic in the 15th month. The average DEHP concentrations ranged from 1,7 to 185,7 μg g–1 of plastic at the beginning, 3,9 to 45,4 μg g–1 of plastic in the 3rd month, 1,4 to 252,4 μg g–1 of plastic on the 6th month, 5,1 to 41,7 μg g–1 in the 9th month, and 2,3 to 87,5 μg g–1 in the 15th month. In particular, higher concentrations were found in soft plastics. An interesting finding is that the content of both phthalates (DBP and DEHP) was also present in leather materials. According to the comparison of phthalates concentrations during storage, it can be stated that phthalates concentration decreases with time and, after approximately 15 months of storage, concentrations decreased: for DBP by 20 to 90% (in 44% samples) and for DEHP by 30 to 90% (in 70% samples). For the other samples, the increase of both phthalates was probably due to depolymerisation of the plastics by UV exposure, temperature and storage time. Phthalates are human carcinogens and they can cause the death or tissue malformation. They are dangerous for liver function. The legislation of the Czech Republic and European Union has not yet dealt with these problems.

Correlation Functions of Quantum Toroidal gln Algebra

In this paper, we study the correlation functions of the quantum toroidal \mathfrak{gl}_n algebra. Their first key properties are established in analogy to those of the correlation functions of quantum affine algebras U_q\mathfrak{n}_+. The core of the paper is the proof of the vanishing of those correlation functions at length 3 wheel conditions, which is done separately for n = 1, n = 2, n≥ 3 cases with different ``Master Equalities" of formal series (the n = 1 case has been previously discussed by the author in (Cui, 2021)). Another important contribution is the discovery of cubic Serre relations for the quantum toroidal.

Acoustic emission based grinding wheel wear monitoring: Signal processing and feature extraction

Surface grinding, being one of the later stages of a manufacturing process, is crucial to achieve the desired workpiece quality by maintaining proper wheel condition. Machine learning and acoustic emission (AE) detection have demonstrated potentials for developing an effective on-line wheel wear monitoring system. However there are inconsistencies among existing studies regarding to signal sampling and feature extraction methods. In the present paper, surface grinding experiments using medium carbon steel workpieces were conducted to determine the effects of using different signal analysis window lengths, feature types, and AE sensor bandwidths on the wheel wear detection performances. In addition, the use of AE transient spikes occurred during wheel entry and exit were analyzed. Feature were extracted from time domain (TD), frequency domain (FD), and time–frequency domain (TFD) representations of the measured AE signals, which were then refined using sequential floating forward selection (SFFS) algorithm to reduce feature redundancy. Support vector machine (SVM) algorithm with linear kernel was used for pattern classification. A number of insights for future monitoring system development were obtained. Firstly, while the lower frequency AE sensor (below 100 kHz) provided adequate classification performances, the higher frequency AE sensor (above 100 kHz) produced more robust results. Secondly, 100% classification rate was achievable using features derived from Fast Fourier Transform (FFT) in combination with descriptive statistics. Thirdly, it was determined that a window length as short as 1 ms (1000 data points) could capture enough signal information using FFT with the higher frequency AE sensor. Lastly, the choice sampling instance did not significantly impact classification outcomes.

Tracking changes in grinding wheel condition using reduced features of acoustic emission signals

In this paper, a universal method is proposed to accurately identify wheel state during grinding brittle materials. In contrast with conventional methods relying on adequate repetitive experimental data under the same process conditions, only history acoustic emission (AE) data in the same life cycle of a grinding wheel are needed for current wheel state identification. During grinding process, AE spectra samples are acquired sequentially on a series of nodes with equal interval. Samples on each node are categorized into the same class of wheel state. In the theoretical part, AE spectrum is proved to be suitable for feature representation of the degradation of wheel condition. Linear discriminant analysis is used to project AE spectra samples into a two-dimensional feature space, and the changes of the projection gives a clue to the evolution of wheel state. Two types of commonly used diamond wheels for optical surface grinding, which are straight wheel and cup wheel, were used to verify the general applicability of the method. The experiments were carried out on different machine tools and the two wheels also possessed different degradation process, yet for all of them, the evolution of wheel state can be accurately identified. It was proved that the proposed method can adaptively trace wear stage change of diamond grinding wheel.

Effect of passing metro trains on uncertainty of vibration source intensity: monitoring tests

The source intensity of metro-induced vibrations is defined as the vertical weighted vibration acceleration level on a tunnel wall. It is traditionally determined using the empirical method or analogical test. However, recent research has indicated that tunnel vibration responses vary significantly according to the passing train, likely owing to the influence of wheels worn out-of-round. To analyse the associated uncertainty of the vibration source intensity, five monitoring tests were performed in a metro tunnel over the course of one year. Each test lasted 24 h, during which the vibration responses of all the passing trains were recorded. Then, information describing the operating mileage for all passing trains was collected to quantitatively analyse the relationship between the vibration source intensity and the wheel conditions. The results indicate that different trains can generate source intensities with significant differences greater than 10 dB in most 1/3 octave bands between 40 and 315 Hz. The tunnel vibration responses increased with the net operating mileage of the passing trains, and the vibrations between 40 and 315 Hz decreased by more than 20 dB after wheel re-profiling. The maximum and average values of the tunnel vibration responses decreased by 8 and 5 dB, respectively, after rail grinding.

On-board vibration-based remaining useful life estimation of hollow worn railway vehicle wheels

Abstract The feasibility of on-board remaining useful life (RUL) estimation of hollow worn railway vehicle wheels under varying travelling speed using vibration signals from the vehicle’s bogie, is for the first time explored. A variant of the Multiple Model Power Spectral Density (MM-PSD) method is employed, treating RUL estimation as a multi-class classification problem, where each class corresponds to a specific condition of hollow worn wheels associated with an empirical remaining mileage. The method classifies the current, unknown, condition of hollow worn wheels to one of the available by examining the vehicular dynamics similarity among them through the amplitude of the sharp periodic valleys (antiresonances) induced due to the Wheelbase Filtering (WF) effect. The vibration signals are obtained from Monte Carlo experiments based on the high-fidelity SIMPACK software, while the remarkable performance of the method is demonstrated via thousands of inspection cases.