Wheel Condition Research Articles

Identifying risk factors for wheelchair damage, part failure, and adverse consequences to the user

No tools or technologies exist to inform data-driven inspection schedules for wheelchairs. To develop such a schedule, this study identifies risk factors linked with manual wheelchair damage, part failures, and consequences and evaluates preferences for a new wheelchair servicing technology. A mixed methods study was performed with manual wheelchair users at The Ohio State University Martha Morehouse Clinic. Demographic data, wheelchair information, failure counts, and consequences suffered by the user were collected using surveys. Wheelchair usage was collected for a month using a sensor. A servicing smartphone app that connects with the sensor was demonstrated as a new servicing technology, and participant preferences were recorded. Thirty participants completed the survey testing procedures. Twenty-three collected usage data and eighteen collected it for over a week. At least 215 wheelchair part failures with an average of 13.4 ± 14.8 self-reported part failures and 4.7 ± 4.8 high-risk failures occurred in 12 months before the first study visit. Two weeks of collected data from 18 participants showed that normalised road shocks, age, and weight were associated with the condition of wheels and frames, as well as self-reported caster failures. Participants responded with a favourable preference for the new wheelchair servicing technology, with more than half of them interested in buying and using it. Risk factors like road shocks and user’s age and weight are associated with part damage towards failures and self-reported failures that risk injury. These factors can be modelled to develop and test the efficacy of wheelchair inspection schedules.

M-CLUSTER: multistage clustering for unsupervised train wheel condition monitoring

Wheel out-of-roundness (OOR) is a common wheel defect that raises maintenance costs and increases the risk of failure or damage to track components. This paper proposes a novel multistage clustering framework (M-CLUSTER) for unsupervised condition monitoring of train wheels. The framework initially extracts time-domain features from raw acceleration responses collected by rail-mounted sensors. Sensitive features are then selected through an unsupervised feature selection algorithm called local learning-based clustering (LLC). Next, a detector model is trained using density-based spatial clustering of applications with noise (DBSCAN), a data clustering method effective for clusters with similar density. Since this algorithm does not originally involve separate training and testing phases, a new two-step mechanism is introduced: (1) training on a healthy dataset and (2) testing on an unlabelled dataset. Finally, the severity of train wheel defects is classified by K-means, with cluster validity indices (CVI) automatically determining the number of severity clusters (classes). The framework’s efficiency is demonstrated through the detection of defective wheels using the Alfa Pendular passenger model. Results indicate that M-CLUSTER accurately identifies train wheel flats and polygonal wear without labelled data, achieving 98% accuracy by selecting 10 features from the set.

Active compliance control of grinding process for stripping enameled copper wire

The hairpin motor is among the motors used in eco-friendly automobiles. Unlike the random-winding method, this method features a square cross-sectional enamel coil formed into several hundred hairpins inserted into the stator. This offers an increased space factor, which leads to enhanced motor power. With the growing use of hairpin motors, there is an increasing demand for automated equipment in their production. With an aim to improve the manufacturing quality of hairpin motors, many countries are conducting research and development. The process of enamel removal is one of the key steps in hairpin-forming equipment and can significantly affect the performance of the motor. A precise machining process technology is required to improve the enamel removal process, achieve higher enamel removal rates, and reduce wire loss rates. Grinding is a technique used for enamel removal. In this study, an adaptive control algorithm was applied to the grinding process to enhance the enamel removal machining performance. During the machining process, the vertical position of the spindle was controlled to maintain a consistent machining depth for tracking the target grinding force. An experimental setup, which included a system for measuring the spindle torque to calculate the grinding force in real time, was created. To validate the performance of the adaptive control technology, experiments were conducted using this setup. Furthermore, experiments were conducted to observe the changes in the grinding force ratio based on the conditions of the grinding wheel. The correlation between the grinding force ratio and tool condition was analyzed, and based on this, the feasibility of assessing tool condition and determining the timing for tool replacement through real-time monitoring of the grinding force ratio was confirmed.

Influence of rotating wheels on Reynolds number sensitivity of passenger vehicle drag

Rotating wheels are essential to replicate realistic forces and flow fields during aerodynamic testing of passenger vehicles. Previous studies have found that the drag coefficient typically reduces with increased speed under rotating wheel conditions but is stable with stationary wheels. This paper investigates this velocity sensitivity for two vehicles, the aerodynamic research vehicle DrivAer and a production vehicle. Experiments with the DrivAer indicate that the drag reduction with increased speed cannot be explained by pressure changes on the vehicle body; instead, the effect is attributed to local flow changes around the wheels. Using numerical simulations, it is found that the separation at the front wheels' outer tire shoulder increases for lower velocities, thus resulting in higher drag coefficient and causing Reynolds number sensitivity. The degree of drag reduction is less for the DrivAer squareback than for the notchback due to the separation over the notchback's rear window. No direct difference is measured between various tires and rims. To assess the generality of the findings with the DrivAer, the results are compared to experiments with a production vehicle. It is observed that the drag sensitivity is less and only occurs if the rear wheels are rotating. Pressure measurements in the wheelhouses and around the wheel drive units confirm the front wheel separation's dependence on velocity and highlight the complex interaction between the front wheel wakes and rear wheel rotational state.

A Study on the Analysis of Machining Characteristics and Derivation of Optimal Machining Conditions of Grinding Wheel for Gear Grinding

A Study on the Analysis of Machining Characteristics and Derivation of Optimal Machining Conditions of Grinding Wheel for Gear Grinding

Blockchain-enabled Data Mining for Pre-Condition Monitoring in Railway Infrastructure

Early detection of abnormalities in railway systems is crucial for risk mitigation and cost-effective maintenance. Damaged wheels in freight trains cause a significant threat to transport infrastructure integrity, particularly when wheel flats occur. Monitoring wheel conditions relies on sensing mechanisms, requiring robust data integrity checks. This paper proposes a data-driven approach utilising blockchain for pre-condition monitoring to enhance data reliability. To achieve this goal, a data mining procedure is developed to establish a local blockchain. The blockchain continuously expands with new blocks, with each block linked to the previous one via a hash function. Only validated data are recorded in order to enhance the reliability of train measurements.

Assessment of fatigue crack growth in metro cast manganese steel frogs and inspection strategy

Fatigue cracking is one of the notable failure mechanisms of railway turnout frogs due to high contact forces induced by the wheel-rail impact. However, the treatments specific to the frogs of cast high manganese steel to prevent sudden failure are rarely provided and are independent of the crack conditions, which can significantly affect the fatigue crack growth rate. To address this shortcoming, the fatigue crack growth in a cast high manganese steel frog is computationally investigated with varied wheel and crack conditions. The dynamic wheel-frog impact is first simulated to obtain the distribution of contact pressure imposed on the frog, and the propagation of the fatigue crack is then predicted using the finite element formulation in fracture mechanics. The simulation results show that the train speed and axle load can change the maximum contact stress. Nevertheless, when representing the growth rate in terms of the cumulative passing axle load, the effect of axle load and train speed on the crack growth is minimal and moderate, respectively. In contrast, the initial crack conditions, including the location and angle, have an overweighted influence. As a result, an inspection scheduling strategy for identified cracks with different conditions is proposed to consider both safety and operational efficiency.

Increasing the Traffic Safety Level of Rolling Stock by Wheel Condition Monitoring Using an Automated Measuring Complex

Increasing the Traffic Safety Level of Rolling Stock by Wheel Condition Monitoring Using an Automated Measuring Complex

A Method for Identifying the Wear State of Grinding Wheels Based on VMD Denoising and AO-CNN-LSTM

Monitoring the condition of the grinding wheel in real-time during the grinding process is crucial as it directly impacts the precision and quality of the workpiece. Deep learning technology plays a vital role in analyzing the changes in sensor signals and identifying grinding wheel wear during the grinding process. Therefore, this paper innovatively proposes a grinding wheel wear recognition method based on Variational Mode Decomposition (VMD) denoising and Aquila Optimizer—Convolutional Neural Network—Long Short-Term Memory (AO-CNN-LSTM). The paper utilizes Acoustic Emission (AE) signals generated during grinding to identify the condition of the grinding wheel. To address noise interference, the study introduces the VMD algorithm for denoising the sample dataset, enhancing the effectiveness of neural network training. Subsequently, the dataset is fed into the designed Convolutional Neural Network—Long Short-Term Memory (CNN-LSTM) structure with AO-optimized parameters. Experimental results demonstrate that this method achieves high accuracy and performance.

Simulation Study on Performance of Solar-Powered Desiccant Wheel and Ground Source Heat Pump Air Conditioning in Qingdao

In China, a large amount of the total energy consumption is made up of building energy, particularly in humid regions. The conventional vapor compression refrigeration systems cannot effectively control the indoor humid and thermal environment. Therefore, this article proposes a solar-powered desiccant wheel and ground-source heat pump (SDW-GSHP) air conditioning system. The energy consumption of the system is mainly from sustainable sources of solar and geothermal energy, showcasing excellent energy efficiency and environmental friendliness. The desiccant wheel (DW) processes latent heat loads, and the GSHP processes the sensible heat load. The regeneration air of the DW is heated by a solar collector. The operational performance of the system was simulated by using TRNSYS during the typical summer week (15 July to 22 July) in Qingdao. The simulation results indicated that indoor temperature was maintained within 25.8–26.2 °C and the relative humidity was maintained in the range of 57–61%. The COP of the SDW-GSHP air conditioning system was 42.1% higher than that of the DW air conditioning system with electric heating regeneration, and electricity saved 43.7%.

Flutter analysis of a rigid-flexible coupled composite space structure with momentum wheels under thermal load

The communication and observation spacecraft generally consists of the spacecraft body, the composite connecting beams, the working payload and the CMG/momentum wheel for integrated attitude-vibration control. When such complex space structure operates in near-Earth orbit, the sudden change in thermal load may cause a solar flux shock to the structure, which is likely to excite the flutter motion of the structure. The flutter mechanism of a rigid-flexible coupled composite space structure configured with the momentum wheel is investigated. In this study, the heat conduction equation of the composite beam appendage is derived. Then, the dynamic equations of the system influenced by the momentum wheel and composite material parameters are obtained based on the Kane's equations. Using the Routh–Hurwitz stability criterion, the stability boundaries of the flexible appendage are obtained for different composite material parameters, different operating conditions of the momentum wheel, and different attitude angular velocities. Finally, the flutter behavior is analyzed based on the dynamic response of the flexible structure and the flutter stability boundaries are verified.

Techniques for Accelerometer Reading Processing on Railway Transport Using Wavelet Transform

Introduction. Safety issues of railway transport are inevitably connected with the condition of railway tracks and railway wheels. Various defects, such as irregularities of the railway track, may lead to emergencies and incidents. Therefore, it is important to measure and calculate short and impulse irregularities. A joint analysis of vibration acceleration signals is needed in order to study the types and size of railway track irregularities.Aim. Development of an algorithm for irregularity search based on accelerometer readings with the vertical measurement axis.Materials and methods. The research encompassed wavelet transformation and wavelet-based signal processing including discrete-time signal processing and continuous wavelet processing. In addition, time-frequency analysis based on Fourier transform and continuous wavelet scalogram was used. These methods provide for time-frequency localization for irregularity detection and measurement.Results. Algorithms for vibrational signal processing using the continuous and discrete-time wavelet transform are proposed. The results show that the discrete wavelet transform is effective for multiresolution and multiband analysis, and continuous wavelet transform and wavelet scalogram allows extraction of irregularities and determination of their parameters. The relative error for irregularity depth was improved by 18 %, and the absolute error for irregularity length determinations was reduced by 7 times.Conclusion. Application of the discrete Fourier transform and Fourier spectrogram provides for fine resolution in the frequency domain. However, separation of signal components in the time-frequency domain is impeded. The continuous-time wavelet transform ensures sufficient resolution in the low-frequency domain for component localization and visualization of irregularities.

Automatic Gear Tooth Alignment in Vision Based Preventive Maintenance

Thorough maintenance of industrial equipment is crucial for the finances of companies. Whereas the purchase of new tools can be an expensive business, reconditioning special gear often costs just a fraction. In this paper, preliminary steps for an accurate visual based preventive maintenance of hobbing wheels are investigated. To perform robust and reliable decisions about the wheel's condition, tool department specialists require precise taken captures of it. For this reason, a visual control cell is built, which depends on correctly aligned hobbing wheels in its image acquisition construction. The tool needs to be placed on a turn-table and rotated, so that a single tooth is centered in the field-of-view of the camera mounted on a robot arm. For this alignment task, three different main approaches with various preprocessing steps are investigated, a brute-force algorithm, an orb-feature approach and an image regression model. The results show that even a brute-force algorithm can be outperformed by a moderate deep neural network.

Impact of daily and seasonal temperature variation on rolling contact fatigue damage in the rail

Rolling contact fatigue is the most frequent degradation mode observed on the rail. Many mechanical studies have revealed the main physical parameters that generate the crack initiation, which are all linked with the track geometry, the dynamic of the wheel and contact conditions. Except for extreme climate, exogenous phenomena such as ambient temperature have not been analysed. The main reason is that temperature variations induce stress cycles in the rail that are under the endurance limit of the material. Therefore, most fatigue life estimation methods do not consider these cycles. In this paper, 6 years of daily temperature measurement over all the French railway network are used and integrated in a machine learning model developed for computing residual lifespan of each rail segment before crack initiation by rolling contact fatigue. Temperature data are aggregated using the continuum damage mechanics model developed by Lemaitre and Chaboche that gives the possibility to let low amplitude cycles to contribute to fatigue damage. Classification models are then implemented, and importance variables are finally quantifed. Results show that daily temperature variations have more impact than seasonal variations and that its contribution to fatigue is significant.

Fabrication of Vitrified Bond Diamond Grinding Wheel via LCD Photopolymerization

In this paper, a liquid crystal display (LCD) photopolymerization method is proposed, and a vitrified bond diamond grinding wheel is successfully prepared. A high-performance vitrified bond was obtained by melting SiO2-B2O3-Al2O3-Na2O ceramic raw materials and used for grinding wheel preparation. LCD photopolymerization technology is characterized by high precision in shaping, fast processing speed, and superior quality, making it a promising technology for fabricating vitrified bond diamond grinding wheels. The preparation of vitrified bond slurry with high solid content and low viscosity was extensively investigated to meet the fabrication requirements. The effects of dispersant, the particle size of the vitrified bond, and solid content on the viscosity of the slurry were systematically analyzed. The vitrified bond slurry with solid content up to 65 wt.% (approximately 45.5 vol.%) was successfully prepared and met the requirements for printing. Furthermore, we explored the optimal formulation of the grinding wheel, debinding and sintering conditions, sintering temperature, grit-to-bond ratio, and the evaluation of the grinding performance of the wheel on hard and brittle materials, such as silicon carbide ceramic. Vitrified bond and abrasive slurry systems with a solid content of 65 wt.% (approximately 42.8 vol.%) were prepared. The results show that the vitrified bond diamond grinding wheel exhibits optimal comprehensive performance, with a sintering temperature of 680 °C and a grit-to-bond ratio of 4:6. The minimum surface roughness of the workpiece after grinding was 1.767 μm, the material removal rate was 5.08 mg/s, the grinding ratio was 9.78, and the friction coefficient was stabilized at about 0.5 during grinding. This paper guides the manufacturing of vitrified bond diamond grinding wheels via LCD photopolymerization.

Nonreciprocal Elasticity and Nonuniform Thickness of Curved Spokes on the Top-Loading Ratio, Vertical Stiffness, and Local Stress of Nonpneumatic Wheels

<div>The nonreciprocal elastic behavior of flexible spokes is essential for designing a top-loading condition of nonpneumatic wheels to distribute the vehicle load throughout the upper circumferential region of a wheel to replicate the loading mode of their pneumatic counterparts. However, most ad hoc spoke designs had been conducted without considering the top-loading mechanics. Moreover, minimizing the stress concentration on the spokes is also significant for preventing potential failures; however, modification of the geometry to reduce the local stress on the spokes has not yet been studied. In this work, we investigate the effect of nonreciprocal elastic behaviors of curved spokes on the top-loading distribution of nonpneumatic wheels. We also study the geometric effect of nonuniform curved spokes on reducing the local stress concentration. Curved beam spokes with greater curvature can contribute to a high top-loading ratio of nonpneumatic wheels. The nonuniform thickness of curved spokes with the spoke’s ends and center regions can reduce the local stress level by up to 24%. Our design method with varying curvature and nonuniformity of the curved spokes can provide significant design guidelines for nonpneumatic wheels for determining the top-loading ratio, tuning the vertical stiffness, and minimizing local stress on the spokes.</div>

A Study on Wheel Member Condition Recognition Using 1D–CNN

The condition of a railway vehicle’s wheels is an essential factor for safe operation. However, the current inspection of railway vehicle wheels is limited to periodic major and minor maintenance, where physical anomalies such as vibrations and noise are visually checked by maintenance personnel and addressed after detection. As a result, there is a need for predictive technology concerning wheel conditions to prevent railway vehicle damage and potential accidents due to wheel defects. Insufficient predictive technology for railway vehicle’s wheel conditions forms the background for this study. In this research, a real-time tire wear classification system for light-rail rubber tires was proposed to reduce operational costs, enhance safety, and prevent service delays. To perform real-time condition classification of rubber tires, operational data from railway vehicles, including temperature, pressure, and acceleration, were collected. These data were processed and analyzed to generate training data. A 1D–CNN model was employed to classify tire conditions, and it demonstrated exceptionally high performance with a 99.4% accuracy rate.

Condition monitoring for fault diagnosis of railway wheels using recurrence plots and convolutional neural networks (RP-CNN) models

RPThe wheel condition monitoring when the train in operation is significant task to prevent the occurrence of unexpected event. In this study, the piezoelectric sensors were installed on the railway track to collect the dynamic voltage-and-strain signals when the train wheels pressed them. These one-dimensional time series signals were transformed to the two-dimensional Recurrence Plots (RP) images as an input data sets for two deep learning models, Xception and EfficientNet-B7. The binary classification, Normal or Faulty as the diagnostical output to indicate the health state of the train wheels in that time. Five metrics were selected to evaluate the performance of two models, namely Accuracy, Precision, Recall, Miss Rate, and AUC. The results show that both models perform the high accuracy of 91.1% to the wheel condition classification. Furthermore, EfficientNet-B7 shows better performance in Recall, Miss-rate, and AUC metrics than those of Xception to express the premium ability in defective wheel identification, which is crucial for this application. Therefore, the efficientNet-B7 is selected as a favorable machine learning classifier for the fault diagnosis of rolling stock wheels. It is significant contribution to train wheel condition monitoring and health management since it provides the effective diagnostic information for maintenance decision to decrease the occurrence of unexpected event.

Conditioning and monitoring of grinding wheels: A state-of-the-art review

<p>Grinding wheel condition monitoring is an important step towards the prediction of grinding wheel faulty conditions. It is beneficial to define techniques to minimize the wear of the grinding wheels and finally enhance the life of the grinding wheels. Grinding wheel condition monitoring is done by two techniques such as (i) direct and (ii) indirect. Direct monitoring employs optical sensors and computer vision techniques, and indirect monitoring is done by signal analysis such as acoustic emission (AE), vibration, cutting force, etc. Methods implemented for grinding wheel monitoring in the published research papers are reviewed. The review is compiled in five sections: (a) process parameters measurement, (b) data acquisition systems, (c) signal analysis techniques, (d) feature extraction, and (e) classification methods. In today’s era of Industry 4.0, a large amount of manufacturing data is generated in the industry. So, conventional machine learning techniques are insufficient to analyze real-time conditioning monitoring of the grinding wheels. However, deep learning techniques such as artificial neural network (ANN), convolutional neural network (CNN) have shown prediction accuracy above 99%.</p>

Deep learning-based fault detection in railway wheelsets using time series analysis

Maintenance of Railway rolling stock is usually scheduled based. However, the mechanical parts, especially the wheelset may wear down prematurely due to several factors such as excessive braking and traction forces and environmental conditions. This makes the scheduled maintenance less effective and sometimes it results in derailments. This paper presents a deep learning-based technique to detect wheel conditions so that maintenance can be performed promptly and efficiently. A time series dataset of axle vibrations is generated using a simulation model of the wheelset. The dataset is then used to train and test the deep learning model. Long short-term memory (LSTM) architecture is selected for this application since it is designed to perform better for time series datasets. The results show good performance in terms of training and testing accuracy. The model is tested in different defect scenarios and the mean square error in the prediction of railway wheelset parameters is around 15%.