Wheel Sensor Research Articles

Microcontroller-based track axle counter system design

Abstract Axle counting technology is an extremely important part of many aspects in ensuring the safety of railroad operation. Axle counting equipment is set up on the railroad tracks, and through the operation of sensors, it detects the trains that drive into the train operating area where the axle counting equipment is arranged. A common axle counting system uses a detection device that utilizes a wheel sensor that senses the movement of the wheel cutting through magnetic lines of inductance causing a change in the magnetic field within the zone. This paper introduces a track axle counter system, the system will be a microcontroller as the core, the use of ultrasonic detection in the train interval of the train wheel distance changes, instead of the existing axle counting system using wheel sensors to sense the electromagnetic signals generated by the wheels, to achieve the axle counting and counting function, so as to determine the track occupancy.

The Push Forward in Rehabilitation: Validation of a Machine Learning Method for Detection of Wheelchair Propulsion Type.

Within rehabilitation, there is a great need for a simple method to monitor wheelchair use, especially whether it is active or passive. For this purpose, an existing measurement technique was extended with a method for detecting self- or attendant-pushed wheelchair propulsion. The aim of this study was to validate this new detection method by comparison with manual annotation of wheelchair use. Twenty-four amputation and stroke patients completed a semi-structured course of active and passive wheelchair use. Based on a machine learning approach, a method was developed that detected the type of movement. The machine learning method was trained based on the data of a single-wheel sensor as well as a setup using an additional sensor on the frame. The method showed high accuracy (F1 = 0.886, frame and wheel sensor) even if only a single wheel sensor was used (F1 = 0.827). The developed and validated measurement method is ideally suited to easily determine wheelchair use and the corresponding activity level of patients in rehabilitation.

Deep Learning-Based Approach for Autonomous Vehicle Localization: Application and Experimental Analysis

In a vehicle, wheel speed sensors and inertial measurement units (IMUs) are present onboard, and their raw data can be used for localization estimation. Both wheel sensors and IMUs encounter challenges such as bias and measurement noise, which accumulate as errors over time. Even a slight inaccuracy or minor error can render the localization system unreliable and unusable in a matter of seconds. Traditional algorithms, such as the extended Kalman filter (EKF), have been applied for a long time in non-linear systems. These systems have white noise in both the system and in the estimation model. These approaches require deep knowledge of the non-linear noise characteristics of the sensors. On the other hand, as a subset of artificial intelligence (AI), neural network-based (NN) algorithms do not necessarily have these strict requirements. The current paper proposes an AI-based long short-term memory (LSTM) localization approach and evaluates its performance against the ground truth.

Алгоритмы управления карьерным самосвалом при проскальзывании колес

Mine dump trucks with a carrying capacity of 90 tons or more are equipped with an electromechanical transmission that controls the traction motors of the driving wheels. Due to unfavorable operation conditions of dump trucks, there are strict requirements to the driving safety along unpaved roads of open pits. The results of BELAZ dump trucks tests and authors maintenance experience have shown that the active security system is especially needed and gives essential assistance to a driver. Loss of control of а loaded truck, for example, during sharp braking on a slippery icy road, can lead to а serious accident and additional costs for repairing mining equipment. Thus, the development of traffic safety systems of mine dump trucks with electromechanical transmission is an important task. The methods of automatic control, mathematical modeling and numerical solution of differential equations are used to develop control algorithms for a mine dump truck. To test the proposed solutions, the method of full-scale experiment is used in real-life driving conditions along the dirt and icy roads. Control algorithms that use data of additional front (driven) wheels sensor are proposed. The results of tests of a BELAZ dump truck with a carrying capacity of 90 tons are presented. The results confirm the effectiveness of the developed control system in comparison with the one currently used in stock-produced items. The developed ABS system with speed sensors of the front wheels of a dump truck has proved the braking effectiveness along a slippery road. It makes it possible to improve the controllability of the dump truck significantly under conditions of lack of road adhesion, preventing the dump truck from skidding and wheels blocking. And it makes it possible to reduce mechanical effects on the transmission and anti-slip system sensitivity to different traffic conditions. The adaptive system remains operational if one of the front wheel sensors fails and if compared to the standard system it works equally effectively in various traffic conditions. The implementation of the obtained results will improve traffic safety and reduce the accident rate of expensive mining equipment.

Establishment of a Murine Chronic Anorexia Nervosa Model.

Anorexia nervosa (AN) is associated with hyperactivity, amenorrhea, and brain atrophy. The underlying pathophysiology is mostly unknown, and new targets for therapeutic interventions are needed. This study aimed to systematically establish a murine AN model with the parameter extent of starvation, animal age, and length of starvation for functional studies. The activity-based anorexia (ABA) model combines food restriction with running wheel access. Early adolescent and adolescent mice received 40% of their baseline food intake until a 20% or 25% weight reduction was reached (acute starvation). To mimic chronic starvation, body weight loss was maintained for another two weeks. Running activity was examined using wheel sensors, while amenorrhea was investigated by analysis of vaginal smears. Brain sections were used to analyze cerebral cortex volumes. Acute starvation did not lead to either AN-related symptoms, whereas chronic starvation led to hyperactivity and amenorrhea except in the adolescent cohort with 20% weight reduction. Only ABA mice with 25% weight reduction revealed a cortex volume reduction. The optimal parameters to mirror AN-related symptoms included a 25% weight reduction, early adolescent or adolescent mice, and chronic starvation. The ABA model enables functional analysis of the impact of chronic AN on the underlying hormonal, behavioral, and brain pathophysiology.

Triboelectric nanogenerator sensors for intelligent steering wheel aiming at automated driving

This paper reports a novel intelligent steering wheel developed based on the concept of triboelectricity aiming at automated driving to reduce traffic accidents. A sandwich-type sensor is designed to be integrated into the steering wheel with the aim of identifying driver’s steering intention. The steering wheel of a vehicle is furnished with a triboelectric nanogenerator (TENG)-based sensor for detecting driver intention. The superiority of the TENG-based sensor is demonstrated by comparing it to other available sensors within a vehicle. By employing different machine learning techniques, we develop classification models based on driving data from multiple drivers. We show that the faster reaction time of the TENG-based sensor can aid in emergency obstacle avoidance when compared to the regular steering wheel sensor through the use of model-predictive control. The fusion of data generated by the proposed TENG-based sensor and advanced control model represents a crucial step towards the development of an intelligent steering wheel for automated systems. This will improve the human–machine interaction for vehicle control, ultimately resulting in more efficient and effective control of the vehicle.

In-Cylinder Pressure Estimation from Rotational Speed Measurements via Extended Kalman Filter.

Real-time estimation of the in-cylinder pressure of combustion engines is crucial to detect failures and improve the performance of the engine control system. A new estimation scheme is proposed based on the Extended Kalman Filter, which exploits measurements of the engine rotational speed provided by a standard phonic wheel sensor. The main novelty lies in a parameterization of the combustion pressure, which is generated by averaging experimental data collected in different operating points. The proposed approach is validated on real data from a turbocharged compression ignition engine, including both nominal and off-nominal working conditions. The experimental results show that the proposed technique accurately reconstructs the pressure profile, featuring a fit performance index exceeding 90% most of the time. Moreover, it can track changes in the engine operating conditions as well as detect the presence of cylinder-to-cylinder variations.

Food Restriction in Mice Induces Food-Anticipatory Activity and Circadian-Rhythm-Related Activity Changes.

Anorexia nervosa (AN) is characterized by emaciation, hyperactivity, and amenorrhea. To what extent AN-related symptoms are due to food restriction or neuronal dysfunction is currently unknown. Thus, we investigated the relevance of food restriction on AN-related symptoms. Disrupted circadian rhythms are hypothesized to contribute to the pathophysiology of AN. Starvation was induced by restricting food access in early adolescent or adolescent mice to 40% of their baseline food intake until a 20% weight reduction was reached (acute starvation). To mimic chronic starvation, the reduced weight was maintained for a further 2 weeks. Locomotor activity was analyzed using running wheel sensors. The circadian-rhythm-related activity was measured using the tracking system Goblotrop. Amenorrhea was determined by histological examination of vaginal smears. All cohorts showed an increase in locomotor activity up to 4 h before food presentation (food-anticipatory activity, FAA). While amenorrhea was present in all groups except in early adolescent acutely starved mice, hyperactivity was exclusively found in chronically starved groups. Adolescent chronically starved mice showed a decrease in circadian-rhythm-related activity at night. Chronic starvation most closely mimics AN-related behavioral changes. It appears that the FAA is a direct consequence of starvation. The circadian activity changes might underlie the pathophysiology of AN.

Predicting Driver Stress Levels with a Sensor-Equipped Steering Wheel and a Quality-Aware Heart Rate Measurement Algorithm.

Unobtrusive monitoring of driver mental states has been regarded as an important element in improving the safety of existing transportation systems. While many solutions exist relying on camera-based systems for e.g., drowsiness detection, these can be sensitive to varying lighting conditions and to driver facial accessories, such as eye/sunglasses. In this work, we evaluate the use of physiological signals derived from sensors embedded directly into the steering wheel. In particular, we are interested in monitoring driver stress levels. To achieve this goal, we first propose a modulation spectral signal representation to reliably extract electrocardiogram (ECG) signals from the steering wheel sensors, thus allowing for heart rate and heart rate variability features to be computed. When input to a simple logistic regression classifier, we show that up to 72% accuracy can be achieved when discriminating between stressful and non-stressful driving conditions. In particular, the proposed modulation spectral signal representation allows for direct quality assessment of the obtained heart rate information, thus can provide additional intelligence to autonomous driver monitoring systems.

UNIFIED VERIFICATION METHOD OF ELECTROMAGNETIC COMPATIBILITY BETWEEN ROLLING STOCK AND TRAIN DETECTION SYSTEMS

Purpose. Axle counters are more and more often applied in train detection systems. The wheel sensor is a main part of each axle counter system. In parallel, more and more complex railway vehicles, especially traction ones, are a potential source of interferences influencing the operation of these train detection systems. It is the reason to verify the electromagnetic compatibility (EMC) between the signalling equipment, particularly train detection systems and new vehicles in the process of obtaining the permission for their exploitation. The measurement of interfering magnetic fields generated by vehicles is one of tests to be carried out. Methodology. For the simplification and unification purpose of the applied interference test methods the EN 50238 standard and TS 50238-3 technical specification were developed. The specification defines unified testing procedures. However, it is necessary to verify if it may replace different testing methods used in particular European states. It is the goal of the European research project financed from the TEN-T network resources. Findings and originality. This project is part of the larger project of facilitation and speeding up the ERTMS system deployment. One of nine measurement campaigns planned in the frame of this project was conducted in Poland by Railway Research Institute.

Modeling the Impact on Wheel Sensor Readouts by Eddy Current Brakes Installed in High-Speed Trains

This paper presents a model to anticipate the impact of Eddy Current Brakes (ECBs) installed in high-speed trains on the readouts of rail-side wheel sensors. The purpose is to anticipate false positive readouts of train wheels when traversing, one of the main obstacles for full ECB deployment. The ECB type EWB 154 from Knorr-Bremse and Wheel Sensor types RSR180 and RSR123 from Frauscher Sensor Technology are represented in a comprehensive model, integrating LTSpice and CST Microwave Studio. The wheel sensor predicted readout error is 4% compared to measurements when DC current is not applied to the ECB (passive case). It is demonstrated that the RSR180 is not compatible with ECBs, whereas the RSR123 is. The impact of active (DC current fed) brakes is analyzed when performing running tests with a high-speed ICE 3 train equipped with ECBs. The model is adjusted to study the saturation of the rail and ECB pole cores. The extra damping of the wheel sensor fingerprint is modeled by an extra 6% drop that may well be applicable to passive tests in a laboratory setting to shift to active tests without actually performing them. In this way, cost and time would be saved. Based on the model outcomes, a test bench is recommended for laboratory tests to emulate active behavior.

Refined detection technique for bridge frequencies using rocking motion of single-axle moving vehicle

A self-designed single-axle vehicle is used to detect the bridge frequencies from its rocking motion, caused by wheels moving over an uneven surface. To include both the rocking and vertical motions, the single-axle vehicle is modeled as a two degree-of-freedom (DOF) system, unlike the previous single-DOF model. The rocking frequency, an inborn property of the vehicle, may pollute the frequency extraction of bridges in the test. The sensors mounted near the wheels (called the wheel sensors) for catching the rocking are of higher fidelity than the central sensor for the vertical motion. Both the vertical and rocking frequencies of the vehicle will be eliminated in the newly derived responses of the wheel-bridge contact points (CPs). In the flat road test, the vertical frequency of the test vehicle is identified from the average of the two wheel sensors responses, and rocking frequency from the angular response calculated. From the field test conducted on a two-span girder bridge, it is concluded that: (1) the new formulas for the CPs are reliable for scanning the bridge frequencies, while removing vehicle’s frequencies; (2) the present model enables us to look into the mechanism of the wheel-bridge contact for uneven surface roughness; and (3) temporary stop of the test vehicle on the bridge can help sharpen the bridge frequencies identified.

Vital Sign Monitoring in Car Seats Based on Electrocardiography, Ballistocardiography and Seismocardiography: A Review.

This paper focuses on a thorough summary of vital function measuring methods in vehicles. The focus of this paper is to summarize and compare already existing methods integrated into car seats with the implementation of inter alia capacitive electrocardiogram (cECG), mechanical motion analysis Ballistocardiography (BCG) and Seismocardiography (SCG). In addition, a comprehensive overview of other methods of vital sign monitoring, such as camera-based systems or steering wheel sensors, is also presented in this article. Furthermore, this work contains a very thorough background study on advanced signal processing methods and their potential application for the purpose of vital sign monitoring in cars, which is prone to various disturbances and artifacts occurrence that have to be eliminated.

Intelligent wearable heart rate sensor implementation for in-vehicle infotainment and assistance

Based on today’s estimation most of the older drivers are likely to get into an accident or collision based on their varying health conditions while driving. Nowadays, middle-age and younger drivers are also facing similar issues. Studies show that drivers who had crashes that have been precipitated by medical emergencies are not related to vehicle design or roadway integrity as indicated by the type of crashes and manner of collisions. Six in ten Americans live with at least one chronic disease like heart disease, stroke and diabetes. In this paper we compare and measure heart rate and heart variability data for arising heart disease and stroke or any emergency condition using electrocardiogram (ECG) and photoplethysmography (PPG) sensors. While driving most of these conditions arise for drivers who are precipitated by chronic conditions, especially the stroke like ischemic stroke and intracerebral hemorrhage stroke. Automakers are trying with methods that involve measuring data via seats sensors and steering wheel sensors, but for measuring sensor data also requires right placement of sensor in the body. In this paper we are implementing a high-resolution proximity sensor with programmable LED current at the ear and electrical change sensor along with electrodes at the chest to measure heart rate and heart rate variability data while driving and compare those results to integrate the data to display in vehicle infotainment to bring in assistance, this data is also stored in the cloud for doctors to monitor the varying conditions while driving.

Measuring the Normal Stress Distribution Acting on a Locked-Wheel of Push-Pull Locomotion Rovers via a Wheel Sensor System.

The resistance force generated when the locked-wheel acts on the soil is critical for deciding the traveling performance of push–pull locomotion. The resistance force depends on the tangential force of the sliding soil wedge beneath the wheel, and the tangential force depends on the forces of the soil and the wheel perpendicular to the tangential direction. Hence, the normal stress distribution of the locked-wheel can affect the resistance force. Previous studies indicated different insights that describe either a uniform or non-uniform shape of the normal stress distribution. The distribution of the locked-wheel still needs to be examined experimentally. This study measured the normal stress distribution using the wheel sensor system, and the variation of the contact area and slip surface beneath the wheel were also observed in PIV analysis. Those results showed that the normal stress distribution was non-uniform along the wheel contact area, and the change of the distribution was confirmed with the change of the contact area and slip surface. Then, the resistance force calculated by a preliminary model based on the measured data was compared with the total resistance force of the wheel measured by a separate sensor. This comparison provided a theoretical consideration for the measured data.

Driver trust & mode confusion in an on-road study of level-2 automated vehicle technology

Contextual investigations of automated vehicle technology have so far been rare, however they are crucial to uncover the challenges that exist around its acceptance and safe use. Twenty-one drivers used a partially automated vehicle on a public highway in unaltered traffic conditions, while their behaviour was observed. Subjective measures of technology acceptance were taken before and after the drive, as well as post-drive ratings of engagement, workload, and perceived safety for manual versus automated driving. Post-drive interviews were conducted to better understand participants’ attitudes and their behaviours that were observed. Technology acceptance was higher following the drive as indicated by increased trust and perceived safety. Engagement was lower for automated driving, while no differences were reported for workload or safety between the driving modes. Perceptions of level-2 technology were highly positive, though complacent attitudes and behaviours were apparent. There was dissatisfaction with the hands-on steering wheel sensor, and observed behaviours indicate that it fails to adequately measure driver engagement. Many unintentional automation disengagements occurred due to problems interacting with the physical controls. Concerningly, this sometimes led to situations where the participant incorrectly thought they were still in automated mode, but were in fact responsible for primary driving tasks. Insufficient cues to inform the driver of mode changes and the current automation status contributed to the mode confusion observed. These findings can help inform the design of automated vehicle technology as well as policies and regulations, to ensure the inherent risks in partial driving automation can be managed.

Train distance and speed estimation using multi sensor data fusion

The accurate distance and speed estimates of train and individual coaches are necessary for the safe operation of the high-speed train system. Often, the train system does not rely on a single sensor for its distance and speed measurements as the sensors are susceptible to diverse operating conditions such as snow, rain, fog, tunnel, hilly region, slip, slide, etc. Hence, the information from a combination of sensors which can complement each other under certain operating conditions is required for the correct estimation. For distance measurements, Global Navigation Satellite System (GNSS) and balise are generally used. For speed sensing, the combination of wheel sensor, radar and GNSS are chosen. The diversity of sensors in terms of sampling rate and noise characteristics, etc. greatly affect the overall estimation accuracy and reliability if the measurements are used directly. Hence, this work presents a probabilistic weighted fusion algorithm which is based on the nonlinear longitudinal train dynamic model. The fusion algorithm combines the state estimates from distributed and sensor-specific extended Kalman filters. The effectiveness of the proposed fusion algorithm is demonstrated on the simulated sensor measurements along with a wide range of noises, spurious measurements, train operating conditions and track environmental disturbances.

Contact-free respiratory monitoring using bed wheel sensors: a valid respiratory monitoring technique with significant potential impact on public health.

Contact-free respiratory monitoring using bed wheel sensors: a valid respiratory monitoring technique with significant potential impact on public health.

Precursors of Instability for a Vehicle Traveling in Curve

Nowadays, the automotive field takes care of human safety in the everyday driving. In this sense, the vehicle dynamics control systems and ADAS (Advanced Driver Assistance System) play a crucial role for both active and passive safety. In terms of handling, the critical issues for the everyday driver are during braking or steering maneuvers: the main idea of this paper is to investigate the possibility to recognize some critical behaviors leading to vehicle instability, using only the wheel sensors signals, already available as mandatory in actual passenger car (in particular for ABS). In order to do this, a seven degrees of freedom model of a vehicle that runs in a curve with constant longitudinal speed is formulated. With no particular reference to the power unit (IC motor or electrical motors), three different traction schemes configurations are considered. The numerical solutions of the model are analyzed to evaluate their stability, the associated modal shapes and the modal participation factors. A systematic analysis allows recognizing some vehicle behaviors that may constitute instability precursors useful to develop a suitable control logic. Results provided by both a standard vehicle dynamics simulation software and a on-road test are presented and discussed to verify the actual feasibility of the proposed precursors employment.

Particle Filter and Its Application in the Integrated Train Speed Measurement

Particle filter (PF) can solve the problem of state estimation under strong non-linear non-Gaussian noise condition with respect to traditional Kalman filter (KF) and those improved KFs such as extended KF (EKF) and unscented KF (UKF). However, problems such as particle depletion and particle degradation affect the performance of PF. Optimizing the particle set to high likelihood region with intelligent optimization algorithm results in a more reasonable distribution of the sampling particles and more accurate state estimation. In this paper, a novel bird swarm algorithm based PF (BSAPF) is presented. Firstly, different behavior models are established by emulating the predation, flight, vigilance and follower behavior of the birds. Then, the observation information is introduced into the optimization process of the proposal distribution with the design of fitness function. In order to prevent particles from getting premature (being stuck into local optimum) and increase the diversity of particles, Levy flight is designed to increase the randomness of particle’s movement. Finally, the proposed algorithm is applied to estimate the speed of the train under the condition that the measurement noise of the wheel sensor is non-Gaussian distribution. Simulation study and experimental results both show that BSAPF is more accurate and has more effective particle number as compared with PF and UKF, demonstrating the promising performance of the method.