Wheel Speed Signal Research Articles

Indirect tire pressure monitoring method based on the fusion of time and frequency domain analysis

Monitoring tire pressure conditions and issuing timely warnings of tire deflation are important to ensure driving safety and avoid unnecessary fuel consumption. Although installing pressure sensors is a direct and accurate monitoring method, it may raise system cost and complexity. The indirect monitoring method with no reliance on pressure sensors is attractive for economical cars. This paper proposes a fusion-based indirect tire pressure monitoring method using existing on-board wheel speed sensors. The screening basis of reasonable wheel speed signals was determined by data mining of typical driving scenarios. Three typical signal distortions were effectively corrected to ensure the signal quality. Tire deflation analysis methods based on time and frequency domain signals were designed and fused using fuzzy logic. Validation results of the proposed two analysis methods and their fusion strategy showed that the proposed fusion strategy can achieve more comprehensive tire deflation monitoring with fast response and high accuracy.

Design of Indirect Tire Pressure Monitoring System Based on Wheel Speed Signal

Keeping the pressure of the automobile tire in the normal working range is an important guarantee for the safety of the automobile. When the tire leaks, the driver should be reminded to check or replace the tire before the potential danger of blowing out. Aiming at the safety problem of commercial vehicle driving, a set of tire pressure monitoring alarm systems is designed by combining the radius method and frequency method. The magneto-electric wheel speed sensor is used to collect and process the tire speed, and then through the data processing, the full range of monitoring each tire’s possible air leakage hazards, such as emergency can be quickly warned, thus providing a reference for the driver’s driving. The experimental results show that the system can effectively improve the accuracy and stability of tire pressure monitoring.

A low-cost in-tire-pressure monitoring SoC using integer/floating-point type convolutional neural network inference engine

Tire pressure state is crucial for driving performance and fuel efficiency and is the welfare of the driver. Tire inflation pressure greatly affects fuel consumption, driving dynamics and the tire lifespan. Hence, the state of tire pressure must be continuously sensed for comfort riding, optimal vehicle handling, and safety, and it is necessary for all types of transport. The existing detection methods are high cost and without any redundancy, if the sensor gets fails, which retard its wide application. The support vector machine (SVM) learning method is introduced for tire pressure loss detection. But, this method is not so accurate in detecting the normal and pressure loss judgement of tires. This research presents a novel approach for a tire pressure monitoring system (TPMS) to reduce the cost of sensor-based frames. The proposed model comes under three stages: pre-processing, feature extraction and tier pressure detection. Initially, an adaptive and unbiased Kalman filter (KF) with a recursive least square (RLS) algorithm (AUK-RLS) approach is introduced to eliminate manufacturing errors in speed gear. The second stage is the extraction of features. Statistical, frequency domain, and fitted frequency domain characteristics features are extracted from the wheel speed signal in this stage. It is fed to the deep learning (DL) model to perform the judgement of tier state. While using floating point model for the operation and storage process, the hardware cost of the DL model tends to be high. To tackle this issue, the convolutional neural network (CNN) hardware design will be optimized by combining both Integer/Floating-Point type inference engines. The design modules of the CNN interference engine are insisted of buffers, on-chip/off-chip memories, controllers, fine state machine (FSM), processing engine (PE) and routers. The PE module needs several adders and multiplier units, and this performs the convolution operation. Therefore, to diminish the area, delay and power consumption, the modified adder and multiplier design named Vedic multiplier based Carry select adder with simplified combinational logics (CSLA-SCL) is emphasized. The proposed system is implemented using Verilog code synthesis in the Xilinx ISE 14.5 simulator platform. The performance measure of pressure loss detection in terms of accuracy (99.25%) and errors are examined. Also, the performance measure of the detection model with the proposed adder and multiplier design is examined in terms of area utilization, power (145 mW), frequency (756 MHz) and delay (7.12 ns).

Design and Experiments of Autonomous Path Tracking Based on Dead Reckoning

Path tracking is an important component of autonomous driving and most current path tracking research is based on different positioning sensors, such as GPS, cameras, and LIDAR. However, in certain extreme cases (e.g., in tunnels or indoor parking lots), if these sensors are unavailable, achieving accurate path tracking remains a problem that is worthy of study. This paper addresses this problem by designing a dead reckoning method that is solely reliant on wheel speed for localization. Specifically, a differential drive model is first used for estimating the current relative vehicle position in real time by rear wheel speed, and the deviation between the current path and the reference path is then calculated using the pure pursuit algorithm as a means of obtaining the target steering wheel angle and vehicle speed. The steering wheel and vehicle speed signals are then output by two PID controllers in order to control the vehicle, and the automatic driving path tracking is ultimately realized. Through exhaustive tests and experiments, the stop position error and tracking process error are compared under different conditions, and the effects of vehicle speed, look-ahead distance, starting position angle, and driving mode on tracking accuracy are analyzed. The experimental results show the average error of the end position to be 0.26 m, 0.383 m, and 0.505 m when using BMW-i3 to drive one lap automatically at speeds of 5 km/h, 10 km/h, and 15 km/h in a test area with a perimeter of approximately 200 m.

Wheel Bearing Fault Detection for Automobiles using Wheel Speed Sensor

Wheel bearing fault diagnosis and monitoring for automobiles are getting more attention as the market shifts towards electric vehicles (EV) and autonomous vehicles (AV). Electric drivetrains are expected to have longer lifespan than the reliable life of low-cost wheel bearing designs. The probability of having a failed wheel bearing in an EV thus increases. In an AV setting, most of the rides are taken by people who are unfamiliar to the vehicle, removing the human as a viable sensor to detect wheel bearing defects. Fleet managers will be required to pay a high price to inspect potential defects. The implementation of a wheel bearing fault detection system can help reduce these costs.
 In our previous work, a method for injecting wheel bearing brinelling failure was developed. In this paper, we propose a wheel bearing brinelling fault detection algorithm using wheel speed sensor. The proposed algorithm detects wheel bearing faults by identifying peaks in the wheel speed spectrum at bearing critical frequencies. To implement this approach, we leverage phase domain transform technique to normalize against effect of varying speed. A wavelet filtering technique is employed to enhance the bearing fault signatures prior transforming the wheel speed signal into frequency domain. Post-processing techniques are developed to smooth the high-variance wheel speed spectra and improve the detectability of the wheel bearing faults. A regression model is then developed to calculate the wheel bearing health indicators. The results show that the proposed algorithm achieves high performance in detecting wheel bearing faults.

An in-tire-pressure monitoring SoC using FBAR resonator-based ZigBee transceiver and deep learning models

In automobiles, tires play an important role in ensuring safe driving. Hence tire pressure monitoring system (TPMS) has become the most important safety system in automobile vehicles. In this work, a new indirect TMPS is proposed that measures the pressure of all the tyres indirectly using the available sensors already mounted for typical vehicle dynamics control purposes. The available sensors in vehicles usually contain a power supply module, a microcontroller, and transceiver and resonator circuitry. The available wheel speed sensor is modified by reducing the wireless receiver system in communication protocols to minimize the power consumption. The transceiver structure is modified using a suitable local oscillator (LO) frequency plan based on a temperature-compensated thin film bulk acoustic wave resonator (FBAR). In addition, the proposed TPMS SoC uses a deep-stacked autoencoder to detect the adherence conditions of the tires and a pressure loss based on wheel speed. This module will initially pre-process the speed signals to remove the speed error caused by manufacturing error. After pre-processing, different features such as statistical, frequency-domain and the fitted frequency-domain characteristics are extracted from the wheel speed signal and fed to the learning model for the judgment of the tire state. The proposed system was designed in Verilog synthesis and layout were made in cadence tool. For ASIC implementation, we use TSMC 65 nm CMOS Library for LNA/hybrid mixer design. The performance measures of the pressure detection model in terms of accuracy and errors are evaluated. Also, the performance metrics of the transceiver architecture in terms of gain, NF, IIP3, S11 and FOM are evaluated. The experimental evaluation describes that the model procures a maximum gain of 58 dB, NF of 2.15 dB and IIP3 of 6.6 dBm respectively. The die area is 0.45 mm2with 0.8 supply voltage and 0.83 mW power. In addition to this, the relationship between peak amplitude and speed vehicle regarding time and frequency domain features are evaluated.

Design of Teaching Platform for ABS Wheel Speed Sensor

In order to ensure driving safety, ABS brake systems are currently installed on cars. When the ABS is working, it needs to detect the wheel speed signal through the wheel speed sensor to realize continuous braking and relaxation to prevent the wheels from locking up. In daily teaching, it is difficult for students to observe the working process of the wheel speed sensor, and then understand the working condition of ABS in the braking process. So according to the working principle of electromagnetic induction wheel speed sensor, a simple experimental bench was designed and manufactured. This bench can clearly display the changes of the voltage generated by the sensors at different speeds during the rotation of the wheel, and then simulate the changes in the signal when the ABS is working.

Health Indicators to Estimate Health of Magnetic Wheel Encoder

Performance of several vehicle safety features, such as anti-lock brake system (ABS), traction control system (TCS) and electronic stability control (ESC) rely on the quality of wheel speed signal. One potential failure mode for the wheel speed encoders is gradual deposition of foreign paramagnetic debris on the surface of the encoder. This results in reduced strength of the magnetic field, and impacts the quality of the wheel speed signal. Noisy wheel speed signal jeopardizes performance of safety critical features, affecting safety, stability, drivability, and negatively impacts customer’s experience.
 In this paper, several faulty encoders with various levels of faults have been used in data collection in a test bench. A prognostics methodology is proposed to evaluate the magnetic wheel encoder’s health. This method leverages time domain and frequency domain-based health indicators to monitor the deterioration in wheel encoder. Time domain-based health indicators include VDA (Verband der Automobilindustrie) signals that are generated by advanced wheel speed sensors, and an enveloping filter of the wheel speed signal’s noise. Frequency domain-based health indicator include root mean square amplitude of average order spectrum of wheel speed noise. The performance of individual/combination of these health indicators are compared to assess the separation between healthy encoder and degraded encoders. Results indicate that it is possible to monitor the degradation process due to magnetic debris accumulation, using the proposed method.

Wheel-slip estimation for advanced braking controllers in aircraft: Model based vs. black-box approaches

As of today, most aircraft are endowed with Anti-lock Braking Systems that are active during landings and rejected take-off manoeuvres, ensuring the maximum exploitation of road-friction capability. Due to strict certification issues, the braking controller must function using only signals that are local to the landing gear, which is the aircraft sub-system hosting the braking actuators. In the most common scenarios, the only available signals are the wheel speed and the braking pressure. This limited set of information prevents the use of advanced braking control architectures which are now mainstream in the automotive sector, i.e., those based on the regulation of the wheel slip, so that typical aircraft Anti-lock Braking Systems are usually based on the regulation of the wheel deceleration. This paper investigates how a reliable wheel slip estimation can be achieved using wheel speed and braking pressure signals, analysing different wheel slip observers options. In particular, a model-based approach that uses a sliding-mode observer is proposed, together with a black-box approach based on Nonlinear Auto-Regressive models with eXogenous input, with a neural network implementation. Both approaches are tested and compared on experimental data, proving that the obtained estimation performance are adequate for use in closed-loop braking systems.

Robust estimation method of tire torsional resonance frequency to detect decrease in tire inflation pressure

Indirect tire pressure monitoring system monitors inflation pressures in pneumatic tires using wheel speed sensor (WSS) signals acquired from anti-lock braking system. In order to monitor the tire pressures indirectly, frequency analysis and wheel radius analysis are operating in parallel. This paper focuses on the frequency analysis. The resonance frequency of tire torsional vibration decreases as the tire pressure decreases. Therefore, the resonance frequency needs to be estimated to detect tire pressure decrease. This paper describes the resonance frequency estimation method with the WSS. Firstly, toothed wheel error of the WSS is calculated from the wheel speed signals and the wheel speed is corrected with the toothed wheel error. A notch filter is designed based on the engine rotational speed to eliminate the engine noise. The resonance frequency is estimated by model parameter estimation based on auto regressive model. Finally, compensation of the estimated frequency, detection of snow chain and monitoring of severe driving environments have been proposed to prevent false alarms at normal tire pressures. Vehicle tests have been conducted to evaluate the proposed estimation method of the resonance frequency. It has been shown that the proposed method provides satisfactory performance for warning indication of tire pressure loss from the test results.

Experimental study on braking and stability performance during low speed braking with ABS under critical road conditions

During braking with ABS (Anti-Lock Brake System), low braking initial speed causes to build-up and reduce brake pressure in longer time period due to extending application time of brake pressure and response time of solenoid valve. Also, the decision about speed and acceleration variations of wheel becomes very difficult due to weak speed signals arising from low speed. This results in lower braking torque than needed on the wheels. Therefore, effects of weak wheel speed signal become more pronounced in critical road conditions due to sudden changes in wheel speed. In this study, the effects of these problems occurring in low speed were investigated on both control and braking performance of ABS under critical road conditions. For this, ABS tests were carried out for low and high speeds on four different road types, which include slippery, wet and µ-jump tests (from wet to slippery and from slippery to wet). In this way, the results were compared to both vehicle speed and road type. The test results show that the low speed worsens braking stability, control and braking performance of ABS, regardless of road conditions. For this reason, it is proposed that ABS control algorithm must be divided in two parts as low and high speed control.

Research on Zero-speed Detector Based on INS / Wheel speed and Vehicle gear

The zero-speed detector is required for the correction of the positioning error and the initial static alignment of the autonomous vehicle. The zero-speed detector that only depends on the INS (Inertial Navigation System) has zero-speed misjudgment. The performance of the detector after the fusion of wheel speed has been improved, but the wheel speed is still not integrated in scenarios such as long periods of stationary, emergency stop and reverse. Because the wheel speed signal only outputs the vehicle speed when the vehicle reaches a certain speed, there is a long initial alignment time, and the wheel speed is not fused in the complex driving process. This paper proposes a Vehicle gear information added to the INS / wheel speed fusion zero speed detector to achieve a 10s increase in the initial alignment time, the effect of wheel speed fusion has been improved, and thus the zero speed The accuracy of detection.

Investigation on Identifying Road Anomalies using In-Vehicle Sensors for Cooperative Applications and Road Asset Management

One of the main challenges for road authorities is to maintain the quality of the road infrastructure. Road anomalies can have a significant impact on traffic flow, the condition of vehicles, and the comfort of occupants of vehicles. Strategies such as pavement management systems use pavement evaluation vehicles that are equipped with state-of-the-art devices to assist road authorities in identifying and repairing these anomalies. The quantity of data available is limited, however, by the limited availability and, therefore, coverage of these vehicles. To address this problem, several investigations have been conducted on the use of smartphones or equipping vehicles with additional sensors to identify the presence of road anomalies. This paper aims to add to this arsenal by using sensors already available in production vehicles to identify road anomalies. If production vehicles could be used to identify road anomalies, then road authorities would be equipped with an additional fleet of mobile sensors (vehicles traveling on a particular road) to receive initial insights into the presence of anomalies. This information could then be used to assist road authorities to deploy their staff and equipment more precisely at these locations, such that appropriate equipment reaches the right place at the right time. In this paper, an algorithm that uses lateral acceleration and individual wheel speed signals, which are commonly available vehicular variables, was developed to detect potholes using machine learning techniques. The results of the algorithm were validated with real life test scenarios.

The influence of bicycle fork bending on brake control

Bicycle forks are prone to bending, introducing a flexible structure between front wheel and vehicle body. This has substantial influence on brake control, not only by altering the system dynamics but also causing serious estimation problems. These consist in considerable measurement errors in the incremental wheel speed and longitudinal tyre velocity signals, both affecting the wheel slip estimation. This paper provides an in-depth analysis of this problem that represents a profound difference to classical vehicle dynamics. For this purpose, measurements are recorded in test manoeuvres using an extensive set of measurement variables. Based on this data, the complex nonlinear couplings of longitudinal and vertical dynamics are included in a full-vehicle multibody model. The measurement errors as well as compensation methods are evaluated using both simulation and experimental data. In order to examine the influence on closed-loop brake control a linear control-oriented model is derived. Finally numerical full-vehicle simulations show the influence of fork bending on discrete anti-lock brake control systems (ABS), distinguishing the sheer dynamic phenomenon and the related estimation problem.

타이어 저압감지를 위한 타이어 공진 주파수 추정

Indirect tire pressure monitoring system(i-TPMS) monitors the inflation pressures in pneumatic tires by using wheel speed signals acquired from the anti-lock braking system(ABS). Frequency analysis and wheel radius analysis are performed at the same time in order to monitor the tire pressures indirectly. The resonance frequency of the tire torsional vibration decreases as the tire pressure decreases. It is necessary to estimate the resonance frequency in order to detect a reduction in tire pressure. This paper discusses the method of estimating the resonance frequency with the rotational wheel speed signal. First, the toothed wheel error of the wheel speed sensor and the wheel speed corrected with the toothed wheel error are calculated from the wheel speed signals. The engine noise caused by the explosion in the four-stroke of engine is eliminated by the notch filter, which is designed by using the engine rotational speed. Finally, the resonance frequency is evaluated via model parameter estimation based on the autoregressive(AR) model. Vehicle tests have been conducted in order to evaluate the proposed estimation method of resonance frequency. The findings of the vehicle test have shown that the proposed method provides satisfactory performance for the warning indication of tire pressure loss.

A high efficiency aircraft anti-skid brake control with runway identification

Anti-skid brake system is an important aircraft subsystem to ensure safe take-off and landing of aircraft. Accurate runway circumstances identification is the key point to achieve the high efficiency of the anti-skid brake control. Inaccurate identification may cause frequent skidding and longer brake distance, even cause the extremely dangerous wheel lock phenomenon. Moreover, many nonlinear factors such as the variation of the aerodynamic drag can seriously affect the brake efficiency and the aircraft safety. In this paper, we propose an anti-skid brake control algorithm to identify the maximum friction force by analyzing the runway/tire friction characteristics that can only based on the aircraft wheel speed signal. The aerodynamic disturbances are observed and compensated by radical basis function (RBF) neural network, and the brake disc friction coefficient variation is also estimated based on the observation of energy consumption. Software simulations and the inertial test bench experiments are carried out. The result reveals that this method can accurately identify the runway circumstances and greatly improve the braking efficiency.

Research on the compensation method of Indirect Tire Pressure Monitoring under Sinusoidal Driving Condition

Indirect tire pressure monitoring method (ITPMS) is based on the wheel speed signal differences indirectly infer that the change of the tire pressure. In the steering mode, there is a large yaw rate, and there is a big deviation between the four wheels’ speed. The traditional indirect tire pressure monitoring method based on wheel speed comparison method is invalid. That the automobiles which is matched electronic stability control system estimate the four-wheel speed according to the yaw rate and steering wheel angle become an important method to improve the accuracy of Indirect tire pressure monitoring. Four-wheel vehicle kinematics model for turning condition is established to compensate for the wheel speed in the steering mode, avoid the misjudgment of indirect tire pressure monitoring system. The seven degree of freedom vehicle model and the four wheel speed model are established firstly, and analyse the four wheel speed change of the vehicle under the condition of a certain corner input in the simulation. Then conduct a test on the vehicle road, calculated using the four wheel speed distribution module model of vehicle wheel speed on the theoretical value of the actual value of the wheel speed compensation, so it can monitor the tire pressure in turning vehicle. When the error of the wheel speed is beyond the threshold, the wheel can be judged to be in the state of lack of gas.

Research on preprocessing method of tractor wheel speed signal based on wavelet transform

As one of the most important parameters in the measurement of tractor slip ratio, wheel speed signal must ensure its accuracy in order to accurately measure the tractor slip ratio. Noises make tractor wheel speed signal a significant fluctuation, which may cause control system failure. Fault points elimination method suitable for tractor wheel speed signal was determined based on the characteristics of tractor wheel speed signal during working process. Meanwhile, soft-hard compromise threshold function wavelet denoising method is designed according to the characteristics of tractor wheel speed signal and trials. We use Carsim simulation software to get actual tractor wheel speed signal, and add white noise which SNR (signal to noise ratio) is 60 to the original signal. From the results of several wavelet denoising methods we can conclude that the soft-hard compromise threshold function wavelet denoising method is better than any other ordinary wavelet denoising methods. The SNR of denoised signal is 56.440 and the MSE (mean square error) is 0.0042. The wavelet transform denoising method is feasible to remove noise from tractor driving wheel speed signal.

Cloud-based shaft torque estimation for electric vehicle equipped with integrated motor-transmission system

In order to improve oscillation damping control performance as well as gear shift quality of electric vehicle equipped with integrated motor-transmission system, a cloud-based shaft torque estimation scheme is proposed in this paper by using measurable motor and wheel speed signals transmitted by wireless network. It can help reduce computational burden of onboard controllers and also relief network bandwidth requirement of individual vehicle. Considering possible delays during signal wireless transmission, delay-dependent full-order observer design is proposed to estimate the shaft torque in cloud server. With these random delays modeled by using homogenous Markov chain, robust H∞ performance is adopted to minimize the effect of wireless network-induced delays, signal measurement noise as well as system modeling uncertainties on shaft torque estimation error. Observer parameters are derived by solving linear matrix inequalities, and simulation results using acceleration test and tip-in, tip-out test demonstrate the effectiveness of proposed shaft torque observer design.

Real-time pedaling rate estimation via wheel speed filtering

In this paper we propose a strategy for cycling cadence estimation from bicycle wheel speed, which does not require any physical cadence sensor. Specifically, the pedaling rate is obtained from the estimation of the employed gear ratio through a proper Kalman filtering of the processed wheel speed signal. The analysis is supported by experimental tests carried out with an instrumented bicycle.