Study on Soil Throwing Performance and Ditch Depth Stability of Ditching Device in Sandy Orchards in Southern Xinjiang

In order to solve the problems of serious soil reflux and poor stability of ditch depth in the existing ditching organic fertilizer fertilization device in grey desert and loess orchards, rotary tillage theory and software simulation were used to conduct kinematic analysis of soil particles and ditching blade in the ditching process, and meanwhile, modeling and simulation are carried out for sand soil particles by using EDEM software, so as to determine the action mechanism of soil, blade and fairing in ditching process of grey desert and loess. The abstract on this basis, the quadratic orthogonal regression-rotation combination experiment was designed. The soil bin test was carried out by taking the cutter wheel speed, ditching depth and inclination of curved surface as the influencing factors, and the throwing distance and the stability of ditch depth as the test indexes. And it was concluded that the order of the influence of the operating parameters of the ditching device on the soil throwing distance is ditching depth > inclination of curved surface > cutter speed, and the order of the influence on the stability of the ditch depth is ditching depth > cutter speed > Inclination of curved surface. Finally, the optimized operating parameters of the ditching device are as follows: the cutter wheel speed is 119.61 r·min−1, the inclination of curved surface is 30.07°, the ditching depth is 35.52 mm, the soil throwing distance is 57.31, and the stability of ditch depth is 87.43. With these parameters as test objects, 10 groups of single factor tests were carried out to obtain that the soil throwing distance is 58.33, and the stability of ditch depth is 86.51, which were basically consistent with the expected results of the optimization test, and also in line with the relevant agronomic standards.

Learning-based fault diagnosis of air brake system using wheel speed data

Faults in the air brake system used in Heavy Commercial Road Vehicles (HCRVs) would adversely affect the vehicle’s dynamic performance, and hence their prompt detection is critical for vehicle safety. This paper first investigates the effect of air brake system faults through extensive hardware-in-loop experiments. These faults were observed to degrade the braking response, yaw stability, and vehicle braking distance. In many countries, an antilock brake system is mandatory in HCRVs, and wheel speed data are readily available. Inspired by this, the feasibility of using wheel speed data to detect faults is investigated in this study. As an initial step of predictive maintenance, a fault diagnostic scheme based on a supervised learning algorithm, Support Vector Machine (SVM) that uses only wheel speed data has been developed. The SVM algorithm’s efficacy was tested for 1937 test cases that encompassed a wide range of operating conditions. It was found that a Gaussian kernel SVM (G-SVM) provided a good classification accuracy of 96.54%, demonstrating its ability to predict a faulty condition accurately. The standard deviation of G-SVM’s prediction accuracy for five groups of data sets with 100 instances was found to be 1.57%, which shows that the model is more precise to predict the fault/no-fault condition of the air brake system.

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.

Microstructural and mechanical studies of feedstock material in continuous extrusion process

The challenge encountered in continuous forming process is the variation in mechanical strength of product formed with respect to process variables like extrusion wheel speed and diameter of product. In this research article, the micro-structural investigation of the aluminum (AA1100) feedstock material of 9.5-mm diameter has been carried out at various extrusion wheel speeds and diameter of product before and after deformation on commercial continuous extrusion setup TBJ350. The mechanical properties like yield strength as well as percentage elongation have been estimated and optimized using two variables with 3 levels through central composite rotatable design (CCRD) method. The mathematical modeling has been carried out to predict the optimum combination of process parameters for obtaining maximum value of yield strength and percentage elongation. The statistical significance of mathematical model is verified through analysis of variance (ANOVA). The optimum value of yield strength is found to be 70.939 MPa at wheel velocity of 8.63 rpm and product diameter of 9 mm respectively, whereas the maximum percentage elongation recorded is 46.457 at wheel velocity of 7.06 rpm and product diameter of 7.18 mm. The outcome may be useful in obtaining the best parametric combination of wheel speed and extrusion ratio for best strength of the product.

Wheel Speed Effect on Transient Lateral Force and Its Characterization by Ramp-Step Steer Test Method

ABSTRACT The concept “relaxation length” serves as one of several ways to characterize the transient lateral response for a rolling tire. Most test methods developed to identify relaxation length tightly link to Pacejka's single-contact-point linear transient model. Its underlying assumption is that the traveled distance during the transition interval is always a constant regardless of the wheels' linear rolling speed. The current research provides physical data against this strong assumption. The data is acquired through a newly-developed test method named the “ramp-step steer method”. The ramp-step steer method features a nonstop, high rolling speed, and fast-changing slip angle procedure that cannot be fulfilled by the conventional “start-stop-resume” step steer method. Thanks to the high dynamic capability of the equipment in GCAPS Corp., the proposed test method becomes feasible. A novel data postprocessing scheme accompanies the test method as well. The ramp-step steer method is independent of any specific models and replicates the scenario of a rolling tire subjected to a sudden slip angle change from on-vehicle to an indoor environment. The wheel speed effect on the tires' transient lateral response is reflected through a proposed quantity, Ly, which is a more general descriptor and can downscale to relaxation length under specific circumstances. Ly itself does not associate with any model, so the remaining study explains the speed effect through an updated model. The present research aims to provide a better way of characterizing tires' lateral transient behavior and is not an alternative to identify the key parameter “relaxation length” in Pacejka's model. Another contribution of the research is categorizing and separating the hierarchy of various transient tire models.

Robust Tightly Coupled Pose Measurement Based on Multi-Sensor Fusion in Mobile Robot System

Currently, simultaneous localization and mapping (SLAM) is one of the main research topics in the robotics field. Visual-inertia SLAM, which consists of a camera and an inertial measurement unit (IMU), can significantly improve robustness and enable scale weak-visibility, whereas monocular visual SLAM is scale-invisible. For ground mobile robots, the introduction of a wheel speed sensor can solve the scale weak-visibility problem and improve robustness under abnormal conditions. In this paper, a multi-sensor fusion SLAM algorithm using monocular vision, inertia, and wheel speed measurements is proposed. The sensor measurements are combined in a tightly coupled manner, and a nonlinear optimization method is used to maximize the posterior probability to solve the optimal state estimation. Loop detection and back-end optimization are added to help reduce or even eliminate the cumulative error of the estimated poses, thus ensuring global consistency of the trajectory and map. The outstanding contribution of this paper is that the wheel odometer pre-integration algorithm, which combines the chassis speed and IMU angular speed, can avoid the repeated integration caused by linearization point changes during iterative optimization; state initialization based on the wheel odometer and IMU enables a quick and reliable calculation of the initial state values required by the state estimator in both stationary and moving states. Comparative experiments were conducted in room-scale scenes, building scale scenes, and visual loss scenarios. The results showed that the proposed algorithm is highly accurate—2.2 m of cumulative error after moving 812 m (0.28%, loopback optimization disabled)—robust, and has an effective localization capability even in the event of sensor loss, including visual loss. The accuracy and robustness of the proposed method are superior to those of monocular visual inertia SLAM and traditional wheel odometers.

Design and Analysis of an Open Differential

A transmission or gearbox provides speed and torque conversions from a rotating power source to another device using gear ratios. The most common use is in motor vehicles, where the transmission adapts the output of the internal combustion engine to the drive wheels. Such engines need to operate at a relatively high rotational speed, which is inappropriate for starting, stopping, and slower travel. The transmission reduces the higher engine speed to the slower wheel speed, increasing torque in the process. We have designed a differential gearbox and tried to create the frictional contact between two mating gears. And we have performed the structural analysis on gear box by providing the torque to the assembly of crown gear and pinion gear, assembly of inner gears- spider gears and side gears and crown gear with the cage to attach spider gears. We have selected two kinds of alloy steel and have compared the factor of safety and structural analysis of the both.

Bridging GNSS Outages with IMU and Odometry: A Case Study for Agricultural Vehicles

Nowadays, many precision farming applications rely on the use of GNSS-RTK. However, when it comes to autonomous agricultural vehicles, GNSS cannot be used as a stand-alone system for positioning. To ensure high availability and robustness of the positioning solution, GNSS-RTK must be fused with additional sensors. This paper presents a novel sensor fusion algorithm tailored to tracked agricultural vehicles. GNSS-RTK, an IMU and wheel speed sensors are fused in an error-state Kalman filter to estimate position and attitude of the vehicle. An odometry model for tracked vehicles is introduced which is used to propagate the filter state. By using both IMU and wheel speed sensors, specific motion characteristics of tracked vehicles such as slippage can be included in the dynamic model. The presented sensor fusion algorithm is tested at a composting site using a tracked compost turner. The sensor measurements are recorded using the Robot Operating System (ROS). To analyze the achievable accuracies for position and attitude of the vehicle, a precise reference trajectory is measured using two robotic total stations. The resulting trajectory of the error-state filter is then compared to the reference trajectory. To analyze how well the proposed error-state filter is suited to bridge GNSS outages, GNSS outages of 30 s are simulated in post-processing. During these outages, the vehicle’s state is propagated using the wheel speed sensors, IMU, and the dynamic model for tracked vehicles. The results show that after 30 s of GNSS outage, the estimated horizontal position of the vehicle still has a sub-decimetre accuracy.