Study on the Control of Torque Distribution of 4WD Corn Harvester Operation Drive

In response to the poor adaptability of existing harvesters to complex operating conditions in the field, this study took a three-row four-wheel-drive (4WD) corn harvester as the research object, designed a traveling transmission system layout, proposed a control strategy of driving torque distribution, simulated, and analyzed each of the four states of harvester drive wheels slippage. The results showed that under the driving wheels slipping condition, after applying torque control, the adjustment time was 43.3% shorter than that without control in the case of single wheel slipping, 11.1% shorter than that without control in the case of two wheels slipping on the same axle, 41.4% shorter than that without control in the case of two wheels slipping on different axles, and 36.6% shorter than that without control in the case of three driving wheels slipping. The application of drive torque distribution control could significantly improve the traction and passing ability of the corn harvesters during operation, as well as made the harvester travel more smoothly, thus improving the harvest quality. The drive torque distribution control can be applied not only to the three-row corn harvester, but also to other types of harvesters, and self-propelled agricultural machinery to enhance their adaptability, improving their operation quality. It has a significant reference value for the development of the driving system on walking agricultural machinery.

Research on Decoupled Optimal Control of Straight-Line Driving Stability of Electric Vehicles Driven by Four-Wheel Hub Motors

The optimal control strategy for the decoupling of drive torque is proposed for the problems of runaway and driving stability in straight-line driving of electric vehicles driven by four-wheel hub motors. The strategy uses a hierarchical control logic, with the upper control logic layer being responsible for additional transverse moment calculation and driving anti-slip control; the middle control logic layer is responsible for the spatial motion decoupling for the underlying coordinated distribution of the four-wheel drive torque, on the basis of which the drive anti-skid control of a wheel motor-driven electric vehicle that takes into account the transverse motion of the whole vehicle is realized; the lower control logic layer is responsible for the optimal distribution of the driving torque of the vehicle speed following control. Based on the vehicle dynamics software Carsim2019.0 and MATLAB/Simulink, a simulation model of a four-wheel hub motor-driven electric vehicle control system was built and simulated under typical operating conditions such as high coefficient of adhesion, low coefficient of adhesion and opposing road surfaces. The research shows that the wheel motor drive has the ability to control the stability of the whole vehicle with large intensity that the conventional half-axle drive does not have. Using the proposed joint decoupling control of the transverse pendulum motion and slip rate as well as the optimal distribution of the drive force with speed following, the transverse pendulum angular speed and slip rate can be effectively controlled with the premise of ensuring the vehicle speed, thus greatly improving the straight-line driving stability of the vehicle.

Improvements to torque versus tension relationship considering nut dilation effects

Torque as a tightening method is a simple technique that can be used to tighten a bolt to a given pre-load. Therefore, it is important to theoretically derive an accurate torque vs tension relationship for threaded fasteners as this would enable the industry to achieve a reliable pre-load. Various attempts were made to develop a complete theoretical relationship between torque and tension. Due to the thread angle there exists a nut dilation force causing the nut to expand radially out wards. This effect is more prominent in nuts with smaller height (Style 0 hex nut, refer ISO 4035 1 ). This nut dilation force creates a combined frictional effect with the drive torque thus affecting the torque tension relationship. This paper proposes a novel 3D formulation for torque tension relationship taking into consideration the nut dilation effect. This paper further develops new formulae for tightening and loosening torque, retaining torque, tension vs nut rotational angle relationship as well as formula for nut dilation force for both tightening and loosening.

Duomoto sport car with two-strolled transmission systems

The sports car, powered by two engines, designed and built by a team of four graduates of the Faculty of Mechanical Engineering at the Gdańsk University of Technology is prezented. One (engine) of them was placed at the front of the vehicle. It transmits the torque to the wheels of the front axle through a combined system. The second engine, located at the rear of the vehicle, in the same way transmits the drive torque to the wheels of the rear axle. The car named Duomoto was built on the basis of two Volkswagen Golf GT. It was subjected to road tests. The test results and conclusions are included in this article.

Machine Learning Approaches to Anomaly Detection of Top Drive Torque Causing Drill Pipe Failure

The scientific drillship Chikyu, operated by JAMSTEC has faced the problems with drill pipe damage and failure. After a failure, we examined the surface-measured drilling data, including Fourier transform analysis and spectrogram, and conducted an outlier investigation with an autoregressive model. We found some possible indications of top drive torque anomalies among the surface-measured drilling data. The purpose of this study is to attempt anomaly detection of top drive torque by applying machine learning approaches to such pre-examinations. Data acquired during the drilling operations of the Chikyu are first analyzed to observe the operations and determine the characteristics of the drilling data. Several hundred sets of surface drilling data are extracted and prepared as labeled learning data. Machine learning is then performed using several algorithms to build predictive models for anomaly detection of top drive torque. Simulations using these predictive models are then conducted for other sets of drilling data, including the anomaly conditions acquired during the operations of the Chikyu. The results demonstrate the validity of the predictive models. This study applies machine learning technique to the anomaly detection of the top drive torque with aim of preventing the drill pipe damage and failure. This paper describes our approaches in detail and discuss the results.