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Feeding rates serve as a vital indicator for adjusting the working parameters of the combine harvester. A non-invasive diagnostic approach to predicting the feed rates of combine harvesters by collecting vibration signals of the inclined conveyor was introduced in this study. To establish a feed rate prediction model, the correlation between feeding rates and vibration signal characteristics was investigated. Vibration signal characteristics in both the time domain and frequency domain were also analyzed in detail. The RMS (root mean square) value and the total RMS value of the one-third octave extracted from the vibration signal were utilized to establish a feed rate prediction model, and field tests were conducted to verify the model performance. The experimental results indicated that the relative errors of the established model range from 3.1% to 4.9% when harvesting rice. With the developed feed rate prediction system, the control system of the combine harvester can acquire feed rate information in real time, and the working parameters can be adjusted in advance, thereby, it can be expected to greatly enhance the working performance of the combine harvesters.

The cooperative formation tracking control is a key problem for the cooperative work of multiple agricultural machines on farmland. In view of the problem, this paper proposes an observer-based robust cooperative formation tracking control method, and the cooperative harvesting system of the combine harvester group is chosen to verify the effectiveness of the proposed method. Firstly, a second-order model is used to describe the combine harvester, and both matched and mismatched disturbances are taken into account. The disturbances are then observed using an observer with a cascade structure that combines NDO and ESO. On this basis, the observer-based robust cooperative formation tracking controller is designed based on multiple agent theory and the SMC method. In addition, the APF method is also employed to achieve the goal of preventing collisions among combine harvesters or between the combine harvesters and obstacles during the collaborative harvesting process. The results demonstrate that the observer-based robust tracking control method proposed in this paper can successfully achieve the cooperative formation tracking control of the combine harvester group without a collision. Moreover, the disturbance compensation method reduces the tracking errors of the combine harvester's working trajectory and increases the robustness of the cooperative formation harvesting system. [ABSTRACT FROM AUTHOR]

Because of the influence of harsh and variable working environments, the vibration signals of rolling bearings for combine harvesters usually show obvious characteristics of strong non-stationarity and nonlinearity. Accomplishing accurate fault diagnosis using these signals for rolling bearings is a challenging subject. In this paper, a novel fault diagnosis method based on composite-scale-variable dispersion entropy (CSvDE) and self-optimization variational mode decomposition (SoVMD) is proposed, systematically combining the nonstationary signal analysis approach and machine learning technology. Firstly, an improved SoVMD algorithm is developed to realize adaptive parameter optimization and to further extract multiscale frequency components from original signals. Subsequently, a CSvDE-based feature learning model is established to generate the multiscale fault feature space (MsFFS) of frequency components for the improvement of fault feature learning ability. Finally, the generated MsFFS can serve as the inputs of the Softmax classifier for fault category identification. Extensive experiments on the vibration datasets collected from rolling bearings of combine harvesters are conducted, and the experimental results demonstrate the more superior and robust fault diagnosis performance of the proposed method compared to other existing approaches. [ABSTRACT FROM AUTHOR]

A combine harvester can be one of the most important links for the potatoes gathering and conveying. However, the current potato combine harvester cannot fully meet the large-scale production in recent years, such as the non-ideal performance of impurity removal, the high rate of potato damage, bruising and impure content in the soil cleaning. In this study, a potato combine harvester was designed with the elastic rubbing technology. The harvesting process was also selected by the "double vibration separation, elastic rubbing type, buffering and loss reducing potatoes gathering and conveying". The main components included the excavation, hydraulic control potato soil separation, elastic rubbing impurity removal, as well as the buffering and reducing losses potatoes gathering and conveying device. A systematic analysis was implemented to clarify the movement track and collision characteristics of potatoes, according the optimal structure of the whole machine, the characteristics of harvesting, and the working principle. A combination of structural parameters were then determined for the key components, in order to fully meet the requirements of efficient separation and impurity removal, as well as the loss reduction and prevention. The whole harvesting process was divided into the three sections of the double vibrating potato soil separation, the elastic rubbing type soil cleaning and impurity removal, as well as the buffer potatoes gathering and conveying. The frequency and amplitude of the vibration were adjusted under the harvest conditions in the double vibration adjustable separation device in time. The rates of the potato damage and bruising were then reduced during the process of the potato and soil separation. The longitudinal non-equidistant soil cleaning and impurity removal device was used to reduce the collision frequency of potato, and then slow down the drop impact response, indicating the higher harvest quality, and the lower impurity content. The buffering and reducing losses potatoes gathering and conveying device was optimized to accurately control the drop height and position of potato at the end of the conveyor belt, in order to effectively avoid the drop damage during potato harvesting. The experiment result showed that the loss rates of the prototype were 1.17% and 1.43%, the potato damage rates were 1.30% and 1.27%, the bruising rates were 1.98% and 1.84% and the productivity values were 0.41 and 0.54 hm2 /h, respectively, when the operating velocity values were 3.17 and 4.16 km/h, respectively. All performance indicators were met the requirements of national standards. Once the harvester was operated at a high speed, there was the significant increase in the material flow entering the elastic rubbing impurity removal device in a unit time, indicating the increasing burden of soil cleaning and impurity removal. Among them, part of the soil, seedlings, and vines were remained to mix in the potato cluster, resulting in a high impurity content. By contrast, the low operation speed was greatly contributed to the reduced impurities entering the soil cleaning and impurity removal stage. A better performance was achieved in the removal from the surface soil layer of potatoes in the soil cleaning and impurity removal device. But there was the weak protective effect of the soil on the surface of potatoes, leading to an increase in the frequency of direct contact between potatoes and the soil cleaning and impurity removal wheel. As such, the impurity content was much lower, whereas, there was an increase in the damage and bruising rate of potatoes, compared with the high operation speed. This finding can provide a strong reference for the subsequent research, equipment development, and optimization improvement. [ABSTRACT FROM AUTHOR]

Accurately diagnosing blockages in a threshing cylinder is crucial for ensuring efficiency and quality in combine harvester operations. However, in terms of blockage diagnostic methods, the current state of affairs is characterized by model-based approaches that can be highly time-consuming and difficult to implement, while data-driven approaches lack interpretability. To address this situation, we propose a temporal association rule mining (TARM)-based fault diagnosis method for identifying threshing cylinder blockages and discovering knowledge. This study performs field trials by varying the actual feed rate and obtains datasets for three blockage classes (slight, moderate, and severe). Firstly, a symbolic aggregate approximation (SAX) method is employed to reduce the data dimensionality and to construct the transaction set with a sliding window. Next, a cSpade method is used to mine and extract strong association rules by applying improved support, confidence, and lift indicators. With the established strong association rules, this study can comprehensively elucidate the variation pattern of each characteristic under several blockage failure conditions and can effectively identify blockage faults. The results demonstrate that the proposed method effectively distinguishes between three levels of blockage faults, achieving an overall diagnostic accuracy of 0.94. And the method yields precisions of 0.90, 0.92, and 0.99 and corresponding recalls of 0.90, 0.93, and 0.98 for slight, medium, and severe levels of blockage faults, respectively. Specifically, the knowledge acquired from the extracted strong association rules can effectively explain the operational characteristics of a combine harvester when its threshing cylinders are blocked. Furthermore, the proposed approach in this study can provide a reasonable and reliable reference for future research on threshing cylinder blockages. [ABSTRACT FROM AUTHOR]

To improve the operational quality of paddy combine it was proposed to include additional innovative operations in plant process of pre-threshing preparation of rice biomass with anticipating seeds selection which increases seeds separation, reduces injuries, forms stable operation of threshing-separating device of combine harvester. Retrofit feeder house is a solution to ensure the stable uniform feed of assorted biomass into the threshing separation device. Equipped with tool package – harvester feeder house turned to the running control equipment for incoming flow of biomass. The movement of threshed heap inside the modified geometry of feeder house with tools effectively affect on caulis, panicle of rice – screed, and brings the biomass vibration mode i. e., lead to the dynamic separation of rice headed seeds to threshing rotor. Vibration (tossing) of biomass make it possible for free rice seeds pass through caulis to the bottom of feeder house. Development program of innovative driving devices of multifunctional rice seed harvester combine considered the development of preproduction explorational program ensuring the information on harvest loss during various rice harvesting, their separation between main harvester aggregates – collector, auger, elevator, threshing mill. It is proposed to combine, in a single complex, all the control parameters, subordinating their actions, into an active control device of the technological process, affecting the shapes of rolls formed from various varieties and yields of rice. [ABSTRACT FROM AUTHOR]

Existing combine harvesters used for rapeseed still have challenges. To simplify the structure of the harvester and reduce losses a new type of cyclone is proposed as a cleaning device. It has several spiral arranged adjustable magnetic blocks fitted at the bottom of the cyclone to disrupt and reduce any dead zone in the airflow field. To establish the effect of altering the dead zone of the airflow field, the motion of a single rapeseed in the cyclone was analysed based on the kinematics and dynamics. A simulation model of the cyclone, based on computational fluid dynamics, was established to optimise the arrangement of the magnetic blocks within the cyclone. The influences of helix angle, distance and spiral number of the magnetics blocks on the airflow were explored through a single-factor simulation experiment, which determined the optimal number of spirals as 4. Subsequently, the air velocity of the inlet and materials other than grain (MOG) at the outlet were added as factors to carry out the Box-Behnken designed experiment. Results showed the optimal combination of parameters were an inlet air velocity 4.2 m s−1, a MOG outlet air velocity 27.4 m s−1, a helix angle 64.2° and distance between magnetics blocks 52.4 mm. Bench tests and field experiment were conducted to validate the performance of the cleaning device under the optimum conditions. The results showed that the average cleaning ratio and loss ratio of the rapeseed were 88.96% and 4.12%, respectively. • Magnetic blocks were utilised to improve the performance of the cyclone. • Motion of a single rapeseed inside the dead zone of airflow was analysed. • Effects of magnetic blocks on airflow status was illustrated based on CFD. • Optimal parameters of the cyclone were validated by bench and field experiments. [ABSTRACT FROM AUTHOR]

Rice threshing is one of the important factors affecting grain losses, quality and quantity. The wearing of the secondary threshing drum forks and bearing occurs crop blockage inside the threshing housing that lowers the combine harvester field capacity and increases the total grain losses. To overcome this problem, this study aimed to modify the combine harvester rice threshing device by replacing the secondary drum forks with knife blades. The combine harvester was evaluated using the modified drum, compared with the original drum. The tested treatments were rice grain moisture content (20%, 23% and 25% d.b.) and operational time (50, 100, 150 and 200 hrs.). The experiment was established and statistically designed as a factorial completely randomized block design with three replications. The obtained results revealed that modified secondary drum achieved lower values of wearing rate, lower values of rice grain total losses and higher values of threshing efficiency, compared with the original drum. So, it is recommended to use the modified secondary drum, especially at 23% grain moisture content and 50 hrs. operating time. [ABSTRACT FROM AUTHOR]

Crawler combine harvesters are mostly used in paddy field operation in South China. The field operation load is complex and variable, which leads to fatigue fracture and other accidents of gearboxes under the impact of variable loads and seriously affects the trouble-free working time of the whole machine. In order to solve the problem of high fault rate of gearboxes of crawler combine harvesters, the single HST hydro-mechanical differential steering gearbox developed by the team is taken as the test object, this study designs and builds the gearbox assembly fatigue test bench, and then carries out the fatigue test of differential steering gearboxes. The test results show that the test bench can well test the comprehensive working performance and durability of differential steering gearboxes, and has advantages of simple structure, convenient installation, etc. This is of great significance to study the fatigue damage rule of differential steering gearboxes, optimize and improve the mechanical structure of gearboxes and reduce the fault rate of its internal parts in the future.

India is the second largest wheat producer in the world after Russia. Wheat harvesting in the country was traditionally done using a sickle, a hand tool. However, in the last two decades, combined harvesters have been extensively used. The rapid development of mechanization has resulted in the production of dust and straw particles during the harvesting operation of wheat. These particles have severe health hazards for the machine operator. Exposure to various types of particulate matter has a variety of effects on human health. Such an effect can be minimized if the concentration of the generated particle is maintained within a permissible limit. Hence, the present study has been conducted to evaluate and categorize dust and straw particles in the workspace of a combine harvester operator during wheat harvesting. An image-processing technique was used to study a field data sample collected on sticky paper. It describes a novel method of collecting dust and straw particles while harvesting wheat. Few studies have been conducted in developing countries to analyse the characteristics of dust and wheat straw exposure of combined harvester operators. The number of dust and straw particles deposited per square millimetre was 9-12, with sizes ranging from 10 to 1400 µm. The extracted data were divided into three groups, viz. thoracic, inhalable and straw and modelled using machine learning algorithms, including support vector machine (SVM) and k-nearest neighbor. With an accuracy of 96%, SVM outperformed the other methods for categorising dust and straw particles, whereas linear discriminant analysis performed poorly with an accuracy of 88%. [ABSTRACT FROM AUTHOR]

Harvesting of high-moisture corn ears poses a challenge due to the high level of grain damage. In the present study, a series of concaves adapted to moist corn ears threshing was developed and evaluated. The key improvements include a concave arc shape and oblique crossbars to reduce corn grain damage and threshing losses. Results show that the geometrical shape of the concave arc and its crossbars have a significant influence on the grain detachment from the ears, grain separation through the concave, and grain damage during the threshing process of moist ears of corn. Studies show that replacing the concave rounded crossbars with oblique ones can increase threshing performance of moist corn ears. A concave with an Archimedes' spiral arc and oblique concave crossbars is an effective approach to improve corn grain quality and reduce harvest losses due to grain damage. We identify the optimal design for threshing corn ears as an experimental concave with an Archimedes' spiral arc of 8 mm height, with 19 mm wide crossbars of the concave with an oblique working plane (tilt angle 25°). This design achieves minimal threshing grain losses (0.03%) when threshing moist ears (grain moisture content ~35%), and damaged grain in the threshing apparatus does not exceed the permissible limit of 3% at an ear feed rate of 16.8 kg s−1. [ABSTRACT FROM AUTHOR]

Agricultural mechanization characteristics of Azerbaijan were investigated in this study. Based on the statistical data of Azerbaijan, number of tractors, number of agricultural tools and machines and agricultural mechanization level indicators were calculated and summarized. Respectively in the years 2010 and 2021, average tractor power was identified as 68.2 and 74.7 HP, tractor power per cultivated area as 916 and 1672.2 HP/ha, number of tractors per 1000 hectares as 13.4 and 22.2, number of combine harvesters per 1000 hectares as 1.2 and 2.1, cultivated area per tractor as 75 and 45 ha, cultivated area per combine harvester as 821.5 and 458.3 ha, agricultural field per capita as 0.52 and 0.47 ha and finally number of people per unit area as 1910 and 2120. [ABSTRACT FROM AUTHOR]

A cost-effective harvesting method and equipment for small-scale farms is essential to ensure the viability and sustainability of their operations. This study aims to retrofit and test a pull-type combine harvester for effective and efficient operations in small grains. A 1960s-era functional combine harvester was retrofitted with a stripper header rotor in place of the sickle bar cutting system to increase the harvesting capacity. Preliminary field testing found that the original header auger and feeder house could not handle the changed crop composition at an increased capacity and did not properly convey the grain from the auger to the feeder house and onto the threshing system. Therefore, redesign modifications were conducted for the auger and feeder house to help increase the efficiency and capacity required to use the stripper header. Various design concepts were proposed, analyzed, and prototyped in this study. The machine performance of the material-conveying efficiency of the wheat-material-other-than-grain mixture was evaluated at various material throughput rates. The results showed that the auger shim and feeder house paddle redesign increased the efficiency from 80.68% to 98.56%, as compared to the original machine configuration. The cost-effective and high-performance pull-type combine harvester proposed in this study has a high potential in solving the bottleneck problem of local production of small grains by small-scale farming operations. [ABSTRACT FROM AUTHOR]

Agricultural mechanization plays a major role in food production. Its proper implementation requires knowing the indicators that affect its performance. The aim of the present investigation was to evaluate the main mechanization indices of Aldeia Nova Farm in Cela Municipalty. For this, the execution of the study consisted of a non-experimental descriptive investigation, which was divided into two parts. The first consisted of collecting personal data for the operation of the machine and tractor park, maintenance and repair, as well as production. Subsequentle, the mechanization indicators were determined by following methodological recommendations established by various authors. Within the results, the ratio values of tractors per hectare (0,008 tract ha-1), the ratio of average power per tractor (108,86 kW tract-1), the machines per tractor ratio (2,05), the ratio of arable hectares per tractor (126,5 ha tract-1) and the ratio of arable hectares per combine (631,25 ha col-1) were magnitudes studied that behaved outside the established. Finally, the level of mechanization of Aldeia Nova Farm was evaluated using the indices established for this purpose, and it was shown that it is low, since although the value of the available power per hectare (0,86 kW ha-1) is higher than the 0,75 kW ha-1 established in the specialized literature, the rest of the indicators register unfavorable magnitudes, except technical assistance indicators. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the efficiency of a new type of corn header equipped with flail knives that shreds corn stubble close to the ground. A field trial was carried out to quantify the influence of the shredding tool design (flail knives, standard knives, horizontal choppers disengaged), harvesting speed (1.5, 3.0, 4.5, 6.0 km/h) and cutting height (approx. 20 cm variation, 4 levels) on the power consumption of the corn header, engine load and fuel consumption of the combine harvester. The regression analyses revealed that modifications of the shredding tool assembly have significant effects on the parameters of the functional relationships. The power consumption of the flail knives configuration was 15 kW/row unit at 6 km/h at the lowest cutting height setting, making it compatible with most current combine harvester models. The additional power demand and fuel consumption that arise when switching to the new shredding tool design (flail knives) show a wide range, depending on the initial situation. Compared to the standard knives at 6 km/h, the additional power consumption was 3.6–5.5 kW/row unit and the additional fuel consumption was 2.6–3.9 L/ha. Compared to the configuration with disengaged horizontal choppers, it was 5.3–6.6 kW/row unit and 3.9–5.1 L/ha, respectively. At 90% engine load the additional power requirement was 1.6–3.1 kW/row unit and the additional fuel consumption was 2.9–5.6 L/ha compared to the standard knives configuration and 2.6–3.6 kW/row unit and 5.6–7.2 L/ha respectively, compared to the configuration with disengaged horizontal choppers. [ABSTRACT FROM AUTHOR]

The study has been conducted to assess the farm mechanization level and tried to identify the determinants. The research utilizes primary data, collected through personal interview of 397 farm households located in the four districts of central and southeast Oromia region of Ethiopia. Multi-stage, purposive and random sampling procedures were used to select the respondents using probability proportional to size from each district. Mechanization Index (MI) based on the matrix use of animate and mechanical energy inputs that incorporate cost factors was used to estimate farm mechanization level, while Tobit model was employed to analyze factors that determine the farm mechanization level. Household’s sex, educational background, experience in farming, family labor availability and social capital, location of household, access to all-weather roads and distance to farm mechanization service providers centers, participation in market and off-farm activities, landholding, land fragmentation and size of livestock owned (TLU) are statistically significant in determining level of farm mechanization. Land consolidation, availing infrastructural facilities and facilitating adult education and short-term trainings are important recommendations to enhance farm mechanization level in the study area.

The European corn borer is a major pest of corn that overwinters in corn stubble and stalks. Shredding these residues disrupts the larvae’s habitat or directly harms them. A corn header has been engineered with a new type of cutting tool on its horizontal choppers, featuring sharp edges and dulled flails, to shred corn stubble near the soil surface. This study investigated the effect of the dulled flails on the shredding intensity of corn stover. Field trials compared flail knives with standard knives for particle size distribution of corn stover and structural integrity of corn stalk segments. Additionally, a common two-step method, which involved a standard knives-equipped corn header followed by tractor-driven flail mowers, was tested. The flail knives reduced the mean particle size by 3.6 mm compared to the standard knives. Subsequent processing with tractor-driven flail mowers, following the corn header using standard knives, led to a reduction in mean particle size by 11.8 mm. It also further reduced the number of incompletely destroyed stalk segments. However, completely intact internodes were scarce in all methods. Given that flail knives enhance shredding intensity without a second processing step, this concept is concluded to be effective for corn stover shredding.

Aimed at addressing the problems of the existing straw choppers on combine harvesters, such as a large cutting resistance and poor cutting effect, combined with bionic engineering technology and biological characteristics, a bionic model was used to extract the characteristics of the cutting blades of locusta migratoria manilensis’s upper jaw. A 3D point cloud reconstruction and machine vision methods were used to fit the polynomial curve of the blade edge using Matlab 2016. A straw-cutting process was simulated using the discrete element method, and the cutting effect of the bionic blade was verified. Cutting experiments with rice straws were conducted using a physical property tester, and the cutting resistance of straw to bionic blades and general blades was compared. On the whole, the average cutting force of the bionic blades was lower than that of the general blades. The average cutting force of the bionic blade was 18.74~38.23% lower than that of a smooth blade and 1.63~25.23% lower than that of a serrated blade. Similarly, the maximum instantaneous cutting force of the bionic blade was reduced by 2.30~2.89% compared with the general blade, which had a significant drag reduction effect. By comparing the time–force curves of different blades’ cutting processes, it was determined that the drag-reducing effect of the bionic blade lies in shortening the straw rupture time. The larger the contact area between the blade and the straw, the more uniform the cutting morphology of the straw after cutting. Field experiment results indicate that the average power consumption of a straw chopper partially installed with bionic blades was 5.48% lower than one with smooth blades, measured using a wireless torque analysis module. In this research study, the structure of the straw chopper of an existing combine harvester was improved based on the bionic principle, which reduced resistance when cutting crop straw, thus reducing the power consumption required by the straw chopper and improving the effectiveness and stability of the blades.

Cleaning is one of the most important steps in the harvesting process, and the prolonged and high-load operation of the vibrating sieve can decrease its reliability. To uncover the structural flaws of the cleaning sieve in the crawler combine harvester and establish a foundation for quality inspection, this paper proposes a method for durability testing and analysis using vibration and strain signals. Via the modal analysis of the cleaning sieve, the most susceptible areas for fault signals are identified. Subsequently, a specialized test rig exclusively designed for the examination of the durability of the cleaning sieve is constructed. After following 96 h of uninterrupted operation, the vibration plate of the cleaning sieve sustains damage, resulting in atypical noise. A signal analysis reveals that the primary vibration signal of the cleaning sieve primarily consists of a fundamental frequency of 5 Hz, corresponding to the driving speed, as well as a frequency doubling signal of 50 Hz. After the occurrence of damage, the peak amplitude of the received vibration signal increases by over 86.3%. Furthermore, the strain gauge sensor situated on the support plate of the rear sieve detects anomalous signals with frequencies exceeding 300 Hz, which are accompanied by a considerable rise in the power spectral density. This research has significant importance for enhancing the service life of the cleaning sieve and optimizing the overall machine efficiency.

Driven by increasing food demand and the urgent need for intelligent land use, the size and complexity of agricultural machinery has increased significantly. Monitoring the operation of machines during long working days is a very challenging task for the operators. Automatic monitoring systems can lighten the load and also pave the way towards fully autonomous machines. This paper proposes an automatic condition monitoring system for detecting operation anomalies on agricultural machine components with reciprocating motion, and applies it to a use-case on a combine harvester header cutter bar. Cyclostationary analysis techniques are employed to develop filtering algorithms to extract informative features, which are monitored through the implementation of statistical process control (SPC) using control charts. Together with a comparison of several filtering and feature extraction techniques, an analysis is provided on the influence of sensor type and position on anomaly detection performance. Filtering benefit was found to be highly dependent on the considered sensor type and its location, with increases in Matthews correlation coefficient (MCC) ranging from 0 to 50%, resulting in maximal MCC values of 1. Suitable feature calculation resulted in average prediction performance improvements over 10% in MCC values for nearly all considered sensor types and their locations. These results highlight the importance of intelligent sensor selection for condition monitoring purposes on agricultural machinery and the added value of SPC involving cyclostationary analysis techniques for anomaly detection. • Knife impacts are detected with a Matthews correlation coefficient of 0.995. • Sensors mounted closer to the cutter bar performed best. • The proposed synchronous filters clearly improved performance. • The standard deviation per rotation captured the signal response during impact well. [ABSTRACT FROM AUTHOR]

Traditional harvesters of tiger nuts (Cyperus esculentus) face problems including low harvesting efficiency, high loss rate, high impurity rate, and high labor intensity. To solve these problems and improve the harvesting efficiency and quality of tiger nuts, a crawler-type tiger-nut combine harvester that integrates digging, soil removal, picking, screening, and collection was designed. The machinery comprises crawler devices and working devices. The key devices were designed through theoretical analysis. Therein, the digging and hoisting devices consist of digger blades, combined soil-breaking blades, and vibrating hoisting chains. The tuber picking and screening device is composed of the tuber picking drum, double-deck heterodromous vibrating screens, impurity removal blowers, and soil-crushing guide rollers. The crawler devices include the track assemblies and the hydraulic driving systems. SolidWorks was used to establish the virtual prototype model. Combined with simulation using the discrete element software, the law of motion of tiger-nut tubers in the digging, elevation, and screening processes was studied, which verified the feasibility of the design. Finally, a prototype was manufactured and fabricated to conduct field harvesting tests on tiger nuts. Field test results indicate that the harvesting efficiency, harvest rate, and impurity rate of the tiger-nut harvester are separately 0.216 ha/h, 98.14%, and 3.24%, which meet the harvesting requirements for tiger-nut growers. [ABSTRACT FROM AUTHOR]

China is a large flax-growing country, with planting area and production ranking among the top three in the world. However, the cultivation range of flax in China is very broad, complex, and diverse, resulting in different planting scales and patterns, making it difficult to apply foreign large combine harvesters, and China lacks a dedicated flax combine harvester. This research improved the design of the 4LZ-4.0 crawler-type flax combine harvester for the regional features and flax cropping patterns in China. First, the structure, technical parameters, and working principles of the machine were introduced; second, the theoretical analysis and optimization of key components were performed; and finally, with the advancing speed of the machine, the speed of the threshing drum, and the speed of the suction fan as independent variables and the rate of removal and the total loss rate as response values, a three-factor, three-level response surface analysis method was used. For each component and response value, a mathematical model was created, and the factors and their interactions were evaluated and confirmed. The results demonstrated that the three parameters impact the threshing drum speed, advancing speed, and centrifugal fan speed in that order of priority, as well as the total loss rate in that order of priority. The machine's optimal operating settings were 1.5 m·s−1 advancing speed, 788.49 r·min−1 threshing drum speed, and 885.34 r·min−1 centrifugal fan speed, and the validation test results indicated that under the typical dryland dense flax cultivation mode, it had a 97.46% threshing rate and 2.99% total loss rate after the test. This demonstrated that optimizing operational parameters may decrease losses in the process of mechanical flax harvesting, enhance harvesting efficiency, and satisfy the marketable flax harvesting standards. [ABSTRACT FROM AUTHOR]

To improve the automation level and operation quality of China's beet harvester and reduce the loss due to damaged and missed excavation, this study used a self-developed sugar beet combine harvester and field simulation experiment platform, based on the single-factor bench test of the automatic row following system in the early stage, taking hydraulic flow A, spring preload B, and forward speed C which have significant influence on performance indices as test factors, and taking the missed excavation rate, breakage rate and reaction time as performance indices, the orthogonal experimental study on the parameter optimization of the three-factor and three-level automatic row following system with the first-order interaction of various factors was carried out. The results of the orthogonal experiments were analyzed using range analysis and variance analysis. The results showed that there were differences in the influence degree, factor priority order and first-order interaction, and the optimal parameter combination on each performance index. A weighted comprehensive scoring method was used to optimize and analyze each index. The optimal parameter combination of the overall operating performance of the automatic row following system was A2B2C1, that is, the hydraulic flow was 25 L/min, the forward speed was 0.8 m/s, and the spring preload was 198 N. Under this combination, the response time was 0.496 s, the missed excavation rate was 2.35%, the breakage rate was 3.65%, and the operation quality was relatively good, which can meet the harvest requirements. The comprehensive optimization results were verified by field experiments with different ridge shapes and different planting patterns. The results showed that the mean values of the missed excavation rate of different planting patterns of conventional straight ridges and extremely large "S" ridges were 2.23% and 2.69%, respectively, and the maximum values were 2.39% and 2.98%, respectively; the average damage rates were 3.38% and 4.14%, and the maximum values were 3.58% and 4.48%, which meet the industry standards of sugar beet harvester operation quality. The overall adaptability of the automatic row following system is good. This study can provide a reference for research on automatic row following harvesting systems of sugar beets and other subsoil crop harvesters. [ABSTRACT FROM AUTHOR]

Existing development for cyclone separation cleaning components of the rapeseed combine harvester, which employs the suspending airflow to separate the rapeseeds from the materials other than grain (MOG), has the challenge to figure out the optimal working parameters, highlighting a need for exploration of the invisible airflow based on Computational Fluid Dynamics (CFD). The airflow status was mainly affected by the air velocities of the inlet, and the outlet for the MOG. The single factor and response surface experiments were carried out. It could be found that the inlet and MOG outlet velocities affected the air velocities through the change in the air quantity. Furthermore, the mathematical model of the relationship between the air velocities inside the cyclone and the working parameters was built, and the optimal combination of working parameters was obtained by multi-objective optimization. The inlet and outlet velocities of the optimal combination were 4.25 m/s and 29.87 m/s, respectively. Under this condition, the cleaning ratio and loss ratio of the cleaning device was 94.62% and 5.39%, respectively, as validated by the field experiment. The findings provide references for the improvement of cleaning systems for rapeseed combine harvesters. [ABSTRACT FROM AUTHOR]

Increasing traffic intensities in agricultural work processes increase the risk of subsoil compaction. The objective of this study is to investigate whether the geometry of the field and size of the machine influence the traffic intensity. This study focuses on analysing the influence of the machinery and field geometry on the traffic intensities in the field based on wheat harvest operations performed with one combined harvester. For this purpose, 177 routes on 59 different fields are simulated with a route planner, and subsequently, the tracks and traffic intensities are modelled. The structure of the field was specified using eight different shape indices. The analyses showed that the configuration of the machines mainly influences the total passed area and the wheel load distribution on the field. Independent of the machine, the geometry of the field has an influence on the area proportions in the headland with wheel passes above 5 and 10. The indices that highly correlate with traffic intensity are the mean interior edge ratio, the interior area ratio and the mean fractal dimension. It means that the larger the field area in proportion to the perimeter and the infield area, the larger the area fraction with more than 5 or 10 passes. Moreover, this area share increases with the complexity of the field geometry, expressed by the mean fractal dimension. Consequently, the size of the field mainly influences the traffic in the headland because of the removal of the crop. [ABSTRACT FROM AUTHOR]

Wheat lodging provides important reference information for self-adaptive header control of a combine harvester. Aimed at real-time detection of wheat lodging, this paper proposed a detection method of wheat lodging location and area based on binocular vision. In this method, the angle relationship between the stem and vertical direction when wheat is upright, inclined, and lodging was determined by mechanical analysis. The discrimination condition of the wheat lodging degree was proposed based on the height of the visual point cloud on the surface of wheat crops. The binocular camera was used to obtain the image parallax of wheat within the harvesting region. The binocular camera optical axis parallel model was used to calculate the three-dimensional coordinate of wheat. Then, the height of the wheat stem was obtained by further analysis and calculation. According to the wheat stem height detected by vision, the location and area of wheat lodging within the combine harvester's harvesting region were analyzed. A field experiment showed that the detection error of the wheat stem height was 5.5 cm and the algorithm speed was under 2000 milliseconds, which enabled the analysis and calculation of the wheat lodging location, contour, and area within the combine harvester's harvesting region. This study provides key information for adaptive header control of combine harvesters. [ABSTRACT FROM AUTHOR]

Impurity rate is one of the key performance indicators of the rice combine harvester and is also the main basis for parameter regulation. At present, the tracked rice combine harvester impurity rates cannot be monitored in real time. Due to the lack of parameter regulation basis, the harvest working parameters are set according to the operator's experience and not adjusted during the operation, which leads to the harvest quality fluctuating greatly in a complex environment. In this paper, an impurity-detection system, including a grain-sampling device and machine vision system, was developed. Sampling device structure and impurity extraction algorithm were studied to enhance the impurity identification accuracy. To reduce the effect of impurity occlusion on visual recognition, an infusion-type sampling device was designed. The sampling device light source form was determined based on the brightness histogram analysis of a captured image under different light irradiations. The effect of sampling device structures on impurity visualization, grain distribution, and mass flow rate was investigated by the discrete element method (DEM). The impurity recognition algorithm was proposed based on Mask R-CNN, which mainly includes an impurity feature extraction network, an ROI generation network, and a target segmentation network. The test set experiment showed that the precision rate, recall rate, average precision, and comprehensive evaluation indicator of the impurity recognition model were 92.49%, 88.63%, 81.47%, and 90.52%, respectively. The conversion between impurity pixel number and its actual mass was realized according to the pixel density calibration test and impurity rate correction factor. The bench test result showed that the designed system has a good detection accuracy of 91.15~97.26% for the five varieties. The result relative error was in a range of 5.71~11.72% between the impurity-detection system and manual method in field conditions. The impurity-detection system could be applied to tracked rice combine harvesters to provide a reference for the adjustment of operating parameters. [ABSTRACT FROM AUTHOR]

In the last 30 years, we often hear about the computer aided design method with the help of the developments in adapting the innovations in the field of computer to the field of design. This method is divided into two sub-methods as forward and reverse. In this study, two methods were applied and compared for a combine harvester part used in the agriculture field. In the reverse engineering method, part was scanned with the laser scanner and the resulting point clouds were made into a mesh structure. These network structures have been improved and optimized with the necessary steps. Then the solid model was created with optimized mesh. In the forward engineering method, a 1/1 scale solid model of the part was created with the help of a caliper. The accuracy analysis of the solid model created with the network model was performed and the deviations from the real values were calculated and interpreted. At the end of the study, the three-dimensional models created by both methods were compared morphologically and the results were evaluated. The average accuracy value between the two models was calculated as 98.24%. [ABSTRACT FROM AUTHOR]

A combine harvester has been widely employed for harvesting paddy in Malaysia. However, it is one of the most challenging machines to operate when harvesting grain crops. Improper handling of a combine harvester can lead to a significant amount of grain loss. Any losses during the harvesting process would result in less income for the farmers. Grain loss sensing technology is automated, remote, and prospective. It can help reduce grain losses by increasing harvesting precision, reliability, and productivity. Monitoring and generating real-time sensor data can provide effective combine harvester performance and information that will aid in analyzing and optimizing the harvesting process. Thus, this paper presents an overview of the conventional methods of grain loss measurements, the factors that contribute to grain losses, and further reviews the development and operation of sensor components for monitoring grain loss during harvest. The potential and limitations of the present grain loss monitoring systems used in combine harvesting operations are also critically analyzed. Several strategies for the adoption of the technology in Malaysia are also highlighted. The use of this technology in future harvesting methods is promising as it could lead to an increase in production, yield, and self-sufficiency to meet the increasing demand for food globally.

It was noted that in waterlogged soil conditions, the existing methods for assessing field grain losses when harvesting soybeans using low-cut headers are not always suitable. (Research purpose) To improve the methodology for assessing soybean losses during harvesting in the waterlogged soil conditions, using the possibility of sampling them separately behind the header and thresher of the combine. (Materials and methods) The methodological approach was based on: the impact of crop heterogeneity; the principle of adequate accuracy in loss assessment, regardless of soil conditions during harvesting; accessibility, simplicity and minimal labor intensity of the method; the possibility of differentiating grain losses behind the header to identify their main sources. Three main loss zones were identified and the characteristics of their sources were considered. The authors presented a scheme for applying square metal frames with a 1-meter side length on crops and on a harvested field area after the passage of the combine. The authors described the sequence of laboratory and field assessment and formulae for determining natural grain losses, losses behind the header and their types, soybean losses behind the thresher and total losses behind the combine. (Results and discussion) A methodology for soybean loss assessment was developed and tested during the trial of the Case IH AF6140 combine with a New Holland 740CF-30DD(SF) header. It was found out that this combine meets all the norms for soybean grain losses at a combine speed of 6 kilometers per hour. Loose grains were revealed to be the largest share of losses behind the header. To further mitigate losses, it was proposed to control the number of reel revolutions. (Conclusions) We have developed a technical approach and an algorithm for grain loss assessment with the use of rigid metal frames, which ensures high reliability, efficiency and lower labor intensity when considering losses and calculating actual and permissible (from biological yield) values for the specific and relative soybean losses behind a header and combine harvester as a whole.

• Construction method of a lightweight digital twin system for combine harvester. • Modelling method for complex kinematic relationships of critical components. • Construct a fuel consumption prediction model based on LightGBM algorithm. • The MRE and RMSE under full harvest conditions are 1.09 % and 0.67 L/h, respectively. How to further improve the working performance of combine harvester is increasingly focused. Because the harvest time of crops in agricultural production is generally short, the number of field trials of harvesters is limited, which severely hindered the study of combine harvesters. The digital twin system can conduct a large number of simulation tests on the harvester in a virtual environment, and it is not limited by operation time and operation scenarios, which has obvious advantages. Addressing the issue that current digital twin systems for agricultural machinery rely heavily on large-scale physical engines and the combine harvester digital twin system lacks a method for modeling multi-component complex transmissions; this study introduces a lightweight network-based approach to construct a digital twin system for combine harvester, encompassing multiple subsystems such as physical, virtual, model calculation, data interaction, and human–computer interaction. Among them, it studies the complex transmission and motion pattern classification of critical components of combine harvester, proposing a method for modeling the kinematic relationships of complex motions in critical components, accurately modeling different types of physical activities provides crucial technical support for the precise mapping of digital twin systems. Ultimately, using the Lovol GM100 combine harvester as a case study and leveraging the CMOnlineLib and HTML lightweight network, a digital twin system was developed for the combine harvester, including creating a LightGBM model capable of predicting fuel consumption. Field tests demonstrate that the digital twin system for the combine harvester operates stably and reliably, with the fuel consumption prediction model under full-load conditions achieving an average error of 0.24 L/h, a maximum error of 0.84 L/h, and an average relative error of only 1.09 %. This research offers a novel approach to enhancing the digital twin technology and increasing the intelligence level of combine harvester. [ABSTRACT FROM AUTHOR]

• Based on the principle of electromagnetic vibration, the wheat image acquisition device was designed and developed. • The DeepLab-EDA semantic segmentation model for the detection of wheat broken grains and impurities was proposed. • To realize the detection of wheat broken rate and impurity rate during harvesting, the detection system was constructed. • Through indoor grouping ratio experiment and field application, the accuracy and effectiveness of the wheat broken rate and impurity rate detection method were fully verified. The broken rate and impurity rate of wheat are important indicators for assessing the quality of combine harvester operations. In view of the overlapping, occlusion and dense adhesion between the scattered grains during the operation of the combine harvester, it is difficult to obtain the grain crushing characteristics and impurity mass, which leads to low detection accuracy. In this paper, a method for detecting wheat broken rate and impurity rate based on DeepLab-EDA semantic segmentation model was proposed, and a detection system was built. In the detection system, an image acquisition device was designed and developed based on the principle of electromagnetic vibration, and the deep learning model was deployed in the embedded processor. Through the human–computer interaction interface design, the online processing and analysis of wheat image data and the display of the detection results of broken rate and impurity rate were realized. Comparative experiments with traditional semantic segmentation models showed that the MIoU, MP and MR of the DeepLab-EDA model were 89.41 %, 95.97 % and 94.83 %, respectively, representing improvements of 9.94%, 7.41%, and 7.52% over the baseline model, and indicating a significant enhancement in the accurate identification and segmentation of broken grain and impurities. Based on this, indoor group matching experiments were conducted with three groups of broken rate and impurity rate levels set at 0.5%, 1.5%, and 2.5%, showing the average errors of 7.54% and 6.30% for broken rate and impurity rate detection systems, respectively. Furthermore, the detection device was installed under the grain outlet of the GM80 combine harvester for field experiments, which showed average errors of 13.32% and 9.77% for wheat broken rate and impurity rate, respectively. The effectiveness and accuracy of the wheat broken rate and impurity rate detection system meet the requirements, which can provide a data basis for intelligent control of combine harvester operation parameters by the operator. [ABSTRACT FROM AUTHOR]

Energy management strategies are crucial for Plug-in Hybrid Electric Combine Harvester (PHECH). However, many existing approaches rely on rigid, pre-setting rules that struggle to adjust to the PHECH operational conditions. This paper first introduces a power estimation model tailored to the quasi-periodic process of harvester activity. Then, Dynamic Programming (DP) is applied to derive optimal samples of engine power ratio across various scenarios. Building on the samples, a Neural Network (NN) is developed to enhance the strategy's economic and real-time performance. Simulation tests evaluate the proposed algorithm's efficacy and its energy conservation potential. The findings suggest that, compared to fuel-driven harvesters, the NN strategy achieves similar energy cost savings to the DP approach, exceeding 11%, which is better than the Charge Depleting and Charge Sustaining (CDCS) strategy's 7.22% and the MPC-ECMS strategy's 7.87%. Moreover, the NN strategy reduces the time expense to roughly one-fifth of that required by the DP approach. [ABSTRACT FROM AUTHOR]

• Real-time monitoring and electric adjustment of threshing and cleaning multi-parameter. • Identification method for the multi-parameter system based on PSO and WNN. • Optimized multi-parameter automatic control method based on MPC algorithm. The operating environment of grain combine harvesters is complex, and operators' experience is highly required. To meet the demand for high-quality and efficient harvesting in modern agriculture, harvesters must have the ability to control operational performance automatically. The core issue is to achieve multi-parameter cooperative control of the threshing and cleaning devices. The threshing and cleaning devices are located in the same rack space. The interaction mechanism between the internal agricultural material movement, mechanical structure, and airflow field is very complex, and it is a system with characteristics such as time-varying, hysteresis, and multi-parameter coupling. In response to the above challenges, real-time collection and adjustment methods for the main indicators and parameters of the threshing and cleaning devices were studied separately. A field experiment plan for identifying the threshing and cleaning system was designed. Based on the state space model and NARMAX (nonlinear auto-regressive moving average with exogenous input) model as the system structures, a fusion method of PSO (particle swarm optimization) and WNN (wavelet neural network) was proposed to optimize the structural parameters of the two models. Determine the optimal identification model for the threshing and cleaning system through testing. Then, adopt the MPC (model predictive control) method to regulate the multi-parameter of threshing and cleaning. The control process simulation was conducted in Simulink, and the software and hardware of the multi-parameter control system for the threshing and cleaning of the combine harvester were developed. The execution process of the control program was optimized, and field validation was conducted. The system could adjust the performance indicators to near the ideal set values in less than or equal to 3 s (indicator differences ≤ 0.23 %). In addition, the trigger times of the adjustment process were also few in the long-term continuous harvesting operation, which could make the performance indicators meet the requirements in the whole process. This indicated that the modeling and control methods proposed in this paper had good adaptability and stability, and laid the foundations for improving the intelligent level of harvesting machinery and achieving intelligent agriculture. [ABSTRACT FROM AUTHOR]

• An integrated optimizing way for harvesting and unloading paths is developed. • A five-step unloading method is proposed. • Tank capacity and unloading points are considered. • The entire crop harvesting process is simulated. Optimization of path planning is becoming increasingly valuable aspect for unmanned combine harvesters. Considering the need to unload grain dozens of times daily, constituting roughly more than one-third of the overall time, the unloading process emerges as a crucial factor that demands inclusion in full coverage path planning. However, current research frequently neglects key factors such as tank capacity and unloading positions. This paper presents a comprehensive path planning approach for both harvesting and unloading processes, specifically designed for a single tracked combine harvester. The entire crop harvesting process was simulated using turtle graphics in Python. The results revealed that cumulative time variations in unloading paths was a significant contributor to overall time fluctuations. The costs associated with maneuvering the grain truck outweighed the benefits, advocating for a fixed unloading point on the longest side of the farmland as the optimal strategy. Unloading accounted for at least 35.02% of the overall time, highlighting the potential for enhanced efficiency through collaborative unloading technology. This research establishes a valuable reference for a comprehensive full coverage path planning method for the entire crop harvesting process, emphasizing the need for integrated strategies considering both harvesting and unloading aspects. [ABSTRACT FROM AUTHOR]

This study investigates the adaptive cleaning system of a soybean combine harvester, addressing the issue of low adaptability in matching the cleaning parameters of the air-and-screen cleaning device of domestic combine harvesters to varying soybean extract characteristics. This mismatch results in high cleaning loss and impurity rates during soybean machine harvesting. Through cleaning experiments, we examine the impact on soybean machine harvesting, where the cleaning loss rate accounts for approximately 10.08% of the total loss rate. The weight of the cleaning loss rate is lower than that of the impurity rate. Additionally, we establish a linear relationship between cleaning parameters and the corresponding cleaning loss rate and impurity rate. We design an adaptive control strategy workflow chart and integrate the adaptive cleaning system into the soybean combine harvester. Verification tests confirm the effectiveness of the adaptive control function. Comparative analysis reveals a reduction of 0.19% in cleaning loss rate and 0.98% in impurity rate compared to the air-and-screen cleaning device. The adaptive cleaning system significantly improves cleaning quality during soybean machine harvesting and enhances the intelligent capabilities of the air-and-screen cleaning device. The results provide practical insights and theoretical guidance for the development of high-quality, low-loss cleaning technology in soybean machine harvesting in China.

Aiming at the problems of poor applicability of traditional header height detection mechanism, poor stability and large lag of automatic control system of combine harvesters, an automatic control system of header height of combine harvester was designed, which mainly included the profiling mechanism, controller, proportional valve, manual operation handle and display module. The profiling detection mechanism was composed of angle sensor, profiling plate, torsion spring and other structures. The key structural parameters of the profiling mechanism were determined by using the Adams simulation software and its working performance was verified. The gray prediction PID algorithm of header height was used to reduce the lag of the control model. The control system detected the height of the header from the ground through the profiling mechanism. After being processed by the controller, the height of the header was changed by adjusting the expansion of the header oil cylinder. The field test results showed that the working performance of the header automatic control system was stable. Under the working conditions of preset header height of 100 mm and 200 mm, the average deviation of the control system was within 21 mm, which met the real-time control demand of header height during the normal operation of the combine harvester. This research could provide intelligent design methods of combine harvesters. [ABSTRACT FROM AUTHOR]

In real-world problems, engineering data often suffer from outliers due to deficiencies in measurement techniques, recording errors, or other reasons. In this work, a robust prediction method is proposed based on the Kriging method and fuzzy c-means algorithm. An outlier detection strategy is designed based on the fuzzy c-means algorithm, in which Kriging method is used to evaluate the relationship between the input and output. The membership of each training sample is calculated based on the prediction error of the Kriging model of each cluster and is used to judge whether the training sample is an outlier. The detected outliers are removed from the training samples, and then the remaining training samples are used to construct the final prediction model. The effect of the parameters of the proposed method on its performance is studied through an one-dimensional and a four-dimensional numerical problem, and ten benchmark functions are used to test its performance thoroughly. The results indicate that the proposed method produces much better performance in terms of outlier detection accuracy and prediction accuracy than the conventional outlier detection method and the Kriging method. Similar results can be found in the experiments on engineering problems. The proposed method is applied to model and analyze the operation data of the cleaning device of a combine harvester. The effect of the operation parameters of the cleaning device on the grain impurity ratio is studied, and the operation parameters of the cleaning device are optimized based on the prediction model of the proposed method. The analysis and optimization results provide a reference for the operation and control of the cleaning device of a combine harvester. [ABSTRACT FROM AUTHOR]

t—As the main harvesting machinery, the combine harvester is often due to improper adjustment of its operating parameters resulting in increased crushing rate and grain waste during the harvesting process. To quickly obtain the working range of key operating parameters under low crushing rate, this study conducted field tests on the relevant parameters affecting the crushing rate and finally selected the travel speed, feed rate, threshing drum speed, concave clearance, and crushing rate as node variables for the construction of the Bayesian network model. Based on the “search-and-score” algorithm, the best network structure can be obtained using the combination of the Akaike Information Criterion (AIC) scoring function and the hill-climbing method. In the obtained network, adjusting the proportion of the lowest level of the crushing rate nodes to 100 %, the operation strategy under the condition of low broken rate obtained by the network reasoning was: feed rate < 6 kg/s, travel speed < 5 km/h, concave clearance = 10 mm, threshing drum speed < 900 rpm. Three field trials were carried out using this optimized operation strategy, and the measured crushing rates were 0.93 %, 0.95 %, and 1.07 %, respectively, and the average crushing rate was 0.98 %. At the same time, when the optimized strategy was not used, the crushing rates were, respectively, 1.12 %, 1.41 %, and 1.93 %, and the average crushing rate was 1.48 %. The test results prove that the operation strategy based on Bayesian network inference can effectively reduce the crushing rate in the harvesting process. [ABSTRACT FROM AUTHOR]

In order to realize the demand for high-quality and high-efficiency harvest in modern agriculture, the grain combine harvesters must have the ability to intelligently adjust the operation parameters. The difficult problem is to establish the multi-parameter control system model for threshing and cleaning devices. The threshing and cleaning devices are located in the same rack space, and the interaction mechanism among agricultural material movement, mechanical structure, and airflow field is very complex. In view of the difficulties in the theoretical modeling of threshing and cleaning devices, a large number of operating parameters and performance indicators, strong coupling, and high requirements for real-time control, the system identification method was used to model the threshing and cleaning system in this paper. Firstly, the amplitude modulated PRBS input signals were designed as the input parameters of the system identification test, and the output signals acquisition test was carried out in the field. Then, the multi-input and multi-output signals of the system were used as training data, and the fusion method of the PSO (particle swarm optimization) algorithm and WNN (wavelet neural network) was proposed to identify it, and the optimal state-space model was obtained. Finally, the model identification and verification experiments were carried out on the threshing and cleaning system of various crops during the actual harvest. The VAF (variance-accounted-for) values of system identification model verification results were greater than or equal to 81.7%, and the RMSE (root mean square error) values were less than or equal to 0.602. The modeling method has high accuracy and adaptability, which laid a good foundation for realizing multi-parameter coordinated control of threshing and cleaning devices. [ABSTRACT FROM AUTHOR]

The single HST (Hydro Static Transmission) mechanical differential transmission gearbox of crawler combine harvester is subjected to the impact of alternating loads in the field operation, resulting in its fatigue failure. The traditional durability fatigue test can improve the fault-free working time of machinery, but it is not suitable for agricultural machinery with time-varying load frequency bandwidth and stress amplitude. Therefore, in this paper, a fault diagnosis method based on order analysis was proposed considering the comprehensive influence of load amplitude and frequency on the fatigue life of gearbox. The location and the corresponding type of fault were found by comparing the spectral line peak changed before and after. Then, the test verification was carried out on the gearbox assembly fatigue test bench according to the compiled load spectrum. The results show that the analysis results of the fault diagnosis method based on order analysis were consistent with the unpacking inspection, which had reference significance for the research on the fault diagnosis method of the crawler combined harvester gearbox. [ABSTRACT FROM AUTHOR]

Introduction. The article describes the approach to solving the problem of complex technical system troubleshooting based on expert knowledge modeling. Intelligent information systems are widely used to solve the problems of diagnostics of multilevel systems including combine harvesters. The formal description of the subject domain knowledge is the framework for building the knowledge base of these systems. The sequence of creating an expert system knowledge base in accordance with production rules is considered. Materials and Methods. The approach is founded on the fault function table. As the object of diagnostics, one of the subsystems of the combine harvester electric equipment “opening the hopper roof flaps” is considered. The basis for constructing a sequence of elementary checks is a system of logical equations describing both the serviceable and possible faulty states of the subsystem. Results. A structural logic model is developed. As a result of analyzing the fault function table, the sets of elementary checks are determined. Four criteria have been used to analyze the weight of these checks. The authors have determined optimal sequence of checks and have developed a decision tree, which allows finding the cause of the malfunction and is the basis for creating the knowledge base of an intelligent information system. A fragment of the knowledge base is given. Discussion and Conclusion. The proposed approach of expert knowledge modelling increases the efficiency of the unit for troubleshooting of the intelligent decision support system. It makes possible to structure the base of expertise and establishing the optimal sequence of elementary checks. This allows determining the optimal sequence of application of the knowledge base production rule that makes it possible to reduce the time of restoring the serviceability of combines.