Pengfei Qian

The knot-forming motions of knotters are difficult to directly observe, and knotting tests are labor-intensive and time-consuming. To address these issues, a novel test bench motion scheme was proposed to simulate the bundling process of a bale, and a performance test bench for a single-knot knotter was developed under controllable indoor conditions. The bench can verify the working principles of different knotters and assess the knotting success rate. To meet the requirements of unattended and safe operations for long-term continuous knotting tests, an automatic control process for the test bench was constructed to identify the knot-releasing failure and twine breakage fault based on monitoring the pressure of the simulated bale. By establishing the corresponding relationships between knotting actions and image states, a test and analysis method for detecting the motion timing sequences of the knotter was developed. This allowed us to obtain a motion timing diagram of the knotter, which can provide references for its reverse design or performance evaluation. The results of the continuous knotting tests show that the tested knotter achieved a knotting success rate of 99.57%, and the test bench can meet the performance testing requirements of the knotter.

Aiming at the problems of low separation efficiency and high impurity content in the cleaning process of moist rice threshed materials, this study systematically explored the influence mechanism of moisture on the segregation behavior of rice threshed products by combining physical experiments and CFD-DEM coupling simulation. Physical test results show that moist conditions significantly change the properties of threshed materials: the impurity mass fraction increases from 3.1% to 6.8%, the straw breakage rate rises from 5.97% to 6.99%, and the working load and unbalanced dynamic load of the threshing unit increase noticeably. On this basis, a gas–solid coupling simulation model of the cleaning device embedded with the Johnson–Kendall–Roberts (JKR) cohesive contact model is established. It is found that moist threshed materials exhibit an obvious stratified movement on the sieve surface, in which the bottom straw layer moves slowly while the upper material layer flows rapidly, resulting in a 50% increase in the sieve surface load. This phenomenon directly accounts for the rising impurity content observed in experiments. Through integrated experimental and simulation analysis, this research clarifies the macroscopic laws of moisture effects and reveals three key pathways for moisture to deteriorate cleaning performance: changing physical characteristics of threshed materials, enhancing inter-particle adhesion, and forming stratified movement. The findings provide an innovative research scheme and reliable theoretical support for the design and optimization of high-efficiency cleaning devices for rice combine harvesters.

The recorded seismic signals are often marred by unwanted noise and artifacts, detracting from the quality and clarity required for accurate analysis. The critical task of removing this undesirable noise is essential for the effective processing of seismic data. In this work, the focus is primarily on the challenge of denoising by using a convolutional neural network operating on a transformation. By employing the polynomial fitting and localized time-frequency analysis, the low-dimensional seismic signals are processed into high-dimensional signals. The combination of time-frequency wavelet domain representation and polynomial interpolation with deep learning techniques results in a notable increase in the Signal-to-Noise Ratio (SNR), which indicates the efficiency of the proposed method. By comparing our method against the traditional transformed and non-transformed based approaches, including the widely used DnCNN denoiser, we observed that our approach consistently achieves higher SNR values, with the transformation step playing a key role in this improvement. Moreover, the methodology showed reasonably consistent performance across a range of mother wavelets, indicating limited sensitivity to the particular wavelet selected. The practical applications of this enhanced denoising method are vast, with its impact extending to the monitoring of volcanic activity, where data clarity can be lifesaving, and to earthquake detection, where early and accurate recognition is essential.

Excessive swing of the cantilevered conveyor trough is a key issue restricting the working efficiency and operational stability of combine harvesters. To suppress its swing, this study established a dynamic model of the conveyor trough to reveal the influence mechanisms of the initial angle, overall length, and cylinder pivot length on its swing characteristics. Orthogonal experimental design and multi-factor analysis of variance were employed to systematically analyze the significance of these three factors on swing amplitude, identifying cylinder pivot length as the most dominant factor. Optimization results determined the optimal parameter combination as an initial angle of 48.33°, an overall length of 1.45 m, and a cylinder pivot length of 1.1 m. Field tests verified that this optimized scheme reduces the swing amplitude by 11.62%, with a minimal error of 0.57% between theoretical and measured values, providing a reliable theoretical and experimental basis for the low-vibration design of combine harvester conveying mechanisms.

The vibration of the combine harvester header assembly directly affects harvesting efficiency and operational quality. To address the insufficient dynamic characterization of the cantilever conveying trough under complex field excitations, this study systematically analyzes the vibration response characteristics of the header assembly under multi-source coupled excitation through field experiments and theoretical modeling. Acceleration sensors arranged at three measurement points on the header bottom collected vibration data, revealing that the dominant vibration frequency of the header has a deterministic harmonic relationship with the threshing drum’s operating frequency (3rd harmonic on the left side, 1.5th harmonic on the right side), demonstrating dynamic coupling effects within the integrated system. Through acceleration response analysis at four symmetric measurement points on the connection, the external excitation force was quantified as a sinusoidal function correlated to the feed quantity (F = 1094.4 sin(50πt/3)). A damped pendulum model of the cantilever conveying trough was established using the Lagrange method. Validation results show that the error between the predicted steady-state swing amplitude and measured values is only 1.11–4.3%, confirming the effectiveness of this simplified model in characterizing the system’s steady-state response. This research provides a theoretical foundation and methodological support for dynamic characterization, parameter optimization, and stability control of the cantilever header system in combine harvesters.

The cleaning of wet rice threshed outputs presents a critical constraint on the operational efficiency of harvesters during humid conditions. This study aimed to quantify the specific impact of wet-state conditions on the physical properties of rice threshed outputs. Utilizing the Hertz–Mindlin with Johnson–Kendal–Roberts contact model, Plackett–Burman screening experiments were designed in Design-Expert to identify contact parameters significantly influencing the dynamic angle of repose. For the six most significant parameters, a six-factor, three-level Box–Behnken experimental design was implemented. Analysis of variance was performed on the experimental data, establishing a multivariate nonlinear regression model relating the dynamic angle of repose to the key contact parameters. Systematic measurements were conducted to determine the compositional content and key physical parameters of the constituent materials: the elastic modulus of paddy was 216.3 MPa; the elastic moduli of stalks (categorized by diameters of 4, 5, and 6 mm) were 5.59, 6.28, and 8.93 MPa, respectively; the paddy friction coefficient was 0.518; the stalk sliding friction coefficient was 0.566 and rolling friction coefficient was 0.115; and the surface moisture contents were 7.76% for paddy and 11.8% for stalks, respectively. The optimized contact parameter group of wet paddy and stalks was compared with the measured values. The relative error of the predicted angle of repose for the paddy was only 0.23%, and the error for the rice stalks was 1.3%. The precise contact parameters established in this study provide crucial foundational support for the development of subsequent CFD-DEM coupled numerical models.

Combine harvesters, as essential equipment in agricultural engineering, frequently experience structural faults due to their complex structure and harsh working conditions, which severely affect their reliability and operational efficiency, leading to significant downtime and reduced agricultural productivity during critical harvesting periods. Therefore, developing accurate and timely Fault Detection and Diagnosis (FDD) techniques is crucial for ensuring food security. This paper provides a systematic and critical review and analysis of the latest advancements in research on data-driven FDD methods for structural faults in combine harvesters. First, it outlines the typical structural sections of combine harvesters and their common structural fault types. Subsequently, it details the core steps of data-driven methods, including the acquisition of operational data from various sensors (e.g., vibration, acoustic, strain), signal preprocessing methods, signal processing and feature extraction techniques covering time-domain, frequency-domain, time–frequency domain combination, and modal analysis among others, and the use of machine learning and artificial intelligence models for fault pattern learning and diagnosis. Furthermore, it explores the required system and technical support for implementing such data-driven FDD methods, such as the applications of on-board diagnostic units, remote monitoring platforms, and simulation modeling. It provides an in-depth analysis of the key challenges currently encountered in this field, including difficulties in data acquisition, signal complexity, and insufficient model robustness, and consequently proposes future research directions, aiming to provide insights for the development of intelligent maintenance and efficient and reliable operation of combine harvesters and other complex agricultural machinery.