Search Results (69)

Intelligent fault diagnosis for rotary machinery often suffers performance degradation under domain shifts between training and deployment environments. To address this, we propose a robust fault diagnosis framework incorporating three key components: (1) an order-frequency-based preprocessing method to normalize rotational variations, (2) a U-Net variational autoencoder (U-NetVAE) to enhance adaptation through reconstruction learning, and (3) a test-time training (TTT) strategy enabling unsupervised target domain adaptation without access to source data. Since existing works rarely evaluate under true domain shift conditions, we first construct a unified cross-domain benchmark by integrating four public datasets with consistent class and sensor settings. The experimental results show that our method outperforms conventional machine learning and deep learning models in both F1-score and recall across domains. Notably, our approach maintains an F1-score of 0.47 and recall of 0.51 in the target domain, outperforming others under identical conditions. Ablation studies further confirm the contribution of each component to adaptation performance. This study highlights the effectiveness of combining mechanical priors, self-supervised learning, and lightweight adaptation strategies for robust fault diagnosis in the practical domain. Full article

To investigate the interaction mechanism between agricultural tillage machinery and soil, this study established a precise simulation model by integrating physical and numerical experiments using typical yellow cinnamon soil collected from western Henan Province, China. The discrete element parameters for soils with varying moisture contents were calibrated based on the Hertz–Mindlin (no slip) contact model. Through Plackett–Burman screening, steepest ascent optimization, and Box–Behnken response surface methodology, a predictive model correlating moisture content, parameters, and repose angle was developed, yielding the optimal contact parameter combination: interparticle static friction coefficient (0.6), soil–65Mn static friction coefficient (0.69), and interparticle rolling friction coefficient (0.358). For the Bonding model, orthogonal experiments coupled with NSGA-II multi-objective optimization determined the optimal cohesive parameters targeting maximum load (673.845 N) and displacement (9.765 mm): normal stiffness per unit area (8.8 × 10⁷ N/m³), tangential stiffness per unit area (6.85 × 10⁷ N/m³), critical normal stress (6 × 10⁴ Pa), critical tangential stress (3.15 × 10⁴ Pa), and bonding radius (5.2 mm). Field validation using rotary tillers and power harrows demonstrated less than 6% deviation in soil fragmentation rates between simulations and actual operations, confirming parameter reliability and providing theoretical foundations for constructing soil-tillage machinery interaction models. Full article

Artificial Intelligence (AI) is revolutionizing proactive repair systems by enabling real-time identification of bearing faults in industrial machinery. However, traditional fault detection methods often struggle in dynamic environments due to their dependence on specific training conditions. To address this limitation, a transfer learning (TL)-based methodology has been developed for bearing fault detection, so that the model trained under some specific training conditions can perform accurately under significantly different real-time working conditions, thereby significantly improving diagnostic efficiency while reducing training time. Initially, a deep learning approach utilizing convolutional neural networks (CNNs) has been employed to diagnose faults based on vibration data. After achieving high classification performance at source domain conditions, the performance of the model is re-evaluated by applying it to the Case Western Reserve University (CWRU) dataset as the target domain through the TL method. short-time Fourier transform is employed for signal preprocessing, enhancing feature extraction and model performance. The proposed methodology has been validated across various CWRU dataset configurations under different operating conditions and environments. The proposed approach achieved a 99.7% classification accuracy in the target domain, demonstrating effective adaptability and robustness under domain shifts. The results demonstrate how TL-enhanced CNNs can be used as a scalable and efficient way to diagnose bearing faults in industrial environments. Full article

Health indicators (HIs) are quantitative indices that assess the condition of engineering systems by linking sensor data with monitoring, diagnostic, and prognostic methods to estimate the remaining useful life (RUL). Digital twins (DTs), which serve as digital representations of physical assets, enhance system monitoring, diagnostics, and prognostics by operationalizing analytic capabilities derived from sensor data. This paper explores the integration of HIs and DTs, illustrating their roles in condition-based maintenance and structural health monitoring. The methodologies discussed span data-driven and physics-based approaches, emphasizing their applications in rotary machinery, including bearings and gears. These approaches not only detect anomalies but also predict system failures through advanced modeling and machine learning (ML) techniques. The paper provides examples of HIs derived from vibration analysis and soft sensors and maps future research directions for improving health monitoring systems through hybrid modeling and uncertainty quantification. It concludes by addressing the challenges of data labeling and uncertainties and the role of HIs in advancing performance engineering, making DTs a pivotal tool in predictive maintenance strategies. Full article

The sugarcane cultivation has used heavy machinery on a large scale, which causes soil compaction. The minimum tillage has been used to reduce the traffic of machines on the crop, but there is a lack of appropriate tools for the implementation of this technique, especially in sugarcane areas. The University of Campinas—UNICAMP developed a conservation soil tillage tool called “Rotary paraplow”, the idea was to join the concepts of a vertical milling cutter with the paraplow, which is a tool for subsoiling without inversion of soil. The rotary paraplow is a conservationist tillage because it mobilizes only the planting line with little disturbance of the soil surface and does the tillage with the straw in the area. These conditions make this study pioneering in nature, by proposing an equipment developed to address these issues as an innovation in the agricultural machinery market. We sought to evaluate soil tillage using rotary paraplow and compare it with conventional tillage, regarding soil physical properties and yield. The experiment was conducted in an Oxisol in the city of Jaguariuna, Brazil. The comparison was made between the soil physical properties: soil bulk density, porosity, macroporosity, microporosity and penetration resistance. At the end, a biometric evaluation of the crop was carried out in both areas. The soil properties showed few statistically significant variations, and the production showed no statistical difference. The rotary paraplow proved to be an applicable tool in the cultivation of sugarcane and has the advantage of being an invention adapted to Brazilian soils, bringing a new form of minimal tillage to areas of sugarcane with less tilling on the soil surface, in addition to reducing machine traffic. Full article

This paper presents a first-of-its-kind evaluation of integrating Large Language Models (LLMs) within a Human-In-The-Loop (HITL) framework for risk analysis in machinery functional safety, adhering to ISO 12100. The methodology systematically addresses LLM limitations, such as hallucinations and lack of domain-specific expertise, by embedding expert oversight to ensure reliable and compliant outputs. Applied to four diverse industrial case studies—motorized gates, autonomous transport vehicles, weaving machines, and rotary printing presses—this study assesses the applicability of ChatGPT in routine risk analysis tasks central to machinery functional safety workflows, such as hazard identification and risk assessment. The results demonstrated substantial improvements: during HITL involvement and the subsequent iterations of risk assessment with expert feedback, a complete agreement with ground truth was achieved across all four use cases. ChatGPT also identified additional scenarios and edge cases, enriching the risk analysis. Efficiency gains were notable, with time efficiency rated at 4.95 out of 5, on average, across case studies. Overall accuracy (4.7 out of 5) and usability (4.8 out of 5) ratings demonstrated the robustness of the HITL framework in ensuring reliable and practical outputs. Likert scale evaluations reflected high confidence in the refined outputs, emphasizing the critical role of HITL in enhancing both trust and usability. The study also highlights the importance of prompt design, revealing that longer initial prompts improve accuracy, while shorter iterative prompts maintain usability without compromising efficiency. The iterative HITL process further ensures that refined outputs align with safety standards and practical requirements. This evaluation underscores the transformative potential of generative AI in functional safety workflows, enhancing routine activities while ensuring rigorous human oversight in safety-critical, regulated industries. Full article

Rotary friction welding is a solid-state welding process that can manufacture high-integrity joints between similar and dissimilar materials with short weld times. However, access to expensive and complex industrial-grade friction welding machines is not always possible. This study explores the design process and functionality of a laboratory-scale friction welding setup following the fundamentals of large-scale machinery. The proposed setup is designed to be easily manufactured, employing the use of a calibrated drill press and load cell, thus ensuring welding parameters such as rotational speed and applied axial load are monitored. The decision to investigate rotary friction welding of aluminium bronze Ca104 to austenitic stainless steel AISI316 was taken to explore the limitations of this bespoke friction welding machine for prospective applications in the nuclear energy sector. The workpieces were friction welded at four sets of rotational speeds with constant friction and forging pressures. The microstructural evolution and mechanical properties of the dissimilar material welds were investigated via optical and scanning electron microscopy with energy dispersive spectroscopy, 4-point bend testing and microhardness measurements. Results show a change in the hardness along the weld interface and evidence of metallic diffusion between the dissimilar materials, demonstrating the successful application of the small-scale experimental setup. Full article

There are extensive areas of mugwort cultivation in China, making efficient harvesting crucial for the industry’s economic performance. However, the lack of specialized harvesting machinery for hilly and mountainous regions leads to reliance on manual operations, characterized by high labor intensity and low efficiency. To address these issues, a self-propelled mugwort harvester is designed based on mugwort planting patterns and the physical characteristics of mugwort during the harvesting period. Key structural components, such as drum dimensions, tooth shapes, and tine arrangements, are developed, and a defoliation force model is established to identify factors influencing the net rate of mugwort leaf harvesting, impurity rate, and mugwort leaf usability. The harvester employs a fully hydraulic drive system, for which the hydraulic system is designed and components are selected. A quadratic regression orthogonal rotary test determines the optimal parameters: a forward speed of 0.8 m/s, drum speed of 200 r/min, and cutting table height of 50 mm. Field tests show that the harvester achieves a net rate of mugwort leaf harvesting of 93.78%, an impurity rate of 13.96%, a mugwort leaf usability of 86.23%, and an operational efficiency of 0.155 hm²/h, while maintaining stable operation under field conditions. Beyond these performance metrics, the harvester reduces dependency on manual labor, lowers operational costs, and increases profitability for farmers. By improving the sustainability and mechanization of mugwort harvesting, this study provides an efficient solution for mugwort cultivation in hilly and mountainous regions and contributes to the sustainable development of the industry. Full article

Objective: This study was conducted to address the harsh working environment of agricultural machinery and improve the wear resistance of soil-contacting components such as rotary tiller blades, thereby extending their service life. Method: Plasma-cladding technology was employed to prepare an iron-based wear-resistant coating on the surface of rotary tiller blades. The following parameter combination was optimized using response surface methodology (RSM): a cladding current of 144A, a cladding speed of 23 mm/s, a powder feeding rate of 23 g/min, and a cladding distance of 12 mm. The microstructure morphology, phase composition, microhardness, and wear resistance of the wear-resistant cladding layer were investigated. Results: The results indicate that the interface of the cladding layer is clean and free from significant porosity or defects, exhibiting good metallurgical bonding with the substrate. The primary phases identified in the cladding layer include α-Fe, Cr₇C₃, Cr₂Fe₁₄C, and Cr-Ni-Fe-C solid solutions. The average hardness of the cladding layer is 1171 Hv0.5, approximately 2.9 times that of the substrate. In wet sand–rubber wheel wear tests under identical conditions, the weight loss of the cladding layer is only 1/21 that of 65Mn steel, with minimal wear morphology. Field trials showed that the wear of the cladding layer rotary tiller blade was reduced by 24.5% compared with the unclad blade. The presence of the cladding layer significantly protected the integrity of the cutting edge, ensuring the functionality of the rotary tiller blade in cutting and throwing soil; thus, its original appearance was maintained even after prolonged wear. The findings of this study can provide a valuable reference for the enhancement of wear resistance for other soil-contacting components. Full article

The increasing use of tractors and implements is replacing manual labor, but adds financial burdens on small-scale farmers due to rising costs. Many farmers have turned to leasing and renting machinery to mitigate these expenses, while repair and maintenance costs remain significant. Government interventions aim to alleviate these burdens, but income disparities between urban and rural areas persist, and the impact of machinery use on climate change and the environment poses further challenges. Strategies like omitting some operation steps and adopting versatile machinery are proposed to cut costs and promote economic sustainability for small-scale farmers. Therefore, this study assessed the economic benefits of using versatile machinery in farming, especially for small-scale rural farmers. Farming processes were divided into field preparation and crop season activities. Field preparation included rotary tillage, ridge formation, and mulching, whereas crop season activities included harvesting and transportation. Annual usage and production cost analyses per hectare, including labor, fuel, and interest, alongside purchasing cost surveys, were conducted. Versatile machinery reduced annual usage costs for field preparation and crop season activities by 63.54% and 71.71%, respectively. This effect was more pronounced for farms under 2 ha, especially those employing manual harvest and transportation. Small-scale farmers, such as those cultivating hot pepper farms, are strongly encouraged to adopt versatile machinery to mitigate expenses and labor costs. The significance of adopting studied methodology will be amplified with the rising cost of labor. Consequently, utilization of versatile machinery in field farming for small-scale farms is projected to increase incomes not through enhanced production, but by significantly reducing the annual usage costs associated with agricultural machinery. This approach not only alleviates financial burdens but also enhances the sustainability of farm management, ensuring long-term viability and environmental stewardship. Full article

As a crucial step in food production, tillage and land preparation play a pivotal role in achieving sustainable crop production and improving the soil environment. However, accurate assessment of the load that agricultural machinery implements during the operation process has always been a vexing problem that needs urgent solutions. In this paper, an extrapolation and reconstruction framework for the time-domain load is constructed based on the probability-weighted moments (PWM) estimation and the peaks-over-threshold function, and the load spectrum is obtained for agriculture general power machinery. Firstly, the load acquisition system was developed, the traction resistance and output torque of the tractor were measured, and the collected load signals were preprocessed. Next, the mean excess function and PWM estimation are introduced to select the optimal threshold and generalized Pareto distribution (GPD) fitting parameters and the extreme load distribution that exceeds the threshold range is fitted. The extreme points in the original data are replaced by generating new extreme points that follow the GPD distribution, and the extrapolation of the load spectrum is achieved. Finally, the real extrapolated load spectrum was validated based on statistical characteristics and rainflow counting analysis, and the correlation coefficient between the fitting data and the extreme load samples was greater than 0.99. It can retain the load sequence characteristics of the original load to a great extent, truly reflecting the load state during the operation of agricultural machinery. Meanwhile, the characteristics of the load spectrum can be accurately obtained, such as extreme, mean, and amplitude values, and the real load during deep loosening and rotary tillage are accurately described. The values provide more authentic and reliable data support for the subsequent selection of optimal operating parameters, reliability design of the power transmission system, and the life assessment of the agricultural implements. Full article

This research focuses on the development of a machine learning-based approach for the early diagnosis of blade rubbing in rotary machinery. In this paper, machine learning-based diagnostic methods are used for blade rubbing early diagnosis, and the faults are simulated using experimental models. The experimental conditions were simulated as follows: Excessive rotor vibration is generated by an unbalance mass, and blade rubbing occurs through excessive rotor vibration. Additionally, the severity of blade rubbing was also simulated while increasing the unbalance mass. And then, machine learning-based diagnostic methods were applied and the trends according to the severity of blade rubbing were compared. This paper provides a signal processing method through feature analysis to diagnose blade rubbing conditions in machine learning. It was confirmed that the results of the unbalance and blade rubbing represent different trends, and it is possible to distinguish unbalance from blade rubbing before blade rubbing occurs. The diagnosis using machine learning methods will be applicable to rotating machinery faults like blade rubbing; furthermore, the early diagnosis of blade rubbing will be possible. Full article

Deep vertical rotary tillage (DVRT) is an innovative soil tillage technology that has been widely adopted in China and shown significant potential in enhancing soil quality, optimizing water use efficiency, and increasing crop yields across diverse ecological and agronomic conditions. DVRT utilizes a vertical spiral drill bit for deep plowing, which preserves soil structure, reduces soil compaction, and improves water retention, making it particularly effective in regions facing climatic challenges such as drought. This review synthesizes the effects of DVRT on soil’s physical and chemical properties, crop root systems, photosynthesis, and water use efficiency, demonstrating its advantages in promoting robust root development and improving nutrient utilization. Although the technology has been applied successfully across various crops and regions, its nationwide adoption remains limited. This paper emphasizes the need for further research to refine the theoretical framework of DVRT and develop tailored strategies for different local conditions. Additionally, integrating DVRT with other agronomic practices and advancing machinery design, supported by policy measures, is essential for maximizing its benefits. In conclusion, DVRT presents a promising approach for sustainable agriculture in China, contributing to improved soil quality, increased crop yields, and enhanced food security. Full article

Cylindrical hydraulic dampers are commonly utilized to mitigate vibrations in machinery and structural applications. These devices generally feature a single linear stroke and are often linked to rotary joints to handle complex loading conditions. However, their installation in confined spaces, such as vehicle suspensions, poses considerable difficulties. In this research, we introduce an innovative bifurcated origami hydraulic damper with nonlinear damping capabilities. Initially, we formulated the collapsible conditional equations essential for the design of the bifurcated origami hydraulic dampers. We then examined the fluid dynamics within the damper and its flow channels, determining that the damping force is proportional to the square of the velocity. Furthermore, we developed motion equations based on the derived damping force and suggested vibration analysis methods using the Runge–Kutta approach. For the mass-spring vibration system, we created an experimental setup with the bifurcated origami hydraulic damper and performed vibration tests using noise signals recorded from a vehicle traveling on a gravel road, thus validating its damping performance and efficacy. Additional tests, which varied the orifice size at the end of the origami structure, as well as the type and temperature of the internal fluid, showed that the orifice size had a more pronounced effect on damping efficiency than the fluid type and temperature. This confirmed the vibration-damping effectiveness of the bifurcated origami hydraulic damper. Full article

China’s small arched shed-building machinery suffers from a low degree of mechanization, building efficiency, and qualification rate for frame insertion. Therefore, we designed a rotary double-insertion device and established the equation for its motion trajectory. The analysis shows that in the rotary insertion process, a better point of entry into the soil exists. A simulation model was constructed in ADAMS, and the static and dynamic trajectories were analyzed. Additionally, the optimal planting and insertion speed ratios were determined. Considering the qualified rate of the insertion frame as the evaluation index to establish a regression model, we adopted a three-factor three-level experimental design and established the planting speed ratio, center distance of the planting arm, and length of the pressing rod arm as the main influencing factors. We used Design-Expert 13 to perform the analysis of variance and determined the optimal parameter combinations. The experimental results show that the planting speed ratio was 0.7, the center distance of the planting arm group was 554 mm, the length of the pressing rod arm was 923 mm, and the qualification rate of trellis planting at this time was 98.05%. The bench was adjusted and tested based on the optimal parameter combination. The average value of the measured trellis qualification rate was 96.73%, and the relative error between the test value and the theoretical optimization value was 1.32%, thereby verifying the reliability of the optimal parameter combination. Field verification test results show that the rotary double-insertion device had a planting speed ratio of 0.7 and a trellis qualified rate of 95.74% compared with the theoretical optimization value of 2.31%. Conforming to the design requirements of small arch shed-building machinery, the prototype operation performance was stable. Full article