Gearbox condition monitoring is essential for ensuring the reliability of power transmission systems. However, the existing methods are constrained by shallow feature extraction and unidirectional temporal modeling. To address these limitations, this study proposes a novel fault diagnosis framework that integrates multiscale depthwise separable convolution, bidirectional gated recurrent units, and a squeeze-and-excitation attention mechanism. This approach enables multiscale feature extraction from vibration signals, bidirectional temporal modeling, and the enhancement of critical fault-related information. The experimental results demonstrate that the proposed method significantly outperforms conventional models in terms of fault diagnosis accuracy, noise robustness, and adaptability to varying operating conditions. The attention mechanism effectively suppresses noise interference, while bidirectional temporal modeling accurately captures fault propagation characteristics, thereby improving adaptability to dynamic conditions. This research provides a highly robust solution for intelligent gearbox fault diagnosis in complex industrial environments.

Tractors equipped with hydro-mechanical transmissions (HMTs) typically deliver excellent fuel-saving performance but are expensive. To improve the fuel economy of cheaper tractors, the authors of this study have designed an HMT for a tractor that uses a simple, single planetary gear to merge the power and analyze its consumption of energy and fuel. First, we introduce the principle of transmission of the HMT and formulate a model to calculate its speed, torque, and efficiency. Second, we analyze the parasitic power of the HMT and simulate its characteristics of efficiency. Finally, we compare the efficiency of transmission and fuel consumption of HMTs with a single planetary gear and Simpson planetary gears. The results showed that parasitic power was obtained when the displacement of the variable pump was negative and the maximum ratio of hydrostatic power in each range was 45–46%. The highest efficiency of the proposed HMT in ranges R_L (low range) and R_H (high range) were 87% and 89%, respectively. It has a simpler structure than the HMT with Simpson planetary gears and consumes lower amounts of energy and fuel. These attributes make it suitable for use as a transmission system for large- and medium-power tractors with a continuously variable transmission.

To enhance the reliability of unmanned hydro-mechanical transmission tractors, a fault diagnosis method for their power-shift system was developed. First, fault types were identified, and sample data was collected via a test bench. Next, a feature extraction method for data dimensionality reduction and a deep learning network called W_SCBAM were introduced for fault diagnosis. Both W_SCBAM and conventional algorithms were trained 20 times, and their performance was compared. Further testing of W_SCBAM was conducted in various application scenarios. The results indicate that the feature extraction method reduces the sample length from 46 to 3. The fault diagnosis accuracy of W_SCBAM for the radial-inlet clutch system has an expectation of 98.5% and a variance of 1.6%, respectively, outperforming other algorithms. W_SCBAM also excels in diagnosing faults in the axial-inlet clutch system, achieving 97.6% accuracy even with environmental noise. Unlike traditional methods, this study integrates the update of a dimensionality reduction matrix into network parameter training, achieving high-precision classification with minimal input data and lightweight network structure, ensuring reliable data transmission and real-time fault diagnosis of unmanned tractors.

The hydro-mechanical transmission (HMT) of continuously variable transmission tractors typically achieves speed regulation using a 2Z-X(A) planetary gear system. Long-term use of this setup has created a strong patent barrier, hindering further HMT structural innovation. This study systematically examines the energy consumption characteristics of HMTs based on a 2Z-X(B) planetary gear set configuration, aiming to provide a theoretical reference for developing new HMT tractors. First, the powertrains of both 4-range and 2-range HMTs using this configuration are described. Next, a mathematical model of the 4-range HMT is developed, and its hydrostatic power portion, transmission efficiency, and fuel consumption are analyzed. Finally, the energy consumption characteristics of the 2-range HMT are compared with those of the 4-range HMT, highlighting their performance differences. Results indicate that HMTs based on the 2Z-X(B) planetary gear set exhibit similar efficiency characteristics to traditional systems, with a maximum efficiency exceeding 90%. The impact of efficiency on HMT fuel economy is greater than that of engine fuel consumption itself, suggesting that an efficiency-prioritized power matching control strategy is feasible.

The power-shift system employed in agricultural multi-range continuously variable transmissions (CVTs) features a complex structure and control logic, presenting significant challenges to the reliability of agricultural machinery. To enable timely detection of faults, constructing an intelligent fault diagnosis classifier to monitor the system’s health status is essential. Typically, fault samples utilized for classifier development originate from ideal bench tests, characterized by uniform patterns and limited diversity, thereby hindering the algorithm’s generalization capability. This study addresses this issue by proposing a generative adversarial network (GAN) model, integrated with a triple loss function and a novel generator architecture, to augment the fault dataset under laboratory conditions. The generator architecture comprises a variational autoencoder module and an oil pressure point attention mechanism, enabling the generation of diverse and fluctuating virtual samples. Building on this augmented dataset, a fault classifier based on one-dimensional ConvNeXt was developed. Experimental results indicate that the classifier achieves an accuracy of 99.73%. While classifier accuracy decreases with increasing noise levels, the GAN-generated dataset provides more comprehensive training, resulting in an accuracy approximately 3% higher than that achieved using the original dataset.