In light of the challenges posed by the complex structural characteristics and significant coupling of vibration signals in rotating machinery, this study proposes an adaptive noise reduction method called Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). Additionally, an enhanced threshold screening Singular Value Decomposition (SVD) algorithm is introduced to address the issues pertaining to noise identification and feature extraction in the context of vibration signals from rotating machinery, which are subjected to complex noise interference. The effectiveness of the proposed approach is substantiated through the evaluation of key metrics, such as the signal-to-noise ratio (SNR), as well as the utilization of advanced signal analysis techniques, including Short Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT). The experimental results validate the finding that the combination of the improved CEEMDAN and the enhanced threshold screening SVD algorithm effectively reduces noise interference in vibration signals from rotating machinery. This integrated denoising approach successfully preserves the informative characteristics of the vibration signals, thereby laying a foundation for the subsequent fault diagnosis of rotating machinery.

This paper systematically reviews research progress in semi-active suspension systems for agricultural machinery seats, focusing on key technologies and methods to enhance ride comfort. First, through an analysis of the comfort evaluation indicators and constraints of seat suspension systems, the current applications of variable stiffness and damping components, as well as semi-active control technologies, are outlined. Second, a comparative analysis of single control methods (such as PID control, fuzzy control, and sliding mode control) and composite control methods (such as fuzzy PID control, intelligent algorithm-based integrated control, and fuzzy sliding mode control) is conducted, with control mechanisms explained using principle block diagrams. Furthermore, key technical challenges in current research are summarized, including dynamic characteristic optimization design, adaptability to complex operating environments, and the robustness of control algorithms. Further research could explore the refinement of composite control strategies, the integrated application of intelligent materials, and the development of intelligent vibration damping technologies. This paper provides theoretical references for the optimization design and engineering practice of agricultural machinery suspension systems.

In recent years, more and more attention has been paid to fuel cell-based hybrid tractors. In order to optimize the global power distribution of tractors and further improve the fuel economy and fuel cell durability of the system, this paper designs an energy management strategy to maximize external energy efficiency based on fuel cell/lithium battery/supercapacitor hybrid tractors. This strategy aims to reduce the real-time hydrogen consumption of the system while maximizing the external energy output so as to reduce the impact of load randomness on the output power of the fuel cell. Under the typical ploughing conditions of the tractor, the simulation is compared with the state machine strategy and the equivalent hydrogen consumption minimization strategy. The results show that the proposed strategy meets the power requirements of a given ploughing condition, and compared with the two traditional strategy systems, the performance characteristics of auxiliary energy are more fully exerted. It reduces the burden on fuel cells and improves the durability of fuel cells. The hydrogen consumption of the system was reduced by 11.03 g and 16.54 g, respectively, improving the overall economy of the hybrid system.

In response to the problems of limited algorithms and low diagnostic accuracy for fault diagnosis in large tractor transmission systems, as well as the high noise levels in tractor working environments, a defect detection approach for tractor transmission systems is proposed using an enhanced convolutional neural network (CNN) and a bidirectional long short-term memory neural network (BILSTM). This approach uses a one-dimensional convolutional neural network (1DCNN) to create three feature extractors of varying scales, directly extracting feature information from different levels of the raw vibration signals. Simultaneously, in order to enhance the model’s predicted accuracy and learn the data features more effectively, it presents the multi-head attention mechanism (MHA). To overcome the issue of high noise levels in tractor working environments and enhance the model’s robustness, an adaptive soft threshold is introduced. Finally, to recognize and classify faults, the fused feature data are fed into a classifier made up of bidirectional long short-term memory (BILSTM) and fully linked layers. The analytical findings demonstrate that the fault recognition accuracy of the method described in this article is over 98%, and it also has better performance in noisy environments.

The transmission system of a tractor is a crucial component, so it is crucial to promptly identify and correctly diagnose faults in it. However, due to the limited samples of faults occurring during its operational processes, employing existing fault diagnosis methods directly yields unsatisfactory results. This paper proposes a fault diagnosis model combining Time Generative Adversarial Networks (Time GAN) and Transformer. To enhance diagnostic accuracy, we first employ Time GAN for data augmentation, addressing the issue of imbalanced fault samples in practical scenarios. Then, we integrate a Transformer network with improved multi-head self-attention mechanisms, leveraging the advantages of the Transformer’s encoder-decoder architecture and attention mechanism to enhance diagnostic performance. Bearing data from Case Western Reserve University (CWRU) was used to validate the diagnostic performance of the proposed model, while gear data from an experimental rig built by the author was used to validate the model’s generalization capability. Experimental results indicate that the accuracy reached 98.96% and 95.36% in CWRU Dataset and Self-made Dataset respectively. In strong noise environments, the accuracy remains above 93%. In conclusion, the diagnostic model presented in this paper can reliably diagnose tractor transmission system problems in few-sample conditions and noise environments compared to traditional machine learning models.