To overcome the limitations of the hybrid tractor bumping tests, which include extended cycle times, high costs, and impracticality for single-part reliability verification, this study focuses on the exhaust system mounting bracket of a hybrid tractor. A novel approach that combines multi-objective particle swarm optimization (MOPSO) and wavelet decomposition algorithms was employed to enhance the reconstruction of shock vibration signals. This approach aims to enable the efficient acquisition of input signals for subsequent shaker table testing. The methodology involves a systematic evaluation of the spectral correlation between the original signal and the reconstructed signal at the stent’s response position, along with signal compression time. These parameters collectively constitute the objective function. The multi-objective particle swarm optimization algorithm is then deployed to explore a range of crucial parameters, including wavelet basic functions, the number of wavelet decomposition layers, and the selection of wavelet components. This exhaustive exploration identifies an optimized signal reconstruction method that accurately represents shock vibration loads. Upon rigorous screening based on our defined objectives, the optimal solution vector was determined, which includes the utilization of the dB10 wavelet basic function, employing a 12-layer wavelet decomposition, and selecting wavelet components a12 and d3~d11. This specific configuration enables the retention of 95% of the damage coefficients while significantly compressing the test time to just 46% of the original signal duration. The implications of our findings are substantial as the reconstructed signal obtained through our optimized approach can be readily applied to shaker excitation. This innovation results in a notable reduction in test cycle time and associated costs, making it particularly valuable for engineering applications, especially in tractor design and testing.

The noise level in a tractor cab directly affects the driving experience. Reducing the noise level in a tractor cab is an important means of improving the image of the product brand and protecting the physical and psychological health of employees. To reduce medium- and low-frequency noise in a tractor cab, the influence of the installation of coupled resonance acoustic materials (CRAMs) on tractor driver ear noise is investigated in this study. Theories for sound absorption by perforated plates and sound insulation by thin membranes are used to design a CRAM composed of a perforated plate, elastic membrane and additional mass. The acoustic impedance and sound absorption coefficient of the CRAM are obtained by an equivalent circuit method, and the resonance frequency of a local resonance system composed of an elastic membrane and additional mass is calculated. The effects of different hole diameters and porosities of the perforated plate on the CRAM sound absorption coefficient are compared. The results of tests performed using impedance tubes over a wide frequency range of 50 Hz~1600 Hz show that the maximum sound absorption coefficient is 0.99 and several transmission loss peaks of 15 dB~26 dB. Measurement of the acoustic response of the CRAM installed on the cab front panel shows that an ear noise reduction of approximately 1.3 dB(A) for a tractor working speed of 2200 rpm. The results show that the designed CRAM has a relatively good effect on reducing driver ear noise over a wide frequency band and is a feasible scheme for noise reduction in a tractor cab.

Long-term exposure to high levels of vibration and noise can have detrimental effects on the health of tractor drivers. This study aimed to evaluate the subjective comfort experienced by drivers operating large-horsepower tractors. A total of 10 tractors sourced from 5 different manufacturers were subjected to testing. The assessment encompassed three operational conditions, namely, idle, maximum torque, and rated power. Objective measurements, including A-weighted sound pressure level (A-SPL), loudness, sharpness, roughness, articulation index (AI), hand vibration, and seat vibration, were collected. Additionally, subjective comfort evaluations were carried out using a paired comparison test. To predict the subjective comfort of tractor drivers, a novel prediction model was developed by employing a simulated annealing (SA) algorithm to optimize a backpropagation neural network (BPNN). The model successfully achieved accurate predictions of subjective comfort, yielding a maximum prediction error of 4.4%. The study findings revealed that vibration had a more pronounced impact on driver comfort in environments with lower-amplitude noise, whereas high-decibel noise exerted a masking effect on vibration-induced discomfort. In conclusion, the SA-BPNN model, utilizing A-SPL, loudness, sharpness, roughness, AI, hand vibration, and seat vibration as objective parameters, effectively predicted the subjective comfort of tractor drivers. This discovery holds significant implications for tractor manufacturers, who can employ the model to optimize the design of tractor cabs and enhance driver comfort.