Bearings, as commonly used elements in mechanical apparatus, are essential in transmission systems. Fault diagnosis is of significant importance for the normal and safe functioning of mechanical systems. Conventional fault diagnosis methods depend on one or more vibration sensors, and their diagnostic results are often unsatisfactory under strong noise interference. To tackle this problem, this research develops a bearing fault diagnosis technique utilizing a multi-channel, multi-scale spatiotemporal convolutional cross-attention fusion network. At first, continuous wavelet transform (CWT) is applied to convert the raw 1D acoustic and vibration signals of the dataset into 2D time–frequency images. These acoustic and vibration time–frequency images are then simultaneously fed into two parallel structures. After rough feature extraction using ResNet, deep feature extraction is performed using the Multi-Scale Temporal Convolutional Module (MTCM) and the Multi-Feature Extraction Block (MFE). Next, these traits are input into a dual cross-attention mechanism module (DCA), where fusion is achieved using attention interaction. The experimental findings validate the efficacy of the proposed method using tests and comparisons on two bearing datasets. The testing findings validate that the suggested method outperforms the existing advanced multi-sensor fusion diagnostic methods. Compared with other existing multi-sensor fusion diagnostic methods, the proposed method was proven to outperform the five existing methods (1DCNN-VAF, MFAN-VAF, 2MNET, MRSDF, and FAC-CNN).

In the harvesting operation, the stubble height of the grain is a vital parameter index in the combined harvesting operation; if the stubble is too high or too low, it will directly affect the harvesting quality and the service life of the header. At present, the profiling control system can only control the lift of the header in the vertical direction but not the horizontal direction and the angle of the cutter profiling. This study proposes a contouring control strategy and system for grain harvesting by analyzing the designed contouring adjustment mechanism and simulating the control method and hydraulic system through Amesim2404 software to simulate and analyze the control method and hydraulic system. Finally, different forward speeds of the harvester (5, 7, 9, and 11 km/h) and other cutting heights of the harvester were analyzed based on static and field tests and different stubble heights (100, 150, 200, and 250 mm) on the test indexes. The results of the field test showed that for different operating speeds, the error between the mean value of stubble height and the target value was small, the absolute error was less than 2 mm, the mean value of the coefficient of variation of stubble height was 4.53%, and the mean value of control accuracy is 94%. The developed adaptive control system of the profiled grain header has high precision and stability, which can provide a reference for the all-terrain profiling control technology of the combined harvester header deck.

In the harvesting operation, the stubble height of the grain is an important parameter index in the combine harvesting operation, the stubble is too high or too low will directly affect the harvesting quality and the service life of the cutting table. At present, the profiling control system can only control the lift of the cutting table in the vertical direction, but not the horizontal direction and the angle of the cutter profiling. The purpose of this study is to propose a contouring control strategy and system for grain harvesting by analyzing the designed contouring adjustment mechanism, and to simulate the control method and hydraulic system through Amesim software to simulate and analyze the control method and hydraulic system. Finally, different forward speeds of the harvester (5、7、9、11km/h) and different cutting heights of the harvester were analyzed based on static and field tests and different stubble heights (100、150、200、250mm) on the test indexes. The results of the field test showed that for different operating speeds, the error between the mean value of stubble height and the target value was small, and the absolute error was less than 2mm, the mean value of the coefficient of variation of stubble height was 4.53% and the mean value of control accuracy is 94%.The developed adaptive control system of the profiled grain cutting deck has high accuracy and stability, which can provide reference for the all-terrain profiling control technology of combine harvester cutting deck.