Diagnosing agricultural machinery faults is critical to agricultural automation, and identifying vibration signals from faulty bearings is important for agricultural machinery fault diagnosis and predictive maintenance. In recent years, data–driven methods based on deep learning have received much attention. Considering the roughness of the attention receptive fields in Vision Transformer and Swin Transformer, this paper proposes a Shift–Deformable Transformer (S–DT) network model with multi–attention fusion to achieve accurate diagnosis of composite faults. In this method, the vibration signal is first transformed into a time–frequency graph representation through continuous wavelet transform (CWT); secondly, dilated convolutional residual blocks and efficient attention for cross–spatial learning are used for low–level local feature enhancement. Then, the shift window and deformable attention are fused into S–D Attention, which has a more focused receptive field to learn global features accurately. Finally, the diagnosis result is obtained through the classifier. Experiments were conducted on self–collected datasets and public datasets. The results show that the proposed S–DT network performs excellently in all cases. With a slight decrease in the number of parameters, the validation accuracy improves by more than 2%, and the training network has a fast convergence period. This provides an effective solution for monitoring the efficient and stable operation of agricultural automation machinery and equipment.

Mechanical fault diagnosis is of great significance to industrial automation, and extracting vibration fault signals is one of the important tasks in mechanical health monitoring and fault diagnosis. However, due to the complex working environment of rolling bearings, a large amount of noise makes it difficult to extract vibration fault signals. Denoising the vibration signal of bearings can remove interference noise, simplify the early identification of signal features, and thus improve diagnostic accuracy and mechanical maintenance efficiency. This paper proposes a rolling bearing fault signal denoising algorithm, which constructs a new feature extraction and denoising function. This method first decomposes the noisy signal into Intrinsic Mode Functions (IMFs) by Intrinsic Computing Expressive Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). Secondly, a new adaptive information entropy threshold function is constructed to extract the noisy IMFs from it. Then, the noisy IMF signal is denoised by a new wavelet threshold function. Finally, the noise-free IMFs are reconstructed to reconstruct the denoised signal. To verify the actual performance of the algorithm, comparative experiments were conducted on a self-collected dataset and a public dataset, the results show that this method improves the continuity of signal reconstruction and can remove various types of vibration fault noise signals more effectively and accurately, thereby improving the fault detection accuracy by 2%–9%.

The Micro-Electro-Mechanical System (MEMS) LiDAR point cloud in autonomous vehicles has a large deflection range, which results in slow registration speed and poor applicability. To maximize speed, an improved Normal Distribution Transform (NDT) method that integrates point cloud density features has been proposed. First, the point cloud is reduced using a modified voxel filter and a pass-through filter. Next, the Intrinsic Shape Signature (ISS) algorithm is utilized to analyze the point cloud features and extract key points; the Four-Point Congruent Set (4PCS) algorithm is then employed to calculate the initial pose under the constraints of the key point set to complete the coarse registration. Finally, the self-adaptive segmentation model is constructed by using a K-D tree to obtain the density features of key points, and the NDT algorithm is combined with this model to form an SSM-NDT algorithm, which is used for fine registration. Each algorithm was compared on the autonomous vehicle dataset PandaSet and actual collected datasets. The results show that the novel method increases the speed by at least 60% and takes into account good registration accuracy and strong anti-interference.

Ground segmentation is a fundamental prerequisite for autonomous navigation, environmental perception, and object detection in ground mobile platforms. To address the under-segmentation of ground points caused by sparse long-range point clouds, ground undulations, and interference from non-ground structures in complex road scenarios, this paper proposes a two-stage ground segmentation method based on the Adaptive Concentric Zone Model. First, an Adaptive Concentric Zone Model is constructed to dynamically determine the number of sectors in each ring, thereby forming local zones with more balanced point distributions. Based on this model, a two-stage ground segmentation method is developed. In the coarse segmentation stage, a lowest-height seed constraint and height-decay weighting are introduced to establish a weighted principal component analysis plane fitting model, from which ground candidate points are extracted. In the fine segmentation stage, a reflectance intensity consistency constraint is employed to distinguish high-confidence ground points from uncertain points, and the uncertain points are further refined based on the local height stability of high-confidence neighborhoods. Experimental results show that the proposed method achieves Precision, Recall, and F1-score values of 99.12%, 96.24%, and 97.66% on the SemanticKITTI dataset, and 98.72%, 100.00%, and 99.36%, respectively, on a self-collected point cloud acquired using a RUBY-PLUS. The results demonstrate that the proposed method can effectively adapt to the range-dependent distribution characteristics of LiDAR point clouds, which are dense at near ranges and sparse at far ranges. It reduces the misclassification of non-ground points while maintaining ground point recall, thereby effectively improving the stability of ground segmentation.