(1) Background: with the development of intelligent transportation, effectively collecting and identifying the working state of vehicles is conducive to the analysis and processing of vehicle information by internet of vehicles, so as to reduce the occurrence of traffic accidents. Aiming at the problem of low identification accuracy of the mechanical vibration fault signal, a signal identification method based on time-frequency detection is introduced; (2) Methods: this paper constructs a parameter model of the synchroextracting S transform on the basis of the poor time-frequency concentration of the original S transform; (3) Results: in the case of SNR = −5~+30 dB, compared with other transformations, the Rényi entropy value of SEST is the smallest, and the Rényi entropy value is 0.5246 when SNR = +22 dB; (4) Conclusions: through simulation comparison and analysis, the excellent time-frequency concentration and anti-noise characteristics of the SEST are highlighted, and the rotor vibration fault signals such as rotor misalignment, unbalance and bearing wear are identified by SEST.

This paper proposed the additional fractional gradient descent identification algorithm based on the multi-innovation principle for autoregressive exogenous models. This algorithm incorporates an additional fractional order gradient to the integer order gradient. The two gradients are synchronously used to identify model parameters, thereby accelerating the convergence of the algorithm. Furthermore, to address the limitation of conventional gradient descent algorithms, which only use the information of the current moment to estimate the parameters of the next moment, resulting in low information utilisation, the multi-innovation principle is applied. Specifically, the integer-order gradient and additional fractional-order gradient are expanded into multi-innovation forms, and the parameters of the next moment are simultaneously estimated using multi-innovation matrices, thereby further enhancing the parameter estimation speed and identification accuracy. The convergence of the algorithm is demonstrated, and its effectiveness is verified through simulations and experiments involving the identification of a 3-DOF gyroscope system.

In modern manufacturing, cutting tools are essential for cutting processes, and their wear state directly affects the processing accuracy, production efficiency, and product quality. Identification of the tool-wear state using a single sensor is insufficient to satisfy the requirements of high-precision, high-efficiency machining. To address this problem, this paper proposes a novel approach to identify the tool-wear state using information fusion technology and the sparrow search algorithm (SSA)–backpropagation (BP) neural network framework. This method uses a principal component analysis (PCA) to fuse multi-domain features extracted from three-way vibration signals, power signals, and temperature signals. Subsequently, the optimal initial threshold and weight of the BP neural network are optimized using the SSA to prevent the network from falling into the local optimum and accelerate the convergence of the algorithm. Lastly, a tool-wear-state identification model based on the SSA–BP neural network is constructed. Experimental results show that the proposed method has an identification accuracy of 98.33%, precision rate of 98.81%, recall rate of 97.96%, and F1 score of 98.36%.

Integer-order models cannot characterize the dynamic behavior of the flexible two-link manipulator (FTLM) system accurately due to its viscoelastic characteristics and flexible oscillation. Hence, this paper proposes a fractional-order modeling method and identification algorithm for the FTLM system. Firstly, we exploit the memory and history-dependent properties of fractional calculus to describe the flexible link’s viscoelastic potential energy and viscous friction. Secondly, we establish a fractional-order differential equation for the flexible link based on the fractional-order Euler–Lagrange equation to characterize the flexible oscillation process accurately. Accordingly, we derive the fractional-order model of the FTLM system by analyzing the motor–link coupling as well as the symmetry of the system structure. Additionally, a system identification algorithm based on the multi-innovation integration operational matrix (MIOM) is proposed. The multi-innovation technique is combined with the least-squares algorithm to solve the operational matrix and achieve accurate system identification. Finally, experiments based on actual data are conducted to verify the effectiveness of the proposed modeling method and identification algorithm. The results show that the MIOM algorithm can improve system identification accuracy and that the fractional-order model can describe the dynamic behavior of the FTLM system more accurately than the integer-order model.

3D point cloud object detection plays a critical role in data acquisition and model recognition for autonomous driving systems. However, it suffers from low accuracy when handling complex scenes, such as long-distance small objects and occluded objects. To address this issue, this paper proposes AM-SECOND, a 3D object detection network that integrates Adaptive Spatial Channel Self-Attention (ASCA) and Multi-scale Context Gated Fusion (MCGF). The network utilizes SECOND as the basic architecture. Firstly, spatial and channel dual attention is embedded into each convolution stage of the backbone network. This module effectively enhances the spatial and channel representation capabilities of the backbone features, enabling the network to distinguish small objects from complex backgrounds better. Secondly, efficient multi-scale feature fusion is achieved in the multi-scale feature fusion stage via intra-group context interaction, adaptive normalization, and multiple gating mechanisms. This strategy enhances the feature representation of small and occluded objects. Finally, experimental results on the KITTI dataset demonstrate that AM-SECOND outperforms the baseline by improving mAP for cars, cyclists, and pedestrians by 6.39\%, 13.11\%, and 13.20\%, respectively. These results prove that the AM-SECOND algorithm effectively improves detection performance in complex scenes.