Yajun Wang

Bearings are one of the critical components in rotating machinery. Bearing failures can lead to equipment damage, reduced performance, and even major safety accidents. Therefore, improving the ability to diagnose bearing faults can help improve the availability, reliability, and safety of rotating machinery. However, the original vibration signals in rotating machinery often contain noise and irregularities, making it difficult for traditional vibration analysis to extract effective high-dimensional features. Inspired by the construction of spatio-temporal graphs and dual-branch graph networks, a bearing fault diagnosis method based on dual-branch spatio-temporal graph networks (DBSGN) is proposed. Firstly, the vibration signal is modeled based on spectrum theory and the spectrum analysis method to construct a spatio-temporal graph. Secondly, Laplace-based spectral decomposition is used to extract the feature vectors of samples in the spatio-temporal graph. Finally, we designed a dual-branch fusion network to train and verify the bearing data and adjusted the model’s learning of the bearing data through a dynamic attention mechanism. The experimental results on three benchmark datasets indicate that DBSGN outperforms traditional models in terms of stability and accuracy.

In warehouses with vast quantities of heavy goods, heavy-duty forklift Automated Guided Vehicles (AGVs) play a key role in facilitating efficient warehouse automation. Due to their large load capacity and high inertia, heavy-duty forklift AGVs struggle to automatically navigate optimized routes. Additionally, rapid acceleration and deceleration can pose safety hazards. This paper proposes a velocity-adaptive model predictive control (MPC)-based path tracking method for heavy-duty forklift AGVs. The movement of heavy-duty forklift-type AGVs is categorized into straight-line and curve-turning motions, corresponding to the straight and curved sections of the reference path, respectively. These sections are segmented based on their curvature. The best driving speeds for straight and curved sections were 1.5 m/s and 0.3 m/s, respectively, while the optimal acceleration rates were 0.2 m/s² for acceleration and −0.2 m/s² for deceleration in straight paths and 0.3 m/s² for acceleration with −0.15 m/s² for deceleration in curves. Moreover, preferred sampling times, prediction domain, and control domain were determined through simulations at various speeds. Four path tracking methods, including pure tracking, Linear Quadratic Regulator (LQR), MPC, and the velocity-adaptive MPC, were simulated and evaluated under straight-line, turning, and complex double lane change conditions. Field experiments conducted in a warehouse environment demonstrated the effectiveness of the proposed path tracking method. Findings have implications for advancing path tracking control in narrow aisles.