The rack-and-pinion drive mechanism (RPD) is a critical transmission component in the battery swap system (BSS) of electric heavy trucks (EHTs). The transmission mechanism in the RPD typically operates under conditions of low speed, speed fluctuations, and short-term sampling, posing challenges for accurate fault diagnosis. To address these issues, this study proposes a fault diagnosis method based on the Heterospectral-Symmetric-Derived Point Cloud Feature Tree (HS-PCFT): First, multiple SDP (symmetric point pattern) variants are generated using Hybrid-dimensional vibration signals to construct multi-perspective symmetric patterns. Based on this, these images are converted into coordinate points and a structured point cloud feature tree is constructed to capture geometric differences across different fault modes. Subsequently, the point cloud is voxelized and input into a lightweight 3D convolutional neural network (3D CNN) for classification. The fault diagnosis algorithm was validated on the BSS platform, achieving an accuracy rate of 97.83%. Comparative studies indicate that the proposed method outperforms traditional 2D/3D networks and conventional machine learning methods. This research proposes an effective representation path from signals to structured geometry, providing an effective solution for fault diagnosis in low-speed and complex industrial environments.
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