Objective With the rapid development of urban rail transit, linear motor has become one of the core driving devices due to its high efficiency and stable performance. Accurate measurement of air and slot gaps is crucial for the operational safety of linear motors. However, current measurements for linear motor air and slot gaps at the bottom of subway vehicles mainly rely on traditional manual methods and laser ranging. These methods have limitations such as low efficiency, high cost, and difficulty in fault localization. It is necessary to study an algorithm that meets urban rail transit demands for high-precision and high-efficiency measurement.

Method To meet the demands for real-time fault localization and detection, an algorithm based on 3D point clouds for measuring the linear motor air and slot gaps is proposed. This algorithm collects point cloud data from the subway train bottom surface through a 3D camera, then extracts the linear motor region by combining the denoising and region segmentation technologies. Subsequently, an improved geometric algorithm is used to calculate the upper/lower limit positions and values of the air gap and slot gap respectively.

Result & Conclusion With a less than ±2 mm deviation from maintenance personnel's verified values, this algorithm can precisely measure the upper/lower limits of the air and slot gaps and provide their positional information, and effectively meet the demand for precise measurement in urban rail transit equipment, thereby providing crucial technical support for the optimization design and quality control of linear motors.