Owing to its precise positioning capability and fast dynamic behavior, the permanent magnet synchronous linear motor (PMSLM) has found broad application in advanced manufacturing and related industrial systems. However, air-gap nonuniformity faults, which are typical faults in PMSLMs, can cause thrust fluctuations and other adverse effects, thereby impairing stable equipment operation and potentially leading to safety accidents. Therefore, accurate diagnosis of these faults is essential. To address this issue, a dual-branch multiscale diagnostic method based on complementary feature utilization and joint decision-making is proposed. First, a 1-D diagnostic branch is developed to extract discriminative features from subtle fault-induced variations in the external magnetic field signal. Meanwhile, a Hankel-matrix mapping converts each 1-D external magnetic field signal into a grayscale image, which is subsequently processed by a dedicated 2-D branch to capture spatial structures and multiscale texture patterns. Finally, a joint decision layer is designed to comprehensively exploit the outputs of the two branches for fault identification. Simulation results show that the proposed method achieves a fault diagnosis accuracy of 99.83% under noise-free conditions and maintains an accuracy of 93.53% under white Gaussian noise at an SNR of 20 dB. Experiments using measured data collected from the developed PMSLM experimental platform, together with deployment experiments on edge computing platforms, further verify the effectiveness of the proposed model.