Permanent magnet synchronous motors are widely used in the field of propulsion motors due to their high efficiency, high torque density, and other advantages. This article focuses on the research of fault diagnosis methods for common stator turn short circuits and rotor eccentricity in surface mounted permanent magnet synchronous motors. Most existing methods are based on stator port voltage and current to extract fault features, but the aggregated parameter information obtained from the motor ports is easily affected by winding structure, pole slot coordination, and other factors, resulting in effective fault signals being overwhelmed and small information dimensions, leading to low signal-to-noise ratio and poor dynamic performance in detection. In order to obtain internal magnetic field information that can directly characterize the state of the motor, and considering the compact structure of high-power density motors, this paper uses a flexible printed circuit board (FPCB) with small space occupation and a large number of turns to make a detection coil, and arranges it in the stator slot to capture magnetic field information. A fast fault diagnosis method based on fault feature extraction is proposed for inter turn short circuit and eccentricity faults. For mixed faults, decoupling of fault diagnosis cannot be achieved through simple coefficient correction. A fault discrimination scheme based on convolutional neural networks is proposed, and the performance of different types of learning methods is compared. The experimental results show that under mixed fault conditions, an accuracy evaluation of about 98% for inter turn short circuits is achieved, and the eccentricity detection error of AlexNet is only about 5% when the training data proportion is 60%.