Transfer Learning-Based Fault Detection System of Permanent Magnet Synchronous Motors

Automatic fault detection is currently combined with deep networks owing to the possibility of dispensing signal processing, which significantly accelerates reactions to faults. Changing the type of defect or object forces repetition of the network learning process and its implementation. The design of universal systems for detecting different faults can be developed using transfer learning techniques. This paper presents the application of the transfer learning of a convolutional neural network to the fault diagnosis of permanent magnet synchronous motors. The crucial point of this research was to develop accurate diagnostic applications based on the data obtained from the motor field model and to use their functionality for a real object. This study compares the accuracy of diagnostic systems using three currently known techniques: neural network-based, instance-based, and mapping-based transfer learning. The experimental verification of the systems was carried out on an experimental bench with a 2.5 kW motor.