AI-Enabled End-of-Line Quality Control in Electric Motor Manufacturing

Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), excel at automatically learning feature representations directly from raw or minimally processed data. This eliminates the need for manual feature engineering, making them ideal for complex signal analysis in EoL inspection. Techniques like Mel-Frequency Spectrograms (MFSs) convert time-series data into 2D representations, which CNNs then process to detect fault patterns. Hybrid architectures combining CNNs and RNNs (like BiGRUs) capture both spectral and temporal dependencies, improving fault detection under various operating conditions. However, DL requires large labeled datasets, incurs high computational demands, and raises interpretability concerns.

Transfer Learning (TL) addresses the critical challenge of limited labeled fault data in industrial EoL inspection, especially for new motor types. By reusing knowledge from pre-trained models or related tasks, TL significantly reduces training time and data acquisition costs. In feature-based ML, this involves reusing feature importance rankings or hyperparameters. In DL, it means fine-tuning pre-trained neural networks with limited new data, adapting higher layers to specific fault patterns. TL enhances scalability across product families and production lines, but challenges include avoiding negative transfer and ensuring model reliability and interpretability in safety-critical environments.