AI-Based Percussive Acoustic Signal Classification for Fastener Strength Inspection of Stator Wedge

摘要

This paper proposes a two-stage AI-based method for automatically inspecting the fastener strength of generator stator wedges. This approach includes a ‘Noise removal stage’ employing a CNN-based autoencoder to eliminate industrial noise, and a ‘Classification stage’ extracting various features from the denoised signals to classify the fastener strength. Each stage of the proposed system demonstrates high accuracy and objectivity, significantly improving inspection efficiency by eliminating the rotor removal process. Moreover, the system effectively filters out industrial noise and is scalable, making it suitable for practical use in industrial sites with robotic inspection units.