A Feasibility Study of Deep Learning-Based Motor Defect Screening in a Production Line Using an Airborne Acoustic Signal

Le résumé fourni par la source

Reliable defect detection in motor production lines is important for maintaining manufacturing quality. This study investigates the feasibility of a deep learning-based airborne acoustic screening approach for controlled no-load end-of-line induction motor inspection. Acoustic signals collected under controlled no-load test conditions were used to evaluate Feedforward Neural Networks (FNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and pre-trained models including ResNet50, MobileNetV2, and EfficientNetB0 for detecting rotor unbalance, assembly-induced bearing abnormalities, and their combination. Experimental results show that several models achieved up to 96% classification accuracy, depending on the selected architecture and feature representation. In cross-motor external validation using an unseen motor from the same manufacturer, the CNN with MFCC features achieved the best performance with 96% accuracy and 97% precision, recall, and F1-score, while ResNet50 with spectrogram inputs achieved 93% accuracy and 92% F1-score. These results demonstrate that airborne motor acoustic signals contain discriminative defect-related information under controlled no-load conditions and support the feasibility of low-cost, non-contact airborne acoustic sensing as a complementary screening approach for rapid end-of-line motor inspection.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.