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