Abstract
In industrial manufacturing processes, such as plugging small connectors in, where visual verification is difficult, workers may experience difficulties in detecting failures. Artificial intelligence algorithms can be used to detect and identify the sound of these connectors and mitigate human error. In this work, sound samples of correctly plugged-in connectors and ordinary background noise of the workplace were collected using a recording setup fastened to workers' hand. In order to discriminate anomalies that represent failures, autoencoder models were trained and tested in an unsupervised manner. Experiments with different deep learning architectures for anomaly detection are conducted. Our CNNAE-FT model achieved best results and yielded a ROC-AUC score of 0.85.
Original language | English |
---|---|
Title of host publication | HORA 2023 - 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9798350337525 |
DOIs | |
Publication status | Published - 2023 |
Event | 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2023 - Istanbul, Turkey Duration: 8 Jun 2023 → 10 Jun 2023 |
Publication series
Name | HORA 2023 - 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings |
---|
Conference
Conference | 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2023 |
---|---|
Country/Territory | Turkey |
City | Istanbul |
Period | 8/06/23 → 10/06/23 |
Bibliographical note
Publisher Copyright:© 2023 IEEE.
Keywords
- anomaly detection
- machine learning
- sound detection