會議論文
| 學年 | 112 |
|---|---|
| 學期 | 2 |
| 發表日期 | 2024-05-22 |
| 作品名稱 | Real-Time Anomaly Detection in Grinding Wheels Using a Multimodal Deep Learning Framework |
| 作品名稱（其他語言） | |
| 著者 | Qiaoyun Zhang; Wan-Chi Yang; Hsiang-Chuan Chang; Chia-Ling Ho; Chih-Yung Chang |
| 作品所屬單位 | |
| 出版者 | |
| 會議名稱 | I-DO 2024 |
| 會議地點 | Taipei; Taiwan |
| 摘要 | In the manufacturing process involving grinding wheels, challenges arise in fine-tuninggrindingmachines, typicallyaddressedbycraftsmen through subjective observations of sparks and sounds. This paper introduces a novel mechanism comprising two pivotal phases aimed at optimizing grinding wheel production line efficiency and accuracy. Firstly, an AutoEncoder is employed for spectrogram denoising, effectively isolating grinding sounds from environmental noise. Convolutional Neural Networks (CNNs) in the Encoder extract features across time and frequency domains, while deconvolution in the Decoder gradually restores features. ReLU activation ensures computational efficiency and effectively handles nonlinear features. Secondly, an AI-based assessment determines parameter adjustments using a combination of 3DCNN and CNN. By integrating classification results from both networks, features from video and audio data are identified, thereby enhancing classification effectiveness. Anomalies during grinding operations are detected through combined outputs, indicating the need for parameter adjustments |
| 關鍵字 | Deep learning; 3DCNN; CNN; Anomaly Detection |
| 語言 | zh_TW |
| 收錄於 | |
| 會議性質 | 國內 |
| 校內研討會地點 | 無 |
| 研討會時間 | 20240522~20240524 |
| 通訊作者 | |
| 國別 | TWN |
| 公開徵稿 | |
| 出版型式 | |
| 出處 | |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/126362 ) |