Scholarlink

<p>This study proposes a semi-supervised fault diagnosis framework based on vision transformers (ViTs) to enhance the diagnostic accuracy and generalization in machine cutting tools (MCT), particularly under the constraint of limited labeled data, a common challenge in intelligent manufacturing systems. The proposed method integrates pseudo-label generation, uncertainty quantification, and a dynamic teacher-student knowledge distillation strategy with an adaptive model refinement loop. Time–frequency domain scalograms, generated using continuous wavelet transform (CWT), are employed as input representations to preserve critical temporal and spectral characteristics from the acoustic emission (AE) signals. A ViT-based architecture is used to extract both local and global representations, enabling highly accurate fault diagnosis across MCT components such as bearings, gears, and cutting tools. The framework first trains a teacher model using transfer learning on a small, labeled dataset. Pseudo-labels for unlabeled data are then generated and refined using uncertainty estimation. High-confidence pseudo-labeled samples are merged with labeled data to train a lightweight DeiT-tiny transformer student model, which benefits from knowledge distillation for improved generalization and computational efficiency. The final adaptive refinement loop ensures continual performance improvement by filtering low-confidence samples and updating the model iteratively. The proposed framework was validated using real-world AE data collected from a milling machine achieving an accuracy of 99.68% and demonstrating outstanding reliability in identifying small fault variations across both experimental and benchmark datasets. By integrating advanced techniques, this work presents a scalable, data-efficient, and interpretable solution for predictive maintenance and intelligent fault diagnosis in Industry 4.0 environments.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Advanced fault diagnosis in milling cutting tools using vision transformers with semi-supervised learning and uncertainty quantification

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月27日

查看全文
  • Muhammad Farooq Siddique,
  • Muhammad Umar,
  • Wasim Ahmad,
  • Jong-Myon Kim

摘要

This study proposes a semi-supervised fault diagnosis framework based on vision transformers (ViTs) to enhance the diagnostic accuracy and generalization in machine cutting tools (MCT), particularly under the constraint of limited labeled data, a common challenge in intelligent manufacturing systems. The proposed method integrates pseudo-label generation, uncertainty quantification, and a dynamic teacher-student knowledge distillation strategy with an adaptive model refinement loop. Time–frequency domain scalograms, generated using continuous wavelet transform (CWT), are employed as input representations to preserve critical temporal and spectral characteristics from the acoustic emission (AE) signals. A ViT-based architecture is used to extract both local and global representations, enabling highly accurate fault diagnosis across MCT components such as bearings, gears, and cutting tools. The framework first trains a teacher model using transfer learning on a small, labeled dataset. Pseudo-labels for unlabeled data are then generated and refined using uncertainty estimation. High-confidence pseudo-labeled samples are merged with labeled data to train a lightweight DeiT-tiny transformer student model, which benefits from knowledge distillation for improved generalization and computational efficiency. The final adaptive refinement loop ensures continual performance improvement by filtering low-confidence samples and updating the model iteratively. The proposed framework was validated using real-world AE data collected from a milling machine achieving an accuracy of 99.68% and demonstrating outstanding reliability in identifying small fault variations across both experimental and benchmark datasets. By integrating advanced techniques, this work presents a scalable, data-efficient, and interpretable solution for predictive maintenance and intelligent fault diagnosis in Industry 4.0 environments.