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Cross-Condition Tool Wear State Monitoring via Multi-Source Sensor Signal Fusion and Supervised Transfer Learning.

Yifeng Huang1, Xikang Lu1, Daode Zhang1

  • 1School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, China.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
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This study introduces a new method for monitoring tool wear across different machining conditions using sensor fusion and transfer learning. The approach enhances machining quality and production reliability by improving model generalization.

Area of Science:

  • Manufacturing Engineering
  • Machine Learning
  • Signal Processing

Background:

  • Tool wear monitoring is crucial for machining quality and production reliability.
  • Data-driven models struggle with generalization due to shifting signal distributions under varying operating conditions.

Purpose of the Study:

  • To propose a cross-condition tool wear state monitoring method using multi-source sensor signal fusion and supervised transfer learning.
  • To enhance the generalization ability of data-driven models for tool wear monitoring across different machining conditions.

Main Methods:

  • Multi-source sensor signal fusion (X-axis vibration, Z-axis vibration, spindle current) as multi-channel time-series inputs.
  • A deep model integrating multi-scale convolutional neural network, bidirectional long short-term memory, and attention mechanism for feature extraction.
Keywords:
cross-condition recognitionmulti-source sensor signal fusionsupervised transfer learningtool wear state monitoring

Related Experiment Videos

  • Progressive supervised transfer learning (pretraining, warm-up fine-tuning, joint fine-tuning) for target-condition adaptation.

    • Main Results:

    • The proposed method achieved an accuracy of 0.8588 with unified XZI input configuration, outperforming existing methods like CNN-LSTM, DANN, and CORAL.
    • Input ablation studies showed increasing accuracy with more sensor inputs (X: 0.6000, XZ: 0.7647, XZI: 0.8588).
    • Repeated experiments yielded high performance metrics: Accuracy (0.7929 ± 0.0499), Macro-F1 (0.7292 ± 0.0706), and Cohen's Kappa (0.6542 ± 0.0840).

    Conclusions:

    • Multi-source sensor fusion effectively captures discriminative wear-related features.
    • Supervised target-condition adaptation significantly improves model performance across different operating conditions.
    • The developed method demonstrates robust and effective cross-condition tool wear monitoring.