Migration learning based fast prediction algorithm for traveling wave tube lifetime

Traveling Wave Tubes (TWTs) are critical components in high-power microwave systems. Their operational reliability and lifespan prediction accuracy directly impact equipment maintenance strategy formulation and operational safety. Addressing challenges in TWT lifespan prediction under complex operating conditions, including data scarcity, nonlinear degradation patterns, and cross-model generalization difficulties, this study proposes a novel transfer learning framework based on Gated Recurrent Units (GRU) and domain adaptation. Integrating data-driven approaches with physical mechanisms, we developed a preprocessing method incorporating Interquartile Range (IQR) denoising and physically interpretable interpolation. We also constructed a lightweight GRU transfer architecture featuring parameter-selective migration through layer importance analysis. Experimental results on K-band TH4816 and Ka-band TH4626 TWT datasets demonstrate the model achieves high-precision prediction with only partial parameter fine-tuning during transfer, achieving a MAPE of 0.01%. The transferred model predicts a lifespan of 24.67 years for the TH4626 TWT. For engineering applications, this approach meets real-time processing requirements in embedded systems through transfer learningbased parameter adjustment, providing a reliable technical solution for predictive maintenance.

• Publication year

  2025

• Venue

  2025 11th International Conference on Mechanical Engineering, Materials and Automation Technology (MMEAT)

• Publication date

  2025-06-23

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/MMEAT65787.2025.11399206
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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