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Fault Types01:18

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When analyzing a single line-to-ground fault from phase A to ground at a three-phase bus, it is important to consider the fault impedance. This impedance is zero for a bolted fault, equal to the arc impedance for an arcing fault, and represents the total fault impedance for a transmission-line insulator flashover. To derive sequence and phase currents, fault conditions are translated from the phase domain to the sequence domain.
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Adaptive Label Refinement Network for Domain Generalization in Compound Fault Diagnosis.

Qiyan Du1, Jiajia Yao1, Jingyuan Yang2

  • 1School of Mechanical Engineering, Sichuan University, Chengdu 610065, China.

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|November 27, 2025
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Summary
This summary is machine-generated.

This study introduces an adaptive label refinement network (ALRN) for robust compound fault diagnosis. ALRN enhances cross-domain performance with limited data by creating better soft labels, improving accuracy by over 22%.

Keywords:
adaptive label refinementcompound fault diagnosisconvolutional neural networkdomain generalizationlabel refinement stability coefficient

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Industrial Fault Diagnosis

Background:

  • Domain generalization (DG) is crucial for real-world fault diagnosis but challenged by compound faults and limited multi-source data.
  • Existing DG methods often require extensive data, which is impractical for industrial settings due to cost and operational constraints.
  • Hard labels and label smoothing inadequately represent complex fault relationships, hindering cross-domain robustness.

Purpose of the Study:

  • To develop a novel adaptive label refinement network (ALRN) for effective domain generalization in compound fault diagnosis.
  • To enable robust model training using imperfect labels under source-scarce conditions (one or two source domains).
  • To create richer, more robust soft labels that capture inter-class semantic similarities.

Main Methods:

  • An adaptive label refinement network (ALRN) was designed, leveraging a convolutional neural network (CNN) for initial predictions.
  • Iterative label refinement using sample-wise cross-entropy loss as an adaptive weighting factor to compute weighted averages of predictions.
  • A label refinement stability coefficient, based on max-min Kullback-Leibler (KL) divergence ratio, was proposed to assess label quality and determine iteration termination.

Main Results:

  • ALRN achieved accuracy gains exceeding 22% on unseen operating conditions compared to a conventional CNN baseline.
  • The proposed method demonstrates superior performance with only one or two source domains for training.
  • The refined soft labels effectively encode semantic similarities between fault classes, enhancing diagnostic accuracy.

Conclusions:

  • The adaptive label refinement network (ALRN) provides a novel and practical solution for cross-domain compound fault diagnosis with imperfect supervision.
  • ALRN significantly enhances cross-domain diagnostic performance, particularly under source-scarce conditions.
  • The method offers a robust approach to learning with imperfect labels, improving model generalization capabilities.