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June 19, 2026International Journal of Information and Communication TechnologyOpen Access

Unsupervised transfer learning for real-time motor resonance fault diagnosis on programmable logic controllers

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Authors

YLYing Li

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Overview

Randomized trial demonstrates real-time motor resonance fault diagnosis in programmable logic controllers, suggesting effective edge execution strategies.

Key Points

  • The aim is to diagnose motor resonance faults in real-time on programmable logic controllers despite domain shifts and hardware constraints.
  • Introduced a deep causal adversarial migration transfer learning framework.
  • Applied multi-physics signal fusion with a physics-guided attention mechanism for feature extraction.
  • Implemented structured pruning and quantization for lightweight edge execution.
  • Achieved an average accuracy of 96.2% across varying load tasks, outperforming baselines by 3.1%.
  • The final model reached a 31-millisecond inference time, satisfying the sub-100 ms requirement for real-time diagnosis.

Cite This Study

Ying Li (2026) studied this question.

synapsesocial.com/papers/6a34de7065a5b0777af2dd1ehttps://doi.org/10.1504/ijict.2026.154215
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