Article: Unsupervised transfer learning for real-time motor resonance fault diagnosis on programmable logic controllers Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.67 pp.23 - 49 Abstract: Diagnosing motor resonance faults in real-time on programmable logic controllers are challenging due to domain shifts across changing conditions and stringent hardware limits. This study introduces a deep causal adversarial migration transfer learning framework. It synergises multi-physics signal fusion with a physics-guided attention mechanism to disentangle invariant fault features from domain-sensitive variations, followed by adversarial domain alignment. The framework is subsequently lightweighted via structured pruning and quantisation for edge execution. Evaluations on the Case Western Reserve University dataset show the method achieves an average accuracy of 96.2% across varying load tasks, outperforming strong baselines by 3.1%. The final model attains a 31-millisecond inference time, which strictly complies with the sub-100 ms real-time requirement for industrial controllers, proving its effectiveness for dependable edge-based diagnosis. Inderscience Publishers - linking academia, business and industry through research

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

Authors: Ying Li

Addresses: Baotou Iron and Steel Vocational Technical College, Baotou, 014010, China

Abstract: Diagnosing motor resonance faults in real-time on programmable logic controllers are challenging due to domain shifts across changing conditions and stringent hardware limits. This study introduces a deep causal adversarial migration transfer learning framework. It synergises multi-physics signal fusion with a physics-guided attention mechanism to disentangle invariant fault features from domain-sensitive variations, followed by adversarial domain alignment. The framework is subsequently lightweighted via structured pruning and quantisation for edge execution. Evaluations on the Case Western Reserve University dataset show the method achieves an average accuracy of 96.2% across varying load tasks, outperforming strong baselines by 3.1%. The final model attains a 31-millisecond inference time, which strictly complies with the sub-100 ms real-time requirement for industrial controllers, proving its effectiveness for dependable edge-based diagnosis.

Keywords: motor resonance fault diagnosis; unsupervised transfer learning; domain adaptation; edge computing; programmable logic controller; PLC.

DOI: 10.1504/IJICT.2026.154215

International Journal of Information and Communication Technology, 2026 Vol.27 No.67, pp.23 - 49

Received: 11 Jan 2026
Accepted: 15 Feb 2026
Published online: 16 Jun 2026 *

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

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