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November 21, 2025Scientific ReportsOpen Access

Fault diagnosis for pump equipment based on transfer learning: the domain generalization method

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Authors

WWWeihong WangXZXinwen ZhaoYZYongfa Zhang

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Overview

Analysis shows improved classification accuracy using transfer learning in pump fault diagnosis, indicating method's robustness.

Key Points

  • Achieved 96.83% classification accuracy in fault diagnosis with transfer learning methods.
  • FOCAL-TFAM-ResNet-LSTM reduces classification error and enhances performance under varied conditions.
  • Algorithm validated with real-world datasets including reciprocating pump and Case Western Reserve University.
  • Findings suggest transfer learning can effectively address issues in domain generalization for engineering applications.

Cite This Study

Wang et al. (2025) studied this question.

synapsesocial.com/papers/6924e3f2c0ce034ddc34efabhttps://doi.org/10.1038/s41598-025-25210-1
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  4. 4Reciprocating Pump Fault Diagnosis Using Enhanced Deep Learning Model with Hybrid Attention Mechanism and Dynamic Temporal Convolutional Networks2025
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