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Intelligent Detection Method of Gearbox Based on Adaptive Hierarchical Clustering and Subset.

Huimiao Yuan1, Yongwei Tang1,2, Huijuan Hao1

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This study introduces an adaptive hierarchical clustering and subset (AHC-SFD) algorithm for intelligent gearbox fault diagnosis. The novel method achieves over 99.7% accuracy, improving upon traditional deep learning approaches.

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Area of Science:

  • Mechanical Engineering
  • Artificial Intelligence
  • Signal Processing

Background:

  • Deep learning enables automatic feature extraction for intelligent fault diagnosis using mechanical time-frequency signals.
  • Traditional methods often rely heavily on extensive signal processing and expert experience.
  • Misclassification of similar fault samples remains a challenge in current diagnostic systems.

Purpose of the Study:

  • To propose a novel fault diagnosis algorithm, Adaptive Hierarchical Clustering and Subset (AHC-SFD), to address similar sample misclassification.
  • To enhance feature extraction and improve the accuracy of gearbox fault diagnosis.
  • To reduce the dependency on manual signal processing and domain expertise.

Main Methods:

  • Utilized adaptive hierarchical clustering to analyze data characteristics and group similar data points into distinct feature sets.
  • Developed a SubCNN model tailored to each feature group for multiscale feature extraction.
  • Applied the AHC-SFD algorithm to gearbox fault diagnosis.

Main Results:

  • The proposed AHC-SFD method achieved a fault recognition rate exceeding 99.7% on the gearbox dataset.
  • Demonstrated superior performance in distinguishing between similar fault samples.
  • Exhibited strong generalization capabilities for gearbox fault diagnosis.

Conclusions:

  • The AHC-SFD algorithm effectively improves the accuracy and reliability of intelligent fault diagnosis systems.
  • This approach offers a robust solution for gearbox fault diagnosis, minimizing misclassification errors.
  • The method showcases the potential of combining clustering with deep learning for advanced diagnostics.