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Abnormal-Sound Diagnosis for Kaplan Hydroelectric Generating Units Based on Continuous Wavelet Transform and Transfer

Yu Liu1, Zhuofei Xu1,2, Pengcheng Guo1

  • 1School of Water Resources and Hydroelectric Engineering, Xi'an University of Technology, Xi'an 710048, China.

Sensors (Basel, Switzerland)
|December 17, 2024
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Summary

This study introduces a novel method for diagnosing faults in hydroelectric generating units using continuous wavelet transform (CWT) and transfer learning (TL). The approach significantly enhances fault detection accuracy through advanced noise reduction and image-based analysis.

Keywords:
Kaplan hydroelectric generating unitsabnormal-sound diagnosisacoustic signal recognitiontransfer learning

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Area of Science:

  • Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Hydroelectric generating units are critical infrastructure requiring continuous monitoring.
  • Abnormal sound diagnosis is essential for preventing catastrophic failures and ensuring operational efficiency.
  • Existing diagnostic methods may lack accuracy or robustness in noisy environments.

Purpose of the Study:

  • To develop an accurate and efficient method for abnormal sound diagnosis in hydroelectric generating units.
  • To improve fault detection capabilities by combining continuous wavelet transform (CWT) and transfer learning (TL).
  • To validate the proposed method's effectiveness against established deep learning models.

Main Methods:

  • A denoising algorithm using spectral noise-gate technology was applied to enhance fault signals.
  • Continuous Wavelet Transform (CWT) was used to convert acoustic signals into pseudo-color images.
  • A transfer learning model with simplified fully connected layers was developed for feature extraction.
  • Key signal processing parameters were optimized for improved performance.

Main Results:

  • The proposed method achieved high diagnosis accuracy rates, significantly improving upon pre-filtering results (e.g., from 84.83% to 98.88%).
  • The noise-reduction process proved highly effective in enhancing fault characteristics.
  • Comparative analysis showed the proposed model outperformed classic deep learning models like AlexNet, Resnet18, and MobileNetV3.
  • The method demonstrated robust fault diagnosis for Kaplan hydroelectric generating units across various fault states.

Conclusions:

  • The developed method based on CWT and TL offers a highly accurate solution for abnormal sound diagnosis in hydroelectric units.
  • The integrated noise reduction and image-based analysis are crucial for effective fault detection.
  • This approach is vital for the daily monitoring, maintenance, and operational safety of hydroelectric generating units.