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Related Concept Videos

Reducing Line Loss01:18

Reducing Line Loss

194
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
194

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Rolling Bearing Degradation Identification Method Based on Improved Monopulse Feature Extraction and 1D Dilated

Chang Liu1, Haiyang Wu2, Gang Cheng2

  • 1School of Mechanical and Electrical Engineering, Xuzhou University of Technology, Xuzhou 221116, China.

Sensors (Basel, Switzerland)
|July 30, 2025
PubMed
Summary

This study introduces an improved feature extraction method and a one-dimensional dilated residual convolutional neural network (1D-DRCNN) for accurate rolling bearing degradation identification. The combined approach achieves high recognition accuracy, even under complex working conditions.

Keywords:
degradation identificationdilated convolutionfeature extractionresidual connection

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Area of Science:

  • Mechanical Engineering
  • Artificial Intelligence
  • Signal Processing

Background:

  • Conventional methods struggle with extracting rolling bearing degradation information.
  • Existing convolutional networks show insufficient performance for this task.
  • Accurate identification of bearing degradation is crucial for predictive maintenance.

Purpose of the Study:

  • To propose a novel method for rolling bearing degradation state identification.
  • To enhance feature extraction for fault detection.
  • To develop a robust deep learning model for diverse working conditions.

Main Methods:

  • Improved monopulse feature extraction using phase scanning and synchronous averaging.
  • Two-stage grid search for fault characteristic frequency (FCC) calibration.
  • Construction and application of a one-dimensional dilated residual convolutional neural network (1D-DRCNN).
  • Experimental validation using vibration signals from nine degradation states.

Main Results:

  • The proposed feature extraction method reduces computational burden and effectively extracts local fault information.
  • The 1D-DRCNN model successfully identifies different bearing degradation states under complex conditions.
  • t-SNE visualization confirms the network's response to bearing degradation features.
  • Overall recognition accuracy reached 97.33%.

Conclusions:

  • The improved feature extraction and 1D-DRCNN method offers a significant advancement in rolling bearing degradation identification.
  • The approach is effective in overcoming complex working condition influences.
  • The method demonstrates high accuracy and robustness for predictive maintenance applications.