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Related Experiment Video

Updated: Aug 25, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Bolt-Loosening Detection Using 1D and 2D Input Data Based on Two-Stream Convolutional Neural Networks.

Xiaoli Hou1, Weichao Guo1, Shengjie Ren1

  • 1School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, China.

Materials (Basel, Switzerland)
|October 14, 2022
PubMed
Summary

This study introduces a novel Two-Stream Convolutional Neural Network (TSCNN) model for accurate bolt-loosening detection. The TSCNN model achieves high diagnostic accuracy using vibration signals and time-frequency images, even in noisy conditions.

Keywords:
anti-noisebolt connectionfault diagnosistwo-stream convolutional neural networks

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Area of Science:

  • Mechanical Engineering
  • Artificial Intelligence
  • Signal Processing

Background:

  • Bolt loosening is a critical failure mode in machinery, significantly impacting operational safety and efficiency.
  • Current detection methods often lack the required accuracy, necessitating advanced diagnostic approaches.
  • Accurate bolt-loosening diagnosis is essential for predictive maintenance and preventing catastrophic failures.

Purpose of the Study:

  • To develop a highly accurate fault diagnosis model for detecting bolt-loosening states.
  • To improve the precision of bolt-loosening detection by leveraging both raw vibration signals and time-frequency image data.
  • To propose a novel deep learning architecture capable of simultaneously extracting features from diverse data representations.

Main Methods:

  • A Two-Stream Convolutional Neural Network (TSCNN) model was developed, integrating one-dimensional Convolutional Neural Network (1DCNN) and two-dimensional Convolutional Neural Network (2DCNN).
  • The model was enhanced by modifying LeNet-5 architecture, including adjusting convolution kernel parameters and incorporating dropout operations.
  • The TSCNN model utilizes raw vibration signals and their corresponding time-frequency images as input for automatic feature extraction.

Main Results:

  • The proposed TSCNN model achieved an average bolt-loosening fault diagnosis recognition accuracy of 99.58% in experimental validation.
  • The model demonstrated robust performance, achieving over 93% accuracy with a signal-to-noise ratio (SNR) above 0 dB without preprocessing.
  • Experimental results on a machine tool guideway confirmed the model's effectiveness and superiority in end-to-end bolt-loosening fault diagnosis.

Conclusions:

  • The developed TSCNN model offers a highly effective and accurate solution for end-to-end bolt-loosening fault diagnosis.
  • The approach exhibits strong noise immunity, maintaining high accuracy even with noisy input signals.
  • This method provides a significant advancement in machine condition monitoring and fault detection for critical mechanical components.