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Integrated Damage Location Diagnosis of Frame Structure Based on Convolutional Neural Network with Inception Module.

Jianhua Ren1, Chaozhi Cai1, Yaolei Chi1

  • 1School of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038, China.

Sensors (Basel, Switzerland)
|January 8, 2023
PubMed
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This study introduces a novel Inception-based convolutional neural network (BICNN) for accurate damage location diagnosis in frame structures. The enhanced model demonstrates superior anti-noise capabilities and achieves 97.38% accuracy, outperforming existing methods.

Area of Science:

  • Structural Engineering
  • Artificial Intelligence
  • Vibration Analysis

Background:

  • Accurate damage location diagnosis is crucial for frame structure maintenance.
  • Vibration data similarity and noise interference pose significant challenges.
  • Existing methods struggle with complex structures and noisy environments.

Purpose of the Study:

  • To develop a high-precision, noise-resistant neural network for frame structure damage location.
  • To improve upon existing convolutional neural network models for fault diagnosis.
  • To propose an integrated method to overcome single-sensor data limitations.

Main Methods:

  • An improved convolutional neural network, named BICNN (convolutional neural network based on Inception), was developed by integrating the Inception module into TICNN.
Keywords:
anti-noise abilityconvolutional neural networkfault diagnosisframe structureinception

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  • An integrated damage location diagnosis method was proposed to prevent misjudgments from single sensor data.
  • The proposed method was tested on a four-story steel frame model from the University of British Columbia.

    • Main Results:

    • The BICNN model achieved a diagnosis accuracy of 97.38%, surpassing other tested methods.
    • The proposed method demonstrated significant advantages in noise resistance.
    • The integrated approach effectively addressed the misjudgment issue associated with single sensor data.

    Conclusions:

    • The proposed BICNN method offers high accuracy and strong anti-noise ability for damage location diagnosis in frame structures.
    • This approach is effective in solving accurate damage location diagnosis problems in complex frame structures, even under strong noise conditions.
    • The study highlights the potential of advanced neural network architectures for structural health monitoring.