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Updated: Jun 24, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

RGB-based visual encoding of vibration data for gearbox fault diagnosis using U-Net segmentation model.

İrfan Kiliç1, Gülşah Karaduman2, Beyda Tasar3

  • 1Department of Software Engineering, Engineering Faculty, Firat University, Elazig, Turkey.

Plos One
|June 22, 2026
PubMed
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This study converts gearbox vibration data into images for deep learning analysis, achieving 99.87% accuracy in fault diagnosis. This novel image-based approach offers superior performance for signal-based engineering problems.

Area of Science:

  • Engineering
  • Artificial Intelligence
  • Data Science

Background:

  • Gearbox gear faults pose significant challenges in industrial machinery maintenance.
  • Traditional methods for diagnosing these faults often rely on complex signal processing and machine learning techniques.
  • Analyzing numerical vibration data presents limitations in extracting comprehensive fault information.

Purpose of the Study:

  • To develop an innovative image-based deep learning approach for diagnosing gearbox gear faults.
  • To transform numerical vibration sensor data into a visual format for enhanced analysis.
  • To evaluate the effectiveness of deep learning models in classifying gearbox faults using image representations of sensor data.

Main Methods:

  • Utilized the Gearbox Fault Diagnosis Dataset from Kaggle, collecting vibration signals from four sensors.

Related Experiment Videos

Last Updated: Jun 24, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

  • Processed signals by calculating maximum, minimum, and mean values, then normalizing them to a [0-255] range.
  • Mapped normalized values to RGB color channels to generate 256x256 pixel images for each fault category.
  • Trained a pre-trained U-Net deep learning model for segmentation using these image representations over 10 epochs.

    • Main Results:

    • Achieved a high classification accuracy of 99.87% for gearbox fault diagnosis.
    • Obtained a mean average precision (mAP) score of 99.74%, indicating robust model performance.
    • Demonstrated significant advantages of the image-based deep learning approach over traditional machine learning and text-based deep learning methods.

    Conclusions:

    • Converting numerical sensor data into visual RGB images enables highly accurate fault classification using deep learning.
    • The proposed method establishes a new paradigm for solving signal-based engineering problems, particularly in gearbox fault detection.
    • This study highlights the potential of image-based deep learning for analyzing non-visual numerical data in engineering applications.