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A Bearing Fault Classification Framework Based on Image Encoding Techniques and a Convolutional Neural Network under

Rafia Nishat Toma¹, Farzin Piltan¹, Kichang Im²

¹Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Korea.

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|July 9, 2022

Summary

This study introduces an intelligent model using Gramian angular field (GAF) images and a convolution neural network (CNN) for classifying bearing faults in manufacturing. The method achieves over 99% accuracy, offering a superior approach for diagnostics.

Keywords:

bearing fault diagnosis convolutional neural network (CNN)gramian angular field (GAF)motor-current signal time-series imaging

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Area of Science:

Mechanical Engineering
Artificial Intelligence
Signal Processing

Background:

Effective diagnostics of mechanical problems in manufacturing systems are crucial for safety and cost reduction.
Bearing faults are a common issue that can lead to system failures and significant expenditures.
Existing diagnostic methods may require manual feature extraction, which can be time-consuming and less effective under varying operating conditions.

Purpose of the Study:

To propose an intelligent fault classification model for bearing faults using motor-current signals.
To develop a novel approach that combines signal-to-image encoding with a convolution neural network (CNN).
To evaluate the model's performance, particularly its accuracy and efficiency, under different operating conditions.

Main Methods:

A dataset was divided based on operating conditions.
Motor-current signals were segmented and transformed into 2-D images using the Gramian angular field (GAF) algorithm.
A 2-layer deep convolution neural network (CNN) was designed and trained on the generated image dataset for fault classification.

Main Results:

The proposed method achieved a classification accuracy exceeding 99% across various operating conditions.
The Gramian angular field (GAF) algorithm effectively preserved fault characteristics from the current signal.
A simple 2-layer CNN structure proved sufficient for high classification accuracy and efficient computational time compared to more complex structures.

Conclusions:

The intelligent fault classification model based on GAF image conversion and CNN demonstrates superior performance for bearing fault diagnostics.
The method eliminates the need for manual feature extraction, simplifying the diagnostic process.
The proposed approach offers a robust and accurate solution for bearing fault classification, outperforming state-of-the-art techniques under inconsistent working conditions.