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Advanced Bearing-Fault Diagnosis and Classification Using Mel-Scalograms and FOX-Optimized ANN.

Muhammad Farooq Siddique¹, Wasim Zaman¹, Saif Ullah¹

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

Sensors (Basel, Switzerland)

|November 27, 2024

Summary

This study introduces a new bearing-fault diagnosis method using Mel-transformed scalograms and an autoencoder with a FOX optimizer. The approach achieves perfect accuracy for industrial machinery maintenance.

Keywords:

MEL-spectrum artificial neural network fault diagnosis fox optimizer vibrational signals (VS)

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Area of Science:

Engineering
Artificial Intelligence
Signal Processing

Background:

Bearing-fault diagnosis is crucial for industrial machinery efficiency and safety.
Traditional methods may lack accuracy and reliability in detecting diverse faults.
Advanced signal processing and machine learning are needed for robust fault detection.

Purpose of the Study:

To propose a novel and highly accurate method for bearing-fault diagnosis.
To leverage Mel-transformed scalograms and an autoencoder for feature extraction.
To utilize the FOX optimizer for enhanced artificial neural network performance.

Main Methods:

Vibrational signals (VS) were transformed into Mel-transformed scalograms.
An autoencoder with convolutional and pooling layers extracted robust features.
An artificial neural network (ANN) optimized with the FOX optimizer performed classification.

Main Results:

The proposed model achieved perfect precision, recall, F1-scores, and AUC of 1.00.
The FOX optimizer demonstrated superior performance over traditional backpropagation.
t-SNE plots confirmed clear separability between different fault classes.

Conclusions:

The novel method offers an efficient and accurate solution for bearing-fault diagnosis.
This approach is highly reliable for real-time predictive maintenance in industrial settings.
The findings significantly advance the field of machinery health monitoring.