BROWSE
Detail
Journal Article
Advanced Bearing-Fault Diagnosis and Classification Using Mel-Scalograms and FOX-Optimized ANN
- Authors
- Muhammad Farooq Siddique, Wasim Zaman, Saif Ullah, Muhammad Umar, Faisal Saleem, Dongkoo Shon, Tae Hyun Yoon, Dae-Seung Yoo, Jong-Myon Kim
- Issue Date
- 2024-11
- Citation
- Sensors, v.24, no.22, pp.1-20
- ISSN
- 1424-8220
- Publisher
- Multidisciplinary Digital Publishing Institute (MDPI)
- Language
- English
- Type
- Journal Article
- Abstract
- Accurate and reliable bearing-fault diagnosis is important for ensuring the efficiency and safety of industrial machinery. This paper presents a novel method for bearing-fault diagnosis using Mel-transformed scalograms obtained from vibrational signals (VS). The signals are windowed and pass through a Mel filter bank, converting them into a Mel spectrum. These scalograms are subsequently fed into an autoencoder comprising convolutional and pooling layers to extract robust features. The classification is performed using an artificial neural network (ANN) optimized with the FOX optimizer, which replaces traditional backpropagation. The FOX optimizer enhances synaptic weight adjustments, leading to superior classification accuracy, minimal loss, improved generalization, and increased interpretability. The proposed model was validated on a laboratory dataset obtained from a bearing testbed with multiple fault conditions. Experimental results demonstrate that the model achieves perfect precision, recall, F1-scores, and an AUC of 1.00 across all fault categories, significantly outperforming comparison models. The t-SNE plots illustrate clear separability between different fault classes, confirming the model’s robustness and reliability. This approach offers an efficient and highly accurate solution for real-time predictive maintenance in industrial applications.
- KSP Keywords
- Accurate solution, Artificial neural network (ann), Diagnosis and classification, Fault classes, Fault diagnosis, Filter bank, Highly accurate, Industrial Applications, Mel filter, Predictive maintenance, Proposed model
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
