Optimizing Bearing Fault Diagnosis in Rotating Electrical Machines Using Deep Learning and Frequency Domain Features
Year: 2025
Type: article
Abstract: This study uses deep learning techniques to optimize fault diagnosis in rolling element bearings of rotating electrical machines. Leveraging the Case Western Reserve University bearing fault database, the methodology involves transforming one-dimensional vibration signals into two-dimensional scalograms, which are used to train neural networks via transfer learning. By employing SqueezeNet—a pre-trained convolutional neural network—and optimizing hyperparameters, this study significantly reduces the computational resources and time needed for effective fault classification. The analysis evaluates the effectiveness of two wavelet transforms (amor and morse) for feature extraction in correlation with varying learning rates. Results indicate that precise hyperparameter tuning enhances diagnostic accuracy, achieving a classification accuracy of 99.37% using the amor wavelet. Scalograms proved particularly effective in identifying distinct vibration patterns for faults in bearings’ inner and outer races. This research underscores the critical role of advanced signal processing and machine learning in predictive maintenance. The proposed methodology ensures higher reliability and operational efficiency and demonstrates the feasibility of transfer learning in industrial diagnostic applications, particularly for optimizing resource-constrained systems. These findings improve the robustness and precision of machine fault diagnosis systems.
Source: Applied Sciences
DOI: 10.3390/app15063132
Institution: Universitat Politècnica de València
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Award: G2396635141
OpenAlex ID: W4408404869