November 30, 2025TechnologiesOpen Access

Novel End-to-End CNN Approach for Fault Diagnosis in Electromechanical Systems Based on Relevant Heating Areas in Thermography

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Proposed convolutional neural network detects faults in electromechanical systems, indicating enhanced monitoring for Industry 4.0 environments.

Achieving 99.7% accuracy indicates effective fault diagnosis under realistic conditions.
The CNN architecture utilizes feature extraction techniques to enhance analysis of thermal behavior.
Data augmentation with controlled noise injection improves resilience against image variability.
The integration of relevant heating areas highlights potential improvements in system reliability during operation.

Alvarado-Robles et al. (2025) studied this question.

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Proposed convolutional neural network detects faults in electromechanical systems, indicating enhanced monitoring for Industry 4.0 environments.