Gear Fault Detection Based on Convolutional Neural Networks and Support Vector Machines

As a critical component of mechanical transmission systems, gears play a vital role in ensuring industrial production runs smoothly.Undetected gear failures can lead to mechanical breakdowns, production interruptions, and even safety hazards.Therefore, an efficient gear fault detection method is essential for maintaining industrial continuity and safety.This paper proposes a hybrid model that integrates convolutional neural networks (CNN) and support vector machines (SVM) for gear fault detection.The model leverages CNNs to automatically extract key features from vibration signals, while SVMs enhance classification accuracy, resulting in a high-precision fault diagnosis system.On a publicly available gear fault dataset, the proposed model achieved an impressive accuracy of 0.9922, significantly outperforming single-classifier models.Moreover, the model exhibits a short training time, demonstrating its computational efficiency.This research provides an effective and automated approach to gear fault detection, offering significant potential for industrial applications.