Broken Tooth Gear Fault Detection Using Vibration Signals Based on Convolutional Neural Network

Gear faults are a major concern in industrial settings, leading to performance degradation and potential system failures. This paper explores the use of Convolutional Neural Networks (CNNs) for broken tooth fault detection in gear systems. Traditional methods for fault detection rely on manual feature extraction from vibration signals, which can be time-consuming and may not capture all relevant information. CNNs, on the other hand, can automatically learn complex patterns from data, making them well-suited for this task. In this paper we develop a computationally tractable deep learning (DL) based CNN model that can be used for broken tooth fault diagnosis in various industrial settings, irrespective of the type of gearbox or gears being used. The authors further compare the performance of developed CNN model with traditional signal processing techniques and Support Vector Machine (SVM)-based classification. The CNN model achieved superior accuracy (98.6%) in distinguishing between broken and healthy teeth across various operating conditions for one experimental setup. For a second setup with a less severe broken tooth fault, the accuracy was 93%. In contrast, SVM models achieved a maximum accuracy of 90.7% using manually selected features. These findings underscore the superiority of the proposed deep learning (DL) based CNN model for broken teeth gear fault detection. Moreover, it exhibits greater resilience to fluctuations in operating conditions and fault types compared to conventional techniques. Comparisons with established deep learning models such as VGG16, AlexNet etc. demonstrate that the proposed model surpasses all others in terms of classification accuracy.