Fault Detection in Gears Using Machine Learning

Gears are essential components in various machinery, including automobiles and rotating equipment.When gears fail, it can lead to significant losses. This paper discusses a method for detecting and analysinggear faults to mitigate such risks. To achieve this, a specialized test setup is constructed to examine gearfaults experimentally. This setup collects vibration data from both healthy gears and those deliberatelymade faulty. The collected data is then analysed using the Fast Fourier Transform (FFT) technique,implemented through LabVIEW software. This analysis provides insights into the time and frequencydomains of the vibration signals, aiding in identifying fault patterns. Subsequently, an Artificial NeuralNetwork (ANN) is trained using MATLAB with the processed vibration data. This trained ANN forms asystem capable of detecting faults in gears based on the patterns learned from the experimental data. Insummary, this research presents a comprehensive approach to gear fault detection, utilizing experimentalstudies, signal processing techniques, and machine learning algorithms.