Enhanced Gear Damage Prediction Using Convolutional Neural Networks on Time-Series Sensor Data

Gearboxes are essential in various industries, serving as the core mechanism for power transmission in machines like wind turbines and vehicles. Traditional fault detection primarily uses vibration analysis, which, although effective, often fails to precisely quantify gear damage. This paper introduces a novel approach by integrating artificial intelligence (AI) with vibration data to enhance gearbox fault diagnosis. By employing machine learning algorithms, our model not only detects but also quantifies the severity of gear faults. This method involves collecting and analyzing vibration signals under different operational conditions to train a deep learning model that can accurately predict fault severity. This innovative approach, to the best of our knowledge, is the first to use AI to quantify gearbox faults, potentially revolutionizing maintenance strategies and improving industrial machinery reliability.