Vibration analysis of Gear defects using Machine learning approach

Gear drives are considered as the most effective transmission method in automobiles as well as in various industries because of its high efficiency, reliability and high velocity ratio. As a result, from its trustful usage, failure in any part may lead to a large and unpredictable production loss along with massive service cost and safety concerns. Periodic maintenance and condition monitoring are the only solution to avoid the above scenario. Vibration analysis are most sounded term in the fault detection due to its runtime condition monitoring and low cost. Nowadays, vibration analysis was off set to machine learning methods, which is a modern technique enable us the automation such that the system can learn from the input data and make decision with a nominal human interface were as conventional methods are highly operator dependent. Here in this study, the effectiveness of a machine learning based gear fault diagnosis system is studied. Gear defects like surface pitting, misalignment and broken teeth were considered. No combinations of these defects were considered. The experimental setup consists of a paired spur gear shafts, driven by a variable speed motor with a belt drive along with a vibrational data acquisition system. Vibration signals for three defects were collected. Vibration signals of defect free gear is also collected. TensorFlow is used to implement machine learning model. The proposed method is successful in detecting gear defects while the machine is in running condition. The new method is fast and can be automated. This reduces the human intervention to a minimum level.