FAULT DIAGNOSIS OF AUTOMOBILE GEARBOX USING ARTIFICIAL NEURAL NETWORK

Gears are very critical and essential device in power transmission systems, industries etc.. to vary speed and load conditions according to the requirements. It has a wide area of application in the field of rotating machinery. In recent years, the operational accuracy of gears are increasing as demand increases. Some minor errors like gear misalignment, backlash and tooth cracks after running for some period of time, will create some form of vibration which will affect the supporting components like bearings, shafts etc.. to fail. So it is very important to monitor the condition of gears to avoid the run time breakdown of machines. Vibration measurements are used to monitor the condition of the machine for predictive maintenance and to predict the gearbox faults successfully. In this work propose the use vibration signal for automated fault diagnosis of gearbox and the statistical features that extracted from the vibrational signal are given as an input to the Deep learning algorithm ANN (Artificial neural networks) for fault identification. We can collect the vibrational datas from vibrational sensors that placed on gears. By using vibrational data and the load in the vehicle we can predict the fault in the gear box accurately.