Fault Diagnosis in Bevel Gearbox Using Coiflet Wavelet and Fault Classification Based on ANN Including DNN

Condition monitoring plays a vital role in predictive maintenance of machinery in today’s world. It is very important to detect the faults in the machinery as early as possible in order to stop the propagation of faults which may lead to heavy damages. The objective of our study is to investigate the vibration analysis of bevel gearbox to detect the faults. An experimental setup was developed to carry out the vibration analysis. Various types of faults were induced in the gearbox. The vibration analysis was carried out for different types of faults with different lubrication levels in the gearbox. MATLAB toolbox was used for signal processing in which Coiflet wavelet was used for denoising the signal. An artificial neural network (ANN) and a deep neural network (DNN) were used to detect the faults in the bevel gearbox automatically. The results were promising in detecting the faults with high accuracy.