Health Monitoring of Gears Based on Vibrations by Support Vector Machine Algorithms

Health monitoring of gears is very critical for satisfactorily overall working of the complex machinery. Thus, the ability to detect gear faults and classify them based on their nature becomes very important aspect of health monitoring of machines. In this paper, SVM algorithms have been used for the multiclass prediction of faults with the help of time domain vibration signals obtained from the gearbox casing operated in a suitable speed range. Moreover, it tries to examine the performance of the SVM technique by optimizing its parameters on utilization of time domain data from multi-fault gear box. The SVM software was fed with the training data and testing data at similar operating speeds for three types of defects and no defect case, and classification ability of SVM was noted and found to be excellent. The sensitivity analysis of optimized parameters is studied and conclusions are drawn.