Detection of Gear Wear and Faults in Spur Gear Systems Using Statistical Parameters and Univariate Statistical Process Control Charts

Abstract

In this study, the detection of wear faults in spur gears was examined using vibration analysis, statistical process control method, and statistical parameters. For this purpose, a closed-loop test rig with a power transmission system was established. Defect-free gears were attached to the test assembly, and the system was operated at a specific torsional load and number of cycles until the gears were worn. Vibration amplitudes at vertical and horizontal directions, received via sensors on the bearings, were transferred to the computer with a digital-analog converter. The control charts were plotted by sampling 30 data points per hour. Upper and lower control limits were determined by using the data obtained from the defect-free gears. The gears are worn in the process due to the effect of applied torque and the operation conditions suitable for the formation of defects. As a result, the vibration amplitudes were increased. The accuracy and convergence of the statistical process control method were verified by the statistical parameters root mean square, kurtosis value, skewness value, crest factor, and peak-to-peak values. It was emphasized that great convergence and accuracy between the statistical process control results and statistical parameters results are achieved. The present study showed that the detection of abrasion of a robust gear could be graphically demonstrated through a real-time experimental study. The statistical process control method is convenient and easily applicable, which allows constructing a real-time early warning system detecting malfunctions at the start.