Application of statistical process control method to monitor wear and broken tooth faults in spur gears using vibration data

Gears are machine elements used extensively in many areas of industry to transmit power and motion. Damage occurs due to working under compulsive conditions effective gear fault detection is crucial to prevent greater drawbacks and economic losses. The objective of this study is to monitor the existence of different types of gear defects by examining changes in the vibrations of spur gears due to faults using vibrations analysis and statistical process control methods. For this purpose, a closed-loop test rig was constructed. Firstly, good gears were attached to the test assembly and vibrations data which received at radial direction via sensors on the bearings were recorded by operating the system at various loading and number of cycle’s conditions. Thus, a reference spectrum was obtained. Then the system was operated with artificial wear and fracture which were created on the surfaces of spur gears at various loading and number of cycles conditions. Vibrations data which were received from the bearings were recorded. These data were plotted on statistical control carts and frequency spectrum. Graphs of data obtained from good gears and defective gears were compared with each other. Thus it was possible to detect the existence of worn gear, one tooth broken pinion gear and one tooth broken from each pinion and wheel gears.