Research Highlights / Full Story

Nenad Nenadic, research associate professor at RIT's Golisano Institute for Sustainability (GIS), is part of a team that is developing specialized predictive tools to better assess equipment failures in military aircraft. This includes a study with the Army Research Laboratory to assess and predict failure in gears used in Army helicopters.

"Large metal gearing is a major component in standard helicopter transmissions and there have been examples of fatigue and cracking of gear teeth that have led to equipment failure," Nenadic says. "Of four dominant gear failure modes (breakage, pitting, scoring, and wear), breakage is the most dangerous, because the user perceives its occurrence as an abrupt failure with no prior warning."

Nenadic's research is focused on developing the first statistically significant data set for comparing performance of existing vibration- based condition indicators with respect to their ability to detect cracks and assess the damage.

The GIS team utilized precision gears designed by NASA's Glenn Research Center and manufactured locally by Gleason Corp. The project included the development of two test fixtures to analyze how and why gears fail.

The first fixture employs a fatigue tester and a unique approach for efficient initiation of realistic cracks. The second, based on a dynamometer, measures crack propagation and collects vibration data. The vibration data is accompanied with ground truth data of actual cracks obtained using crack-propagation sensors. The results will assist the team in developing a predictive failure model that can be used to improve gear maintenance and design.

The team presented its initial results at the 2011 Conference on Structural Dynamics, sponsored by the Society for Experimental Mechanics. They are also expanding the data analysis through the development of novel condition indicators with support from the Office of Naval Research.

"The empirical data we are developing will serve the gear research community for better gear crack detection, assessment, and prediction of remaining useful life," Nenadic adds.