Decatur industrial, business leaders tour UAH Reliability and Failure Analysis Lab

RFAL Associate Director for Reliability Mark Gauldin, right, explains lab equipment to Decatur industrial representatives.

Glenn Baeske

Twenty-three representatives of eight Decatur industrial firms toured the Reliability and Failure Analysis Laboratory (RFAL) at UAH on Friday.

The Morgan County Economic Development Association (MCEDA) and the UAH Office of the Vice President for Research and Economic Development arranged the tour.

"Morgan County Economic Development Association was happy to facilitate today's tour at the UAH Reliability and Failure Analysis Lab, which fosters the relationship between UAH and Morgan County industries. This lab will offer industries an additional resource to assist in increasing the reliability of their equipment," said MCEDA President & CEO Jeremy Nails.

"Dr. Ray Vaughn made the introduction to the Morgan County Economic Development Office and they felt my lab might be able to assist some of their local businesses," said Mark Gauldin, associate director for reliability at RFAL.

Companies represented were:

• Toray Fluorofibers America Inc.
• Toray Composite Materials America Inc.
• OCI Peroxygens LLC
• 3M Decatur
• Nucor Steel
• Daikin America Inc.
• AlphaPet Inc.
• GE Appliances

A part of UAH's Research Institute, RFAL's research focuses on expanding the body of knowledge in the reliability engineering discipline. By looking at the physics of failure, the lab investigates possible failure mechanisms acting on a part through math modeling and physical testing. Its primary focus is reliability, defined as the probability that a system or component will perform its required functions under stated conditions for its mission duration.

The lab has the capability to model the reliability and availability of complex systems so they can be designed for reliability and maintainability. RFAL uses U.S. Dept. of Defense Unique Identification (UID) marking compliant procedures to track parts or test articles in the lab to model failure, lifecycle costing and sparing. It can also be applied in a Reliability Centered Maintenance solution to track equipment to aid in increasing the reliability of the equipment.

RFAL recently developed a machine learning algorithm for the U.S. Army that is used to determine component health and convert unscheduled maintenance into scheduled maintenance. This algorithm has gained interest in the industrial sector to assist in production facilities maintenance.

RFAL has the ability to assist their customers in the entire product lifespan in ensuring a reliable product.