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Bio: David Rosen is a Professor in the School of Mechanical Bio: Dr. Kai Goebel is a Principal Scientist in the System
Engineering at the Georgia Institute of Technology (on leave). Sciences Lab at Palo Alto Research Center (PARC). His interest
Additionally, he is the Research Director of the Digital is broadly in condition-based maintenance and systems health
Manufacturing and Design Centre at the Singapore University management for a broad spectrum of cyber-physical systems
of Technology & Design. He received his Ph.D. at the University in the transportation, energy, aerospace, defense, and
of Massachusetts in 1992 in mechanical engineering. manufacturing sectors. Prior to joining PARC, Dr. Goebel
His research interests lie at the intersection of design, worked at NASA Ames Research Center and General Electric
manufacturing, and computing with specific focus on additive Corporate Research & Development center. At NASA, he was
manufacturing (AM), computer-aided design, and design a branch chief leading the Discovery and Systems Health tech
methodology. He has industry experience, working as a area, which included groups for machine learning, quantum
software engineer at Computervision Corp. and a Visiting computing, physics modeling, and diagnostics & prognostics.
Research Scientist at Ford Research Laboratories. He is a He founded and directed the Prognostics Center of Excellence,
Fellow of ASME and has served on the ASME Computers and which advanced our understanding of the fundamental aspects
Information in Engineering Division Executive Committee. of prognostics. He holds 18 patents and has published more
He is the recipient of the 2013 Solid Freeform Fabrication than 350 papers, including a book on Prognostics. Dr. Goebel
Symposium, International Freeform and Additive Manufacturing was an adjunct professor at Rensselaer Polytechnic Institute
Excellence (FAME) Award, and he is the co-author of a leading and is now adjunct professor at Lulea Technical University.
textbook in the AM field. He is a member of ASME, co-founder of the Prognostics and
Health Management Society, and associate editor of the
Track 18: Conference Wide Symposium International Journal of PHM.

18-1-1: DESIGN, SYSTEMS AND COMPLEXITY
Wednesday, November 13, 8:45AM–9:30AM
Room 355B,

Calvin L. Rampton Salt Palace Convention Center

Failure Is Not an Option: Avoiding Operational Disruptions
With Mechanistic and Data-Driven Damage Prognostics ‒
Sponsored by the NDPD Division
(IMECE2019-14012)

Kai Goebel
Palo Alto Research Center

Abstract: We are in an age where pervasive sensing, high
communication bandwidth, and advances in AI have arrived
at industrial equipment. The question is how one can leverage
these advances for operational gain. To uphold operational
functionality, these techniques flow into a Condition-Based
Maintenance (CBM) strategy, where maintenance is only
performed on evidence of need identified through direct or
indirect monitoring. Knowledge of an asset’s condition and
how it will evolve is required such that the remedial action can
be prescribed with sufficient lead time to minimize the cost and
operational impact of the occurrence of a potential disruption.
This strategy differs from “on-condition” maintenance in that
an understanding of how much time is available before the
asset loses functionality can be leveraged. The basic concept
entails collecting and assessing data from NDE inspections
and in situ sensors to estimate remaining life of the system in
question. This is done using either mechanistic, physics-based
models, or as suitable, data-driven AI techniques. This talk lays
out a roadmap of the tools and methods that are to be used to
realize the promise of making failure not an option.

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