ENHANCING GEAR PHYSICS-OF-FAILURE MODELS WITH SYSTEM LEVEL VIBRATION FEATURES Gregory J. Kacprzynski Michael J. Roemer , Carl S. Byington, Girish A. Modgil and Andrea Palladino Impact Technologies, LLC 125 Tech Park Drive Rochester, NY 14623 mike.roemer@impact-tek.com Kenneth P. Maynard Applied Research Laboratory The Pennsylvania State University State College, PA 16801 Abstract: To truly optimize the deployment of DoD assets, there exists a fundamental need for predictive tools that can reliably estimate the current and reasonably predict the future capacity of complex systems. Prognosis, as in all true predictions, has inherent uncertainty, which has been treated through probabilistic modeling approaches. The novelty in the current prognostic tool development is that predictions are made through the fusion of stochastic physics-of-failure models, relevant system or component level health monitoring data and various inspection results. Regardless of the fidelity of a prognostic model or the quantity and quality of the seeded fault or run-to-failure data, these models should be adaptable based on system health features such as vibration, temperature, and oil analysis. The inherent uncertainties and variability in material capacity and localized environmental conditions, as well as the realization that complex physics-of-failure understanding will always possess some uncertainty, all contribute to the stochastic nature of prognostic modeling. However, accuracy can be improved by creating a prognostic architecture instilled with the ability to account for unexpected damage events, fuse with diagnostic results, and statistically calibrate predictions based on inspection information and real-time system level features. In this paper, the aforementioned process is discussed and implemented first on controlled failures of single spur gear teeth and then on a helical gear contained within a drivetrain system. The stochastic, physics-of-failure models developed are validated with transitional run-to-failure data developed at Penn State ARL. Future work involves applying the advanced prognostics process to helicopter gearboxes. Keywords: Prognostics, Material Capacity, State Awareness, Gears Introduction: Military personnel currently have limited information to evaluate their assets’ operational capability or the next mission’s effect on that capability. Thus, they must make “go”/“no go” decisions that can result in the underutilization of assets with some remaining capability and the risk of overtaxing deployed assets that have insufficient capability to meet mission requirements. The assessment of current health state and the prediction of future capability for complex mechanical systems is a core enabling technology for such decision aiding.