Tools For Predictive Assessment of Structural Integrity of Complex Mechanical Systems

Impact Technologies, in cooperation with the Penn State ARL, propose the development and validation of prognostic tools for predicting the remaining useful life of complex mechanical systems through fusion of stochastic physics-based failure mode models,relevant system or component level health monitoring data and inspection results. The proposed life determination and prediction strategies will be implemented within a probabilistic framework to directly identify confidence bounds associated withspecific component life consumption. A major thrust for improving the accuracy of structural integrity predictions that is addressed in this proposal is related to minimizing the current level of uncertainty that exists in the critical parameters thatdrive specific component failure modes. The link between advanced health monitoring, feature extraction, inspection results and model-based prognostics is key to determining and reducing this uncertainty thus enabling effective risk-based maintenancepractices, higher system availability and improved safety.The prognostic tools generated under this program will contain many generic elements that can be transitioned to a broad range of applications. These elements include the ability to account for unexpected damage events, fuse diagnostic information withmodel-based predictions, and statistically calibrate predictions with inspection information. Validation of these tools will be accomplished using transitional run-to-failure data available through Penn State ARL for bearing or gears. Additional sourcesof validation data available to Impact include seeded fault test results from F100 and F110 engines and CH-46 transmission components.The prognostic tools will be capable of performing probabilistic Remaining Useful Life (RUL) predictions with high fidelity physical models governed by real-time health data and maintenance/inspection results. The improved accuracy of this lifedetermination approach can be used within the framework of military equipment support cost models to reduce the risk of safety or system availability related failures. More accurate time-to-failure predictions can also reduce costly inspection routines andpremature component replacements by using a risk-based, maintenance optimization technique. Commercial applications for the component life assessment technologies developed can be realized on gas turbine applications including commercial aviation engines,electric power generators, industrial drive systems, and naval propulsion systems.