NOVEL STRATEGY FOR BIOSUBSTRATE PROTEIN KINASE INHIBITOR
Small Business Information
HAWAII BIOTECHNOLOGY GROUP, INC.
99-193 AIEA HEIGHTS DR, STE 236, AIEA, HI, 96701
Name: PAUL GROTHAUS
Phone: () -
Phone: () -
Phone: (808) 486-5333
AbstractNot Available Impact Technologies in cooperation with the Applied Research Laboratory (ARL) at Penn State University proposes the development and implementation of metric strategies and algorithms to impartially evaluate the performance and effectiveness of diagnostic and prognostic technologies. The metrics to be developed will be implemented utilizing test-bed data, in-service data including health monitoring and CBM database data, and model-based simulation data. Specific performance and accuracy of the diagnostic algorithms at the component or subsystem level will be evaluated with performance metrics, while system level capabilities in terms of achieving the overall operational goals of the diagnostic system will be evaluated with effectiveness measures. The metrics process to be developed will utilize a standard set of mathematical ground rules and a statistical framework to directly identify confidence bounds, robustness measures, and various diagnostic thresholds associated with specific mechanical diagnostic technologies. The diagnostic metrics strategy integrates state-of-the-art test-bed and in-service data with quantitative and statistical analysis techniques to provide impartial and accurate comparisons among different diagnostic algorithms. The developed diagnostic metrics will be calibrated and verified using gearbox seeded fault and accelerated failure data taken with the MDTB (Mechanical Diagnostic Test Bed) at the ARL at Penn State University. In addition, performance degradation diagnostic data from a F100 accelerated mission test will be used to develop and verify metrics for diagnosing degraded condition performance. Finally, the proposed metrics will be tested on several different real-time, diagnostic technologies developed for the Navy by Impact Technologies and the ARL at Penn State, utilizing neural networks (supervised and unsupervised), wavelets, fuzzy logic systems, and probabilistic diagnostic methods.
* information listed above is at the time of submission.