S. J. Hudak Jr., R. C. McClung, M. P. Enright, Southwest Research Institute, San Antonio, TX; H. Millwater, University of Texas at San Antonio, San Antonio, TX
The development and implementation of prognosis technology for integrated system health management (ISHM) has the potential to significantly enhance the reliability and readiness of high-value assets, while concurrently decreasing sustainment costs. The systems approach includes the acquisition and fusion of on-line sensor information, combined with physics-based models for damage accumulation, and higher order reasoning for decision making. This presentation will summarize and demonstrate the benefits of several recently developed ISHM-enabling technologies. First, enhancements to the DARWIN® probabilistic fracture mechanics code that are relevant to military engine application will be summarized, and example applications will be given. These enhancements will be employed to assess the benefits of tracking actual engine usage and monitoring material damage (i.e. fatigue cracking) in assessing remaining fatigue life and forecasting probability of component failure. Specifically, an advanced fracture mechanics model, which explicitly treats crack nucleation, small crack propagation, and large crack propagation, will be described. This model will be used to demonstrate the benefits of usage tracking to the component level by assess the conservatism in employing Total Accumulated Cycles (TACs) versus actual usage data to predict fatigue life. A probabilistic method for mission identification will also be described and assessed using actual usage data from flight data recorders at USAF bases. The utility of this method for forecasting the impact of projected changes in mission mixing on future fatigue damage accumulation and probability of failure will also be discussed. The development of a statistical model for sensor uncertainty, based on laboratory data on a thin film magnetostrictive sensor for crack detection, will also be described. This sensor model will then be combined with probabilistic simulation to assess the potential benefits of embedded sensors for on-line detection and monitoring of defects, as compared to the more traditional mid-life depot inspection.
Summary: Emerging sensing and life prediction technologies will be presented within a probabilistic analysis framework. Potential applications for these technologies will be discussed, and example computations provided; where applicable, analytical results will be compared with laboratory data.
