Ogden, UT · September 11, 2002 · Health status of testers, instruments, interface test adapters, and units under test can be determined using test results automatically collected from automatic testing equipment (ATE) and processed using statistical methods.
Monitoring the health status in terms of failure rates and performance can immediately identify the testing capability of a test station and its associated instrumentation. Mission capability rates can be directly inferred from this information.
Test results, repair actions, statistical methods, artificial intelligence technology, and a knowledge base were all integrated into our product, Decision Support Integrated Environment (DSide), to allow technicians and software engineers troubleshoot and diagnose test failures more efficiently.
Suspicion that CND/RTOK occur due to discrepancies in TPS can now be supported and identified by comparing actual measurements with pass/fail limit criteria. Stack tolerance, also identified as one of the causes of CND/RTOK, can be eliminated by analyzing measurement statistics and statistical patterns. Test results, never collected and stored before, provide now a performance history that can identify degradation and potential failures in avionic systems along the time.
Monitoring performance through test results and statistical analysis provide means to forecast failures in complement to the traditional mean-time-between-failure (MTBF) information typically used to estimate failure intervals. The performance method is particularly valuable in avionic and other electronic systems that present random failure rates (exponential distributions). Performance information is especially useful when failures are random, or if failure datum is not available.
The methodology developed in this SBIR developed a 'never-had-before' tool to provide system health status visibility and support preventive maintenance and sustainment programs in the USAF.
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