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• W2176781498 abstract "Statistically based metrics for gas turbine engine diagnostic systems are required to evaluate competing products fairly and to establish a convincing business case. Diagnostic algorithm validation often includes engine testing with implanted faults. The implantation rate is rarely, if ever, representative of the true fault occurrence rate. A technique is presented to convert a confusion matrix with a non-representative fault distribution to one representative of the expected distribution. The small sample size associated with fault implantation studies requires a confidence interval on the results to provide valid comparisons and a method for calculating them is presented. The use of cost matrices to weight confusion matrices, based on the associated cost of each outcome, is demonstrated. The calculation of metrics on the resulting weighted confusion matrix will measure the relative cost of the diagnostic technique, rather than just its accuracy. In this paper, the generation and use of cost matrices is presented and their application to a hypothetical test case is demonstrated. Receiver operating characteristic (ROC) curves evaluate diagnostic system performance across a range of threshold settings. This allows an algorithm’s ability to be assessed over a range of possible usage. Cost curves are analogous to ROC curves but offer several advantages. The techniques for applying cost curves to diagnostic algorithms are presented and their advantages over ROC curves are outlined." @default.
• W2176781498 created "2016-06-24" @default.
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• W2176781498 date "2009-01-01" @default.
• W2176781498 modified "2023-09-25" @default.
• W2176781498 title "Application of Cost Matrices and Cost Curves to Enhance Diagnostic Health Management Metrics for Gas Turbine Engines" @default.
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• W2176781498 doi "https://doi.org/10.1115/gt2009-59630" @default.
• W2176781498 hasPublicationYear "2009" @default.
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