A Three-Dimensional Receiver Operator Characteristic Surface Diagnostic Metric

Abstract

Receiver Operator Characteristic (ROC) curves are commonly applied as metrics for quantifying the performance of binary fault detection systems. An ROC curve provides a visual representation of a detection system's True Positive Rate versus False Positive Rate sensitivity as the detection threshold is varied. The area under the curve provides a measure of fault detection performance independent of the applied detection threshold. While the standard ROC curve is well suited for quantifying binary fault detection performance, it is not suitable for quantifying the classification performance of multi-fault classification problems. Furthermore, it does not provide a measure of diagnostic latency. To address these shortcomings, a novel three-dimensional receiver operator characteristic (3D ROC) surface metric has been developed. This is done by generating and applying two separate curves: the standard ROC curve reflecting fault detection performance, and a second curve reflecting fault classification performance. A third dimension, diagnostic latency, is added giving rise to three-dimensional ROC surfaces. Applying numerical integration techniques, the volumes under and between the surfaces are calculated to produce metrics of the diagnostic system's detection and classification performance. This paper will describe the 3D ROC surface metric in detail, and present an example of its application for quantifying the performance of aircraft engine gas path diagnostic methods. Metric limitations and potential enhancements are also discussed.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2010
Accession Number
ADA562645

Entities

People

  • Donald L. Simon

Organizations

  • National Aeronautics and Space Administration

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Aircraft Engines
  • Classification
  • Detection
  • Detectors
  • Engines
  • Gas Turbines
  • High Pressure
  • Machine Learning
  • Measurement
  • Neural Networks
  • Numerical Integration
  • Propulsion Systems
  • Three Dimensional
  • Turbines
  • Turbofan Engines
  • Two Dimensional
  • United States

Readers

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  • Instructional Design and Training Evaluation.
  • Radar Systems Engineering.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference