Performance metrics for fault prognosis of complex systems

Author

Vachtsevanos, George

Author_Institution

Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA

fYear

2003

fDate

22-25 Sept. 2003

Firstpage

341

Lastpage

345

Abstract

This paper presents a methodology for estimating confidence bounds associated with the task of prediction. A data-driven confidence prediction neural network architecture is introduced that accommodates a learning scheme intended to ´shrink´ the uncertainty bounds, as more information becomes available. Performance metrics are discussed and statistical techniques are employed to define confidence bounds; the methodology is applied to a typical shipboard process.

Keywords

failure analysis; neural nets; radial basis function networks; statistical analysis; uncertainty handling; GRNN; PNN; RBFN; confidence bound statistical techniques; data-driven confidence prediction neural network architecture; failure analysis; fault prognosis performance metrics; general regression neural network; learning scheme; prediction confidence bounds; probabilistic neural network; radial basis function networks; shipboard process; Artificial neural networks; Asset management; Computer architecture; Delay estimation; Drives; Fault detection; Fault diagnosis; Measurement; Neural networks; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

AUTOTESTCON 2003. IEEE Systems Readiness Technology Conference. Proceedings

ISSN

1080-7725

Print_ISBN

0-7803-7837-7

Type

conf

DOI

10.1109/AUTEST.2003.1243597

Filename

1243597