NDLI: Neural networks for model-based prognostics

Content Provider	IEEE Xplore Digital Library
Author	Jaw, L.C.
Copyright Year	1999
Description	Author affiliation: Scientific Monitoring Inc., Tempe, AZ, USA (Jaw, L.C.)
Abstract	The increasing demand for performance and durability of advanced aerospace systems has increased the need for health management of these systems. Effective health management involves seamless integration of failure diagnostics, failure prediction, part life estimation, and maintenance logistics. These capabilities have only partially been implemented in current health management systems. Hence the effectiveness of current management systems is compromised. To achieve the goal of effective prognostic and health management (PHM), promising technologies from various disciplines must be integrated. One of these technologies is artificial neural network (ANN), or neural network (NN). This paper presents a view that NNs have matured in the area of modeling. NN's learning feature is effective in capturing the time-varying, individual behavior of a complex physical system; therefore, NNs can improve the fidelity of the models used onboard aerospace systems to facilitate PHM. This paper supports the view of NN-based models with a numerical example. The example concerns the modeling of a compressor tip clearance of in a gas turbine engine. The modeling approach blends NNs with the traditional modeling paradigm offering significant advantages. This cooperative approach to modeling also provides a framework for certifying the adaptive portion of the overall model, enabling NN-based algorithms to be implemented in onboard PHM systems. The cooperative approach may also be applied to pattern classification problems.
Starting Page	21
Ending Page	28
File Size	436516
Page Count	8
File Format	PDF
ISBN	0780354257
DOI	10.1109/AERO.1999.789761
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-03-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Prognostics and health management Artificial neural networks Life estimation Logistics Time varying systems Numerical models Turbines Engines Pattern classification
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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