Power Plant Surveillance and Diagnostics: Applied Research with Artificial Intelligence / Edition 1

Power Plant Surveillance and Diagnostics: Applied Research with Artificial Intelligence / Edition 1

by Da Ruan
     
 

ISBN-10: 3540432477

ISBN-13: 9783540432470

Pub. Date: 06/10/2002

Publisher: Springer Berlin Heidelberg

Edited book reporting recent results in AI research in power plant surveillance and diagnostics. High quality and applicability of the contributions through a thorough peer-reviewing process. Condition Monitoring and Early Fault Detection provide for better efficiency of energy systems, at lower costs.

Inhalt

Featured Topics: Analysis of important issues

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Overview

Edited book reporting recent results in AI research in power plant surveillance and diagnostics. High quality and applicability of the contributions through a thorough peer-reviewing process. Condition Monitoring and Early Fault Detection provide for better efficiency of energy systems, at lower costs.

Inhalt

Featured Topics: Analysis of important issues relating to specification, development and use of systems for computer-assisted plant surveillance and diagnosis.- Empirical and analytical methods for on-line calibration monitoring and data reconciliation.- Noise analysis methods for early fault detection, condition monitoring, leak detection and loose part monitoring.- Predictive maintenance and condition monitoring techniques.- Empirical and analytical methods for fault detection and recognition.

Product Details

ISBN-13:
9783540432470
Publisher:
Springer Berlin Heidelberg
Publication date:
06/10/2002
Series:
Power Systems Series
Edition description:
2002
Pages:
386
Product dimensions:
9.21(w) x 6.14(h) x 0.88(d)

Table of Contents

Featured Topics:
Analysis of important issues relating to specification, development and use of systems for computer-assisted plant surveillance and diagnosis.-
Empirical and analytical methods for on-line calibration monitoring and data reconciliation.-
Noise analysis methods for early fault detection, condition monitoring, leak detection and loose part monitoring.-
Predictive maintenance and condition monitoring techniques.- Empirical and analytical methods for fault detection and recognition.

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