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A Hybrid Approach for Power Plant Fault Diagnostics [electronic resource] / by Tamiru Alemu Lemma.

By: Lemma, Tamiru Alemu [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Computational Intelligence: 743Publisher: Cham : Springer International Publishing : Imprint: Springer, 2018Edition: 1st ed. 2018.Description: XII, 283 p. 161 illus., 138 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319718712.Subject(s): Computational intelligence | Computer vision | Electric power production | Computational Intelligence | Computer Vision | Electrical Power Engineering | Mechanical Power EngineeringAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Introduction -- Literature Review -- Model Identification using Neuro-Fuzzy Approach -- Model Uncertainity, Fault Detection and Diagnostics -- Intelligent Fault Detection and Diagnostics -- Application Studies, Part-I: Model Identification and Validation -- Application Studies, Part-II: Fault Detection and Diagnostics -- Conclusion and Recommendation.
In: Springer Nature eBookSummary: This book provides a hybrid approach to fault detection and diagnostics. It presents a detailed analysis related to practical applications of the fault detection and diagnostics framework, and highlights recent findings on power plant nonlinear model identification and fault diagnostics. The effectiveness of the methods presented is tested using data acquired from actual cogeneration and cooling plants (CCPs). The models presented were developed by applying Neuro-Fuzzy (NF) methods. The book offers a valuable resource for researchers and practicing engineers alike.
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Introduction -- Literature Review -- Model Identification using Neuro-Fuzzy Approach -- Model Uncertainity, Fault Detection and Diagnostics -- Intelligent Fault Detection and Diagnostics -- Application Studies, Part-I: Model Identification and Validation -- Application Studies, Part-II: Fault Detection and Diagnostics -- Conclusion and Recommendation.

This book provides a hybrid approach to fault detection and diagnostics. It presents a detailed analysis related to practical applications of the fault detection and diagnostics framework, and highlights recent findings on power plant nonlinear model identification and fault diagnostics. The effectiveness of the methods presented is tested using data acquired from actual cogeneration and cooling plants (CCPs). The models presented were developed by applying Neuro-Fuzzy (NF) methods. The book offers a valuable resource for researchers and practicing engineers alike.

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