A Hybrid Approach for Power Plant Fault Diagnostics

• Κύριος συγγραφέας: Lemma, Tamiru Alemu (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2018.
• Έκδοση: 1st ed. 2018.
• Σειρά: Studies in Computational Intelligence, 743
• Θέματα: Computational intelligence.; Optical data processing.; Power electronics.; Energy systems.; Computational Intelligence.; Image Processing and Computer Vision.; Power Electronics, Electrical Machines and Networks.; Energy Systems.

 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. 

 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.