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- Author: Rolf Isermann
- Publisher: Springer
- Year: 2005
- Pages: 475
- ISBN-10: 3540241124
- ISBN-13: 9783540241126
- Format: 15.7 x 23.7 x 2.8 cm, minkšti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
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Description
This advanced text gives an introduction to advanced methods of fault detection and diagnosis (FDD). After definitions of important terms, the reliability, availability, safety and systems integrity of technical processes is considered. Then fault-detection methods for single signals without models like limit and trend checking and with harmonic and stochastic models, like Fourier analysis, correlation and wavelets are treated. This is followed by fault detection with process models using the relationships between signals like parameter estimation, parity equations, observers and principal component analysis. The treated fault-diagnosis methods include classification methods from Bayes classification to neural networks with decision trees and inference methods from approximate reasoning with fuzzy logic to hybrid fuzzy-neuro systems. Several practical examples for fault detection and diagnosis of DC motor drives, a centrifugal pump, automotive suspension and tire show applications.
EXTRA 10 % discount with code: EXTRA
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- Author: Rolf Isermann
- Publisher: Springer
- Year: 2005
- Pages: 475
- ISBN-10: 3540241124
- ISBN-13: 9783540241126
- Format: 15.7 x 23.7 x 2.8 cm, minkšti viršeliai
- Language: English English
This advanced text gives an introduction to advanced methods of fault detection and diagnosis (FDD). After definitions of important terms, the reliability, availability, safety and systems integrity of technical processes is considered. Then fault-detection methods for single signals without models like limit and trend checking and with harmonic and stochastic models, like Fourier analysis, correlation and wavelets are treated. This is followed by fault detection with process models using the relationships between signals like parameter estimation, parity equations, observers and principal component analysis. The treated fault-diagnosis methods include classification methods from Bayes classification to neural networks with decision trees and inference methods from approximate reasoning with fuzzy logic to hybrid fuzzy-neuro systems. Several practical examples for fault detection and diagnosis of DC motor drives, a centrifugal pump, automotive suspension and tire show applications.
Reviews