Isbn: 9780367738358 - artificial intelligence tools: decision support systems in condition monitoring and diagnosis (13 Ergebnisse)

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Paperback. Zustand: Brand New. 549 pages. 9.21x6.14x1.26 inches. In Stock.

Zustand: New. Diego Galar Pascual holds an M.Sc and Ph.D from Saragossa University, Zaragoza, Spain. He has been a professor at several universities, including Saragossa University and the European University of Madrid, Spain. At Saragossa University..

Taschenbuch. Zustand: Neu. Artificial Intelligence Tools | Decision Support Systems in Condition Monitoring and DIagnosis | Diego Galar Pascual | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2020 | CRC Press | EAN 9780367738358 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and Diagnosis discusses various white- and black-box approaches to fault diagnosis in condition monitoring (CM). This indispensable resource:Addresses nearest-neighbor-based, clustering-based, statistical, and information theory-based techniquesConsiders the merits of each technique as well as the issues associated with real-life applicationCovers classification methods, from neural networks to Bayesian and support vector machinesProposes fuzzy logic to explain the uncertainties associated with diagnostic processes Provides data sets, sample signals, and MATLAB® code for algorithm testingArtificial Intelligence Tools: Decision Support Systems in Condition Monitoring and Diagnosis delivers a thorough evaluation of the latest AI tools for CM, describing the most common fault diagnosis techniques used and the data acquired when these techniques are applied. 549 pp. Englisch.…

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and Diagnosis discusses various white- and black-box approaches to fault diagnosis in condition monitoring (CM). This indispensable resource:Addresses nearest-neighbor-based, clustering-based, statistical, and information theory-based techniquesConsiders the merits of each technique as well as the issues associated with real-life applicationCovers classification methods, from neural networks to Bayesian and support vector machinesProposes fuzzy logic to explain the uncertainties associated with diagnostic processes Provides data sets, sample signals, and MATLAB® code for algorithm testingArtificial Intelligence Tools: Decision Support Systems in Condition Monitoring and Diagnosis delivers a thorough evaluation of the latest AI tools for CM, describing the most common fault diagnosis techniques used and the data acquired when these techniques are applied.…