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Record Nr. |
UNINA9910787963003321 |
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Autore |
Pascual Diego Galar |
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Titolo |
Artificial intelligence tools : decision support systems in condition monitoring and diagnosis / / Diego Galar Pascual |
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Pubbl/distr/stampa |
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Boca Raton, Florida : , : CRC Press, , [2015] |
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©2015 |
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ISBN |
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1-4987-6019-8 |
0-429-10232-1 |
1-4665-8406-8 |
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Descrizione fisica |
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1 online resource (528 p.) |
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Disciplina |
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Soggetti |
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Industrial equipment - Maintenance and repair - Data processing |
Machinery - Monitoring |
Artificial intelligence - Industrial applications |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters. |
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Nota di contenuto |
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Front Cover; Contents; Preface; Acknowledgments; Author; Chapter 1: Massive Field Data Collection: Issues and Challenges; Chapter 2: Condition Monitoring: Available Techniques; Chapter 3: Challenges of Condition Monitoring Using AI Techniques; Chapter 4: Input and Output Data; Chapter 5: Two-Stage Response Surface Approaches to Modeling Drug Interaction; Chapter 6: Nearest Neighbor-Based Techniques; Chapter 7: Cluster-Based Techniques; Chapter 8: Statistical Techniques; Chapter 9: Information Theory-Based Techniques; Chapter 10: Uncertainty Management; Back Cover |
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Sommario/riassunto |
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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 |
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