Artificial Intelligence Tools

Decision Support Systems in Condition Monitoring and DIagnosis

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 techniques



Considers the merits of each technique as well as the issues associated with real-life application



Covers classification methods, from neural networks to Bayesian and support vector machines



Proposes fuzzy logic to explain the uncertainties associated with diagnostic processes



Provides data sets, sample signals, and MATLAB (R) code for algorithm testing



Artificial 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. Les mer
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Paperback
Legg i
Paperback
Legg i
Vår pris: 725,-

(Paperback) Fri frakt!
Leveringstid: Sendes innen 21 dager
På grunn av Brexit-tilpasninger og tiltak for å begrense covid-19 kan det dessverre oppstå forsinket levering.

Om boka

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 techniques



Considers the merits of each technique as well as the issues associated with real-life application



Covers classification methods, from neural networks to Bayesian and support vector machines



Proposes fuzzy logic to explain the uncertainties associated with diagnostic processes



Provides data sets, sample signals, and MATLAB (R) code for algorithm testing



Artificial 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.

Fakta

Innholdsfortegnelse

Massive Field Data Collection: Issues and Challenges. Condition Monitoring: Available Techniques. Challenges of Condition Monitoring Using AI Techniques. Input and Output Data. Two-Stage Response Surface Approaches to Modeling Drug Interaction. Nearest-Neighbor-Based Techniques. Clustering-Based Techniques. Statistical Techniques. Information Theory-Based Techniques. Uncertainty Management.

Om forfatteren

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, he also served as director of academic innovation, director of international relations, pro-vice-chancellor, and senior researcher in the Aragon Institute of Engineering Research (i3A). In addition, he has been the technological director and CBM manager of international firms such as Volvo, Saab, Boliden, Scania, Tetrapak, Heinz, and Atlas Copco. Currently, he is the professor of condition monitoring in the Division of Operation and Maintenance of the Lulea University of Technology (LTU), Sweden, where he also is involved with the LTU-SKF University Technology Center. Widely published, Dr. Galar Pascual serves as a visiting professor at the University of Valencia (Spain), Polytechnic of Braganza (Portugal), Valley University (Mexico), Sunderland University (UK), University of Maryland (College Park, USA), and Northern Illinois University (DeKalb, USA).