1. Massive field data collection : issues and challenges -- 2. Condition monitoring : available techniques -- 3. Challenges of condition monitoring using AI techniques -- 4. Input and output data -- 5. Two-stage response surface approaches to modeling drug interaction -- 6. Nearest neighbor-based techniques -- 7. Cluster-based techniques -- 8. Statistical techniques -- 9. Information theory-based techniques -- 10. Uncertainty management
Summary
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
Bibliography
Includes bibliographical references and index
Notes
English
Online resource; title from PDF title page (Ebsco, viewed April 27, 2015)
