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Full Description
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® 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.
Contents
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.
