scope:

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.