Artificial Intelligence Tools : Decision Support Systems in Condition Monitoring and DIagnosis

This book discusses white- and black-box approaches to fault diagnosis in condition monitoring, delivering a thorough evaluation of the latest artificial intelligence tools. It addresses nearest-neighbor-based, clustering-based, statistical, and information theory-based techniques, considering the merits of each technique as well as the issues associated with real-life application. It covers classification methods, from neural networks to Bayesian and support vector machines. It proposes fuzzy logic to explain the uncertainties associated with diagnostic processes. It also provides data sets, sample signals, and MATLAB® code for algorithm testing.

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.