Artificial Intelligence Tools

<p><strong>Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and Diagnosis</strong> discusses various white- and black-box approaches to fault diagnosis in condition monitoring (CM). This indispensable resource:</p><p></p><ul> <br><br><li>Addresses nearest-neighbor-based clustering-based statistical and information theory-based techniques</li> <br><br><li>Considers the merits of each technique as well as the issues associated with real-life application</li> <br><br><li>Covers classification methods from neural networks to Bayesian and support vector machines</li> <br><br><li>Proposes fuzzy logic to explain the uncertainties associated with diagnostic processes </li> <br><br><li>Provides data sets sample signals and MATLAB® code for algorithm testing</li> </ul><p></p><p><b>Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and Diagnosis </b>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.</p>