Description
Book SynopsisArtificial 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 w
Trade Review
"… a long overdue publication; the condition monitoring community, from newcomers to experts, will find themselves constantly referring to this book, especially to find definitive answers to often debated issues."
—Chris Pomfret, Society for Machinery Failure Prevention Technology, Dayton, Ohio, USA
"… a good reference book for students, educators, and maintenance engineers who would like to use artificial intelligence (AI) techniques for data fusion and decision making in condition monitoring and diagnosis."
—Zhongxiao Peng, University of New South Wales, Sydney, Australia
"… a detailed and descriptive analysis of the latest thinking on data collection and analyses for maintenance task development. … an important addition to the library of existing knowledge to support asset managers, academics, and engineering students who want to understand the methods and techniques to diagnose the state of an asset and develop a new approach to asset management."
—David Baglee, University of Sunderland, UK
"… very comprehensive and informative in its coverage of condition monitoring and condition-based maintenance for machinery. I’m not aware of any other book on the market that has the breadth of coverage of this book. It will be an excellent resource for practitioners in the field. The book contains well-written and very understandable definitions and descriptions of the techniques used for condition monitoring for machinery, providing a useful resource for students and practicing engineers."
—Peter Sandborn, University of Maryland, College Park, USA
Table of ContentsMassive 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.
