Intelligent Condition Monitoring and Diagnosis Systems: A Computational Intelligence Approach
Written as a result of a several-year research project using Computational Intelligence techniques for solving condition monitoring and diagnosis problems of machineries at the Norwegian University of Science and Technology, this book is about intelligent system development. In order to survive in an uncertain and complex environment, it is necessary to bring Artificial Neural Networks, Fuzzy Logic Systems, Genetic Algorithms and Expert systems together to make a condition monitoring and diagnosis system more effective, reliable, and cost effective than the traditional one. The focus of Intelligent Condition Monitoring and Diagnosis System is on practical applications of intelligent techniques. It provides practicing engineers and scientists with the information they need to solve the problems in both industry and academia.
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agent AMNs amplitude analysis and/or applications Artificial Intelligence Artificial Neural Networks B-Spline network basis functions bearing blackboard system centrifugal pump monitoring centrifugal pump system chromosome complex components condition monitoring crossover defined deltap detection developed diagnosis Expert System diagnosis system domain drive-end Engineering expert systems extract fast AND power fault Figure flow is low fuzzy logic fuzzy relationships fuzzy sets fuzzy system Genetic Algorithms hidden layer Hopfield network human impeller implemented individual input space knots knowledge base learning low AND speed machine measure mechanical equipment membership function membership value method Misalignment MLFF ANN MLFF network monitoring and diagnosis motor multivariate basis functions mutation network structure neurons operator optimization parameters patterns performance possible problem risk procedure processing representation rotational rotor rules schema selection sensors signal simulated solution solve Supervised learning techniques theory torque Unbalance univariate usually variable vector weight