Computational Intelligence in Fault Diagnosis by Vasile Palade, Cosmin Danut Bocaniala PDF

By Vasile Palade, Cosmin Danut Bocaniala

ISBN-10: 1846283434

ISBN-13: 9781846283437

This ebook offers the newest matters and examine ends up in business fault prognosis utilizing clever innovations. It specializes in computational intelligence functions to fault analysis with real-world purposes utilized in diverse chapters to validate different prognosis equipment. The booklet comprises one bankruptcy facing a singular coherent fault analysis dispensed technique for complicated systems.

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Extra info for Computational Intelligence in Fault Diagnosis

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The previous equivalence may be used to insert in the B-spline neural network knowledge from human experts expressed as fuzzy rules (Chen and Patton, 1999; Brown and Harris, 1995). The fact that the (multivariate) B-spline functions can be interpreted linguistically allows the set of fuzzy rules to provide the operator with an explicit description of the causes of the faults. , 1999). , 1999). 4. Genetic Algorithms Genetic algorithms represent the best known and the most commonly used Evolutionary Computing technique.

121-133 33. Isermann R (1997) Supervision, fault-detection and fault-diagnosis methods – an introduction. Control Engineering Practice 5(5): 639-652 34. Isermann R and Ballé P (1997) Trends in the application of model-based fault detection and diagnosis of technical processes. Control Engineering Practice 5(5): 709-719 35. Jones HL (1973) Failure detection in linear systems (PhD thesis). MIT, Massachusetts, USA 36. Kay H (1996) Refining imprecise models and their behaviors (PhD thesis). The University of Texas at Austin, USA 37.

More precisely, the percent obtained by dividing the fitness of an individual to the sum of fitness values of all individuals represents the probability with which that individual will be selected to perform reproduction. Therefore, the probability for selecting the most fit individuals for reproduction is larger than the probability of selecting the less fit individuals. The new individuals are obtained combining the features of selected individuals. The features combination is done by applying the crossover operation for the pairs of strings of genes corresponding to the same feature.

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Computational Intelligence in Fault Diagnosis by Vasile Palade, Cosmin Danut Bocaniala

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