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

By Vasile Palade, Cosmin Danut Bocaniala

This e-book provides the latest matters and examine leads to business fault prognosis utilizing clever recommendations. It specializes in computational intelligence functions to fault analysis with real-world purposes utilized in diverse chapters to validate the various analysis equipment. The booklet comprises one bankruptcy facing a unique coherent fault prognosis dispensed technique for advanced platforms.

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Fuzzy Modeling of Systems with Faults Takagi and Sugeno (1985) use fuzzy rules, with the general form given by Eq. 24, to build the fuzzy model of a system. R: IF x1 is A1 and … and xk is Ak THEN y=p0+p1x1+…+pkxk (24) where y is the output of the system whose value is inferred, x1, …, xk are input variables of the system, A1, …, Ak represent fuzzy sets with linear membership functions standing for a fuzzy subspace, in which the rule R can be applied for reasoning. If the system is described by a set of rules {Ri / i=1,…,n} having the previous form, and the values of input variables x1, x2, …, xk are x10, x20,…, xk0, respectively, the output value y is inferred following the next three steps.

Prentice Hall, New York 6. Beard R V (1971) Failure accommodation in linear system through selfreorganization (PhD thesis). MIT, Massachusetts, USA 7. Bendtsen JD and Izadi-Zamanabadi R (2002) FDI using neural networks – application to ship benchmark engine gain. In: Preprints of the 15th IFAC World Congress, Barcelona, Spain 8. Bocaniala CD, Sa da Costa J and Palade V (2004) A Novel Fuzzy Classification Solution for Fault Diagnosis. International Journal of Fuzzy and Intelligent Systems 15(3-4):195-206 9.

Compared to MLP networks or Computational Intelligence in Fault Diagnosis 15 RNNs, the training of a DNN requires more time, memory, and computational effort. Marcu et al. (1999) study the mixing of three variations of DNNs and their application to generating residuals for a three-tank laboratory system. Marcu et al. (2000) apply two types of DNNs to model the evaporation station from the Lublin sugar factory using real process data. The two types of DNNs are the Dynamic Multilayer Perceptron Networks (DMLPs), previously described, and Dynamic Radial Basis Function (DRBF) Networks that have dynamics provided by the ARMA filters in the hidden layers structure (Ayoubi, 1994).

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