By Vasile Palade, Cosmin Danut Bocaniala
This booklet offers the newest issues and learn leads to business fault prognosis utilizing clever concepts. It makes a speciality of computational intelligence purposes to fault prognosis with real-world purposes utilized in varied chapters to validate the various analysis tools. The e-book comprises one bankruptcy facing a singular coherent fault prognosis dispensed technique for complicated platforms.
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Additional info for Computational Intelligence in Fault Diagnosis (Advanced Information and Knowledge Processing)
This is a novel process that achieves effective diagnosis by means of a rule-based pattern-recognition methodology founded on fuzzy algebra, developed to provide an alternative technology versus conventional estimation algorithms. The inherent capability of fuzzy logic to deal with gas path diagnostics difficulties, thanks to the use of fuzzy set theory and its rule-based nature, is highlighted. First, the problem of noisy measurements is treated at a fuzzy-set level. Second, at the system level the definition of fuzzy rules is used to map input sets of measurements into output faulty classes of performance parameters in a constrained search space; this enables a problem reduction aimed at overcoming the fact that the analytical formulation is undetermined.
1999) and Chen and Patton (1999) use, as input of the neural network, the inputs and the outputs of the system inside a time window. The output of the neural network, the residual r(t), is forced to be 0 when the system operates in normal state, and 1 when a fault occurs in the system. , 1999; Chen and Patton, 1999). The m+1 output values of the network correspond to the normal state (F0) and the faulty states (F1-Fm) of the system. When the system operates in normal state, the corresponding output value, F0, is one and all other output values are zero.
Also, such a system will usually feature a large number of faults. In order to increase the number of faults that can be diagnosed, the number of fuzzy sets used must increase too. If the complexity of the rule base is too large, the neuro-fuzzy systems will experience the curse of dimensionality too. ) ∆ . . . . . . . . . . . ∆ ∆ ∆ ∆ ∆ . . . . . . OR ∆ ∆ . . . . . ∆ ∆ . . . . . . 13. A hierarchical structure of neuro-fuzzy networks. 3. B-Spline Neural Networks The B-spline neural networks are one-layer neural networks with B-spline functions in the hidden layer.
Computational Intelligence in Fault Diagnosis (Advanced Information and Knowledge Processing) by Vasile Palade, Cosmin Danut Bocaniala