By Vasile Palade, Cosmin Danut Bocaniala
This e-book provides the latest matters and study leads to commercial fault prognosis utilizing clever strategies. It makes a speciality of computational intelligence functions to fault prognosis with real-world purposes utilized in diversified chapters to validate different analysis tools. The e-book contains one bankruptcy facing a unique coherent fault prognosis disbursed technique for advanced platforms.
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Additional info for Computational Intelligence in Fault Diagnosis (Advanced Information and Knowledge Processing)
System and Control Series. North-Holland, MIT Press, Cambridge, MA, USA 80. Viswanadham N, Taylor JH and Luce EC (1987) A frequency-domain approach to failure detection and isolation with application to GE-21 turbine engine control systems. ) 81. Waltz D (1975) Understanding line drawings of scene with drawings. In: The psychology of computer vision. McGraw-Hill, New York 82. Willsky AS and Jones HL (1974) A generalized likelihood approach to state estimation in linear systems subjected to abrupt changes.
This method provides more realistic conditions for generating the behavior of the system while undergoing a fault. It also makes the FDI task more difficult because the real data input causes the system to feature the same noise conditions as those in the real plant. 4. Conclusions This chapter surveyed the applications of computational intelligence methodologies to fault diagnosis. Throughout the chapter, a special emphasis has been put on the practical limitations of the applicability of these methodologies.
Calado et al. (2001) propose a hierarchical architecture of several neuro-fuzzy structures (called by the authors fuzzy-neural networks (FNNs)) for fault isolation purposes. The structure aims to correctly classify input symptoms corresponding to both abrupt and incipient faults (single or multiple), using only abrupt faults symptoms and normal state symptoms during the training phase. The symptoms are generated by selecting from residuals, and their combinations, those signals that provide the best distinction between different operating states of the system.