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Technical Computational Intelligence in Fault Diagnosis
Posted on 2010-04-11
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Computational Intelligence in Fault Diagnosis . Vasile Palade . . There are two main categories of fault diagnosis techniques currently in use and each has its own basic support theory. The first class of methodologies used for fault diagnosis-related problems were based on mathematical models of the monitored plant. The differences between the plant model and its actual behavior are called residuals and form the basis for deciding if a fault did or did not occur; and if a fault has occurred, deciding which particular fault occurred. Unfortunately, these techniques provide satisfactory results only when plants exhibit linear behaviour or when the modelling errors can be kept within acceptable limits. Accurate mathematical models can be obtained only for plants with low behavioural complexity. . Recent research efforts have concentrated on finding suitable techniques to model plants with high nonlinear behaviour, noise and uncertainty. These three characteristics have been successfully mastered by using computational intelligence methodologies. These solutions are based on models such as fuzzy systems, neural networks, and genetic algorithms, to name only the most important of them. The above methods are commonly combined to give the desired result. Besides using residuals for diagnosis purposes, the computational intelligence methods may also be used to directly map the sensor measurements to the faults’ space. Thesemethods allow an understanding of plant behaviour using rules obtained directly from sensor measurements. However, even if these techniques can solve the difficult problems posed by nonlinearity, noise and uncertainty, if the complexity of the plant behaviour is very high, the computational load becomes too large for practical purposes. .
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