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Fault detection and diagnosis using neural network approachesNeural networks can be used to detect and identify abnormalities in real-time process data. Two basic approaches can be used, the first based on training networks using data representing both normal and abnormal modes of process behavior, and the second based on statistical characterization of the normal mode only. Given data representative of process faults, radial basis function networks can effectively identify failures. This approach is often limited by the lack of fault data, but can be facilitated by process simulation. The second approach employs elliptical and radial basis function neural networks and other models to learn the statistical distributions of process observables under normal conditions. Analytical models of failure modes can then be applied in combination with the neural network models to identify faults. Special methods can be applied to compensate for sensor failures, to produce real-time estimation of missing or failed sensors based on the correlations codified in the neural network.
Document ID
19950007756
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Kramer, Mark A.
(Massachusetts Inst. of Tech. Cambridge, MA, United States)
Date Acquired
September 6, 2013
Publication Date
October 30, 1992
Publication Information
Publication: Research Inst. for Computing and Information Systems, RICIS Symposium 1992: Mission and Safety Critical Systems Research and Applications
Subject Category
Cybernetics
Accession Number
95N14170
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
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