Abstract
Growing demands of reliability and safety of contemporary industrial systems and technological processes impose development of more efficient fault diagnosis methods. The reliable fault diagnosis is expected even in uncertain conditions when measurement noises, disturbances and model uncertainty could appear. The application of the analytical redundancy on the basis of mathematical models opposite to hardware one in the fault detection and isolation systems gave expectations for radical improvement of engineering systems quality.
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© 2014 Springer International Publishing Switzerland
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Mrugalski, M. (2014). Conclusions and Future Research Directions. In: Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis. Studies in Computational Intelligence, vol 510. Springer, Cham. https://doi.org/10.1007/978-3-319-01547-7_7
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DOI: https://doi.org/10.1007/978-3-319-01547-7_7
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01546-0
Online ISBN: 978-3-319-01547-7
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