PublisherInternational Federation of Automatic Control (IFAC)
Rights accessRestricted access - publisher's policy
A growing demand for technologically advanced systems has contributed to the increase of the awareness of systems safety and reliability. Such a situation requires the development of novel methods of robust fault diagnosis. The application of the analytical redundancy based methods for system fault detection causes that theIr effectiveness depends on model quality. In this paper, a new Methodology for the improvement of the neural model with a D-optimum sequential experimental design technique combined with outer bounding ellipsoid algorithm is proposed. Moreover, a novel method of robust fault detection against neural model uncertainty and disturbances is developed. Such an approach is used for modelling and robust fault detection of the three-screw spindle oil pump.
CitationAkielaszek-Witczak, A., Mrugalska, B., Puig, V., Wyrwicka, M. Robust neural-network-based fault detection with sequential D-optimum bounded-error input design. A: IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes. "IFAC-PapersOnLine (volume 48, issue 21, Pages 1-1496): 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2015 Paris, 2–4 September 2015". París: International Federation of Automatic Control (IFAC), 2015, p. 434-439.
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder. If you wish to make any use of the work not provided for in the law, please contact: email@example.com