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State-Space GMDH Neural Networks for Actuator Robust Fault DiagnosisMRUGALSKI, M. , WITCZAK, M.
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fault diagnosis, robustness, actuators, neural networks, system identification
fault(14), control(12), systems(11), neural(10), witczak(7), networks(7), estimation(7), mrugalski(6), korbicz(6), gmdh(6)
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About this article
Date of Publication: 2012-08-31
Volume 12, Issue 3, Year 2012, On page(s): 65 - 72
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2012.03010
Web of Science Accession Number: 000308290500010
SCOPUS ID: 84865856659
Most fault diagnosis methods focus on the fault detection of the system or sensors and do not take into account the problem of the fault detection and isolation of the actuators, which are an important part of the contemporary industrial systems. To solve such a problem, the system outputs and inputs estimator based on a dynamic Group Method of Data Handling neural network in the state-space representation is proposed. In particular, the methodology of the adaptive thresholds calculation for system inputs and outputs is presented. The approach is based on the application of the Unscented Kalman Filter and Unknown Input Filter is presented. This result enables performing robust fault detection and isolation of the actuators. The final part of the paper presents an application study, which confirms the effectiveness of the proposed approach.
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