Abstract
The fault diagnosis of hybrid systems is a challenging research topic at present. Model based fault diagnosis methods have been paid much attention in recent years, however, because of the complexity of hybrid systems, it is usually difficult to achieve a first principle model. To address this problem, this paper proposes a novel hybrid neural network, and based on it, a gray-box approach to fault detection of hybrid systems is presented, which combines some priori knowledge with neural networks instead of the common first principle model. Simulation results illustrate the effectiveness of the proposed approach.
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© 2004 Springer-Verlag Berlin Heidelberg
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Wang, W., An, D., Zhou, D. (2004). Hybrid Neural Network Based Gray-Box Approach to Fault Detection of Hybrid Systems. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_89
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DOI: https://doi.org/10.1007/978-3-540-28648-6_89
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22843-1
Online ISBN: 978-3-540-28648-6
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