Abstract
Faults in a hydraulic test rig are detected and isolated by using two radial basis function networks (RBF). One RBF is used to model the test rig according to its nonlinear structure. The output prediction error, generated from the real responses and the model responses, is used as a residual to indicate the occurence of any fault. A second RBF is used to enhance the effect of an individual fault while reducing the effects of the other faults, in such a way that the fault is isolated. Simulation using real data collected from the rig demonstrates the effectiveness of this method.
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© 1995 Springer-Verlag/Wien
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Yu, D., Shields, D.N., Daley, S. (1995). Application of Radial Basis Function Networks to Fault Diagnosis for a Hydraulic System. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_28
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DOI: https://doi.org/10.1007/978-3-7091-7535-4_28
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82692-8
Online ISBN: 978-3-7091-7535-4
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