Fault detection method for nonlinear systems based on probabilistic neural network filtering

DOI10.1080/0020772021000046216zbMath1031.93083OpenAlexW2033207803MaRDI QIDQ4808245

Jacquelien M. A. Scherpen, J. Liu

Publication date: 14 October 2003

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: https://pure.rug.nl/ws/files/14623215/2002IntJSystSciLiu.pdf

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Neural networks for/in biological studies, artificial life and related topics (92B20) Nonlinear systems in control theory (93C10) Design techniques (robust design, computer-aided design, etc.) (93B51)

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An application of a discrete wavelet transform and a back-propagation neural network algorithm for fault diagnosis on single-circuit transmission line

Cites Work

• Process fault detection based on modeling and estimation methods - a survey
• A survey of design methods for failure detection in dynamic systems
• Fault detection and isolation in nonlinear dynamic systems: A combined input-output and local approach
• Frequency domain approach to optimally robust residual generation and evaluation for model-based fault diagnosis
• Detection filter design: Spectral theory and algorithms
• Analytical redundancy and the design of robust failure detection systems
• Adaptive observers for single output nonlinear systems
• On-line fault detection in uncertain nonlinear systems using diagnostic observers: a survey
• Computation of optimal output feedback gains for linear multivariable systems
• A bilinear fault detection observer

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