Fault diagnosis in dynamic systems: theory and application
Fault diagnosis in dynamic systems: theory and application
Nonlinear control design: geometric, adaptive and robust
Nonlinear control design: geometric, adaptive and robust
State space neural network. Properties and application
Neural Networks
Robust model-based fault diagnosis for dynamic systems
Robust model-based fault diagnosis for dynamic systems
Artificial Neural Networks for Modelling and Control of Non-Linear Systems
Artificial Neural Networks for Modelling and Control of Non-Linear Systems
Fault diagnosis for nonlinear systems using a bank of neural estimators
Computers in Industry - Special issue: Soft computing in industrial applications
Issues of Fault Diagnosis for Dynamic Systems
Issues of Fault Diagnosis for Dynamic Systems
Sliding mode observers for fault detection and isolation
Automatica (Journal of IFAC)
Brief Sliding mode observers for detection and reconstruction of sensor faults
Automatica (Journal of IFAC)
Nonlinear system fault diagnosis based on adaptive estimation
Automatica (Journal of IFAC)
Automated fault diagnosis in nonlinear multivariable systems using a learning methodology
IEEE Transactions on Neural Networks
Robust local stability of multilayer recurrent neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Improving heat exchanger supervision using neural networks and rule based techniques
Expert Systems with Applications: An International Journal
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This article presents a robust fault detection and diagnosis scheme for any abrupt and incipient class of faults that can affect the state of a class of nonlinear systems. A nonlinear observer which synthesizes sliding mode techniques and neural state space models is proposed for the purpose of online health monitoring. The sliding mode term is utilized to eliminate the effect of system uncertainties on the state observation. The switching gain of the sliding mode is updated via an iterative learning algorithm and an iterative fuzzy model, respectively. Moreover, a bank of neural state space models is adopted to estimate various state faults. Robustness with respect to modeling uncertainties, fault sensitivity, and stability of this neural sliding mode observer-based fault diagnosis scheme are rigorously investigated in theory. Moreover, the proposed fault detection and diagnosis scheme is applied to the model of a fourth-order satellite dynamic system, and the simulation results illustrate the effectiveness of the proposed approach.