State-Space GMDH Neural Networks for Actuator Robust Fault Diagnosis

Robust H-Infinity Actuator Fault Diagnosis with Neural Network

The paper deals with the problem of a robust actuator fault diagnosis for Linear Parameter-Varying (LPV) systems with Recurrent Neural-Network (RNN). The preliminary part of the paper describes the derivation of a discrete-time polytopic LPV model with RNN. Subsequently, a robust fault detection, isolation and identification scheme is developed, which is based on the observer and H∞ framework f...

An unscented Kalman filter in designing dynamic GMDH neural networks for robust fault detection

This paper presents an identification method of dynamic systems based on a group method of data handling approach. In particular, a new structure of the dynamic multi-input multi-output neuron in a state-space representation is proposed. Moreover, a new training algorithm of the neural network based on the unscented Kalman filter is presented. The final part of the work contains an illustrative...

Robust fault detection and accommodation of the boiler unit using state space neural networks

This paper deals with the application of state space neural network models to fault detection and accommodation of the boiler unit. The work describes two aspects. First one is the fault detection and in this paper to diagnose the fault the three methods are described and compared: simple and adaptive threshold and more robust method which is model error modelling. The second part of the paper ...

Robust Freeway Traveltime Prediction with State-space Neural Networks

Using Neural Networks for Fault Diagnosis

In this paper, a universial Fault Instance Model, which aims to solve problems existing in the present technology of fault diagnosis , such as the lack of universiality, the difficulty in the use of real time system and the dilemma of stability and plasticity, is proposed. The experiment demonstrates that the FANNC used can successfully settles the problems mentioned above by its effectively in...