State-Space GMDH Neural Networks for Actuator Robust Fault Diagnosis

Abstract

1 Abstract—Most fault diagnosis methods focus on the fault detection of the system or sensors and do not take into account the problem of the fault detection and isolation of the actuators, which are an important part of the contemporary industrial systems. To solve such a problem, the system outputs and inputs estimator based on a dynamic Group Method of Data Handling neural network in the state-space representation is proposed. In particular, the methodology of the adaptive thresholds calculation for system inputs and outputs is presented. The approach is based on the application of the Unscented Kalman Filter and Unknown Input Filter is presented. This result enables performing robust fault detection and isolation of the actuators. The final part of the paper presents an application study, which confirms the effectiveness of the proposed approach. The m ost desirable feature of the neural models applied to the FDI and FTC schemes is a small modeling uncertainty, which is defined as a mismatch between the model and the system being considered (3). Therefore, it is important to use an approach reducing the contribution of the structure errors and the parameter estimation inaccuracy of the neural model uncertainty. To solve such a challenging problem, a Group Method of Data Handling (GMDH) neural network (10), (12), (20), (13), (18) have been proposed. The concept of the GM DH approach relies on replacing the complex neural model by the set of the hierarchically connected partial models, which can be chosen with the application of appropriate selection methods. Besides the reduction of the neural m odel inaccuracy it is important to calculate the model uncertainty in the form of mathematical description allowing performing the robust fault diagnosis. In this paper, a new approach to the actuator and sensors fault detection and isolation is proposed. To solve such a challenging problem, the system outputs and inputs estimator based on the dynamic GMDH neural network is employed. In order to achieve this goal a new structure of the dynamic neuron in the state-space representation is proposed. This description enables to obtain constraints of the parameter estimates which warranty the stability of dynamic GMDH neural model. In order to obtain the constrained parameter estimates and the neural model uncertainty description the Unscented Kalman Filter (UKF) (7), (27), (28) was used. This knowledge enables to calculate the output adaptive threshold. Moreover, the methodology of estimation of the GMDH neural model inputs and the calculation of the input adaptive threshold with the application of the Unknown Input Filter (UIF) is proposed. The obtained input and output adaptive thresholds allow to perform the robust fault diagnosis and actuators isolation of the dynamic non-linear systems. The paper consists of six sections. After the introduction, presenting the subject of this paper, in Section II a concept of robust fault detection and isolation of sensors and actuators is presented. Section III presents the process of synthesis of GMDH neural model. In section IV, the estimation of neural model inputs is realized with the UIF. The experimental results of the robust fault diagnosis are shown in section V. Finally, section VI concludes the paper.