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The paper shows a unified approach for designing both sensor and actuator fault diagnosis with neural networks. In particular, a general scheme of the group method of data handling neural networks is recalled. Subsequently, a unscented Kalman filter approach for designing the network and determining its uncertainty is briefly portrayed. The achieved results are then used to obtain the so-called robust sensor fault diagnosis scheme. The main contribution of this paper is to show how to use the above-mentioned results for actuator fault diagnosis. In particular, the obtained neural model is used to obtain the input estimates. The achieved estimates are then compared with the original input signals to formulate the diagnostics decisions. The input estimation scheme is based on a chain of robust observers, which guaranties that the input estimates are obtained with a prescribed disturbance attenuation level while ensuring the convergence of the observers. The final part of the paper shows a comprehensive case study regarding the laboratory tunnel furnace, which exhibits the performance of the proposed approach.

Towards Robust Neural-Network-Based Sensor and Actuator Fault Diagnosis: Application to a Tunnel Furnace