Sensor fault detection and diagnosis based on SOMNNs for steady-state and transient operation

Abstract

The paper presents a readily implementable approach for sensor fault detection, identification (SFD/I) and faulted sensor data reconstruction in complex systems based on self-organizing map neural networks (SOMNNs). Two operational regimes are considered, i.e. the steady operation and operation with transients. For steady operation, SOMNN based estimation error (EE) are used for SFD. EE contribution plots are employed for SFI. For operation with transients, SOMNN classification maps are used for SFD/I comparing with the `fingerprint' maps. In addition, extension algorithm of SOMNNs is developed for faulted sensor data reconstruction. The validation of the proposed approach is demonstrated through experimental data during the commissioning of industrial gas turbines.