An approach for fault localization based upon unsupervised neural networks

Nowadays, classical strategies for fault detection and isolation present the lack of availability in order to tackle unknown scenarios. In here, fault localization based upon unsupervised neural networks is presented as an alternative approach in order to determine abnormal situations under certain conditions. This proposal uses two well-known techniques in order to classify patterns and clusters of patterns. This approach is followed in order to enhance the capability of fault localization by avoiding noise ratio and time variant behaviour. Both cluster techniques are connected in cascade mode. These two techniques are trained offline in order to response to on-line time constraints based upon case study