This thesis presents a method for fault detection and precedent-free isolation for two types of channel flow systems, which were modeled with the finite element method. Unlike previous fault detection methods, this method requires no a priori knowledge or training pertaining to any particular fault. The basis for anomaly detection was the model of normal behavior obtained using the recently introduced Growing Structure Multiple Model System (GSMMS). Anomalous behavior is then detected as statistically significant departures of the current modeling residuals away from the modeling residuals corresponding to the normal system behavior. Distributed anomaly detection facilitated by multiple anomaly detectors monitoring various parts of the thermal-fluid system enabled localization of anomalous partitions of the system without the need to train classifiers to recognize an underlying fault. / text