Fault detection and precedent-free localization in numerically discretized thermal–fluid systems

This paper uses the Growing Structure Multiple Model System (GSMMS) method for fault detection and precedent-free localization of unwanted heating anomalies in two different configurations of channel flow systems operated under dynamic conditions: (i) straight channel and (ii) straight channel with an internal flow disruptor. Unlike commonly used fault detection methods, the newly proposed approach does not require prior information regarding the fault location, fault severity or data emitted in the presence of a fault to build the model of that fault and recognize it. The new detection mechanism is based only on the models of normal behavior for various portions of the monitored system. The obtained results indicate that the detection and localization of the unwanted heating element (i.e., heat source) can be achieved through distributed GSMMS-based anomaly detection, with multiple anomaly detectors monitoring different parts of each configuration. The results also suggest that fault detection and localization are strongly related to a system’s configuration and operational conditions.

► A recently introduced method for dynamic system monitoring was modified and applied for the first time to a distributed system.
► The method was demonstrated in simulations of two thermal–fluid systems.
► The new method accomplishes fault detection and isolation using only data corresponding to normal behavior of the system.
► Fault localization is accomplished via distributed anomaly detection, without need for training using fault related data.