Recursive Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Diagnostic system for boilers and furnaces using CFD and neural networks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Proceedings of the 2010 Conference on Grand Challenges in Modeling & Simulation
Improving heat exchanger supervision using neural networks and rule based techniques
Expert Systems with Applications: An International Journal
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
Hi-index | 12.05 |
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.