Early detection and prediction of leaks in fluidized-bed boilers using artificial neural networks

Abstract

Leaks in fluidized-bed boilers are typically characterized by slow escalation. Early detection and prediction of such faults is an important task that has not been solved in practice yet. The paper reports a series of research and development works related to achieving early detection and prediction of leaks in fluidized-bed boilers using ANN (artificial neural networks). The obtained results were used in pilot implementation of a diagnostics and prediction system covering six blocks of a professional power plant. The diagnostics and prediction task is divided into two stages: early fault detection by virtual sensors and leak isolation using classifiers of fault state. Models of process variables were created by employing a novel two-stage structure of ANN. The resulting efficiency of leak detection is presented. Also provided is an example of 12 faults of a fluidized-bed boiler, achieving detection of 11 faults with at least two days advance prediction of a boiler shutdown. These results are compared with detections obtained by the authors previously with the use of neuro-fuzzy models. Then, the paper reports the ability to distinguish between three classes of leaks by the developed classifier of the fault state. Further possible improvements of this fault classification system are discussed.