GRU-Auto-Encoder neural network based methods for diagnosing abnormal operating conditions of steam drums in coal gasification plants

Abstract

Operating conditions of steam drums significantly reflect operating performances of coal gasification processes. It is crucial to accurately diagnose the abnormal operating conditions and pay attention to the associated process variables in time. In response to the problem that it is difficult to obtain a large number of abnormal samples of steam drum operating data, which are insufficient for abnormal features learning with traditional methods, an Auto-Encoder neural network based on gated recurrent unit (GRU-Auto-Encoder) is proposed in this paper. The method generates abnormal samples by considering the temporal dependence of data before operating data which contain the generated abnormal samples are provided to GRU neural network for extracting operating conditions features in a deeper and dynamic manner, helping analyze root causes of abnormalities and monitor operating conditions. The effectiveness of method is demonstrated by experiments with operating data of an industrial coal gasification plant.