Anomaly detection using lstm-autoencoder to predict coal pulverizer condition on coal-fired power plant

Abstract

Coal pulverizing systems reliability can be ensured effectively by using prognostics and health management approach. A mathematical model of coal pulverizing system used for anomaly detection is hard to be constructed due to its dynamic and nonlinear high-dimensional system typically. This paper proposed the use of the Long-Short Term Memory Autoencoder model for anomaly detection of the coal pulverizing system on a coal-fired power plant. The LSTM will solve the gradient reduction problem, and Autoencoder will improve the generalizability of the model. As a result, the proposed model can detect the anomaly successfully before the Sequent of Events occurs.