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Anomaly Detection Using LSTM-Autoencoder to Predict Coal Pulverizer Condition on Coal-Fired Power Plant

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概要 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.続きを見る

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登録日 2021.04.02
更新日 2021.04.02

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