| 作成者 |
|
|
|
|
|
|
|
| 本文言語 |
|
| 出版者 |
|
|
|
| 発行日 |
|
| 収録物名 |
|
| 巻 |
|
| 号 |
|
| 開始ページ |
|
| 終了ページ |
|
| 出版タイプ |
|
| アクセス権 |
|
| Crossref DOI |
|
| 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
|
Mendeley出力
Pages_089-097