An intelligent prediction method of gas concentration in coal mines based onimproved TCN-TimeGAN
| Title: | An intelligent prediction method of gas concentration in coal mines based onimproved TCN-TimeGAN |
|---|---|
| Authors: | Qingsong HU, Shuo ZHENG, Shiyin LI, Yanjing SUN |
| Source: | Meitan kexue jishu, Vol 52, Iss S2, Pp 321-330 (2024) |
| Publisher Information: | Editorial Department of Coal Science and Technology, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Mining engineering. Metallurgy |
| Subject Terms: | gas concentration prediction, timegan, deep learning, artificial intelligence, coal mine intellectualization, Mining engineering. Metallurgy, TN1-997 |
| More Details: | The prediction of gas concentration is of great importantance to ensure the safety of mine production. The gas concentration data has the characteristics of small sample size and time dependence, and traditional machine learning methods are not effective. A time convolution improved time series Generative adversarial network (TCN-TimeGAN) is proposed. Based on the characteristics of generative adversarial network (GAN), the problem of over-fitting of small samples of gas data is improved, and the receptive field is enlarged based on TCN network to read long-term dimension features. In the design of loss function, Wasserstein distance is used to measure the distribution of gas data, and the gradient penalty term of adaptive weight is added to the identification network loss function, so as to solve the problems of data irregularity and gradient disappearance, and improve training stability and prediction accuracy. When conducting model training, the first step is to normalize the gas time series and process missing data values. The processing results are used as input sequences of the embedding network and recovery network to reduce reconstruction loss. Subsequently, the input sequences are also input into the supervised network to reduce supervision loss. Finally, joint training is conducted, and the total loss is the sum of the generated network loss and the discriminative network loss. The experiment results show that the data generated by the proposed model can cover the original data distribution more comprehensively, and the mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE) of the results predicted from the generated data by the improved model are much smaller than those of the comparison model, and the prediction can be stable and accurate in all time periods. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | Chinese |
| ISSN: | 0253-2336 |
| Relation: | https://doaj.org/toc/0253-2336 |
| DOI: | 10.12438/cst.2023-1404 |
| Access URL: | https://doaj.org/article/33a6b05308e24872bacc9dcd32998f04 |
| Accession Number: | edsdoj.33a6b05308e24872bacc9dcd32998f04 |
| Database: | Directory of Open Access Journals |
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