Analysis and Prediction of Hydraulic Support Load Based on Time Series Data Modeling
Hydraulic support plays a key role in ground control of longwall mining. The smart prediction methods of support load are important for achieving intelligent mining. In this paper, the hydraulic support load data is decomposed into trend term, cycle term, and residual term, and it is found that the...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Geofluids |
| Online Access: | http://dx.doi.org/10.1155/2020/8851475 |
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| author | Yi-Hui Pang Hong-Bo Wang Jian-Jian Zhao De-Yong Shang |
| author_facet | Yi-Hui Pang Hong-Bo Wang Jian-Jian Zhao De-Yong Shang |
| author_sort | Yi-Hui Pang |
| collection | DOAJ |
| description | Hydraulic support plays a key role in ground control of longwall mining. The smart prediction methods of support load are important for achieving intelligent mining. In this paper, the hydraulic support load data is decomposed into trend term, cycle term, and residual term, and it is found that the data has clear trend and period features, which can be called time series data. Based on the autoregression theory and weighted moving average method, the time series model is built to analyze the load data and predict its evolution trend, and the prediction accuracy of the sliding window model, ARIMA (Autoregressive Integrated Moving Average) model, and SARIMA (Seasonal Autoregressive Integrated Moving Average) model to the hydraulic support load under different parameters are evaluated, respectively. The results of single-point and multipoint prediction test with various sliding window values indicate that the sliding window method has no advantage in predicting the trend of the support load. The ARIMA model shows a better short-term trend prediction than the sliding window model. To some extent, increasing the length of the autoregressive term can improve the long-term prediction accuracy of the model, but it also increases the sensitivity of the model to support load fluctuation, and it is still difficult to predict the load trend in one support cycle. The SARIMA model has better prediction results than the sliding window model and the ARIMA model, which reveals the load evolution trend accurately during the whole support cycle. However, there are many external factors affecting the support load, such as overburden properties, hydraulic support moving speed, and worker’s operation. The smarter model of SARIMA considering these factors should be developed to be more suitable in predicting the hydraulic support load. |
| format | Article |
| id | doaj-art-3aa317e6cf53421b931984bb558f3c82 |
| institution | Kabale University |
| issn | 1468-8115 1468-8123 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geofluids |
| spelling | doaj-art-3aa317e6cf53421b931984bb558f3c822025-02-03T01:28:26ZengWileyGeofluids1468-81151468-81232020-01-01202010.1155/2020/88514758851475Analysis and Prediction of Hydraulic Support Load Based on Time Series Data ModelingYi-Hui Pang0Hong-Bo Wang1Jian-Jian Zhao2De-Yong Shang3CCTEG Coal Mining Research Institute, Beijing 100013, ChinaChina University of Mining and Technology-Beijing, School of Energy and Mining Engineering, Beijing 100083, ChinaSinosteel Group Corporation Limited, Beijing 100080, ChinaChina University of Mining and Technology-Beijing, School of Energy and Mining Engineering, Beijing 100083, ChinaHydraulic support plays a key role in ground control of longwall mining. The smart prediction methods of support load are important for achieving intelligent mining. In this paper, the hydraulic support load data is decomposed into trend term, cycle term, and residual term, and it is found that the data has clear trend and period features, which can be called time series data. Based on the autoregression theory and weighted moving average method, the time series model is built to analyze the load data and predict its evolution trend, and the prediction accuracy of the sliding window model, ARIMA (Autoregressive Integrated Moving Average) model, and SARIMA (Seasonal Autoregressive Integrated Moving Average) model to the hydraulic support load under different parameters are evaluated, respectively. The results of single-point and multipoint prediction test with various sliding window values indicate that the sliding window method has no advantage in predicting the trend of the support load. The ARIMA model shows a better short-term trend prediction than the sliding window model. To some extent, increasing the length of the autoregressive term can improve the long-term prediction accuracy of the model, but it also increases the sensitivity of the model to support load fluctuation, and it is still difficult to predict the load trend in one support cycle. The SARIMA model has better prediction results than the sliding window model and the ARIMA model, which reveals the load evolution trend accurately during the whole support cycle. However, there are many external factors affecting the support load, such as overburden properties, hydraulic support moving speed, and worker’s operation. The smarter model of SARIMA considering these factors should be developed to be more suitable in predicting the hydraulic support load.http://dx.doi.org/10.1155/2020/8851475 |
| spellingShingle | Yi-Hui Pang Hong-Bo Wang Jian-Jian Zhao De-Yong Shang Analysis and Prediction of Hydraulic Support Load Based on Time Series Data Modeling Geofluids |
| title | Analysis and Prediction of Hydraulic Support Load Based on Time Series Data Modeling |
| title_full | Analysis and Prediction of Hydraulic Support Load Based on Time Series Data Modeling |
| title_fullStr | Analysis and Prediction of Hydraulic Support Load Based on Time Series Data Modeling |
| title_full_unstemmed | Analysis and Prediction of Hydraulic Support Load Based on Time Series Data Modeling |
| title_short | Analysis and Prediction of Hydraulic Support Load Based on Time Series Data Modeling |
| title_sort | analysis and prediction of hydraulic support load based on time series data modeling |
| url | http://dx.doi.org/10.1155/2020/8851475 |
| work_keys_str_mv | AT yihuipang analysisandpredictionofhydraulicsupportloadbasedontimeseriesdatamodeling AT hongbowang analysisandpredictionofhydraulicsupportloadbasedontimeseriesdatamodeling AT jianjianzhao analysisandpredictionofhydraulicsupportloadbasedontimeseriesdatamodeling AT deyongshang analysisandpredictionofhydraulicsupportloadbasedontimeseriesdatamodeling |