Analysis and Prediction of Hydraulic Support Load Based on Time Series Data Modeling

Joint Authors

Wang, Hong-Bo
Pang, Yi-Hui
Zhao, Jian-Jian
Shang, De-Yong

Source

Geofluids

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-15, 15 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-10-23

Country of Publication

Egypt

No. of Pages

15

Main Subjects

Physics

Abstract EN

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.

American Psychological Association (APA)

Pang, Yi-Hui& Wang, Hong-Bo& Zhao, Jian-Jian& Shang, De-Yong. 2020. Analysis and Prediction of Hydraulic Support Load Based on Time Series Data Modeling. Geofluids،Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1165929

Modern Language Association (MLA)

Pang, Yi-Hui…[et al.]. Analysis and Prediction of Hydraulic Support Load Based on Time Series Data Modeling. Geofluids No. 2020 (2020), pp.1-15.
https://search.emarefa.net/detail/BIM-1165929

American Medical Association (AMA)

Pang, Yi-Hui& Wang, Hong-Bo& Zhao, Jian-Jian& Shang, De-Yong. Analysis and Prediction of Hydraulic Support Load Based on Time Series Data Modeling. Geofluids. 2020. Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1165929

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1165929