A Prediction Network for Hydraulic Support Pressure Based on Multitimescale Feature Extraction

The roof water hazard has severely weakened the safety of coal mine production. As the most direct data on the state of the roof, the monitoring big data of the hydraulic support is of great importance to coal mine safety. This paper proposes a neural network model based on multitimescale feature ex...

Full description

Saved in:
Bibliographic Details
Published in:Mathematical problems in engineering 2022-05, Vol.2022, p.1-9
Main Authors: Gao, Tianyu, Li, Zhanli, Li, Hongan
Format: Article
Language:English
Subjects:
Algorithms
Big Data
Coal mines
Coal mining
Disasters
Energy
Feature extraction
Fuzzy logic
Geology
Hydraulics
Machine learning
Methods
Mines
Mining industry
Monitoring
Neural networks
Occupational safety
Roofs
Simulation
Time series
Work face
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The roof water hazard has severely weakened the safety of coal mine production. As the most direct data on the state of the roof, the monitoring big data of the hydraulic support is of great importance to coal mine safety. This paper proposes a neural network model based on multitimescale feature extraction for predicting the pressure-bearing data of hydraulic supports in coal mine working faces, drawing on the idea of multiscale time feature extraction in the empirical modal decomposition algorithm. The proposed model can excel in capturing temporal features at multiple scales in the time series, thus improving the prediction accuracy of the neural network model. We collected 80 days of monitoring data from 17 hydraulic supports in the working face and carried out comparative experiments with several existing models. The experiments show that the neural network model proposed in this paper is stable and reliable, with small prediction errors, and can provide important help for the prevention and control of the roof water hazard.
ISSN:1024-123X
1563-5147
DOI:10.1155/2022/8526744