For Libraries For Publication Open Access Downloads About Us Contact Us
Paper Titles
The Study of Reclaimed Water and Rainwater Utilization Mode of Rural Rainwater Utilization
p.530
Urban Water Conservancy Project Based on Engineering Construction Combined with Ecological Environment
p.534
Model for Evaluation of Water Quality Base on Improved Wavelet Neural Network
p.538
The Trihalomethanes Formation Potential in Treated Waters Exposed to Ozone and Chlorine
p.542
An Anomaly Identification Method of Coal Gas Pre-Drainage Parameters Based on T2 Control Chart
p.549
Study and Application of Hydraulic Fracturing Technology to Increase Permeability in Soft and Low Permeability Coal Seam
p.553
The Study on Eco-Environmental Influence of Mineral Resource Exploitation - Cased by Yalong River Basin in Jiulong County
p.559
Study of Red Mud as Compound Sulfur-Fixing Agent
p.567
Research on Separating Rubidium from Deep Potassium-Rich Old Brine by Solvent Extraction
p.572

An Anomaly Identification Method of Coal Gas Pre-Drainage Parameters Based on T2 Control Chart

Article Preview

Abstract:

The T2 statistic is one important indicator of statistical process control theory to identify anomalies of the multivariate industrial process. In the research field of the coal gas pre-drainage process control, previous achievements mainly based on the univariate control chart, which leaded to huge workload and facilitated some human errors. Against these problems, a more comprehensive and easy-to-use method based on the T2 statistic was proposed. First at all, the basic thought and the principle of T2 control chart was elaborated. Secondly, the data structure and data samples were provided after their principle component analysis. Finally, the multivariate control chart of coal gas pre-drainage process was established. Results show that the proposed anomaly identification method can integrate dozen of univariate control charts into one. Then technicians needn’t deal with many control charts in the same time and many human errors can be avoided.

You might also be interested in these eBooks
Advances in Energy Materials and Environment Engineering View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 700)

Pages:

549-552

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.700.549

Citation:

Cite this paper

Online since:

December 2014

Authors:

Shao Jie Hou*, Xian Zun Meng, Yu Wei Zhang

Keywords:

Anomaly Identification, Coal Mining, Gas Pre-Drainage, PCA, SPC

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2015 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

References

[1] S.J. Hou, Y.W. Zhang and Y.P. Cheng: Advanced Materials Research, Vol. 868(2014), pp.361-364.

Google Scholar

[2] S.J. Hou and Y.W. Zhang: Energy Education Science and Technology Part A: Energy Science and Research, Vol. 31(2013), p.1949-(1956).

Google Scholar

[3] S.J. Hou, J.Y. Liu and Y.W. Zhang: Information Technology Journal, Vol. 12(2013), pp.4918-4924.

Google Scholar

[4] A.J. Duncan: Quality control and industrial statistics (4th ed. ). Homewood, IL: Richard D. Irwin. p. (1974).

Google Scholar

[5] D.C. Montgomery: Introduction to statistical quality control (5th ed. ). John Wiley&Sons. (2005).

Google Scholar

[6] J. Lu and F. Liu: Computer Measurement & Control, Vol. 15(2007), pp.984-993.

Google Scholar

[7] Y. Zheng and J. Zhang: Foundry, Vol. 54(2005), pp.797-799.

Google Scholar

© 2025 Trans Tech Publications Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. For open access content, terms of the Creative Commons licensing CC-BY are applied.
Scientific.Net is a registered trademark of Trans Tech Publications Ltd.