THERMAL SCIENCE

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Xiaoli Meng

DETECTION METHOD FOR DAM DEFORMATION OF TAILING POND BASED ON FAULT DIAGNOSIS ALGORITHM

ABSTRACT
The existing methods of dam deformation detection of tailings reservoir have the problems of poor accuracy and slow speed. Therefore, a fault diagnosis algorithm based on tailing dam deformation detection method is proposed. The grey theory is used to accumulate the original feature sequence, and the first cumulative sequence is obtained. Based on this, the grey detection model is constructed, and then the concrete deformation of tailings dam body is accurately detected by precision test. Experimental results show that the method has high accuracy, high speed and practicability
KEYWORDS
tailing pond, dam deformation, fault diagnosis
PAPER SUBMITTED: 2019-06-09
PAPER REVISED: 2019-09-11
PAPER ACCEPTED: 2019-09-12
PUBLISHED ONLINE: 2020-02-08
DOI REFERENCE: https://doi.org/10.2298/TSCI190609013M
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2020, VOLUME 24, ISSUE Issue 3, PAGES [1489 - 1496]
REFERENCES
  1. Agurto-Detzel, H., Bianchi, M., Assumpção, M., et al. 2016. The Tailings Dam Failure of 5 November 2015 in SE Brazil and Its Preceding Seismic Sequence. Geophysical Research Letters 43(10):4929-4936.
  2. Santoyo, M.A., Martínez-Garzón, P., García-Jerez, A., et al. 2016. Surface Dynamic Deformation Estimates from Local Seismicity: The Itoiz Reservoir, Spain. Journal of Seismology 20(3):1021-1039.
  3. Krupskaya, L.T., Zvereva, V.P., Kirienko, O.A., et al. 2016. Using a Comprehensive Method for Evaluation of Environment State in a Zone of Influence of a Tailing Dam. Russian Journal of General Chemistry 86(13):3012-3014.
  4. Triantafyllidis, S., Loupasakis, C., Tsangaratos, P. 2016. Geochemical Modeling-based Rehabilitation Proposal for Abandoned Sulfidic Flotation Mill Tailings, Kirki, Thrace, NE Greece. Environmental Earth Sciences 75(21):1403.
  5. Zhao, C.H., Wang, X.P., Yao, Z.F. 2016. Hyperspectral Anomaly Detection Based on Approximate Target Posterior Information. Journal of Shenyang University (Natural Science) 28(3):212-218.
  6. Yang, X.S., Jiang, L., Peng, X., et al. 2018. Research on Anomaly Detection Method Based on Big Data. Computer Engineering & Science v. 40; No. 283(07):38-44.
  7. Yu, B.J., Xia, Z.G. 2016. Anomaly Detection Algorithm Based on Gaussian Process Model. Computer Engineering and Design 37(4):914-920.
  8. Miao, Y.M. 2017. Anomaly Detection Technology Based on Big Data Analysis Applied to Petroleum Catalysis. Electronic Measurement Technology 40(7):170-172.
  9. Ahmad, Y, Ali, U., Bilal, M., et al. 2017. Some New Standard Graphs Labeled by 3-total Edge Product Cordial Labeling. Applied Mathematics & Nonlinear Sciences, 2(1):61-72.
  10. Baig, A.Q, Naeem, M., Gao, W. 2018. Revan and Hyper-Revan Indices of Octahedral and Icosahedral Networks. Applied Mathematics & Nonlinear Sciences, 3(1):33-40.
  11. Wang, D.L. 2017. Optimization Simulation of Abnormal Data Detection in Network Database. Computer Simulation 34(5):410-413.
  12. Wei, Z., Long, J., Wei, Z., et al. 2016. Vertical Distribution and Historical Loss Estimation of Heavy Metals in an Abandoned Tailings Pond at HTM Copper Mine, Northeastern China. Environmental Earth Sciences 75(22):1462.
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Detection method for dam deformation of tailing pond based on fault diagnosis algorithm

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence