A Study on Faults Diagnosis and Early-Warning Method of Tailings Reservoir Monitoring Points based on Intelligent Discovery

doi:10.23940/ijpe.17.05.p13.697710

Abstract

Abstract: The tailings reservoir is a major hazard source with high potential energy, which may cause an artificial debris flow. The stability of the tailings reservoir is extremely important to the normal operation of the mining enterprises and the safety of people's lives and property. In order to settle the problem that traditional manual monitoring is scattered, not timely, and difficult to manage, this article takes Huangmailing tailings as an example, and establishes the CMST model to optimize the network topology connection of the tailings monitoring points. BP neural network algorithm is used to discuss the intelligent discovery and early warning of the faults on-line monitoring system of tailings. In this way, the fault-points and the causes can be perceived quickly and accurately, and the risk of the tailings’ safety accident can be reduced. It can be proved by the experimental results and two years stable operation of the system that BP neural network algorithm can accurately predict the value of safety monitoring data.

Submitted on April 20, 2017; Revised on June 30, 2017; Accepted on August 15, 2017
References: 19

Tianyong Wu, Chunyuan Zhang, and Yunsheng Zhao. A Study on Faults Diagnosis and Early-Warning Method of Tailings Reservoir Monitoring Points based on Intelligent Discovery [J]. Int J Performability Eng, 2017, 13(5): 697-710.

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References 0

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