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

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.

Monitoring and Warning Methods of Tailings Reservoir using BP Neural Network

“…It makes sure that the adjustment of the weights is proportional to the gradient of the errors. In this way, errors can be reduced to meet the needs of the application [7]. The BP neural network consists of an input layer, hidden layer and output layer, and its network structure is shown in Figure 1.…”

“…The minimum of the residual sum of square of the regression data and the samples data is 7 6.88 10  , and the value of fitness is 0.917. Therefore, the normal probability of errors is 84.1 percent, and the value of F is 352.969.…”