Study of a Gray Genetic BP Neural Network Model in Fault Monitoring and a Diagnosis System for Dam Safety

Hai-Feng Liu,Zhong-Tian Zheng,Xian-Jian Lu,Yue-Ji Liang,Chao Ren

doi:10.3390/ijgi7010004

Hai-Feng Liu, Zhong-Tian Zheng + Show 3 more

Open Access

https://doi.org/10.3390/ijgi7010004

Copy DOI

Abstract

In this paper, a self-diagnosis system of observer fault with linear and non-linear combination is studied in light of the unstable performance of the automatic monitoring system and the drift of the measured value. The system makes a prediction step ahead of time, compares it with the online measured value, and makes a logical judgment based on the residual error to achieve the purpose of real-time diagnosis of the automatic monitoring system. We developed a novel combined algorithm for dam deformation prediction using two traditional models and one optimization model. The developed algorithm combines two sub-algorithms: the gray model (GM) (1, 1) and the back-propagation neural network (BPNN) model. The GM (1, 1) addresses the effects of the automated monitoring of data from unstable situations; the BPNN model addresses the internal non-linear regularity of the dam displacement. The connection weights and thresholds of the BPNN model can be optimized and determined via the genetic algorithm (GA), which can decrease the uncertainties within the model predictions and improve the prediction accuracy. The results show that the fault self-diagnosis system based on the GM-GA-BP combined model can realize online fault diagnosis better than the traditional single models.

Highlights

With the rapid development of science and technology, dam safety automation monitoring has undergone a qualitative leap [1,2,3,4]
The results show that the statistical regression model, which depends on the sample information amount, and the support vector machine (SVM), which is based on a single kernel function, have low prediction accuracy and unstable performance
The results show that the gray model (GM)-genetic algorithm (GA)-BP model, which is well trained by online automatic monitoring data, can provide highly precise predictions one step early under the conditions of the normally functioning automated monitoring system

Summary

Introduction

With the rapid development of science and technology, dam safety automation monitoring has undergone a qualitative leap [1,2,3,4]. Dam deformation is influenced primarily by the water level and temperature, it has a complex non-linearity, and automated monitoring data are not stable. These factors are unstable and challenging to analyze, hindering the accurate prediction of dam deformation. The design and structure optimization performed for the BPNN generally relies on a time-consuming iterative trial-and-error approach [28,29,30,31,32,33,34] To solve these problems, many researchers have used the model combination method to improve and optimize the neural network (NN).

Modeling with a GM

Modeling with the BPNN Model

Validation and Comparison of Model Performances

Evaluation for the Models

Evaluation Index

Conclusions