The Research of Fault Diagnosis of Nuclear Power Plant Based on ELM-AdaBoost.SAMME

Cheng Li,Tianshu Wang,Ren Yu,Han Zhang

doi:10.1155/2020/6689829

Abstract

A fault diagnosis framework based on extreme learning machine (ELM) and AdaBoost.SAMME is proposed in a nuclear power plant (NPP) in this paper. After briefly describing the principles of ELM and AdaBoost.SAMME algorithm, the fault diagnosis framework sets ELM algorithm as the weak classifier and then integrates several weak classifiers into a strong one using the AdaBoost.SAMME algorithm. Furthermore, some experiments are put forward for the setting of two algorithms. The results of simulation experiments on the HPR1000 simulator show that the combined method has higher precision and faster speed by improving the performance of weak classifiers compared to the BP neural network and verify the feasibility and validity of the ensemble learning method for fault diagnosis. Meanwhile, the results also indicate that the proposed method can meet the requirements of a real-time diagnosis of the nuclear power plant.

Highlights

Structures of the nuclear power plant (NPP) are complicated, which have potential radiation detriment. us, the requirements for their safety and reliability are quite strict
A lot of fault diagnosis methods have been proposed; for example, Wang Hang applied the support vector machine (SVM) and improved particle swarm optimization (PSO) to perform further diagnosis in NPP on the basis of qualitative reasoning by knowledge-based preliminary diagnosis and sample data provided by an online simulation model [2]
As a representative algorithm of ensemble learning, the AdaBoost algorithm combined with the SAMME algorithm [17] is applied in fault diagnosis research in NPP, which selecting extreme learning machine (ELM) as the weak classifier. e contributions of this paper are as follows: (i) A fault diagnosis method based on ELM-AdaBoost.SAMME for the nuclear power plant is proposed

Summary

Introduction

Structures of the nuclear power plant (NPP) are complicated, which have potential radiation detriment. us, the requirements for their safety and reliability are quite strict. One technical way to provide operation supports for the operators is fault diagnosis technology, and its application in NPP can assist operators to find and identify faults timely and accurately It is an effective method for preventing and reducing human factor errors. Shyamapada Mandal addressed an approach for small/minor fault detection of thermocouple sensors in a nuclear power plant using time series analysis methods [6]. As a representative algorithm of ensemble learning, the AdaBoost algorithm combined with the SAMME algorithm (with sagewise additive modeling using a multiclass exponential loss function) [17] is applied in fault diagnosis research in NPP, which selecting extreme learning machine (ELM) as the weak classifier.

Proposed Algorithm

Simulation Tests and Analysis

Comparative Analysis of Simulation Results

Findings

Conclusions