基于人工神经网络的加速器噪声诊断系统

介绍了一种加速器故障诊断系统的原理及实验结果。系统采集加速器运行时的噪声信号，运用快速傅立叶变换做频谱分析，通过人工神经网络进行故障诊断。给出了该系统实验的数据分析结果。     

A neural network to predict reactor core behaviors

The global fuel management problem in BWRs(Boiling Water Reactors) can be understood as a very complex optimization problem,where the variables represent design decisions and the quality assessment of each solution is done through a complex and computational expensive simulation.This last aspect is the major impediment to perform an extensive exploration of the design space,mainly due to the time lost evaluating non promising solutions.In this work,we show how we can train a Multi-Layer Perceptron(MLP) to predict the reactor behavior for a given configuration.The trained MLP is able to evaluate the configurations immediately,thus allowing performing an exhaustive evaluation of the possible configurations derived from a stock of fuel lattices,fuel reload patterns and control rods patterns.For our particular problem,the number of configurations is approximately 7.7×10~(10);the evaluation with the core simulator would need above 200 years,while only 100hours were required with our approach to discern between bad and good configurations.The later were then evaluated by the simulator and we confirm the MLP usefulness.The good core configurations reached the energy requirements,satisfied the safety parameter constrains and they could reduce uranium enrichment costs.     

Fault diagnosis and classification based on wavelet transform and neural network

In this paper we present a novel method in fault recognition and classification in Nuclear Power Plant (NPP) using wavelet transform based Artificial Neural Network (ANN). We first simulate 10 design basis accidents (DBA) of a VVER-1000 using 15 input parameters with employing a Multilayer Perceptron (MLP) Neural Network with Resilient Backpropagation (RBP) algorithm. Afterwards we present the application of wavelet transform for its temporal shift property and multiresolution analysis characteristics to reduce disturbing perturbations in input training set data. Simulation of Artificial Neural Network and wavelet transform was performed using MATLAB software. The results show an enhanced accuracy and speed in fault recognition and high degree of robustness.     

BP网络和OLAM网络在X荧光谱分析中的应用比较

用计算机软件实现了两种神经网络：BP和OLAM，并将两种网络模型用于水泥生料样品的X荧光谱分析，其中OLAM得到了较好的效果，从理论和实际的角度对两种算法的优劣和适用范围进行了讨论。     

Predicting of slow noise and vibration spectra degradation in the IBR-2 pulsed neutron source using a neural network simulator

Employing a neural net model of the noise state of the IBR-2 reactor (JINR, Dubna, Russia) and a model of the vibration state of movable reflectors in the reactor we have predicted slow degradation in reactor noises. Operative diagnostics and prediction of the reactor noise behavior with time involves separating of the reflector degradation trend in power noises. We investigate two neural models. The first concerns the vibrations of the reflectors and the second is a simplified reactor noise model. The predicted results are close, in character, to the experimental data. They show that it is the movable reflectors that are mainly responsible for the degradation of power noises.     

Study on the characteristic points of boiling curve by using wavelet analysis and genetic neural network

Local singularity of a signal includes a lot of important information. Wavelet transform can overcome the shortages of Fourier analysis, i.e., the weak localization in the local time- and frequency-domains. It has the capacity to detect the characteristic points of boiling curves. Based on the wavelet analysis theory of signal singularity detection, Critical Heat Flux (CHF) and Minimum Film Boiling Starting Point (q_min) of boiling curves can be detected by using the wavelet modulus maxima detection. Moreover, a genetic neural network (GNN) model for predicting CHF is set up in this paper. The database used in the analysis is from the 1960s, including 2365 data points which cover a range of pressure (P), from 100 to 1000 kPa, mass flow rate (G) from 40 to 500 kg m⁻² s⁻¹, inlet sub-cooling (ΔT_sub) from 0 to 35 K, wall superheat (ΔT_sat) from 10 to 500 K and heat flux (Q) from 20 to 8000 kW m⁻². GNN mode has some advantages of its global optimal searching, quick convergence speed and solving non-linear problem. The methods of establishing the model and training of GNN are discussed particularly. The characteristic point predictions of boiling curve are investigated in detail by GNN. The results predicted by GNN have a good agreement with experimental data. At last, the main parametric trends of the CHF are analyzed by applying GNN. Simulation and analysis results show that the network model can effectively predict CHF.