摘要
为提高网络异常故障诊断能力,提出基于大数据分析和故障谱特征提取的网络异常故障率智能预测方法。采用相关性频谱特征检测方法进行网络故障异常数据采集,对采集的网络故障信息特征量进行匹配滤波,采用自适应波束形成方法对网络的故障大数据进行波束聚焦处理,对提取的网络故障大数据采用模糊聚类方法进行故障分类识别,在大数据下实现网络异常故障率智能预测。仿真结果表明,采用该方法进行大数据下网络异常故障率智能预测的准确度较高,故障诊断能力较强。
In order to improve the ability of network abnormal fault diagnosis,an intelligent prediction method of network anomaly failure rate based on big data analysis and fault spectrum feature extraction is proposed.The correlation spectrum feature detection method is used to collect the abnormal network fault data,the network fault information feature quantity is filtered by matched filtering,and the adaptive beamforming method is used to process the fault big data of the network.The fuzzy clustering method is used to classify and identify the network fault big data,and the intelligent prediction of the abnormal failure rate of the network is realized under the big data.The simulation results show that the proposed method has a high accuracy and strong fault diagnosis ability for intelligent prediction of network abnormal failure rate under big data.
作者
周挺
ZHOU Ting(Xi'an Aeronautical Polytechnic Institute,Xi'an 710089,China)
出处
《自动化与仪器仪表》
2019年第3期165-168,173,共5页
Automation & Instrumentation
关键词
大数据
网络异常
故障
智能预测
诊断
big data
network anomaly
fault
intelligent prediction
diagnosis
