计算技术与自动化
計算技術與自動化
계산기술여자동화
COMPUTING TECHNOLOGY AND AUTOMATION
2015年
2期
19-23
,共5页
KPCA%EDA%Fisher准则%EMD%信息识别
KPCA%EDA%Fisher準則%EMD%信息識彆
KPCA%EDA%Fisher준칙%EMD%신식식별
KPCA%EDA%fisher criterion%EMD%information identification
基于电子系统状态监测为研究背景,传统的Kernel Principal Component Analysis(核主成份分析法,简称KPCA)在状态监测过程中做数据特征降维处理,使得电路状态数据在消除冗余信息的同时,也能在相应的模型算法计算中很大程度的减少计算步骤,但是KPCA法的降维数据处理过程对数据样本贡献率的识别能力有不足之处,虽然达到了降维的目的,但是对特征样本数据的信息保留能力存在不足。本文中采用经验模态分解法(Empirical Mode Decomposition,简称 EMD)对输出信号进行采集处理作为样本数据,设计基于 Fisher准则的状态信息识别能力分析,采用 Estimation of Distribution Algorithms(种群算法,简称 EDA)对KPCA分析法进行改进研究,通过对数据处理,最大限度的保留状态主信息,使得在电路系统状态监测过程中减小实验误差,为后续故障预测打下基础。
基于電子繫統狀態鑑測為研究揹景,傳統的Kernel Principal Component Analysis(覈主成份分析法,簡稱KPCA)在狀態鑑測過程中做數據特徵降維處理,使得電路狀態數據在消除冗餘信息的同時,也能在相應的模型算法計算中很大程度的減少計算步驟,但是KPCA法的降維數據處理過程對數據樣本貢獻率的識彆能力有不足之處,雖然達到瞭降維的目的,但是對特徵樣本數據的信息保留能力存在不足。本文中採用經驗模態分解法(Empirical Mode Decomposition,簡稱 EMD)對輸齣信號進行採集處理作為樣本數據,設計基于 Fisher準則的狀態信息識彆能力分析,採用 Estimation of Distribution Algorithms(種群算法,簡稱 EDA)對KPCA分析法進行改進研究,通過對數據處理,最大限度的保留狀態主信息,使得在電路繫統狀態鑑測過程中減小實驗誤差,為後續故障預測打下基礎。
기우전자계통상태감측위연구배경,전통적Kernel Principal Component Analysis(핵주성빈분석법,간칭KPCA)재상태감측과정중주수거특정강유처리,사득전로상태수거재소제용여신식적동시,야능재상응적모형산법계산중흔대정도적감소계산보취,단시KPCA법적강유수거처리과정대수거양본공헌솔적식별능력유불족지처,수연체도료강유적목적,단시대특정양본수거적신식보류능력존재불족。본문중채용경험모태분해법(Empirical Mode Decomposition,간칭 EMD)대수출신호진행채집처리작위양본수거,설계기우 Fisher준칙적상태신식식별능력분석,채용 Estimation of Distribution Algorithms(충군산법,간칭 EDA)대KPCA분석법진행개진연구,통과대수거처리,최대한도적보류상태주신식,사득재전로계통상태감측과정중감소실험오차,위후속고장예측타하기출。
Condition monitoring based on electronic system as the research background,the traditional Kernel Principal Component Analysis (Kernel Principal Component Analysis,KPCA)do in the process of condition monitoring data feature dimension reduction process,makes the circuit state data at the same time of eliminating redundant information,as well as the corresponding calculation model algorithm greatly reduces computation steps,but KPCA method of dimension reduction data processing for the contribution rate of the data sample inadequacies in the ability to recognize,though achieved the pur-pose of dimension reduction,but information on the characteristics of the sample data retention capability shortcomings.This article USES the method of Empirical Mode Decomposition (Empirical Mode Decomposition,the EMD)was carried out on the output signal as sample data collection and processing,design based on Fisher criterion of state information recognition a-bility analysis,the Estimation of Distribution Algorithms (population algorithm,referred to as EDA)to improve the KPCA analysis research,through the data processing,maximum retention state master information,make the circuit system de-crease experimental error in the process of condition monitoring,fault prediction to lay the foundation for the follow-up.
