基于EDA算法的改進(jìn)KPCA的某型測角儀的狀態(tài)監(jiān)測與故障預(yù)測研究

孔凡勝+王竹林

摘要：基于電子系統(tǒng)狀態(tài)監(jiān)測為研究背景，傳統(tǒng)的Kernel?Principal?Component?Analysis（核主成份分析法，簡稱KPCA）在狀態(tài)監(jiān)測過程中做數(shù)據(jù)特征降維處理，使得電路狀態(tài)數(shù)據(jù)在消除冗余信息的同時(shí)，也能在相應(yīng)的模型算法計(jì)算中很大程度的減少計(jì)算步驟，但是KPCA法的降維數(shù)據(jù)處理過程對(duì)數(shù)據(jù)樣本貢獻(xiàn)率的識(shí)別能力有不足之處，雖然達(dá)到了降維的目的，但是對(duì)特征樣本數(shù)據(jù)的信息保留能力存在不足。本文中采用經(jīng)驗(yàn)?zāi)B(tài)分解法（Empirical?Mode?Decomposition，簡稱EMD）對(duì)輸出信號(hào)進(jìn)行采集處理作為樣本數(shù)據(jù)，設(shè)計(jì)基于Fisher準(zhǔn)則的狀態(tài)信息識(shí)別能力分析，采用Estimation?of?Distribution?Algorithms（種群算法，簡稱EDA）對(duì)KPCA分析法進(jìn)行改進(jìn)研究，通過對(duì)數(shù)據(jù)處理，最大限度的保留狀態(tài)主信息，使得在電路系統(tǒng)狀態(tài)監(jiān)測過程中減小實(shí)驗(yàn)誤差，為后續(xù)故障預(yù)測打下基礎(chǔ)。

關(guān)鍵詞：KPCA;EDA;Fisher準(zhǔn)則;EMD;信息識(shí)別;

中圖分類號(hào)：TP?????????????????文獻(xiàn)標(biāo)識(shí)碼：A

Electronic?System?Based?on?EDA?Algorithm?improve?the?KPCA?Condition?Monitoring

and?Fault?Prediction?Research

Kong?Fan-sheng?，?Wang?Zhu-lin

（Ordnance?Engineering?College，?Shi?Jiazhuang?，?Hebei，?050003）

Abstract：?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?purpose?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?ability?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?decrease?experimental?error?in?the?process?of?condition?monitoring，?fault?prediction?to?lay?the?foundation?for?the?follow-up.

Key?word：?KPCA;?EDA;?Fisher?criterion;?EMD;Information?identification;

1?摘要

某型測角儀是裝備訓(xùn)練的重要控制設(shè)備，主要對(duì)裝備飛行過程中通過對(duì)誤差信息的接收處理，及時(shí)輸出調(diào)整信號(hào)到主控機(jī)，主控機(jī)輸出控制指令，從而達(dá)到提高裝備命中精度的功能。

基于對(duì)某型測角儀的狀態(tài)監(jiān)測與故障預(yù)測研究過程，選取一定的模型算法對(duì)設(shè)備的電子信號(hào)處理模塊進(jìn)行分析研究，通過對(duì)采集的數(shù)據(jù)進(jìn)行提取降維等一系列算法處理，從而達(dá)到信息特征狀態(tài)的提取分析，為下一步電子信號(hào)模塊的狀態(tài)監(jiān)測與故障預(yù)測研究打下基礎(chǔ)[1]。

2?研究內(nèi)容

本文主要是針對(duì)某型測角儀TV4信號(hào)處理模塊的狀態(tài)監(jiān)測與故障預(yù)測研究，采用HSMM為狀態(tài)監(jiān)測模型基礎(chǔ)，通過EMD（經(jīng)驗(yàn)?zāi)B(tài)分解）信號(hào)特征提取作為數(shù)據(jù)特征提取方法，應(yīng)用KPCA做為數(shù)據(jù)特征降維處理，根據(jù)KPCA具有的局限性，采用EDA算法基于fisher準(zhǔn)則進(jìn)行改進(jìn)處理，使得采用KPCA降維的同時(shí)最大限度保證數(shù)據(jù)主信息的完整性。

3?實(shí)驗(yàn)理論

3.1?KPCA分析法

本文是基于HSMM的電子系統(tǒng)信號(hào)處理模塊研究，由于提取的特征信號(hào)具有冗余和高維的特點(diǎn)[2]，若直接應(yīng)用到實(shí)驗(yàn)中，會(huì)很大限度的降低狀態(tài)監(jiān)測能力，特征降維在于提取包含更多類別信息的狀態(tài)特征，大幅度的消除特征的冗余性，提高狀態(tài)監(jiān)測能力。