本文關(guān)鍵詞： 自動化測試 反饋控制 智能診斷 數(shù)據(jù)集降維 數(shù)據(jù)分類　出處：《蘭州理工大學》2012年碩士論文　論文類型：學位論文

【摘要】：隨著信息技術(shù)、控制理論和人工智能等領(lǐng)域的快速發(fā)展，旋轉(zhuǎn)機械故障診斷技術(shù)已逐漸呈現(xiàn)出自動化、實時化、智能化等信息化技術(shù)水平偏低，難以滿足先進工業(yè)生產(chǎn)實際應(yīng)用需求的問題。因此，將傳統(tǒng)機械設(shè)備拓展為具有控制自動化、監(jiān)測實時化和診斷智能化等功能特性于一體的機械裝備信息化裝置，，將成為機械故障智能診斷研究的前沿。其中故障數(shù)據(jù)分類的科學問題研究，已成為提高故障模式辨識技術(shù)水平的核心內(nèi)容。 本項研究以旋轉(zhuǎn)機械核心部件－轉(zhuǎn)子系統(tǒng)為研究對象，綜合利用常用統(tǒng)計特征與數(shù)據(jù)挖掘方法，對故障特征數(shù)據(jù)的分類問題重點開展了研究，并采用虛擬儀器技術(shù)，開發(fā)了一套雙跨轉(zhuǎn)子實驗臺振動實驗測試與反饋控制系統(tǒng)。開展的具體研究工作內(nèi)容和獲得的主要結(jié)果如下： 1)在分析常用統(tǒng)計特征測度的基礎(chǔ)上，篩選并從信號中提取出具備表征轉(zhuǎn)子不同運行狀態(tài)能力的特征向量，以此建立的故障特征數(shù)據(jù)集表現(xiàn)出維數(shù)過高與可分性差的問題。在此基礎(chǔ)上，總結(jié)出經(jīng)典數(shù)據(jù)集降維方法存在著非線性因素干擾、降維準則不足的問題。 2)通過公式推導(dǎo)，歸納出主成分分析（PCA）法和費歇判別分析（FDA）法的本質(zhì)與內(nèi)在聯(lián)系。以上述理論分析為前提，提出了一種偏費歇判別分析（BFDA）的數(shù)據(jù)集降維方法，并進行實例驗證，結(jié)果表明，BFDA法在達到FDA法降維性能的基礎(chǔ)上，具有更低的算法復(fù)雜度。 3)在提出將核主成分分析（KPCA）法與費歇判別分析法相結(jié)合實施數(shù)據(jù)集降維的基礎(chǔ)上，推導(dǎo)出以費歇準則建立粒子群優(yōu)化算法適應(yīng)度函數(shù)中存在的等價關(guān)系。并進一步提出將KPCA法與BFDA法相結(jié)合的數(shù)據(jù)集降維方法，提出基于偏費歇準則的粒子群優(yōu)化方案。將提出的兩種降維算法應(yīng)用于特征數(shù)據(jù)集降維，并將降維結(jié)果輸入至設(shè)計的分類器進行分類驗證，分類效果均較顯著。 4)利用虛擬儀器技術(shù)，開發(fā)了一套雙跨轉(zhuǎn)子實驗臺振動實驗測試與反饋控制的軟件平臺，全面拓展了現(xiàn)有轉(zhuǎn)子系統(tǒng)的軟硬件功能，且該平臺具有人機交互界面直觀，開發(fā)、維護便捷，功能易擴展等特點。并在常規(guī)控制與監(jiān)測功能實現(xiàn)的前提下，對狀態(tài)信息監(jiān)測功能中存在的若干難點進行了研究與開發(fā)。 研究表明，特征數(shù)據(jù)集中包含了大量能夠反映轉(zhuǎn)子運行狀態(tài)的信息，故如何在數(shù)據(jù)集挖掘算法研究中獲得新突破，及如何將取得的研究成果合理地嵌入于自動化測控系統(tǒng)，將是故障診斷領(lǐng)域開展研究工作的重要方向。
[Abstract]:With the rapid development of information technology, the field of control theory and artificial intelligence, fault diagnosis technology of rotating machinery has been gradually showing the automation, real-time and intelligent information technology level is low, it is difficult to meet the practical application requirements in advanced industrial production. Therefore, the traditional mechanical equipment to expand with automatic control and real-time monitoring. Intelligent diagnosis and other features in one of the mechanical equipment information device, will become the frontier research of intelligent mechanical fault diagnosis. The scientific researches on fault data classification, has become the core content of technology level of fault mode identification.
This study is based on core components of rotating machinery rotor system as the research object, the comprehensive utilization of common statistical features and methods of data mining, the key problems of classification of fault data is studied, and the virtual instrument technology, developed a set of double span rotor experimental platform vibration test and feedback control system. The specific research work the content and the main results are as follows:
1) based on the analysis of characteristics of commonly used statistical measure, screening and extract the feature vector representation with different running state of the rotor ability from the signal, in order to establish the fault feature data set showed high dimension andbad separability problems. On this basis, summed up the classic data set reduction method is nonlinear factors interference reduction rule is insufficient.
2) by formula derivation, summed up the principal component analysis (PCA) method and Fisher discriminant analysis (FDA) method the essence and internal relationship. Based on the above-mentioned theoretical analysis foundation, proposed a partial Fisher discriminant analysis (BFDA) data set reduction method is verified, the results show that the base of BFDA method of reducing dimensional performance in reaching FDA method and has lower complexity.
3) in the kernel principal component analysis (KPCA) method and Fisher discriminant analysis method combining implementation based on dimensionality reduction data set, according to Fisher criterion of particle swarm optimization algorithm to adapt to the existence of equivalent relation function is derived. And further put forward by combining KPCA method and BFDA method for dimensionality reduction of data set this method, partial Fisher principle of particle swarm optimization scheme based on two dimension reduction algorithm proposed was applied to feature data dimensionality reduction and classification will be reduced to the design of the verification result of dimension input classifier, classification results were more significant.
4) the use of virtual instrument technology, developed a set of double span rotor experimental platform vibration test and feedback control software platform, comprehensive development of the hardware and software function of the existing rotor system, and the platform has intuitive man-machine interface, convenient maintenance, easy development, function expansion and other characteristics. The premise and the realization in the conventional control with the function of monitoring, some problems of state information of the monitoring function of research and development.
Research shows that the feature data set contains a large number of information can reflect the running state of the rotor, so how to make a breakthrough in the data mining algorithm, and how to apply the research achievements reasonably embedded in automation control system, will be an important direction of fault diagnosis research.

【學位授予單位】：蘭州理工大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TH165.3

【引證文獻】

相關(guān)碩士學位論文 前1條

1 張恒;基于數(shù)據(jù)驅(qū)動的轉(zhuǎn)子故障特征信息建模方法研究[D];蘭州理工大學;2013年

本文編號：1546559