本文選題：故障診斷 + 數(shù)據(jù)降維　； 參考：《蘭州理工大學(xué)》2017年碩士論文

【摘要】：隨著旋轉(zhuǎn)機(jī)械故障診斷技術(shù)的不斷研究與發(fā)展,它已經(jīng)開始為人們所重視。而在工程實(shí)踐中,隨著設(shè)備的復(fù)雜度和信息量的增加,人們要獲得的原始特征數(shù)據(jù)集變得越來(lái)越困難,呈現(xiàn)出信息量大、知識(shí)匱乏等問(wèn)題。因此,如何從海量監(jiān)測(cè)系統(tǒng)能采集的數(shù)據(jù)中去除不相關(guān)干擾與冗余信息十分重要,是當(dāng)今故障診斷數(shù)據(jù)挖掘領(lǐng)域應(yīng)該重點(diǎn)關(guān)注的問(wèn)題。本項(xiàng)研究充分使用數(shù)據(jù)挖掘方法中的流形學(xué)習(xí)方法,開展對(duì)故障數(shù)據(jù)分類的降維研究。流形學(xué)習(xí)方法是一種能有效發(fā)現(xiàn)潛在于結(jié)構(gòu)本質(zhì)中信息的大數(shù)據(jù)驅(qū)動(dòng)的方法。本研究的工作主要包含以下內(nèi)容:1)將實(shí)驗(yàn)臺(tái)上采集到的數(shù)據(jù)通過(guò)分析小波以及小波包的能量,篩選信號(hào)里存在的表現(xiàn)轉(zhuǎn)子運(yùn)行情況的信息,建立了相關(guān)數(shù)據(jù)集。此數(shù)據(jù)集能夠排除一部分干擾信息,為后續(xù)工作的順利展開奠定了基礎(chǔ)。2)通過(guò)公式推導(dǎo),比較分析主成分分析法(PCA)、核主成分分析法(KPCA)和鄰域保持嵌入法(NPE)。采取實(shí)例驗(yàn)證的方式,對(duì)比得出鄰域保持嵌入法在降維性能上的優(yōu)越性。3)提出一種基于舒爾分解和正交鄰域保持嵌入的降維算法,簡(jiǎn)稱為Schur-ONPE算法。該算法運(yùn)用舒爾分解替代了原本的正交鄰域保持嵌入算法中的正交化迭代計(jì)算,削減了計(jì)算復(fù)雜度,有效提高了運(yùn)算效率和準(zhǔn)確程度。將Schur-ONPE算法的數(shù)據(jù)降維結(jié)果輸入K近鄰分類器之中進(jìn)行的分類驗(yàn)證,發(fā)現(xiàn)得到的分類效果顯著提高。再把不同轉(zhuǎn)速下故障數(shù)據(jù)進(jìn)行降維,也將結(jié)果輸入到K近鄰分類器之中,降維準(zhǔn)確率也是穩(wěn)定的,充分證明了該算法的有效性。4)將Schur-ONPE降維算法嵌入到LAB VIEW虛擬儀器技術(shù)和M ATLAB軟件的混編程序中。結(jié)合了兩種軟件的優(yōu)勢(shì),在原有的雙跨轉(zhuǎn)子實(shí)驗(yàn)軟件平臺(tái)上增加了經(jīng)驗(yàn)?zāi)B(tài)分解模塊和小波分析模塊,進(jìn)一步拓展了原有轉(zhuǎn)子系統(tǒng)的軟硬件功能,使該振動(dòng)實(shí)驗(yàn)測(cè)試與反饋控制平臺(tái)具有更好的人機(jī)交互性與信號(hào)處理能力。通過(guò)研究表明,利用數(shù)據(jù)挖掘算法,能有效挖掘出隱藏在海量檢測(cè)數(shù)據(jù)背后的本質(zhì)結(jié)構(gòu)特征。進(jìn)行創(chuàng)造突破性的研究,讓更多的人認(rèn)同并運(yùn)用已經(jīng)開展的研究,對(duì)該領(lǐng)域的智能化研究工作起導(dǎo)向性作用。
[Abstract]:With the continuous research and development of rotating machinery fault diagnosis technology, it has been paid more and more attention. In engineering practice, with the increase of equipment complexity and the amount of information, it becomes more and more difficult for people to obtain the original feature data set, which presents the problems of large amount of information and lack of knowledge. Therefore, it is very important to remove irrelevant interference and redundant information from the data collected by mass monitoring system, which should be paid more attention to in the field of fault diagnosis data mining. In this study, the manifold learning method of data mining is fully used to reduce the dimension of fault data classification. Manifold learning method is a big data driven method which can effectively discover the potential information in the nature of the structure. The work of this study mainly includes the following contents: 1) by analyzing the energy of wavelet and wavelet packet, selecting the information of rotor operation in the signal, the relevant data set is established. This data set can eliminate some interference information and lay a foundation for the smooth development of the subsequent work. Through formula derivation, the principal component analysis method (PCAA), the kernel principal component analysis (KPCA) and the neighborhood retention embedding method (NPE) are compared and analyzed. In this paper, the superiority of neighborhood preserving embedding method in dimensionality reduction is compared with that of example verification. (3) A dimensionality reduction algorithm based on Schuer decomposition and orthogonal neighborhood preserving embedding is proposed, which is called Schur-ONPE algorithm for short. The Schuer decomposition is used to replace the orthogonal neighborhood preserving embedding algorithm, which reduces the computational complexity and improves the efficiency and accuracy of the algorithm. The dimensionality reduction results of Schur-ONPE algorithm are input into the K-nearest neighbor classifier to verify the classification results, and the classification results are found to be significantly improved. Then reduce the dimension of the fault data at different rotational speeds, and input the results into the K-nearest neighbor classifier. The accuracy of dimension reduction is also stable. The validity of the algorithm. 4) embed the Schur-ONPE dimensionality reduction algorithm into the LAB VIEW virtual instrument technology and the mixed program of M ATLAB software. Combining the advantages of the two kinds of software, the empirical mode decomposition module and wavelet analysis module are added to the original two-span rotor experimental software platform, which further expands the hardware and software functions of the original rotor system. The vibration test and feedback control platform has better human-computer interaction and signal processing ability. The research shows that using the data mining algorithm, we can effectively mine the essential structural features hidden behind the massive detection data. Creative breakthrough research is carried out so that more people can identify with and use the existing research to play a leading role in intelligent research in this field.
【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP311.13;TH17

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 梁秀霞;鄭向博;鄭曉慧;;基于鄰域保持嵌入算法的間歇過(guò)程故障檢測(cè)[J];自動(dòng)化與儀表;2015年10期

2 孫斌;劉立遠(yuǎn);牛，

本文編號(hào)：1889077