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

【摘要】：隨著旋轉機械故障診斷技術的不斷研究與發(fā)展,它已經(jīng)開始為人們所重視。而在工程實踐中,隨著設備的復雜度和信息量的增加,人們要獲得的原始特征數(shù)據(jù)集變得越來越困難,呈現(xiàn)出信息量大、知識匱乏等問題。因此,如何從海量監(jiān)測系統(tǒng)能采集的數(shù)據(jù)中去除不相關干擾與冗余信息十分重要,是當今故障診斷數(shù)據(jù)挖掘領域應該重點關注的問題。本項研究充分使用數(shù)據(jù)挖掘方法中的流形學習方法,開展對故障數(shù)據(jù)分類的降維研究。流形學習方法是一種能有效發(fā)現(xiàn)潛在于結構本質中信息的大數(shù)據(jù)驅動的方法。本研究的工作主要包含以下內(nèi)容:1)將實驗臺上采集到的數(shù)據(jù)通過分析小波以及小波包的能量,篩選信號里存在的表現(xiàn)轉子運行情況的信息,建立了相關數(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。2)通過公式推導,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)。采取實例驗證的方式,對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,簡稱為Schur-ONPE算法。該算法運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算,削減了計算復雜度,有效提高了運算效率和準確程度。將Schur-ONPE算法的數(shù)據(jù)降維結果輸入K近鄰分類器之中進行的分類驗證,發(fā)現(xiàn)得到的分類效果顯著提高。再把不同轉速下故障數(shù)據(jù)進行降維,也將結果輸入到K近鄰分類器之中,降維準確率也是穩(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虛擬儀器技術和M ATLAB軟件的混編程序中。結合了兩種軟件的優(yōu)勢,在原有的雙跨轉子實驗軟件平臺上增加了經(jīng)驗模態(tài)分解模塊和小波分析模塊,進一步拓展了原有轉子系統(tǒng)的軟硬件功能,使該振動實驗測試與反饋控制平臺具有更好的人機交互性與信號處理能力。通過研究表明,利用數(shù)據(jù)挖掘算法,能有效挖掘出隱藏在海量檢測數(shù)據(jù)背后的本質結構特征。進行創(chuàng)造突破性的研究,讓更多的人認同并運用已經(jīng)開展的研究,對該領域的智能化研究工作起導向性作用。
[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.
【學位授予單位】：蘭州理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP311.13;TH17

【參考文獻】

相關期刊論文 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程故障檢測[J];自動化與儀表;2015年10期

2 孫斌;劉立遠;牛，

本文編號：1889077