本文選題：ISOMAP　切入點(diǎn)：數(shù)據(jù)降維　出處：《華南理工大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

【摘要】：智能機(jī)械故障診斷方法研究一直是機(jī)械診斷領(lǐng)域研究的熱點(diǎn)問(wèn)題。隨著人工智能、計(jì)算機(jī)軟件技術(shù)、現(xiàn)代傳感器技術(shù)以及現(xiàn)代信號(hào)處理技術(shù)的飛速發(fā)展，大型機(jī)械設(shè)備的故障診斷信號(hào)數(shù)據(jù)集呈現(xiàn)出維數(shù)高、隨機(jī)性強(qiáng)、數(shù)據(jù)量大的典型特點(diǎn)。在保證數(shù)據(jù)間的幾何關(guān)系和距離測(cè)度不變的前提下，將原始數(shù)據(jù)對(duì)應(yīng)的高維空間的流形映射至低維空間，可以減少相關(guān)計(jì)算量，找出關(guān)鍵特征，全面提高故障診斷效率。 針對(duì)機(jī)械故障信號(hào)高維數(shù)、時(shí)變性、非線性和非高斯分布特征，本文利用流形學(xué)習(xí)理論中改進(jìn)之后的ISOMAP算法對(duì)機(jī)械故障信號(hào)數(shù)據(jù)集進(jìn)行非線性降維處理，使得故障數(shù)據(jù)更加易于分類。論文研究的重點(diǎn)是深入分析經(jīng)典ISOMAP算法的原理和計(jì)算過(guò)程，明確經(jīng)典算法應(yīng)用到機(jī)械故障領(lǐng)域的局限性，提出了一種適用于機(jī)械故障診斷有監(jiān)督的快速ISOMAP算法，利用改進(jìn)的算法對(duì)故障數(shù)據(jù)進(jìn)行非線性降維，將降維之后的數(shù)據(jù)分為訓(xùn)練數(shù)據(jù)集和測(cè)試數(shù)據(jù)集，用訓(xùn)練數(shù)據(jù)集對(duì)支持向量機(jī)進(jìn)行訓(xùn)練，然后利用訓(xùn)練之后的支持向量機(jī)對(duì)測(cè)試數(shù)據(jù)集進(jìn)行預(yù)測(cè)，實(shí)現(xiàn)故障診斷和分類。 論文的主要內(nèi)容包括： (1).探討ISOMAP算法應(yīng)用在機(jī)械故障診斷領(lǐng)域中存在的問(wèn)題，包括噪聲問(wèn)題，，參數(shù)的優(yōu)化選擇問(wèn)題以及算法的泛化能力。 (2).針對(duì)ISOMAP算法應(yīng)用到機(jī)械故障診斷領(lǐng)域存在的問(wèn)題，對(duì)經(jīng)典ISOMAP進(jìn)行改進(jìn)，提出有監(jiān)督的快速ISOMAP算法，采用美國(guó)西儲(chǔ)大學(xué)的電機(jī)軸承故障數(shù)據(jù)，對(duì)提出的新算法進(jìn)行驗(yàn)證。 (3).利用汽車傳動(dòng)試驗(yàn)臺(tái)對(duì)汽車變速箱進(jìn)行無(wú)故障、齒輪點(diǎn)蝕和齒輪剝落模擬試驗(yàn)，在時(shí)域分析、頻域分析和小波分解無(wú)法準(zhǔn)確迅速進(jìn)行故障診斷的情況下，將提出的改進(jìn)ISOMAP算法應(yīng)用到齒輪故障診斷中，并與傳統(tǒng)方法進(jìn)行對(duì)比，證明了該方法在齒輪故障診斷中有效性和優(yōu)越性。 改進(jìn)之后的ISOMAP算法能夠有效約簡(jiǎn)故障數(shù)據(jù)維數(shù)、找出本征維數(shù)，這將會(huì)大大縮短計(jì)算時(shí)間，利于數(shù)據(jù)分類，提高故障診斷效率和正確率。
[Abstract]:The research of intelligent mechanical fault diagnosis method has been a hot issue in the field of mechanical diagnosis. With the rapid development of artificial intelligence, computer software technology, modern sensor technology and modern signal processing technology, The fault diagnosis signal data set of large mechanical equipment presents the typical characteristics of high dimension, strong randomness and large amount of data. Under the premise of ensuring the geometric relationship between data and the invariance of distance measure, Mapping the manifold of the high-dimensional space corresponding to the original data to the low-dimensional space can reduce the relevant computation amount, find out the key features, and improve the efficiency of fault diagnosis in an all-round way. Aiming at the characteristics of high dimension, time variation, nonlinearity and non-#china_person0# distribution of mechanical fault signal, the improved ISOMAP algorithm in manifold learning theory is used to deal with the nonlinear dimensionality reduction of mechanical fault signal data set in this paper. The emphasis of this paper is to analyze the principle and calculation process of the classical ISOMAP algorithm, and to clarify the limitation of the classical algorithm in the field of mechanical fault. A fast ISOMAP algorithm for mechanical fault diagnosis is proposed. The improved algorithm is used to reduce the nonlinear dimension of the fault data. The reduced dimension data is divided into the training data set and the test data set. The support vector machine is trained with the training data set, and then the test data set is predicted by the training support vector machine to realize fault diagnosis and classification. The main contents of the thesis include:. This paper discusses the problems existing in the application of ISOMAP algorithm in the field of mechanical fault diagnosis, including the problem of noise, the optimization of parameters and the generalization ability of the algorithm. In view of the problems existing in the application of ISOMAP algorithm in the field of mechanical fault diagnosis, the classical ISOMAP is improved, and a supervised fast ISOMAP algorithm is proposed. The new algorithm is verified by using the fault data of motor bearings from the University of Western Reserve in the United States. Using the automobile transmission test bench to carry out the fault-free, pitting corrosion and spalling simulation test of the automobile gearbox, when the time domain analysis, the frequency domain analysis and the wavelet decomposition can not accurately and quickly carry on the fault diagnosis, The improved ISOMAP algorithm is applied to gear fault diagnosis, and compared with the traditional method, it is proved that this method is effective and superior in gear fault diagnosis. The improved ISOMAP algorithm can effectively reduce the dimension of fault data and find the intrinsic dimension, which will greatly shorten the calculation time, facilitate data classification, and improve the efficiency and accuracy of fault diagnosis.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TH165.3

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