本文選題：ISOMAP　切入點：數據降維　出處：《華南理工大學》2012年碩士論文　論文類型：學位論文

【摘要】：智能機械故障診斷方法研究一直是機械診斷領域研究的熱點問題。隨著人工智能、計算機軟件技術、現代傳感器技術以及現代信號處理技術的飛速發(fā)展，大型機械設備的故障診斷信號數據集呈現出維數高、隨機性強、數據量大的典型特點。在保證數據間的幾何關系和距離測度不變的前提下，將原始數據對應的高維空間的流形映射至低維空間，可以減少相關計算量，找出關鍵特征，全面提高故障診斷效率。 針對機械故障信號高維數、時變性、非線性和非高斯分布特征，本文利用流形學習理論中改進之后的ISOMAP算法對機械故障信號數據集進行非線性降維處理，使得故障數據更加易于分類。論文研究的重點是深入分析經典ISOMAP算法的原理和計算過程，明確經典算法應用到機械故障領域的局限性，提出了一種適用于機械故障診斷有監(jiān)督的快速ISOMAP算法，利用改進的算法對故障數據進行非線性降維，將降維之后的數據分為訓練數據集和測試數據集，用訓練數據集對支持向量機進行訓練，然后利用訓練之后的支持向量機對測試數據集進行預測，實現故障診斷和分類。 論文的主要內容包括： (1).探討ISOMAP算法應用在機械故障診斷領域中存在的問題，包括噪聲問題，，參數的優(yōu)化選擇問題以及算法的泛化能力。 (2).針對ISOMAP算法應用到機械故障診斷領域存在的問題，對經典ISOMAP進行改進，提出有監(jiān)督的快速ISOMAP算法，采用美國西儲大學的電機軸承故障數據，對提出的新算法進行驗證。 (3).利用汽車傳動試驗臺對汽車變速箱進行無故障、齒輪點蝕和齒輪剝落模擬試驗，在時域分析、頻域分析和小波分解無法準確迅速進行故障診斷的情況下，將提出的改進ISOMAP算法應用到齒輪故障診斷中，并與傳統(tǒng)方法進行對比，證明了該方法在齒輪故障診斷中有效性和優(yōu)越性。 改進之后的ISOMAP算法能夠有效約簡故障數據維數、找出本征維數，這將會大大縮短計算時間，利于數據分類，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.
【學位授予單位】：華南理工大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TH165.3

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