礦井提升機(jī)-外文翻譯

1、外文翻譯部分：英文原文Mine-hoist fault-condition detection based on the wavelet packet transform and kernel PCAAbstract: A new algorithm was developed to correctly identify fault conditions and accurately monitor fault development in a mine hoist. The new method is based on the Wavelet Packet Transform (WPT) a

2、nd kernel PCA (Kernel Principal Component Analysis, KPCA). For non-linear monitoring systems the key to fault detection is the extracting of main features. The wavelet packet transform is a novel technique of signal processing that possesses excellent characteristics of time-frequency localization.

3、It is suitable for analyzing time-varying or transient signals. KPCA maps the original input features into a higher dimension feature space through a non-linear mapping. The principal components are then found in the higher dimension feature space. The KPCA transformation was applied to extracting t

4、he main nonlinear features from experimental fault feature data after wavelet packet transformation. The results show that the proposed method affords credible fault detection and identification.Key words: kernel method; PCA; KPCA; fault condition detection1 IntroductionBecause a mine hoist is a ver

5、y complicated and variable system, the hoist will inevitably generate some faults during long-terms of running and heavy loading. This can lead to equipment being damaged , to work stoppage, to reduced operating efficiency and may even pose a threat to the security of mine personnel. Therefore, the

6、identification of running faults has become an important component of the safety system. The key technique for hoist condition monitoring and fault identification is extracting information from features of the monitoring signals and then offering a judgmental result. However, there are many variable

7、s to monitor in a mine hoist and, also, there are many complex correlations between the variables and the working equipment. This introduces uncertain factors and information as manifested by complex forms such as multiple faults or associated faults, which introduce considerable difficulty to fault

8、 diagnosis and identification 1.There are currently many conventional methods for extracting mine hoist fault features, such as Principal Component Analysis(PCA) and Partial Least Squares (PLS) 2. These methods have been applied to the actual process. However, these methods are essentially a linear

9、transformation approach. But the actual monitoring process includes nonlinearity in different degrees. Thus, researchers have proposed a series of nonlinear methods involving complex nonlinear transformations. Furthermore, these non-linear methods are confined to fault detection: Fault variable sepa

10、ration and fault identification are still difficult problems.This paper describes a hoist fault diagnosis feature exaction method based on the Wavelet Packet Transform (WPT) and kernel principal component analysis(KPCA). We extract the features by WPT and then extract the main features using a KPCA

11、transform, which projects low-dimensional monitoring data samples into a high-dimensional space. Then we do a dimension reduction and reconstruction back to the singular kernel matrix. After that, the target feature is extracted from the reconstructed nonsingular matrix. In this way the exact target

12、 feature is distinct and stable. By comparing the analyzed data we show that the method proposed in this paper is effective.2 Feature extraction based on WPT and KPCA2.1 Wavelet packet transformThe wavelet packet transform (WPT) method 3,which is a generalization of wavelet decomposition, offers a r

13、ich range of possibilities for signal analysis. The frequency bands of a hoist-motor signal as collected by the sensor system are wide. The useful information hides within the large amount of data. In general, some frequencies of the signal are amplified and some are depressed by the information. Th

14、at is to say, these broadband signals contain a large amount of useful information: But the information can not be directly obtained from the data. The WPT is a fine signal analysis method that decomposes the signal into many layers and gives a better resolution in the time-frequency domain. The use

15、ful information within the different frequency bands will be expressed by different wavelet coefficients after the decomposition of the signal. The concept of “energy information” is presented to identify new information hidden the data. An energy eigenvector is then used to quickly mine information

16、 hiding within the large amount of data.The algorithm is: Step 1: Perform a 3-layer wavelet packet decomposition of the echo signals and extract the signal characteristics of the eight frequency components, from low to high, in the 3rd layer. Step 2: Reconstruct the coefficients of the wavelet packe

17、t decomposition. Use 3 j S (j=0, 1, , 7) to denote the reconstructed signals of each frequency band range in the 3rd layer. The total signal can then be denoted as: （1）Step 3: Construct the feature vectors of the echo signals of the GPR. When the coupling electromagnetic waves are transmitted underg

18、round they meet various inhomogeneous media. The energy distributing of the echo signals in each frequency band will then be different. Assume that the corresponding energy of 3 j S (j=0, 1, 7) can be represented as3 j E (j=0, 1, , 7). The magnitude of the dispersed points of the reconstructed signa

19、l 3 j S is: jk x (j=0,1, , 7; k=1, 2, , n), where n is the length of the signal. Then we can get: （2）Consider that we have made only a 3-layer wavelet package decomposition of the echo signals. To make the change of each frequency component more detailed the 2-rank statistical characteristics of the

20、 reconstructed signal is also regarded as a feature vector: （3）Step 4: The 3 j E are often large so we normalize them. Assume that, thus the derived feature vectors are, at last:T= （4） The signal is decomposed by a wavelet package and then the useful characteristic information feature vectors are ex

21、tracted through the process given above. Compared to other traditional methods, like the Hilbert transform, approaches based on the WPT analysis are more welcome due to the agility of the process and its scientific decomposition.2.2 Kernel principal component analysisThe method of kernel principal c

22、omponent analysis applies kernel methods to principal component analysis45.The principal component is the element at the diagonal after the covariance matrix，has been diagonalized. Generally speaking, the first N values along the diagonal, corresponding to the large eigenvalues, are the useful infor

23、mation in the analysis. PCA solves the eigenvalues and eigenvectors of the covariance matrix. Solving the characteristic equation6: （5）where the eigenvalues and the eigenvectors is essence of PCA.Let the nonlinear transformations, : RN F , xX , project the original space into feature space, F. Then

24、the covariance matrix, C, of the original space has the following form in the feature space: （6）Nonlinear principal component analysis can be considered to be principal component analysis ofin the feature space, F. Obviously, all the eigenvalues of C and eigenvectors, V F 0 satisfyV =V . All of the

25、solutions are in the subspacethat transforms from （7）There is a coefficient Let （8）From Eqs.(6), (7) and (8) we can obtain: （9）where k =1, 2, ., M . Define A as an MM rankmatrix. Its elements are: （10）From Eqs.(9) and (10), we can obtain MAa =a . This is equivalent to:Ma =Aa . (11)Make as As eigenva

26、lues, and,as the corresponding eigenvector.We only need to calculate the test points projections on the eigenvectorsthat correspond to nonzero eigenvalues in F to do the principal component extraction. Defining this asit is given by: (12)Principal component we need to know the exact form of the non-

27、linear image. Also as the dimension of the feature space increases the amount of computation goes up exponentially. Because Eq.(12) involves an inner-product computation, according to the principles of Hilbert-Schmidt we can find a kernel function that satisfies the Mercer conditions and makesThen E

28、q.(12) canbe written: (13)Here is the eigenvector of K. In this way the dot product must be done in the original space but the specific form ofneed not be known. The mapping, and the feature space, F, are all completely determined by the choice of kernel function 78.2.3 Description of the algorithmT

29、he algorithm for extracting target features in recognition of fault diagnosis is:Step 1: Extract the features by WPT; Step 2: Calculate the nuclear matrix, K, for each sample, in the original input space, andStep 3: Calculate the nuclear matrix after zero-mean processing of the mapping data in featu

30、re space;Step 4: Solve the characteristic equation Ma =Aa ;Step 5: Extract the k major components using Eq.(13) to derive a new vector. Because the kernel function used in KPCA met the Mercer conditions it can be used instead of the inner product in feature space. It is not necessary to consider the

31、 precise form of the nonlinear transformation. The mapping function can be non-linear and the dimensions of the feature space can be very high but it is possible to get the main feature components effectively by choosing a suitable kernel function and kernel parameters9. 3 Results and discussionThe

32、character of the most common fault of a mine hoist was in the frequency of the equipment vibration signals. The experiment used the vibration signals of a mine hoist as test data. The collected vibration signals were first processed by wavelet packet. Then through the observation of different time-f

33、requency energy distributions in a level of the wavelet packet we obtained the original data sheet shown in Table 1 by extracting the features of the running motor. The fault diagnosis model is used for fault identification or classification.Experimental testing was conducted in two parts: The first

34、 part was comparing the performance of KPCA and PCA for feature extraction from the original data, namely: The distribution of the projection of the main components of the tested fault samples. The second part was comparing the performance of the classifiers, which were constructed after extracting

35、features by KPCA or PCA. The minimum distance and nearest-neighbor criteria were used for classification comparison, which can also test the KPCA and PCA performance. In the first part of the experiment, 300 fault samples were used for comparing between KPCA and PCA for feature extraction. To simpli

36、fy the calculations a Gaussian kernel function was used: 10The value of the kernel parameter, , is between 0.8 and 3, and the interval is 0.4 when the number of reduced dimensions is ascertained. So the best correct classification rate at this dimension is the accuracy of the classifier having the b

37、est classification results. In the second part of the experiment, the classifiers recognition rate after feature extraction was examined. Comparisons were done two ways: theminimum distance or the nearest-neighbor. 80% of the data were selected for training and the other 20% were used for testing. T

38、he results are shown in Tables 2 and 3.From Tables 2 and 3, it can be concluded from Tables 2 and 3 that KPCA takes less time and has relatively higher recognition accuracy than PCA.4 ConclusionsA principal component analysis using the kernel fault extraction method was described. The problem is fir

39、st transformed from a nonlinear space into a linearhigher dimension space. Then the higher dimension feature space is operated on by taking the inner product with a kernel function. This thereby cleverly solves complex computing problems and overcomes the difficulties of high dimensions and local mi

40、nimization. As can be seen from the experimental data, compared to the traditional PCA the KPCA analysis has greatly improved feature extraction and efficiency in recognition fault states. References1 Ribeiro R L. Fault detection of open-switch damage in voltage-fed PWM motor drive systems. IEEE Tra

41、ns Power Electron, 2003, 18(2): 587593.2 Sottile J. An overview of fault monitoring and diagnosis in mining equipment. IEEE Trans Ind Appl, 1994, 30(5):13261332.3 Peng Z K, Chu F L. Application of wavelet transform in machine condition monitoring and fault diagnostics: areview with bibliography. Mec

42、hanical Systems and Signal Processing, 2003(17): 199221.4 Roth V, Steinhage V. Nonlinear discriminant analysis using kernel function. In: Advances in Neural Information Proceeding Systems. MA: MIT Press, 2000: 568574.5 Twining C, Taylor C. The use of kernel principal component analysis to model data

43、 distributions. Pattern Recognition, 2003, 36(1): 217227.6 Muller K R, Mika S, Ratsch S, et al. An introduction tokernel-based learning algorithms. IEEE Trans on Neural Network, 2001, 12(2): 181.7 Xiao J H, Fan K Q, Wu J P. A study on SVM for fault diagnosis. Journal of Vibration, Measurement & Diag

44、nosis,2001, 21(4): 258262.8 Zhao L J, Wang G, Li Y. Study of a nonlinear PCA fault detection and diagnosis method. Information and Control,2001, 30(4): 359364.9 Xiao J H, Wu J P. Theory and application study of feature extraction based on kernel. Computer Engineering,2002, 28(10): 3638.中文譯文基于小波包變換和核

45、主元分析技術(shù)的礦井提升機(jī)的自我故障檢測摘要： 這是一種新的運(yùn)算法，它能正確識別礦井提升機(jī)的故障并且準(zhǔn)確地監(jiān)測礦井提升機(jī)故障的發(fā)展過程。這種方法是基于小波包變換（WPT）和核主成份分析（KPCA，核主成份分析）技術(shù)。對于非線性監(jiān)聽系統(tǒng)，故障檢測的關(guān)鍵是提取主要特征。小波包變換是時間頻率的局部化分析，尤其適合于非平穩(wěn)信號。KPCA就是將最初輸入的數(shù)據(jù)特征透過非線性映射映射到高維特征空間，然后在高維特征空間發(fā)現(xiàn)其主要組成部分。KPCA變換適用于從經(jīng)過小波包變換的實驗故障特征數(shù)據(jù)中提取主要的非線性特征。結(jié)果表示，該方法能提供可靠的故障檢測和鑒定。關(guān)鍵詞：核心方法;主成分分析;核主元分析;故障檢測1介紹

46、因為礦井提升機(jī)是一種復(fù)雜的可變性比較大的系統(tǒng)，提升機(jī)在長期運(yùn)行和重載情況下難免會產(chǎn)生一些故障。這些都有可能損壞設(shè)備，停工，降低工作效率，甚至對我們員工的安全帶來威脅。因此，運(yùn)行中故障的檢測已經(jīng)變成安全系統(tǒng)的一個重要組成部分。 提升機(jī)狀態(tài)監(jiān)測與故障識別的關(guān)鍵技術(shù)是從監(jiān)測信號特征中提取的信息和提供一個判斷的結(jié)果。但是，在礦井提升機(jī)的檢測中有很多不同的情況，而且在各種各樣的工作設(shè)備之間有許多復(fù)雜的相互關(guān)系。這里不確定因素和數(shù)據(jù)由復(fù)雜的形式所表現(xiàn)，如多個故障或相關(guān)故障，這些故障的診斷和鑒定是相當(dāng)困難的。目前有許多傳統(tǒng)的方法可以提取礦井提升機(jī)故障特征，如主成分分析（PCA）和偏最小二乘法（PLS）。這些

47、方法已經(jīng)被熟練的運(yùn)用于我們的實際生產(chǎn)中來。然而，這些方法基本上是一個線性變換方法。但實際監(jiān)測過程包括不同程度的非線性。因此我們的研究員已經(jīng)提出了一系列涉及復(fù)雜的非線性變換非線性方法。此外，這些非線性方法只限于故障檢測，故障變量分離和故障識別仍然是難以解決的問題。這篇論文是介紹了一種基于小波包變換 （WPT）和核主成份分（KPCA）的礦井提升機(jī)故障診斷的特征提取方法。我們用WPT提取特征數(shù)據(jù)然后用核主成分分析變換提取主要數(shù)據(jù)特征，這種變換將低維的監(jiān)測數(shù)據(jù)樣本映射到高維的特征空間。然后我們做了降維和重建并備份到奇異核矩陣。在這之后，目標(biāo)特征從重構(gòu)的非奇異矩陣提取出來。用這樣的方法我們得到清楚又穩(wěn)定

48、的目標(biāo)特征。通過比較分析數(shù)據(jù)，我們得出本文提出的方法是有效的。2基于小波包變換和主成分分析技術(shù)的特征提取2.1小波包變換 小波包變換（小波包變換）方法 3 ，這是一種小波的分解的概括，為信號分析提供了很多可能。傳感器系統(tǒng)收集到的升降器的信號頻帶是非常廣泛的。有用的信息隱藏在大量的數(shù)據(jù)中。一般情況下，某些頻率的信號被放大，某些頻率的信號被抑制。這就是說，這些寬帶信號包含大量有用的信息：但是從這些信息中不能直接獲得有用數(shù)據(jù)。小波包變換是一個很好的信號分析方法，它把信號分解成很多層的信號并在時頻域給出了一個更好的分辨率，不同頻段內(nèi)的有用信息在信號分解后將被不同的小波系數(shù)表達(dá)。該信號的提出，是以確定新

49、的信息隱藏在數(shù)據(jù)的中新信息。然后一種能量特征向量快速挖掘隱藏在大量的數(shù)據(jù)中的有用信息。該算法是：第1步：將回波信號執(zhí)行3層小波包分解，并提取8個頻率成分的信號特征在第三層，從低到高。第2步：重構(gòu)小波包分解的系數(shù)。利用3 j S (j=0, 1, , 7) 指每個重建信號的頻帶范圍內(nèi)的第3層。總的信號就可以被命名為： （1）第3步：構(gòu)建的探地雷達(dá)回波信號的特征向量。當(dāng)電磁波的耦合傳輸他們滿足各種地下非均勻介質(zhì)。能源分布的回波信號在每個頻帶然后將不同：承擔(dān)相應(yīng)的能量 3 j S (j=0, 1, , 7) 可以代表3 j E (j=0, 1, , 7 ).的規(guī)模分散點(diǎn)的重建信號3 j S 是 jk

50、 x (j=0,1, , 7; k=1, 2, , n),其中n是長度的信號。然后，我們可以得到： （2）考慮到我們做的只有3層的回波信號的小波包分解。為了使每個頻率成分的變化更詳細(xì)，重構(gòu)信號的2級的統(tǒng)計特性也被視為一個特征向量： （3）(4)第4步3 j E往往大，所以我們將他們標(biāo)準(zhǔn)化。假設(shè)，從而得出的特征向量是，最后：T = 信號通過小波包變換分解，然后提取有用的特征信息的特征向量通過上述過程。相對于其他傳統(tǒng)方法，像希爾伯特變換，基于小波包變換分析方法更受歡迎，這是由于它敏捷的過程和它的科學(xué)分解。2.2核主成份分析核主成分份析方法就是將核心方法應(yīng)用在主成分分析法中 4-5 。主要組成部分是

51、在對角線元素后，協(xié)方差矩陣，已是結(jié)尾 。一般而言，第一次N值山對角線長，相應(yīng)的大特征值，是有用的信息在數(shù)據(jù)分析.PCA解決了特征值和特征向量的協(xié)方差矩陣。求解特征方程 6 ：如果特征值和特征向量，是屬于PCA的。使非線性變換，RNF ,xX項目原始空間到特征空間，樓然后，協(xié)方差矩陣，中，原來的空間具有下列表格中的功能空間： (6)非線性主成分分析可被認(rèn)為是主成分分析的功能空間，樓顯然，所有的C抗原值和特征向量，V F 0 滿足V V。所有的解決方案是在子這一轉(zhuǎn)變從 （7）使系數(shù) 可以得到 （8）從6 7 8式我們可以得到 (9)使k =1, 2, ,M 定義A是MM的矩陣，它的要點(diǎn)是 （10）

52、從9和10式我們可以得到MAa =a這就相當(dāng)于Ma= Aa . （11） 使作為A的特征值，以及相應(yīng)的特征向量。我們只需要計算測試點(diǎn)的預(yù)測的特征向量對應(yīng)的非零特征值的F這樣做主要成分的提取。界定這種因為它是由： (12) 主要組成部分，我們需要知道確切形式的非線性圖像。還為層面的特征空間增加了計算量隨之呈指數(shù)。由于均衡器。由于式（12）涉及內(nèi)積計算，根據(jù)希爾伯特 - 施密特的原則，我們可以找到一個內(nèi)積函數(shù)，滿足的默瑟條件下，方程（12）可以改寫成 這里是K的一個變量。這樣，點(diǎn)積必須在原來的空間，但(x)的具體形式?jīng)]必要知道。特征空間F，完全取決于選擇的核心特征 7-8 。2.3說明算法在故障診

53、斷的識別中提取目標(biāo)特征的算法是：第1步：用小波包變換提取特征;第2步：計算每個樣本的核矩陣，K 在原始的空間輸入，和第3步：在特征空間進(jìn)行測繪數(shù)據(jù)的均值處理，然后計算核矩陣;第4步：求解特征方程Ma =Aa ;第5步：利用方程提取的K式的重要組成部分(13)，制定出一個新的載體。由于核函數(shù)在核主成分分析要滿足Mercer的條件，可用于代替內(nèi)積的特征空間。沒有必要考慮的具體形式的非線性變換。映射功能可以非線性和特征空間的尺寸可以很高，但有它可能得到有效的主要成分通過選擇合適的核函數(shù)和內(nèi)核參數(shù) 9 。3結(jié)果與討論 礦井提升機(jī)的最常見的故障特征可以在設(shè)備振動信號的頻率中提取出來。實驗中使用礦井提升機(jī)的振動信號作為測試數(shù)據(jù)，將收集到的振動信號首先進(jìn)行小波包處理,然后通過在一個水平的小波包上觀察不同的時頻能量分布，接著我們將獲得的原始數(shù)據(jù)列于表1并提取電機(jī)的運(yùn)行特征。該故障診斷模型用于故障識別或分類。實驗測試被分兩部分進(jìn)行： 第一部分是比較核主元分析和主成分分析從原來的數(shù)據(jù)中提取特征的性能，即：測試故障樣本的主要組成部分的投影分布。那個第二部分是比較分類的性能，這些分類是