矿业矿井外文翻译外文文献英文文献基于pca技术核心的打包和变换的矿井提升机失误的发现

矿业矿井外文翻译外文文献英文文献基于pca技术核心的打包和变换的矿井提升机失误的发现内容摘要：

,..., M   ,as the corresponding eigenvector. We only need to calculate the test points’ projections on the eigenvectors kV that correspond to nonzero eigenvalues in F to do the principal ponent extraction. Defining this as k it is given by: 1( ( ) ) ( ( ) ( ) )Mkki i kiV x x x     (12) principal ponent we need to know the exact form of the nonlinear image. Also as the dimension of the feature space increases the amount of putation goes up exponentially. Because Eq.(12) involves an innerproduct putation, ( ) ( )ixx according to the principles of HilbertSchmidt we can find a kernel function that satisfies the Mercer conditions and makes ( , ) ( ) ( )iiK x x x x Then Eq.(12) can be written: 1( ( ) ) ( ( , ) )Mkki i kiV x K x x   Here is the eigenvector of K. In this way the dot product must be done in the original space but the specific form of  (x) need not be known. The mapping,  (x) , and the feature space, F, are all pletely determined by the choice of kernel function[ 7–8]. Description of the algorithm The 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, ( 1, 2 , ... , )Nix R i N in the original input space, and ( ( ) ( ))ij iK x x Step 3: Calculate the nuclear matrix after zeromean processing of the mapping data in feature space。 Step 4: Solve the characteristic equation M a Aa。 Step 5: Extract the k major ponents 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 precise form of the nonlinear transformation. The mapping function can be nonlinear and the dimensions of the feature space can be very high but it is possible to get the main feature ponents effectively by choosing a suitable kernel function and kernel parameters[9]. 3 Results and discussion The character of the most mon 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 timefrequency 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 part was paring the performance of KPCA and PCA for feature extraction from the original data, namely: The distribution of the projection of the main ponents of the tested fault samples. The second part was paring the performance of the classifiers, which were constructed after extracting features by KPCA or PCA. The minimum distance and nearestneighbor criteria were used for classification parison, which can also test the KPCA and PCA performance. In the first part of the experiment, 300 fault samples were used for paring between KPCA and PCA for feature extraction. To simplify the calculations a Gaussian kernel function was used: 22( , ) ( ) , ( ) e x p ( )2xyK x y x y    10 The value of the kernel parameter,  , is between and 3, and the interval is 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 best classification results. In the second part of the experiment, the classifiers’ recognition rate after feature extraction was examined. Comparisons were done two ways: the minimum distance or the nearestneighbor. 80% of the data were selected for training and the other 20% were used for testing. The 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 Conclusions A principal ponent analysi。