礦井提升機外文翻譯基于小波包變換和核主元分析技術的礦井提升機的自我故障檢測其他專業(yè)

1、中文 中文 3162 字外文翻譯部分： 外文翻譯部分：出處： 出處：Journal of China University of Mining and Technology, 2008, 18(4): 567-570英文原文Mine-hoist fault-condition detection based on the wavelet packet transform and kernel PCAAbstract: A new alg

2、orithm 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) and kernel PCA (Kernel Principal Compone

3、nt 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 charact

4、eristics of time-frequency localization. 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 princ

5、ipal components are then found in the higher dimension feature space. The KPCA transformation was applied to extracting the main nonlinear features from experimental fault feature data after wavelet packet transformation

6、. 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 very complicated and v

7、ariable 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 pos

8、e a threat to the security of mine personnel. Therefore, 2.1 Wavelet packet transformThe wavelet packet transform (WPT) method [3],which is a generalization of wavelet decomposition, offers a rich range of possibilities

9、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 am

10、plified and some are depressed by the information. That 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 s

11、ignal analysis method that decomposes the signal into many layers and gives a better resolution in the time-frequency domain. The useful information within the different frequency bands will be expressed by different wav

12、elet 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 hiding

13、 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

14、 the 3rd layer. Step 2: Reconstruct the coefficients of the wavelet packet 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 t

15、hen be denoted as:（1）730jjs S?? ?Step 3: Construct the feature vectors of the echo signals of the GPR. When the coupling electromagnetic waves are transmitted underground they meet various inhomogeneous media. The energy

16、 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 point