矿井提升机外文翻译--基于小波包变换和核主元分析技术的矿井提升机的自我故障检测

《矿井提升机外文翻译--基于小波包变换和核主元分析技术的矿井提升机的自我故障检测》由会员分享，可在线阅读，更多相关《矿井提升机外文翻译--基于小波包变换和核主元分析技术的矿井提升机的自我故障检测（18页珍藏版）》请在毕设资料网上搜索。

1、PDF外文：http:/ 中文 3162 字  外文翻译部分：  出处： 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 PCA  Abstract: A new algorithm was developed to correctly identify fault con

2、ditions 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 Component Analysis, KPCA). For non-linear monitoring systems the key to fault detection is the extracting of main features. The wavelet pac

3、ket transform is a novel technique of signal processing that possesses excellent characteristics 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. T

4、he principal 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. The results show that the proposed method affords credible fault detec

5、tion and identification.  Key words: kernel method； PCA； KPCA； fault condition detection  1 Introduction Because a mine hoist is a very complicated and variable system, the hoist will inevitably generate some faults during long-terms of running and heavy loading. This can lead to equipment

6、 being damaged , to work stoppage, to reduced operating efficiency and may even pose a threat to the security of mine personnel. Therefore, the identification of running faults has become an important component of the safety system. The key technique for hoist condition monitoring and fault identifi

7、cation is extracting information from features of the monitoring signals and then offering a judgmental result. However, there are many variables to monitor in a mine hoist and, also, there are many complex correlations between the variables and the working equipment. This introduces uncertain facto

8、rs and information as manifested by complex forms such as multiple faults or associated faults, which introduce considerable difficulty to fault diagnosis and identification 1.There are currently many conventional methods for extracting mine hoist fault features, such as Principal Component Analysis

9、(PCA) and Partial Least Squares (PLS) 2. These methods have  been applied to the actual process. However, these methods are essentially a linear transformation approach. But the actual monitoring process includes nonlinearity in different degrees. Thus, researchers have proposed a series of non

10、linear methods involving complex nonlinear transformations. Furthermore, these non-linear methods are confined to fault detection: Fault variable separation and fault identification are still difficult problems. This paper describes a hoist fault diagnosis feature exaction method based on the Wavele

11、t Packet Transform (WPT) and kernel principal component analysis(KPCA). We extract the features by WPT and then extract the main features using a KPCA transform, which projects low-dimensional monitoring data samples into a high-dimensional space. Then we do a dimension reduction and reconstruction

12、back to the singular kernel matrix. After that, the target feature is extracted from the reconstructed nonsingular matrix. In this way the exact target feature is distinct and stable. By comparing the analyzed data we show that the method proposed in this paper is effective.    2 Feature e

13、xtraction based on WPT and KPCA  2.1 Wavelet packet transform The wavelet packet transform (WPT) method 3,which is a generalization of wavelet decomposition, offers a rich range of possibilities for signal analysis. The frequency bands of a hoist-motor signal as collected by the sensor system a

14、re 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. That is to say, these broadband signals contain a large amount of useful information: But the information can not be directly obt

15、ained 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 useful information within the different frequency bands will be expressed by different wavelet coefficients after the decomp

16、osition 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 within the large amount of data. The algorithm is:  Step 1: Perform a 3-layer wavelet packet decomposition o

17、f 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 packet decomposition. Use 3 j S (j=0, 1, , 7) to denote the reconstructed signals of each frequency  band ra

18、nge in the 3rd layer. The total signal can then be denoted as: 730 jjsS                             （ 1）  Step 3: Construct the feature vectors of the echo signals of the GPR. When the coupling electromagnetic waves are trans

19、mitted underground 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 signal 3 j S is: jk x (j=0,1, , 7； k=1, 2, , n), where n is the length