基于PCA技术核心的打包和变换的矿井提升机失误的发现

基于PCA技术核心的打包和变换的矿井提升机失误的发现(中文2900字,英文2400字)
摘要：
一个新的运算法则被正确的运用于证明和监视矿井提升机的过失情况。这种方法是基于小浪小包变换（WPT）和PCA为核心基础的。（KPCA，核心校长成份分析）因为非线性监听系统主要是通过主要特征来发现和吸取系统过失的。小浪小包变换是处理时间-频率局限性的优良特性信号的新技术。它对分析改变时间或短暂的信号是适当的。
KPCA 透过最初的输入的非线性映射映射特征进入较高的尺寸特征空间。主要的成份然后在较高的尺寸特征空间被发现。KPCA 变形被适用于从实验的过失特征小浪后的数据小包变形吸取主要的非线性特征。结果表示，被提议的方法负担可信的过失发现和确认。

关键词：核心方法;主成分分析;核主元分析;故障检测

外文翻译部分：
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 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 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. It is suitable for analysing 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 the 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 detection