礦業(yè)礦井外文翻譯外文文獻(xiàn)英文文獻(xiàn)基于pca技術(shù)核心的打包和變換的礦井提升機(jī)失誤的發(fā)現(xiàn)(已修改)
2025-11-25 15:17 本頁(yè)面
【正文】 外文翻譯部分： 英文原文 Minehoist faultcondition 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 nonlinear 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 timefrequency localization. It is suitable for analysing timevarying or transient signals. KPCA maps the original input features into a higher dimension feature space through a nonlinear mapping. The principal ponents 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 1 Introduction Because a mine hoist is a very plicated andvariable system, the hoist will inevitably generate some faults during longterms of running and heavy loading. This can lead to equipment being damaged,to work stoppage, to reduced operating efficiency andmay even pose a threat to the security of mine personnel. Therefore, the identification of running fault shas bee an important ponent 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 variables to monitor in a mine hoist and, also , there are many plex correlations between thevariables and the working equipment. This introduce suncertain factors and information as manifested by plex 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(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 nonlinearmethods involving plex nonlinear transformations. Furthermore, these nonlinear methods are confined to fault detection: Fault variable separation and fault identification are still difficult paper describes a hoist fault diagnosis featureexaction method based on the Wavelet Packet Transform(WPT) and kernel principal ponent analysis(KPCA). We extract the features by WPT and thenextract the main features using a KPCA transform,which projects lowdimensional monitoring datasamples into a highdimensional space. Then we do adimension reduction and reconstruction back to thesingular kernel matrix. After that, the target feature isextracted from the reconstructed nonsingular this way the exact target feature is distinct and paring the analyzed data we show that themethod proposed in this paper is effective. 2 Feature extraction based on WPT and KPCA Wavelet packet transform The wavelet packet transform (WPT) method[3],which is a generalization of wavelet deposition, offers a rich range of possibilities for signal analysis. The frequency bands of a hoistmotor 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. That is tosay, these broadband signals contain a large amountof useful information: But the information can not bedirectly obtained from the data. The WPT is a finesignal analysis method that deposes the signalinto many layers and gives a etter resolution in thetimefrequency domain. The usefu
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