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This study concerns with the diagnosis of composite defects using pitch-catch method in aircraft material by applying the Wavelet transform (WT) analysis, PCA along with support vector machine (SVM). A novel application is presented exploring the problem of detection and estimation of the various defects; the early detection and classification of aircraft defects is of particular importance, as the...
In this paper, a new method of fault diagnosis for power unit based on wavelet packet PCA-SVM is proposed. Firstly, using wavelet packet transformation to extract each band of energy as the initial samples; Secondly, taking principal component analysis to excavate the features of the initial samples, eliminating the correlation between data while ensure the integrity of data as far as possible, then...
An intelligent fault diagnosis method based on principal component analysis (PCA) and least squares support vector machines (LS-SVM) is proposed. The characteristic parameter set is obtained by wavelet packet transform (WPT). And PCA is used to extract the principal features associated with the diagnosing object. Then, the training data set which is reduced from the original parameters are used as...
Based on the theory of wavelet analysis and principal component analysis, multiscale PCA is introduced which combines the ability of PCA to decorrelate the variables by extracting a linear relationship, with that of wavelet analysis to extract deterministic features and approximately decorrelate autocorrelated measurements to improve the performance of PCA whose modeling is limited to a single scale...
In this paper, a novel approach is proposed to diagnose faults of marine main engine cylinder cover. Considering vibration signal is highly related with various faults of cylinder cover, we propose to diagnose faults of marine main engine cylinder cover based on vibration signal from engine. First, a wavelet analysis method is used to characterize the power spectrum of the vibration signal. Next,...
Feature extraction for fault detection of an induction motor is carried out using the information of stator current. After preprocessing actual data, Fourier and wavelet transforms are applied to detect characteristics under the healthy and various faulted conditions. The most reliable phase current among 3-phase currents is selected by the fuzzy entropy. The fuzzy membership function is also required...
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