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In this paper a new methodology of transient motor current signature analysis (TMCSA) is proposed. The approach consists on obtaining a 2D time frequency plot representing the time-frequency evolution of all the harmonics present on an electric machine transient current. Identifying characteristic patterns in the time-frequency plane, produced by some of the fault related components, permits the machine...
According to the fault of rotor bar breakage, the paper put forward a diagnosis method to extract the fault characteristic signal in stator current, by signal processing with Fourier Transform and wavelet transform theory. To validate its feasibility, simulation and experiment on frequency converter sets are made, and the results show that the given method perform well in the fault diagnosis application.
This paper presents a new algorithm for transformer differential protection, based on pattern recognition of the instantaneous differential currents. A decision logic by wavelet Transform has been devised using extracted feature from differential currents due to internal fault and inrush currents. In this logic, diagnosis criterion is based on time difference of amplitudes of wavelet coefficients...
This paper shows a new polynomial Kernel (a non-Gaussian window) suitable for the implementation of phase corrected wavelet transform, in a recursive manner, which the target is to track harmonics and inter-harmonics and achieve acceleration in the disturbances detection process and help the waveform characterization in power quality analyzers that must be in compliance at the same time with the power...
Due to the weak energy and nonstationarity, incipient fault characteristic signals are usually submerged by vibration signals of rotary machine and noise. Based on the multi-resolution feature and time-frequency localization feature of Wavelet Transform, a method to extract fault characteristic signals by decomposing them into corresponding time-frequency segmentations is presented. The noise is attenuated,...
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