scholarly journals An Efficient Profile Detection Method for Fiber Spectrum Images with Low SNR Based on Wigner Bispectrum

2011 ◽  
Vol 28 (2) ◽  
pp. 144-149 ◽  
Author(s):  
Jia Zhu ◽  
Zhangqin Zhu ◽  
Zhongfu Ye
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Detection Method ◽  
Higher Order ◽  
Accurate Information ◽  
Reconstruction Method ◽  
Analysis Method ◽  
Low Snr ◽  
Higher Order Spectra ◽  
Spectrum Data

AbstractAnovel profile detection method is proposed for astronomical fiber spectrum data with low signalto-noise ratio. This approach can be applied to the pretreatment for 2-D astronomical spectrum data before the extraction of spectra. The Wigner bispectrum, a classical higher-order spectrum analysis method, is introduced and applied to deal with the spectrumsignal in this article.After analyzing the Wigner higher-order spectra distribution of the target profile signal, the combination of the Wigner bispectrum algorithm and the fast Fourier transform algorithm is used to weaken the effect of the noise to obtain more accurate information. Both the reconstruction method of the Wigner bispectrum and inverse fast Fourier transform are used to acquire the detection signal. At the end of this paper, experiments with both simulated and observed data based on the Large Sky Area Multi-Object Fiber Spectroscopy Telescope project are presented to demonstrate the effectiveness of the proposed method.

The Wavelet De-Noising of Vibration Signals for Aircraft Rolling Bearings

2014 ◽  
Vol 912-914 ◽  
pp. 873-877 ◽  
Author(s):  
Feng Kui Cui ◽  
Fei Fei Lv ◽  
Xiao Qiang Wang ◽  
Dong Ying Zhang
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Noise Reduction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Noise Interference ◽  
Low Snr ◽  
Contrast Analysis

Aiming at air rolling bearing vibration signals low SNR and nonstationary characteristics, taking wavelet theory and principles of the wavelet noise reduction for air vibration signals of rolling bearings to conduct wavelet noise reduction processing.By means of the simulation signal wavelet noise reduction processing and fast Fourier transform, the contrast analysis of the vibration signals after wavelet noise reduction and FFT transform and the original signal directly to the result of the fast Fourier transform, and thus prove the validity of the vibration signal wavelet noise reduction. Through the actual vibration signals of bearing conductnoise reduction processing, the result is a further indication of the superiority of wavelet noise reduction in eliminate noise interference.

scholarly journals Diagnostics of separately excited DC motor based on analysis and recognition of signals using FFT and Bayes classifier

2015 ◽  
Vol 64 (1) ◽  
pp. 29-35 ◽  
Author(s):  
Witold Glowacz ◽  
Zygfryd Glowacz
Keyword(s):  
Fourier Transform ◽  
Fourier Analysis ◽  
Fast Fourier Transform ◽  
Dc Motor ◽  
Analysis Method ◽  
Bayes Classifier ◽  
Training Process ◽  
Recognition Method ◽  
Identification Process ◽  
Diagnostic Investigations

Abstract In this article results of diagnostic investigations of separately excited DC motor were presented. In diagnostics were applied a Fourier analysis method based on the fast Fourier transform (FFT) and a recognition method using Bayes classifier. In training process a set of the most important frequencies has been determined for which differences of corresponding signals in two states are the largest. Three categories of signals have been recognized in identification process: faultless state, state of the rotor broken one coil and state of the rotor shorted three coils.

scholarly journals Delay Differential Analysis of Time Series

2015 ◽  
Vol 27 (3) ◽  
pp. 594-614 ◽  
Author(s):  
Claudia Lainscsek ◽  
Terrence J. Sejnowski
Keyword(s):  
Dynamical System ◽  
Time Series ◽  
Spectral Analysis ◽  
Fourier Transform ◽  
Time Domain ◽  
Higher Order ◽  
Series Data ◽  
Differential Analysis ◽  
Delay Differential ◽  
Higher Order Spectra

Nonlinear dynamical system analysis based on embedding theory has been used for modeling and prediction, but it also has applications to signal detection and classification of time series. An embedding creates a multidimensional geometrical object from a single time series. Traditionally either delay or derivative embeddings have been used. The delay embedding is composed of delayed versions of the signal, and the derivative embedding is composed of successive derivatives of the signal. The delay embedding has been extended to nonuniform embeddings to take multiple timescales into account. Both embeddings provide information on the underlying dynamical system without having direct access to all the system variables. Delay differential analysis is based on functional embeddings, a combination of the derivative embedding with nonuniform delay embeddings. Small delay differential equation (DDE) models that best represent relevant dynamic features of time series data are selected from a pool of candidate models for detection or classification. We show that the properties of DDEs support spectral analysis in the time domain where nonlinear correlation functions are used to detect frequencies, frequency and phase couplings, and bispectra. These can be efficiently computed with short time windows and are robust to noise. For frequency analysis, this framework is a multivariate extension of discrete Fourier transform (DFT), and for higher-order spectra, it is a linear and multivariate alternative to multidimensional fast Fourier transform of multidimensional correlations. This method can be applied to short or sparse time series and can be extended to cross-trial and cross-channel spectra if multiple short data segments of the same experiment are available. Together, this time-domain toolbox provides higher temporal resolution, increased frequency and phase coupling information, and it allows an easy and straightforward implementation of higher-order spectra across time compared with frequency-based methods such as the DFT and cross-spectral analysis.

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