Increasing the Efficiency of Using Hardware Resources for Time-Frequency Correlation Function Computation

2014 ◽  
Vol 1040 ◽  
pp. 969-974 ◽  
Author(s):  
Valeriy V. Avramchuk ◽  
E.E. Luneva ◽  
Alexander G. Cheremnov
Keyword(s):  
Fourier Transform ◽  
Correlation Function ◽  
Fast Fourier Transform ◽  
Sample Size ◽  
Optimal Number ◽  
Computing Technique ◽  
Frequency Correlation ◽  
Time Frequency ◽  
Function Computation ◽  
Fixed Base

In the article the techniques of increasing efficient of using multi-core processors for the task of calculating the fast Fourier transform were considered. The fast Fourier transform is led on the basis of calculating a time time-frequency correlation function. The time-frequency correlation function allows increasing the information content of the analysis as compared with the classic correlation function. The significant computational capabilities are required to calculate the time-frequency correlation function, that by reason of the necessity of multiple computing fast Fourier transform. For computing the fast Fourier transform the Cooley-Tukey algorithm with fixed base two is used, which lends itself to efficient parallelization and is simple to implement. Immediately before the fast Fourier transform computation the procedure of bit-reversing the input data sequence is used. For algorithm of calculating the time-frequency correlation function parallel computing technique was used that experimentally allowed obtaining the data defining the optimal number of iterations for each core of the CPU, depending on the sample size. The results of experiments allowed developing special software that automatically select the effective amount of subtasks for parallel processing. Also the software provides the choice of sequential or parallel computations mode, depending on the sample size and the number of frequency intervals in the calculation of time-frequency correlation function.

scholarly journals Reviews of bearing vibration measurement using fast Fourier transform and enhanced fast Fourier transform algorithms

2019 ◽  
Vol 11 (1) ◽  
pp. 168781401881675 ◽  
Author(s):  
Hsiung-Cheng Lin ◽  
Yu-Chen Ye
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Vibration Measurement ◽  
Time Frequency ◽  
Diagnosis And Prognosis ◽  
Vibration Signal Analysis ◽  
Critical Components ◽  
Comparative Results

The rolling element bearing is one of the most critical components in a machine. Vibration signals resulting from these bearings imply important bearing defect information related to the machinery faults. Any defect in a bearing may cause a certain vibration with specific frequencies and amplitudes depending on the nature of the defect. Therefore, the vibration analysis plays a key role for fault detection, diagnosis, and prognosis to reach the reliability of the machines. Although fast Fourier transform for time–frequency analysis is still widely used in industry, it cannot extract enough frequencies without enough samples. If the real frequency does not match fast Fourier transform frequency grid exactly, the spectrum is spreading mostly among neighboring frequency bins. To resolve this drawback, the recent proposed enhanced fast Fourier transform algorithm was reported to improve this situation. This article reviews and compares both fast Fourier transform and enhanced fast Fourier transform for vibration signal analysis in both simulation and practical work. The comparative results verify that the enhanced fast Fourier transform can provide a better solution than traditional fast Fourier transform.

THE FAST FOURIER TRANSFORM AND THE CONTINUOUS WAVELET TRANSFORM ANALYSIS OF THE PHONOCARDIOGRAM SIGNAL

2004 ◽  
Vol 04 (03) ◽  
pp. 257-272 ◽  
Author(s):  
S. M. DEBBAL ◽  
F. BEREKSI-REGUIG ◽  
A. MEZIANE TANI
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Fast Fourier Transform ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Quantitative Measurements ◽  
Signal Characteristics ◽  
Wavelet Transform Analysis ◽  
Pcg Signals

This paper is concerned with a synthesis study of the fast Fourier transform (FFT) and the continuous wavelet transform (CWT) in analysing the phonocardiogram signal (PCG). It is shown that the continuous wavelet transform provides enough features of the PCG signals that will help clinics to obtain qualitative and quantitative measurements of the time-frequency PCG signal characteristics and consequently aid to diagnosis. Similary, it is shown that the frequency content of such a signal can be determined by the FFT without difficulties.

scholarly journals Sparse Time-Frequency Decomposition for Multiple Signals with Same Frequencies

2017 ◽  
Vol 09 (04) ◽  
pp. 1750010
Author(s):  
Thomas Y. Hou ◽  
Zuoqiang Shi
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Frequency Analysis ◽  
Special Structure ◽  
Data Driven ◽  
Multiple Signals ◽  
Time Frequency ◽  
Frequency Decomposition ◽  
Engineering Problems ◽  
Instantaneous Frequencies

In this paper, we consider multiple signals sharing the same instantaneous frequencies. This kind of data is very common in scientific and engineering problems. To take advantage of this special structure, we modify our data-driven time-frequency analysis by updating the instantaneous frequencies simultaneously. Moreover, based on the simultaneous sparsity approximation and the Fast Fourier Transform, we develop several efficient algorithms to solve this problem. Since the information of multiple signals is used, this method is very robust to the perturbation of noise and it is applicable to the general nonperiodic signals even with missing samples or outliers. Several synthetic and real signals are used to demonstrate the robustness of this method. The performances of this method seems quite promising.

Vibration signature analysis of distributed defects in ball bearing using wavelet decomposition technique

2017 ◽  
Vol 48 (1-2) ◽  
pp. 7-18 ◽  
Author(s):  
SS Kulkarni ◽  
AK Bewoor ◽  
RB Ingle
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Wavelet Decomposition ◽  
Ball Bearing ◽  
Vibration Signal ◽  
Extended Defects ◽  
Decomposition Technique ◽  
Outer Race ◽  
Time Frequency ◽  
Extended Type

The analysis of vibration signals acquired from a ball bearing with an extended type of distributed defects is carried out using wavelet decomposition technique. The influence of artificially generated defect and its location on outer and inner race of the ball bearing is observed using vibration data acquired from bearing housing. The comparison of diagnostic information from fast Fourier transform and time frequency decomposition method is made for inner and outer race of ball bearing with single as well as multiple extended defects. To decompose vibration signal acquired from bearing, db04 wavelet technique was implemented. It is observed that impulses appear with a time period corresponding to characteristic defect frequencies. The results observed from wavelet decomposition technique and fast Fourier transform reveal that the characteristic defect frequency is quite consistent even with change in location of defect. The extended type of distributed defects in the ball bearings can also be effectively diagnosed with the help of wavelet decomposition technique and fast Fourier transform.

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