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.

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.

Wavelet decomposition and nonlinear prediction of nonstationary vibration signals

2020 ◽  
Vol 51 (3) ◽  
pp. 52-59 ◽  
Author(s):  
Xiao-bin Fan ◽  
Bin Zhao ◽  
Bing-xu Fan
Keyword(s):  
Wavelet Decomposition ◽  
Wavelet Packet ◽  
Prediction Method ◽  
Vibration Signal ◽  
Signal Denoising ◽  
Maximum Lyapunov Exponent ◽  
Nonlinear Prediction ◽  
Local Method ◽  
Time Frequency ◽  
Nonstationary Vibration

In order to overcome the shortcomings (such as the time–frequency localization and the nonstationary signal analysis ability) of the Fourier transform, time–frequency analysis has been carried out by wavelet packet decomposition and reconstruction according to the actual nonstationary vibration signal from a large equipment located in a large Steel Corporation in this article. The effect of wavelet decomposition on signal denoising and the selection of high-frequency weight coefficients for each layer on signal denoising were analyzed. The nonlinear prediction of the chaotic time series was made by global method, local method, weighted first-order local method, and maximum Lyapunov exponent prediction method correspondingly. It was found the multi-step prediction method is better than other prediction methods.

scholarly journals Application of Choi—Williams Reduced Interference Time Frequency Distribution to Machinery Diagnostics

1995 ◽  
Vol 2 (6) ◽  
pp. 437-444 ◽  
Author(s):  
Howard A. Gaberson
Keyword(s):  
Fourier Transform ◽  
Vibration Signal ◽  
Test Cases ◽  
Short Time Fourier Transform ◽  
Vibration Signals ◽  
Time Frequency ◽  
Machinery Diagnostics ◽  
Short Time ◽  
Amplitude And Frequency Modulation ◽  
Time Frequency Distribution

This article discusses time frequency analysis of machinery diagnostic vibration signals. The short time Fourier transform, the Wigner, and the Choi–Williams distributions are explained and illustrated with test cases. Examples of Choi—Williams analyses of machinery vibration signals are presented. The analyses detect discontinuities in the signals and their timing, amplitude and frequency modulation, and the presence of different components in a vibration signal.

Vibration Analyses of the Bearing Using the Time Frequency Domain Technique

2014 ◽  
Vol 592-594 ◽  
pp. 2091-2096
Author(s):  
H.N. Sharma ◽  
Santosh Verma
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Energy Levels ◽  
Vibration Signal ◽  
Test Rig ◽  
Time Frequency ◽  
Bearing Vibration ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
Bearing System

This work employs the wavelet transform for reading the fault diagnosis in a rotor-bearing system. Initiating with literature review with some relevant studies of bearing fault and the signal processing techniques used followed by the theory of wavelet transform. A bearing test rig is shown which is used for implementing wavelet transform. A faulty bearing vibration signal is measured from the test rig; thereafter the fast Fourier transform is plotted to show the critical frequencies, bearing characteristics frequency and its harmonics. A scalogram showing the energy levels of signal is plotted as result. Faulty signal is analyzed using wavelet 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.

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 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.

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