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.

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 Measuring System of Vibration Diagnostic with the Metrological Self-Control Function

2019 ◽  
Vol 9 (1) ◽  
pp. 6639-6646
Keyword(s):  
Control Function ◽  
Vibration Signal ◽  
Diagnostic Methods ◽  
Self Control ◽  
Measuring System ◽  
Vibration Measurement ◽  
Control Mode ◽  
Structural System ◽  
Time Frequency ◽  
Developing System

In the article, there is given the description of the method of metrological self-control in the measuring system of vibration diagnostic, the structural system of scheme is shown and the peculiarities of its functioning is self-control mode are given. The diagnostic methods, discussed in the article, usually are not completely independent and show the bigger effectiveness in the combined usage. The following problem exists: in the early stages of the appearing of a defect the informative components of vibration signals have extremely small amount of the energy and are covered with the background noise. Therefore, the effective method of the signal processing should assume the extraction of the informative signs of a damage when the signal/noise ratio is less than 1. Currently the biggest interest is presented by the group of the time frequency methods because they allow localizing in time the peculiarities of a vibration signal, therefore, they are potentially more sensitive to occurring defects than the time and spectral methods. Among their disadvantages are the mathematical complexity and the complexity of the realization and the interpretation of the results. The main advantage of developing system is the presence of the high frequency vibration measurement channel and also the built-in functional self-diagnosis system which principle is experimentally confirmed.

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.

THE ROLE OF MOTION MONITORING IN EXPERIMENTS ON SLIDING FRICTION IN FLAT-ON-FLAT METALLIC CONTACTS

Tribologia ◽  
2017 ◽  
Vol 276 (6) ◽  
pp. 39-44
Author(s):  
Jacek ŁUBIŃSKI
Keyword(s):  
Sliding Friction ◽  
Screening Method ◽  
Vibration Signal ◽  
Analysis Data ◽  
Vibration Measurement ◽  
Global Evaluation ◽  
Data Set ◽  
Metallic Contacts ◽  
Critical Components

The paper describes an example of an experiment on sliding friction of metallic materials in which vibration measurement methods were used to identify friction induced vibrations occurring in sliding. The determination of the parameters of the motion of the critical components in the sliding system in different modes of operation allowed using the vibration signal as a source of information pertaining to the observed process. The tests performed with a metallic material were inspired by the earlier research performed with machine ceramics in which the result of vibration analysis allowed the determination of the correctness of measurement with regard to the conditions of contact and the analyses of the nature of the observed effect of vibration on the inflicted friction. The motion analysis data was used as a basis for a screening method eliminating corrupted measurements from the data set used for global evaluation of the friction characteristics. It was confirmed that, in a steel-on-steel sliding system, similar friction and vibration regimes occur as in ceramics-on-ceramics, but the effects of certain vibration modes are opposite in the two systems, despite the same load/velocity conditions.

scholarly journals Vibration Condition Monitoring: Latest Trend and Review

2015 ◽  
Vol 773-774 ◽  
pp. 139-143
Author(s):  
K.H. Hui ◽  
L.M. Hee ◽  
M. Salman Leong ◽  
Ahmed M. Abdelrhman
Keyword(s):  
Decision Making ◽  
Fourier Transform ◽  
Condition Monitoring ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Machine Learning Techniques ◽  
Vibration Condition ◽  
The Past ◽  
Analysis Methods ◽  
Vibration Signal Analysis

Vibration analysis has proven to be the most effective method for machine condition monitoring to date. Various effective signal analysis methods to analyze and extract fault signature that embedded in the raw vibration signals have been introduced in the past few decades such as fast Fourier transform (FFT), short time Fourier transform (STFT), wavelets analysis, empirical mode decomposition (EMD), Hilbert-Huang transform (HHT), etc. however, these is still a need for human to interpret vibration signature of faults and it is regarded as one of the major challenge in vibration condition monitoring. Thus, most recent researches in vibration condition monitoring revolved around using Artificial Intelligence (AI) techniques to automate machinery faults detection and diagnosis. The most recent literatures in this area show that researches are mainly focus on using machine learning techniques for data fusion, features fusion, and also decisions fusion in order to achieve a higher accuracy of decision making in vibration condition monitoring. This paper provides a review on the most recent development in vibration signal analysis methods as well as the AI techniques used for automated decision making in vibration condition monitoring in the past two years.

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.

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