Time - Frequency Analysis Techniques Applied to Automotive Noise and Vibration Signals

2008 ◽  
Author(s):  
Belisario Nina Huallpa ◽  
Jorge Marano ◽  
Jose Roberto de França Arruda
Keyword(s):  
Frequency Analysis ◽  
Vibration Signals ◽  
Noise And Vibration ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Analysis Techniques

Overview of Time-Frequency Analysis Techniques in Vibration Signals of Rotating Machinery

2014 ◽  
Vol 684 ◽  
pp. 124-130
Author(s):  
Hong Li ◽  
Qing He ◽  
Zhao Zhang
Keyword(s):  
Frequency Analysis ◽  
Local Characteristic ◽  
Vibration Signals ◽  
Time Frequency Analysis ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Analysis Techniques ◽  
Advantages And Disadvantages ◽  
Analysis Methods ◽  
Short Time

There is very rich fault information in vibration signals of rotating machineries. The real vibration signals are nonlinear, non-stationary and time-varying signals mixed with many other factors. It is very useful for fault diagnosis to extract fault features by using time-frequency analysis techniques. Recent researches of time-frequency analysis methods including Short Time Fourier Transform, Wavelet Transform, Wigner-Ville Distribution, Hilbert-Huang Transform, Local Mean Decomposition, and Local Characteristic-scale Decomposition are introduced. The theories, properties, physical significance and applications, advantages and disadvantages of these methods are analyzed and compared. It is pointed that algorithms improvement and combined applications of time-frequency analysis methods should be researched in the future.

scholarly journals Frequency and Time-Frequency Analysis of Cutting Force and Vibration Signals for Tool Condition Monitoring

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 6400-6410 ◽  
Author(s):  
Juan C. Jauregui ◽  
Juvenal R. Resendiz ◽  
Suresh Thenozhi ◽  
Tibor Szalay ◽  
Adam Jacso ◽  
...  
Keyword(s):  
Cutting Force ◽  
Condition Monitoring ◽  
Frequency Analysis ◽  
Tool Condition Monitoring ◽  
Vibration Signals ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Tool Condition

Time-Frequency Analysis of Transient Vibrations to Improve Turbo-Compressor Start-Up

2008 ◽  
Author(s):  
Juan C. Jauregui ◽  
Oscar Gonzalez ◽  
Eduardo Rubio
Keyword(s):  
Frequency Analysis ◽  
Fourier Transforms ◽  
Transient Vibration ◽  
Vibration Signals ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Start Up ◽  
Rotational Systems ◽  
Turbo Compressor ◽  
Different Sources

Diagnosis of turbo-compressors during start-up is a particularly challenging task. One of the reason is the reduced set of instruments that monitor this procedure. It is cumbersome to adjust lubrication and steam valves while controlling the speed and dynamic stability. In order to get the turbo-compressor out of a high vibration zone, it is important to be able to predict instabilities associated to the start-up process. Thus, it is necessary to have a measurement system with the ability of fault detection, especially at early stages of fault appearance. In this way, the start-up time can be significantly reduced. Although recent developed diagnosis methods use information from different sources and measurements, data structures are not designed to carry predictive information related to the turbo-compressor health. Therefore, it is important to extract early warning signals related to instability conditions. Vibration signals during machine start-up are non-stationary in nature, and conventional techniques, such as Fourier transforms and time series analysis, have difficulties to extract the full features of the vibrations signature. In this paper, the features of start-up vibrations in rotational systems like those found in turbo compressors are investigated by time-frequency analysis, and appropriate analysis of the transient vibration during compressor start-up is presented.

Stationarity test of vibration signals with surrogate data and time–frequency analysis

2016 ◽  
Vol 20 (8) ◽  
pp. 1143-1154
Author(s):  
Zuo-Cai Wang ◽  
Feng Wu ◽  
Wei-Xin Ren
Keyword(s):  
Frequency Analysis ◽  
Surrogate Data ◽  
Transform Method ◽  
Vibration Signals ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Analysis Methods ◽  
Stationarity Test ◽  
Modulated Signals ◽  
Short Time

The stationarity test of vibration signals is critical for the extraction of the signal features. In this article, the surrogate data with various time–frequency analysis methods are proposed for stationary test of vibration signals. The surrogate data are first generated from the Fourier spectrum of the original signal with keeping the magnitude of the spectrum unchanged and replacing its phase by a random sequence. The local and global spectra of the original signal and the surrogate data are then estimated by four time–frequency analysis methods, which are short-time Fourier transform, multitaper spectrograms, wavelet transform, and S-transform methods. The index of nonstationarity is then defined based on the distances between the local and global spectra. Three kinds of synthetic signals, which are stationary signals, frequency-modulated signals, and amplitude-modulated signals, are tested to compare the efficiency of the four time–frequency analysis methods as mentioned. The results show that with a certain observation scale value, the index of nonstationarity based on the short-time Fourier transform or wavelet transform method may fail to test the stationarity of the signal. The parametric studies and sensitivity analysis of the observation scale and noise-level effect are also extensively conducted. The results show that the index of nonstationarity calculated using the multitaper spectrograms’ method is more suitable for stationarity test of frequency-modulated signals, while the index of nonstationarity calculated using the S-transform method is more suitable for stationarity test of amplitude-modulated signals. The results also show that the noise has a significant effect on the stationarity test results. Finally, the stationarity of a real vibration signal measured from a cable is tested, and the results show that the proposed index of nonstationarity can effectively test the stationarity of real vibration signals.

Blasting Vibration Signals Time-Frequency Analysis Based on S-Transform

2012 ◽  
Vol 588-589 ◽  
pp. 2013-2017
Author(s):  
Dong Tao Li ◽  
Jing Long Yan ◽  
Le Zhang
Keyword(s):  
Frequency Analysis ◽  
Vibration Signal ◽  
Transformation Method ◽  
Blasting Vibration ◽  
Time Duration ◽  
Vibration Signals ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
S Transform ◽  
Vibration Wave

Introduced the theory of S-transform, designed simulation experiment and the frequency components distribution versus time was, verified that the S-transformation method is suitable for blasting vibration signal time-frequency analyzed. Applied it to the time-frequency analysis of measured blasting vibration signals at situ, the results show that S-transform has excellent time-frequency representation ability and higher resolution, reveals the detail information of blasting vibration wave changing with time and frequency, and provides a new way for blasting vibration research. Determined the desired delay intervals through comparing the energy of signal and the time duration of the waveform at characteristic frequency between two-hole blasting vibration signals with different delay intervals.

scholarly journals Signal-based diagnostics of the gasoline injection engine

2021 ◽  
Vol 9 (2) ◽  
pp. 083-091
Author(s):  
Marius Singureanu ◽  
Ion Copae
Keyword(s):  
Frequency Analysis ◽  
Experimental Basis ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Analysis Techniques

The paper addresses one of the methods of diagnosing the gasoline injection engine available on the vehicle, namely signal-based diagnostics. The most used algorithms for signal-based diagnostics are highlighted. The possibilities offered on an experimental basis are presented and the tests performed in this respect are presented. Time-frequency analysis techniques are applied to detect defects caused in the case of the Audi A6 car engine.

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