A Time-Frequency Distribution for Analysis of Signals with Transient Components and Its Application to Vibration Analysis

1999 ◽  
Vol 121 (3) ◽  
pp. 328-333 ◽  
Author(s):  
G. T. Zheng ◽  
P. D. McFadden
Keyword(s):  
Frequency Distribution ◽  
Wigner Distribution ◽  
Vibration Signals ◽  
Frequency Distributions ◽  
Time Frequency ◽  
Frequency Domains ◽  
Harmonic Components ◽  
Cohen Class ◽  
New Distribution ◽  
Time Frequency Distribution

Bilinear time-frequency distributions, which provide simultaneous high resolution in both time and frequency domains, offer advantages for the analysis of vibration signals where the harmonic components and sidebands may be closely spaced. However, the Choi-Williams exponential distribution is found to be unsuitable, and aliasing produced by distributions of the Cohen class also causes problems. An aliasfree exponential time-frequency distribution is introduced, which combines features of distributions of the Cohen class and the generalized Wigner distribution. The new distribution is shown to be well suited to the analysis of signals with transient components.

scholarly journals A Novel Fractional Filter Design and Cross-Term Elimination in Wigner Distribution

2015 ◽  
Vol 2015 ◽  
pp. 1-12
Author(s):  
Jiexiao Yu ◽  
Kaihua Liu ◽  
Liang Zhang ◽  
Peng Luo
Keyword(s):  
Frequency Distribution ◽  
Filter Design ◽  
Wigner Distribution ◽  
Piecewise Linear ◽  
Support Vector ◽  
Svm Classifier ◽  
Cross Term ◽  
Time Frequency ◽  
Fractional Filter ◽  
Time Frequency Distribution

The second and the third sentences of the abstract are changed and the shorter abstract is given as follows. To recover the nonstationary signal in complicated noise environment without distortion, a novel general design of fractional filter is proposed and applied to eliminate the Wigner cross-term. A time-frequency binary image is obtained from the time-frequency distribution of the observed signal and the optimal separating lines are determined by the support vector machine (SVM) classifier where the image boundary extraction algorithms are used to construct the training set of SVM. After that, the parameters and transfer function of filter can be determined by the parameters of the separating lines directly in the case of linear separability or line segments after the piecewise linear fitting of the separating curves in the case of nonlinear separability. Without any prior knowledge of signal and noise, this method can meet the reliability and universality simultaneously for filter design and realize the global optimization of filter parameters by machine learning even in the case of strong coupling between signal and noise. Furthermore, it could completely eliminate the cross-term in Wigner-Ville distribution (WVD) and the time-frequency distribution we get in the end has high resolution and good readability even when autoterms and cross-terms overlap. Simulation results verified the efficiency of this method.

TRANSIENT TIME-FREQUENCY DISTRIBUTION BASED ON MONO-COMPONENT DECOMPOSITIONS

2013 ◽  
Vol 11 (03) ◽  
pp. 1350022 ◽  
Author(s):  
PEI DANG ◽  
TAO QIAN ◽  
YUAN YUAN GUO
Keyword(s):  
Frequency Distribution ◽  
Instantaneous Frequency ◽  
Characteristic Property ◽  
Transient Time ◽  
Frequency Distributions ◽  
Time Frequency ◽  
Fourier Frequency ◽  
New Type ◽  
Negative Time ◽  
Time Frequency Distribution

In this paper we propose a new type of non-negative time-frequency distribution associated with mono-components in both the non-periodic and periodic cases, called transient time-frequency distribution (TTFD), and study its properties. The TTFD of a mono-component signal can be obtained directly through its analytic instantaneous frequency. The characteristic property of TTFD is its complete concentration along the analytic instantaneous frequency graph. For multi-components there are induced time-frequency distributions called composing transient time-frequency distribution (CTTFD). Each CTTFD is defined as the superposition of the TTFDs of the composing intrinsic mono-components in a suitable mono-components decomposition of the targeted multi-component. In studying the properties of TTFD and CTTFD the relations between the Fourier frequency and analytic instantaneous frequency are examined.

Design of a Time-Frequency Distribution for Vibration Monitoring under Corrosions in the Pipe

2006 ◽  
Vol 321-323 ◽  
pp. 1257-1261
Author(s):  
Gi Young Park ◽  
C.K. Lee ◽  
Jung Taek Kim ◽  
K.C. Kwon ◽  
Sang J. Lee
Keyword(s):  
Frequency Distribution ◽  
Vibration Monitoring ◽  
Vibration Signals ◽  
Plant Operation ◽  
Time Frequency ◽  
Analysis Techniques ◽  
Cross Terms ◽  
Conventional Methods ◽  
Time Frequency Distribution

To monitor the wear and degradation on a pipe by corrosion during a plant operation, the vibration signals were measured by an accelerometer and analyzed by several analysis techniques. From the conventional methods, it was difficult to identify the wear and degradation on the pipe. And hence, the time-frequency distribution (TFD) and the adaptive cone-kernel distribution (ACKD) devised for reducing the interfering cross-terms are applied to the acquired data. They can provide the distinguishing peak patterns between the normal and corrosion signals.

Time–Frequency Distribution Analyses of Ku-Band Radar Doppler Echo Signals

Frequenz ◽  
2015 ◽  
Vol 69 (3-4) ◽  
Author(s):  
Dimitrije Bujaković ◽  
Milenko Andrić ◽  
Boban Bondžulić ◽  
Srđan Mitrović ◽  
Slobodan Simić
Keyword(s):  
Frequency Distribution ◽  
Doppler Frequency ◽  
Energy Concentration ◽  
Frequency Distributions ◽  
Signal Energy ◽  
Time Frequency ◽  
Window Functions ◽  
Radar Echo ◽  
Time Frequency Distribution ◽  
Ku Band

AbstractReal radar echo signals of a pedestrian, vehicle and group of helicopters are analyzed in order to maximize signal energy around central Doppler frequency in time–frequency plane. An optimization, preserving this concentration, is suggested based on three well-known concentration measures. Various window functions and time–frequency distributions were optimization inputs. Conducted experiments on an analytic and three real signals have shown that energy concentration significantly depends on used time–frequency distribution and window function, for all three used criteria.

Signal separation and correction with multiple Doppler acoustic sources for wayside fault diagnosis of train bearings

2016 ◽  
Vol 231 (14) ◽  
pp. 2664-2680
Author(s):  
Shangbin Zhang ◽  
Qingbo He ◽  
Haibin Zhang ◽  
Kesai Ouyang ◽  
Fanrang Kong
Keyword(s):  
Time Series ◽  
Fault Diagnosis ◽  
Frequency Distribution ◽  
Signal Separation ◽  
Frequency Distributions ◽  
Time Frequency ◽  
S Transform ◽  
Generalized S Transform ◽  
Original Time ◽  
Time Frequency Distribution

The extraction of single train signal is necessary in wayside fault diagnosis because the acoustic signal acquired by a microphone is composed of multiple train bearing signals and noises. However, the Doppler distortion in the signal acquired by a microphone effectively hinders the signal separation and fault diagnosis. To address this issue, we propose a novel method based on the generalized S-transform, morphological filtering, and time–frequency amplitude matching-based resampling time series for multiple-Doppler-acoustic-source signal separation and correction. First, the original time–frequency distribution is constructed by applying generalized S-transform to the raw signal acquired by a microphone. Based on a morphological filter, several time–frequency distributions corresponding to different single source Doppler fault signals are extracted from the original time–frequency distribution. Subsequently, the time–frequency distributions are reverted to time signals by inverse generalized S-transform. Then, a resampling time series is built by time–frequency amplitude matching to obtain the correct signals without Doppler distortion. Finally, the bearing fault is diagnosed by the envelope spectrum of the correction signal. The effectiveness of this method is verified by simulated and practical signals.

Sign in / Sign up

Export Citation Format

Share Document