scholarly journals A new method for epoch detection based on the Cohen's class of time frequency representations

2001 ◽  
Vol 8 (8) ◽  
pp. 225-227 ◽  
Author(s):  
J.L. Navarro-Mesa ◽  
E. Lleida-Solano ◽  
A. Moreno-Bilbao
Keyword(s):  
New Method ◽  
Time Frequency ◽  
Cohen's Class

scholarly journals MULTIDIMENSIONAL REPRESENTATION OF AUTOMATIC DOSING DEVICE SIGNALS

2018 ◽  
pp. 26-32
Author(s):  
Denis Borisovich Fedosenkov ◽  
Anna Alekseevna Simikova ◽  
Boris Andreevich Fedosenkov ◽  
Stanislav Matveevich Kulakov
Keyword(s):  
Fourier Transform ◽  
Wigner Distribution ◽  
Low Frequency ◽  
Stationary Flow ◽  
Complex Model ◽  
Signal Spectrum ◽  
One Dimensional ◽  
Time Frequency ◽  
Cohen's Class ◽  
Discrete Values

The article describes the development of a special approach based on using multidimensional wavelet distributions principle to monitor and control the feed dozing processes in the mix preparation unit. As a key component, this approach uses the multidimensional time-frequency Wigner-Ville distribution, which is the part of Cohen's class distributions. The research focuses on signals characterizing mass transfer processes in the form of material flow measuring signals in relevant points of the unit. Wigner-Ville distribution has been shown in time terms as Fourier transform of products of multiplied parts of the signal under consideration for past and future time moments; corresponding distribution for the frequency spectrum is shown as Fourier transform of the products of signal parts for high-frequency and low-frequency fragments of the signal spectrum. It has been noted that when using a complex model of a dozing signal, discrete values (samples) of the latter are considered as its real values. The description of the signal parameters (amplitude, phase, frequency) has been carried out with the help of Hilbert transform. In Cohen's class distributions which represent one-dimensional non-stationary flow signals, the concept of ‘instantaneous frequency’ has been introduced. A graphical explanation for the transformation of a process flow signal from a one-dimensional time domain to a time-frequency 2 D/ 3 D -space is presented. The technology of developing a multidimensional image in the form of Wigner distribution for one-dimensional signals of continuous spiral or screw-type feeders has been examined in detail. There have been considered the features to support Wigner distribution, which allow to guess the presence or absence of time-frequency distribution elements in the interval of signal recording. There has been demonstrated how Wigner distribution can be obtained for a continuous-intermittent feeding signal. It has been concluded that for a certain types of the signal for zero fragments of the latter, non-zero time-frequency elements (i.e. virtual, anomalous ones) appear on the distribution. In addition to Wigner distribution, two other distributions - of Rihachek and Page - are considered. They display the same signal and also contain virtual elements, but in different domains of the time-frequency space. A generalized multidimensional compound signal distribution with a so-called distribution kernel available in it is presented, which includes a correction parameter that allows controlling the intensity of the virtual signal energy.

scholarly journals Cohen’s Class Time-Frequency Distributions for Measurement Signals as a Means of Monitoring Technological Processes

2019 ◽  
Vol 49 (4) ◽  
pp. 252-256
Author(s):  
D. B. Fedosenkov ◽  
A. A. Simikova ◽  
S. M. Kulakov ◽  
B. A. Fedosenkov
Keyword(s):  
Frequency Distributions ◽  
Time Frequency ◽  
Technological Processes ◽  
Class Time ◽  
Cohen's Class

A Correlative Method of Machine Condition Monitoring Based on the Choi-Williams Distribution

2005 ◽  
Vol 293-294 ◽  
pp. 777-784
Author(s):  
Guoan Yang ◽  
Zhenhuan Wu ◽  
Jin Ji Gao
Keyword(s):  
Fault Diagnosis ◽  
Correlation Analysis ◽  
Condition Monitoring ◽  
New Method ◽  
Time Varying ◽  
Machine Condition ◽  
Time Frequency ◽  
Machine Condition Monitoring ◽  
Machine Fault Diagnosis ◽  
Feature Extractor

In this paper, a new method for time-varying machine condition monitoring is proposed. By Choi-Williams distribution, the interference terms produced by the bilinear time-frequency transform are reduced and the fault signal is processed by the correlation analysis of the Choi-Williams distribution. For machine fault diagnosis, both the feature extractor and classifier are combined to make a decision. It is particularly suited to those who are not experts in the field. Satisfactory results have been obtained from a real example and the effectiveness of the proposed method is demonstrated.

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