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An order analysis method to overcome the interference of non-order frequency components

A technology of frequency component and order analysis, applied in spectrum analysis, measurement of ultrasonic/acoustic/infrasonic waves, instruments, etc., can solve problems such as broadband peak interference, misleading feature extraction and state judgment, and inaccurate order analysis results, etc., to achieve The effect of improving accuracy and avoiding the influence of non-order components

Active Publication Date: 2020-04-24
UNIV OF SCI & TECH BEIJING
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

These non-order components cause false broadband peak interference in the order spectrum, which is easy to mislead feature extraction and state judgment, making the order analysis results inaccurate

Method used

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  • An order analysis method to overcome the interference of non-order frequency components
  • An order analysis method to overcome the interference of non-order frequency components
  • An order analysis method to overcome the interference of non-order frequency components

Examples

Experimental program
Comparison scheme
Effect test

Embodiment approach 1

[0084] Embodiment 1: (only exclude fixed frequency non-order components)

[0085] 1) Collect the target rotating machinery equipment signals (including vibration signals, noise signals, electrical signals, etc.) at equal time intervals, and synchronously collect the speed signals of the rotating machinery equipment; or Wigner-Ville distribution and other time-frequency analysis methods) to obtain the time-frequency matrix; 3) carry out proxy testing for each row signal (or part of the row signal) of the time-frequency matrix, that is, perform step S3 to locate the non-order of fixed frequency 4) Using Vold-Kalman filter to reconstruct the non-order component waveform of fixed frequency; 5) Subtracting the reconstructed non-order component signal waveform from the original signal to obtain a signal without interference; 6) Using the collected rotational speed signal, Perform angular domain resampling on the obtained interference-free signal to obtain equiangularly spaced sampli...

Embodiment approach 2

[0086] Embodiment 2: (excluding fixed frequency and time-varying frequency non-order components)

[0087] 1) Collect the target rotating machinery equipment signals (including vibration signals, noise signals, electrical signals, etc.) at equal time intervals, and synchronously collect the speed signals of the rotating machinery equipment; or Wigner-Ville distribution and other time-frequency analysis methods) to obtain the time-frequency matrix; 3) carry out proxy testing for each row signal (or part of the row signal) of the time-frequency matrix, that is, perform step S3 to locate the non-order of fixed frequency Component; 4) Estimate the instantaneous frequency of the time-varying non-order component in the time-frequency matrix, resample the original signal in the angle domain, and convert the non-order component of the time-varying frequency into a non-order component of fixed frequency; 5 ) Repeat the above steps 2 and 3 to locate the non-order components of the time-v...

Embodiment approach 3

[0088] Implementation Mode Three: (Use Ridge Line Extraction to Replace Speed ​​Signal Acquisition)

[0089] 1) Collect target rotating mechanical equipment signals (including vibration signals, noise signals, electrical signals, etc.) at equal time intervals; 2) Perform short-term Fourier transform (or continuous wavelet transform, or Wigner-Ville distribution and other time-frequency analysis on the original signal) method) to obtain a time-frequency matrix; 3) estimate the rotational speed signal of the rotating mechanical equipment from the time-frequency matrix; 4) carry out a proxy test for each row signal (or part of the row signal) of the time-frequency matrix, that is, perform step S3, and fix the position Non-order components of frequency; 5) Estimate the instantaneous frequency of time-varying non-order components in the time-frequency matrix, resample the original signal in the angle domain, and convert the non-order components of time-varying frequencies into non-o...

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Abstract

The invention provides an order analysis method for eliminating the non-order frequency component interference. The method comprises: performing time-frequency analysis on a collected rotating mechanical device signal to obtain a time-frequency distribution matrix; carrying out extraction on each row of the obtained time-frequency distribution matrix to obtain an independent one-dimensional line signal; determining whether the extracted one-dimensional line signal corresponds to a real frequency component by using a Fourier transform proxy test and an instantaneous frequency entropy quantization index; reconstructing the determined real frequency component to obtain a time-domain waveform of a non-order frequency component; subtracting the non-order frequency component from the original rotating mechanical device signal; and carrying out angular domain resampling based on the rotational speed signal of the rotating mechanical device and carrying out fast Fourier transform on a resampled signal to obtain an order spectrum with the non-order frequency component removed. Therefore, the accuracy of the order spectrum analysis can be improved effectively; the influence of the non-ordercomponents is eliminated; and the method has the important theoretical significance and application value in state monitoring and fault diagnosis of the rotating machine.

Description

technical field [0001] The invention relates to the technical field of signal time-varying feature extraction, in particular to an order analysis method for overcoming non-order frequency component interference. Background technique [0002] Signal feature extraction technology is one of the key common technologies in aerospace, energy and power fields. By accurately analyzing the amplitude and frequency structure of vibration, noise and other signals of mechanical equipment, the operating characteristics of complex equipment and the health status of internal components can be revealed. Since many equipments operate under time-varying conditions in practical engineering applications, the collected signals present time-varying frequency structure characteristics. Commonly used spectrum analysis methods do not have the ability to reveal time-varying frequency structures, and time-varying signal feature extraction methods are one of the key difficulties in solving precise stat...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01R23/165G01H17/00
CPCG01H17/00G01R23/165
Inventor 陈小旺冯志鹏
Owner UNIV OF SCI & TECH BEIJING