CN110376437B - Order analysis method for overcoming non-order frequency component interference - Google Patents
Order analysis method for overcoming non-order frequency component interference Download PDFInfo
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- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
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Abstract
The invention provides an order analysis method for overcoming non-order frequency component interference, which comprises the following steps: performing time-frequency analysis on the collected rotating mechanical equipment signals to obtain a time-frequency distribution matrix; extracting each row of the obtained time-frequency distribution matrix into an independent one-dimensional row signal; utilizing Fourier transform agent test and instantaneous frequency entropy quantization index to judge whether the extracted one-dimensional line signal corresponds to a real frequency component; reconstructing the judged real frequency components to obtain time domain waveforms of the non-order frequency components; subtracting the non-order frequency components from the original rotating machine signal; and performing angular domain resampling according to a rotating speed signal of the rotating mechanical equipment, and performing fast Fourier transform on the resampled signal to obtain an order spectrum without non-order frequency components. The method can effectively improve the accuracy of order spectrum analysis, avoids the influence of non-order components, and has important theoretical significance and application value for state monitoring and fault diagnosis of the rotary machine.
Description
Technical Field
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
The signal feature extraction technology is one of key common technologies in the fields of aerospace, energy power and the like. By accurately analyzing the amplitude and frequency structure of signals such as vibration and noise of mechanical equipment, the running characteristics of complex equipment and the health state of internal components can be revealed. In practical engineering application, a plurality of devices operate under time-varying working conditions, and acquired signals have the characteristic of a time-varying frequency structure. The conventional frequency spectrum analysis method does not have the capability of revealing a time-varying frequency structure, and the time-varying signal feature extraction method is one of the key difficulties in solving the precision state identification and monitoring diagnosis in various fields.
The order analysis method expresses a series of time-varying frequency components in proportion to each other as a spectrogram of a multiple relation of a certain reference component, namely an order spectrum. Wherein each order peak corresponds to a time-varying frequency component in an order multiple relationship with a reference frequency, thereby reducing the time-frequency two-dimensional information to order information. The order analysis method is suitable for analyzing time-varying frequency structure signals, and particularly attracts great attention in the field of rotary machine signal analysis, because signal components in rotary machines are mostly in a multiple relation with rotor frequency conversion, and time-varying signal structures can be intuitively and simply identified through order spectrums. The calculation order analysis method does not need extra hardware to control equal-angle sampling, but converts signals sampled at equal time intervals into equal-angle sampling signals through a resampling method, so that time-varying frequency components related to the rotating speed in the obtained equal-angle sampling signals are converted into fixed frequency orders to perform order spectrum analysis. The Chinese invention patent 201410253655.X discloses a wind turbine generator gear fault diagnosis scheme based on equal-angle resampling order analysis. Firstly, solving a time-frequency spectrogram of an acquired vibration signal; secondly, fitting an instantaneous frequency curve according to a ridge line extraction algorithm to generate an equiangular key phase time scale; further carrying out equal-angle interpolation resampling on the vibration signal based on the key phase time scale, and carrying out fast Fourier transformation on the resampled equal-angle signal to obtain an order spectrum; and finally, determining the components and the fault degree of the wind turbine generator set with faults by analyzing the characteristic order structure and the amplitude value in the order spectrum.
The method is characterized in that time-varying frequency structure information can be analyzed visually by using a spectrogram, but interference caused by non-order frequency components cannot be avoided, so that false order components exist in the obtained order spectrum besides noise interference. The frequency components which are in a multiple relation with the instantaneous frequency curve obtained by ridge line extraction are defined as order components, and the rest frequency components are non-order components. In the actual implementation process, only the order components are converted into angular domain stationary signals in the resampling process, namely, clear spectral peaks can be represented in a spectrogram; non-order components, such as resonance frequency components in the vibration signal, also exist in the actual signal, and after resampling, the non-order components still appear as time-varying components in an angular domain, and present a broadband distribution in an order spectrum. These non-order components cause false broadband peak interference in the order spectrum, which is prone to misleading feature extraction and state judgment, making the order analysis result inaccurate.
Disclosure of Invention
The invention aims to solve the technical problem of providing an order analysis method for overcoming non-order frequency component interference, which is used for identifying, positioning, reconstructing and removing the non-order components before angular domain resampling and overcoming order spectrum interference caused by the non-order components, thereby obtaining more accurate signal time-varying frequency characteristic representation and being suitable for the fields of state monitoring fault diagnosis of rotary mechanical equipment and the like.
To solve the above technical problem, an embodiment of the present invention provides an order analysis method for overcoming non-order frequency component interference, including the following steps:
s1, carrying out time-frequency analysis on the collected rotating mechanical equipment signals to obtain a time-frequency distribution matrix;
s2, extracting each row of the obtained time-frequency distribution matrix into an independent one-dimensional row signal;
s3, judging whether the extracted one-dimensional line signal corresponds to a real frequency component or not by utilizing Fourier transform proxy test and an instantaneous frequency entropy quantization index;
s4, reconstructing the judged real frequency components to obtain time domain waveforms of the non-order frequency components;
s5, subtracting the non-order frequency component from the original rotating machinery equipment signal;
and S6, performing angular domain resampling according to the rotating speed signal of the rotating mechanical equipment, and performing fast Fourier transform on the resampled signal to obtain an order spectrum without non-order frequency components.
Preferably, in step S1, the rotating mechanical device signal includes a vibration signal, a noise signal and an electrical signal;
the step of performing time-frequency analysis on the collected rotating mechanical equipment signals comprises the following steps:
and carrying out short-time Fourier transform, continuous wavelet transform or Wigner-Ville distribution on the collected signals of the rotating mechanical equipment.
Preferably, the step S3 includes:
calculating the instantaneous frequency of each one-dimensional row signal;
calculating the instantaneous frequency entropy of each one-dimensional row signal;
calculating a preset number of Fourier transform proxy signals of each one-dimensional row signal;
calculating the instantaneous frequency entropy of the obtained Fourier transform proxy signal;
and for each one-dimensional row signal, judging the magnitude relation between the instantaneous frequency entropy of the one-dimensional row signal and the instantaneous frequency entropies of the preset number of Fourier transform proxy signals, and if more than 95% of the Fourier transform proxy signals have the instantaneous frequency entropy larger than that of the original signals, judging that the one-dimensional row signal corresponds to the real frequency component.
Preferably, in step S4, the determined real frequency component is reconstructed by using a Vold-Kalman filter, a band-pass filter or a ridge reconstruction method.
Preferably, the non-order frequency components include fixed frequency components and time-varying frequency components;
for the fixed frequency component, directly performing steps S1-S6;
for the time-varying frequency component, angular domain resampling is performed once to convert the time-varying frequency component into a fixed frequency component, and then steps S1-S6 are performed.
Preferably, the step S6 includes:
performing angular domain resampling according to a rotating speed signal of the rotating mechanical equipment to obtain an equiangular interval signal;
and carrying out fast Fourier transform on the obtained equiangular interval signals to obtain an order spectrum with non-order frequency components removed.
Preferably, in step S6, the rotation speed signal of the rotating mechanical device is obtained by:
collecting rotating speed signals at equal time intervals;
or, the rotating speed signal is estimated from the time-frequency distribution matrix by using ridge line extraction.
The technical scheme of the invention has the following beneficial effects:
the method can effectively improve the accuracy of order spectrum analysis, avoids the influence of non-order components, and has important theoretical significance and application value for state monitoring and fault diagnosis of the rotary machine.
Drawings
FIG. 1 is a flow chart of an order analysis method provided by an embodiment of the present invention;
FIG. 2 is a schematic flow chart of an exemplary order analysis method;
FIG. 3 is a time-frequency distribution of a short-time Fourier transform of a radial displacement signal of a certain turbine rotor;
FIG. 4 is a comparison graph of the order spectrum for overcoming the interference of non-order components proposed by the method of the present invention and the effect of the conventional calculation order spectrum when analyzing the radial displacement signal of the turbine rotor.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
An embodiment of the present invention provides an order analysis method for overcoming non-order frequency component interference, as shown in fig. 1, the method including the steps of:
s1, carrying out time-frequency analysis on the collected rotating mechanical equipment signals to obtain a time-frequency distribution matrix;
s2, extracting each row of the obtained time-frequency distribution matrix into an independent one-dimensional row signal;
s3, judging whether the extracted one-dimensional line signal corresponds to a real frequency component or not by utilizing Fourier transform proxy test and an instantaneous frequency entropy quantization index;
s4, reconstructing the judged real frequency components to obtain time domain waveforms of the non-order frequency components;
s5, subtracting the non-order frequency component from the original rotating machinery equipment signal;
and S6, performing angular domain resampling according to the rotating speed signal of the rotating mechanical equipment, and performing fast Fourier transform on the resampled signal to obtain an order spectrum without non-order frequency components.
The method can effectively improve the accuracy of order spectrum analysis, avoids the influence of non-order components, and has important theoretical significance and application value for state monitoring and fault diagnosis of the rotary machine.
Preferably, in step S1, the rotating mechanical device signal includes a vibration signal, a noise signal, and an electrical signal;
the step of performing time-frequency analysis on the collected rotating mechanical equipment signals comprises the following steps:
and carrying out short-time Fourier transform, continuous wavelet transform or Wigner-Ville distribution on the collected signals of the rotating mechanical equipment.
Preferably, step S3 includes:
calculating the instantaneous frequency of each one-dimensional row signal;
calculating the instantaneous frequency entropy of each one-dimensional row signal;
calculating a preset number of Fourier transform proxy signals of each one-dimensional row signal;
calculating the instantaneous frequency entropy of the obtained Fourier transform proxy signal;
and for each one-dimensional row signal, judging the magnitude relation between the instantaneous frequency entropy of the one-dimensional row signal and the instantaneous frequency entropies of the preset number of Fourier transform proxy signals, and if more than 95% of the Fourier transform proxy signals have the instantaneous frequency entropy larger than that of the original signals, judging that the one-dimensional row signal corresponds to the real frequency component.
Preferably, in step S4, the determined real frequency component is reconstructed by using a Vold-Kalman filter, a band-pass filter or a ridge reconstruction method.
Preferably, the non-order frequency components include fixed frequency components and time-varying frequency components;
for the fixed frequency component, directly performing steps S1-S6;
for the time-varying frequency component, angular domain resampling is performed once to convert the time-varying frequency component into a fixed frequency component, and then steps S1-S6 are performed.
Preferably, step S6 includes:
performing angular domain resampling according to a rotating speed signal of the rotating mechanical equipment to obtain an equiangular interval signal;
and carrying out fast Fourier transform on the obtained equiangular interval signals to obtain an order spectrum with non-order frequency components removed.
Preferably, in step S6, the rotation speed signal of the rotating mechanical device is obtained by:
collecting rotating speed signals at equal time intervals;
or, the rotating speed signal is estimated from the time-frequency distribution matrix by using ridge line extraction.
Fig. 2 is a schematic flow chart of an order analysis method according to an embodiment of the present invention, in which the order analysis method for automatically detecting, reconstructing, and removing non-order frequency components by using a proxy test and filtering method includes:
performing time-frequency analysis on the original signal x (t) to be analyzed to obtain its time-frequency distribution TFRx(t, f), a real matrix of K N;
combining the matrices TFRxEach line of (t, f) is extracted independently to obtain K one-dimensional line signals y of Nx 1k(t)，k＝1,2,…K；
For each one-dimensional row signal yk(t) calculating the instantaneous frequency IF thereofyk(t) the calculation formula is:
φyk(t)＝arctan{H[yk(t)]/yk(t)}，IFyk(t)＝(1/2π)[dφyk(t)/dt]wherein H (-) refers to a Hilbert transform;
for each one-dimensional row signal yk(t), further calculating instantaneous frequency entropy, wherein the calculation formula is as follows:
for each one-dimensional row signal yk(t), calculating NsA Fourier transform proxy signal yk (n)(t)，n＝1,2,…NsThe calculation formula is as follows:
repeating the steps, and calculating the instantaneous frequency entropy of each agent signal;
for each one-dimensional row signal yk(t) determining its instantaneous frequency entropy and its NsThe magnitude relation of the instantaneous frequency entropy of each proxy signal exceeds 95 percent ifIf the proxy signal has a larger instantaneous frequency entropy than the original signal, the one-dimensional row signal is judged to correspond to the real frequency component, i.e. the fixed frequency component yk(t) true presence;
reconstructing all detected real frequency components y according to Vold-Kalman filteringk(t) a time domain waveform;
subtracting the reconstructed fixed frequency components from the original signal;
for the non-order time-varying frequency components, firstly carrying out one-step angular domain resampling to convert the time-varying frequency components into fixed frequency components, and then repeating the steps;
according to a rotation speed signal measured by hardware or extracted by a ridge line, carrying out angular domain resampling on the signal from which all non-order frequency components (including fixed frequency components and time-varying frequency components) are removed to obtain an equiangular interval signal;
and carrying out fast Fourier transform on the obtained signals to obtain an order spectrum for overcoming the interference of non-order frequency components.
Fig. 3 is a time-frequency distribution of short-time fourier transform of a radial displacement signal of a certain hydraulic turbine rotor, and fig. 4 is a comparison graph of an order spectrum for overcoming interference of non-order components proposed by the method of the present invention and an effect of a conventional calculation order spectrum when analyzing the radial displacement signal of the hydraulic turbine rotor. It can be seen that the order spectrum for overcoming the interference of non-order components proposed by the method of the present invention can avoid the interference of the natural frequency components of the water turbine.
The process of the invention is illustrated in detail below by means of four specific examples.
The first implementation mode comprises the following steps: (excluding only fixed frequency non-order components)
1) Collecting target rotating mechanical equipment signals (including vibration signals, noise signals, electric signals and the like) at equal time intervals, and synchronously collecting rotating speed signals of the rotating mechanical equipment; 2) carrying out short-time Fourier transform (or continuous wavelet transform, or Wigner-Ville distribution and other time-frequency analysis methods) on the original signal to obtain a time-frequency matrix; 3) performing a proxy test on each row signal (or partial row signal) of the time-frequency matrix, that is, performing step S3 to locate the non-order component with fixed frequency; 4) reconstructing a non-order component waveform with fixed frequency by using Vold-Kalman filtering; 5) subtracting the reconstructed non-order component signal waveform from the original signal to obtain an interference-free signal; 6) performing angular domain resampling on the obtained interference-free signal by using the collected rotating speed signal to obtain an equal-angle interval sampling signal; 7) and carrying out fast Fourier transformation on the obtained equal-angle interval sampling signals to obtain an order spectrum without non-order component interference.
The second embodiment: (excluding fixed frequency and time varying frequency non-order components)
1) Collecting target rotating mechanical equipment signals (including vibration signals, noise signals, electric signals and the like) at equal time intervals, and synchronously collecting rotating speed signals of the rotating mechanical equipment; 2) carrying out short-time Fourier transform (or continuous wavelet transform, or Wigner-Ville distribution and other time-frequency analysis methods) on the original signal to obtain a time-frequency matrix; 3) performing a proxy test on each row signal (or partial row signal) of the time-frequency matrix, that is, performing step S3 to locate the non-order component with fixed frequency; 4) estimating the instantaneous frequency of the time-varying non-order component in the time-frequency matrix, performing angular domain resampling on the original signal, and converting the non-order component of the time-varying frequency into a non-order component of fixed frequency; 5) repeating the steps 2 and 3, and positioning the non-order component of the time-varying frequency; 6) reconstructing a fixed-frequency non-order component waveform and a time-varying frequency non-order component waveform by using Vold-Kalman filtering; 7) subtracting the reconstructed non-order component signal waveform from the original signal to obtain an interference-free signal; 8) performing angular domain resampling on the obtained interference-free signal by using the collected rotating speed signal to obtain an equal-angle interval sampling signal; 9) and carrying out fast Fourier transformation on the obtained equal-angle interval sampling signals to obtain an order spectrum without non-order component interference.
The third embodiment is as follows: (using ridge extraction instead of speed signal acquisition)
1) Collecting target rotating mechanical equipment signals (including vibration signals, noise signals, electric signals and the like) at equal time intervals; 2) carrying out short-time Fourier transform (or continuous wavelet transform, or Wigner-Ville distribution and other time-frequency analysis methods) on the original signal to obtain a time-frequency matrix; 3) estimating a rotating speed signal of the rotating mechanical equipment from the time-frequency matrix; 4) performing a proxy test on each row signal (or partial row signal) of the time-frequency matrix, that is, performing step S3 to locate the non-order component with fixed frequency; 5) estimating the instantaneous frequency of the time-varying non-order component in the time-frequency matrix, performing angular domain resampling on the original signal, and converting the non-order component of the time-varying frequency into a non-order component of fixed frequency; 6) repeating the steps 2 and 4, and positioning the non-order component of the time-varying frequency; 7) reconstructing a fixed-frequency non-order component waveform and a time-varying frequency non-order component waveform by using Vold-Kalman filtering; 8) subtracting the reconstructed non-order component signal waveform from the original signal to obtain an interference-free signal; 9) carrying out angular domain resampling on the obtained interference-free signal by using the estimated rotating speed signal to obtain an equal-angle interval sampling signal; 10) and carrying out fast Fourier transformation on the obtained equal-angle interval sampling signals to obtain an order spectrum without non-order component interference.
The fourth embodiment: (non-order component reconstruction by band-pass filter or ridge reconstruction)
1) Collecting target rotating mechanical equipment signals (including vibration signals, noise signals, electric signals and the like) at equal time intervals; 2) carrying out short-time Fourier transform (or continuous wavelet transform, or Wigner-Ville distribution and other time-frequency analysis methods) on the original signal to obtain a time-frequency matrix; 3) estimating a rotating speed signal of the rotating mechanical equipment from the time-frequency matrix; 4) performing a proxy test on each row signal (or partial row signal) of the time-frequency matrix, that is, performing step S3 to locate the non-order component with fixed frequency; 5) obtaining a waveform of a non-order component of a fixed frequency by using a band-pass filter; 6) subtracting the reconstructed fixed-frequency non-order component signal waveform from the original signal; 7) estimating the instantaneous frequency of the time-varying non-order component in the time-frequency matrix, performing angular domain resampling on the original signal, and converting the non-order component of the time-varying frequency into a non-order component of fixed frequency; 6) repeating the steps 2, 4 and 5 to obtain a non-order component waveform with fixed frequency, resampling to obtain a corresponding time-varying frequency non-order component waveform, and subtracting the obtained time-varying frequency non-order component waveform from an original signal to obtain an interference-free signal; 7) carrying out angular domain resampling on the obtained interference-free signal by using the estimated rotating speed signal to obtain an equal-angle interval sampling signal; 8) and carrying out fast Fourier transformation on the obtained equal-angle interval sampling signals to obtain an order spectrum without non-order component interference.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (6)
1. An order analysis method for overcoming non-order frequency component interference, comprising the steps of:
s1, carrying out time-frequency analysis on the collected rotating mechanical equipment signals to obtain a time-frequency distribution matrix;
s2, extracting each row of the obtained time-frequency distribution matrix into an independent one-dimensional row signal;
s3, judging whether the extracted one-dimensional line signal corresponds to a real frequency component or not by utilizing Fourier transform proxy test and an instantaneous frequency entropy quantization index;
s4, reconstructing the judged real frequency components to obtain time domain waveforms of the non-order frequency components;
s5, subtracting the non-order frequency component from the original rotating machinery equipment signal;
s6, performing angular domain resampling according to a rotating speed signal of the rotating mechanical equipment, and performing fast Fourier transform on the resampled signal to obtain an order spectrum without non-order frequency components;
the step S3 includes:
calculating the instantaneous frequency of each one-dimensional row signal;
calculating the instantaneous frequency entropy of each one-dimensional row signal;
calculating a preset number of Fourier transform proxy signals of each one-dimensional row signal;
calculating the instantaneous frequency entropy of the obtained Fourier transform proxy signal;
and for each one-dimensional row signal, judging the magnitude relation between the instantaneous frequency entropy of the one-dimensional row signal and the instantaneous frequency entropies of the preset number of Fourier transform proxy signals, and if more than 95% of the Fourier transform proxy signals have the instantaneous frequency entropy larger than that of the original signals, judging that the one-dimensional row signal corresponds to the real frequency component.
2. The order analysis method of claim 1, wherein in step S1, the rotating machine signal includes a vibration signal, a noise signal, and an electrical signal;
the step of performing time-frequency analysis on the collected rotating mechanical equipment signals comprises the following steps:
and carrying out short-time Fourier transform, continuous wavelet transform or Wigner-Ville distribution on the collected signals of the rotating mechanical equipment.
3. The order analysis method as claimed in claim 1, wherein in step S4, the determined real frequency components are reconstructed using a Vold-Kalman filter, a band-pass filter or a ridge reconstruction method.
4. The order analysis method of claim 1, wherein the non-order frequency components include fixed frequency components and time-varying frequency components;
for the fixed frequency component, directly performing steps S1-S6;
for the time-varying frequency component, angular domain resampling is performed once to convert the time-varying frequency component into a fixed frequency component, and then steps S1-S6 are performed.
5. The order analysis method according to claim 1, wherein the step S6 includes:
performing angular domain resampling according to a rotating speed signal of the rotating mechanical equipment to obtain an equiangular interval signal;
and carrying out fast Fourier transform on the obtained equiangular interval signals to obtain an order spectrum with non-order frequency components removed.
6. The order analysis method according to any one of claims 1 to 5, wherein in step S6, the rotation speed signal of the rotating mechanical device is obtained by:
collecting rotating speed signals at equal time intervals;
or, the rotating speed signal is estimated from the time-frequency distribution matrix by using ridge line extraction.
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