CN115683632A - Method, device, equipment and medium for acquiring fault signal of gearbox bearing - Google Patents
Method, device, equipment and medium for acquiring fault signal of gearbox bearing Download PDFInfo
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Abstract
The application discloses a method, a device, equipment and a medium for acquiring fault signals of a bearing of a gearbox. The method comprises the following steps: sampling an original vibration signal of a target gearbox based on a preset sampling frequency and a preset sampling duration to obtain a first candidate signal; carrying out time domain delay on the first candidate signal to obtain a second candidate signal; the target gearbox is a multi-stage gearbox; determining a cross-correlation parameter between the first candidate signal and the second candidate signal, and an autocorrelation parameter of the first candidate signal; and determining a periodic noise signal in the first candidate signal according to the cross-correlation parameter and the self-correlation parameter, removing the periodic noise signal in the first candidate signal, and obtaining a random fault signal of the target gearbox bearing. Therefore, the extraction effect of the fault signal of the bearing of the multistage gearbox is improved.
Description
Technical Field
The present disclosure relates generally to the field of computer technology, and more particularly, to a method, apparatus, device, and medium for acquiring a fault signal of a bearing of a gearbox.
Background
As a key component of the movement of a rotary machine, a rolling bearing is prone to faults such as bearing peeling, poor lubrication, pitting and the like in a multi-stage gear box. In the diagnosis of the bearing fault of the multistage gearbox, a diagnosis method of mounting an acceleration sensor outside the gearbox and measuring a shell vibration signal through the sensor is generally adopted.
At present, because the installation position of a sensor measuring point is generally far away from a bearing in a box body, a fault vibration signal is transmitted to a weak signal at the sensor, and therefore the noise removal processing needs to be carried out on a shell vibration signal measured by an acceleration sensor, so that the fault characteristic of the bearing is extracted, and the fault of the bearing is diagnosed according to the fault characteristic of the bearing.
However, the method for denoising and extracting the bearing vibration signal and the bearing fault in the prior art still has the technical problem of poor fault feature extraction effect.
Disclosure of Invention
In view of the above defects or shortcomings in the prior art, it is desirable to provide a method, a device, equipment and a medium for acquiring a fault signal of a bearing of a gearbox, wherein a noise signal of a vibration signal of a gearbox housing except for the fault signal of the bearing is determined under a frequency domain condition, and the fault signal of the bearing is extracted under a time domain condition, so that the signal-to-noise ratio of the fault signal of the bearing can be improved, and the extraction effect of the fault signal of the bearing of the multi-stage gearbox is improved.
In a first aspect, a method for acquiring a fault signal of a bearing of a gearbox is provided, and the method comprises the following steps:
sampling an original vibration signal of a target gearbox based on a preset sampling frequency and a preset sampling duration to obtain a first candidate signal; carrying out time domain delay on the first candidate signal to obtain a second candidate signal; the target gearbox is a multi-stage gearbox;
determining a cross-correlation parameter between the first candidate signal and the second candidate signal, and an autocorrelation parameter of the first candidate signal; the cross-correlation parameter is used for representing the frequency domain correlation of the first candidate signal and the second candidate signal, and the autocorrelation parameter is used for representing the frequency domain characteristic of the first candidate signal;
and determining a periodic noise signal in the first candidate signal according to the cross-correlation parameter and the self-correlation parameter, removing the periodic noise signal in the first candidate signal, and obtaining a random fault signal of the target gearbox bearing.
In the method, firstly, the vibration signal of the whole shell of the gearbox is obtained through the acceleration sensor arranged outside the multistage gearbox body, the frequency domain noise signal of part of the shell vibration signal is determined by utilizing the frequency domain filter function constructed under the condition of the frequency domain, the frequency domain noise signal is converted into the time domain periodic noise signal to be subjected to de-noising processing, and finally the fault signal of the bearing inside the gearbox (namely the random fault signal of the target gearbox bearing) is obtained. Noise signals of the vibration signals of the gearbox shell except for the bearing fault signals are determined under the condition of a frequency domain, the bearing fault signals are extracted under the condition of a time domain, the signal to noise ratio of the bearing fault signals can be improved, and therefore the extraction effect of the bearing fault signals of the multistage gearbox is improved.
In a second aspect, there is provided a fault signal acquiring apparatus of a bearing of a gear box, the apparatus including:
the acquisition unit is used for sampling an original vibration signal of the target gearbox based on preset sampling frequency and sampling duration to obtain a first candidate signal; carrying out time domain delay on the first candidate signal to obtain a second candidate signal; the target gearbox is a multi-stage gearbox;
a determining unit that determines a cross-correlation parameter between the first candidate signal and the second candidate signal, and an autocorrelation parameter of the first candidate signal; the cross-correlation parameter is used for representing the frequency domain correlation of the first candidate signal and the second candidate signal, and the autocorrelation parameter is used for representing the frequency domain characteristic of the first candidate signal;
and the processing unit is used for determining a periodic noise signal in the first candidate signal according to the cross-correlation parameter and the autocorrelation parameter, removing the periodic noise signal in the first candidate signal and obtaining a random fault signal of the target gearbox.
In a third aspect, a computer device is provided, which comprises a memory, a processor and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the computer device implements the method of any one of the first aspect.
In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, which program, when executed by a processor, performs the method of any of the first aspect above.
In a fifth aspect, there is provided a computer program product comprising instructions that, when executed, perform the method of any of the first aspects above.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments made with reference to the following drawings:
FIG. 1 is a system architecture of an embodiment of the present application;
FIG. 2 is a schematic flow chart of a fault signal acquisition method for a gearbox bearing according to an embodiment of the present application;
FIG. 3 is a waveform diagram and a spectrum diagram of a first candidate signal according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a frequency domain filter function according to an embodiment of the present application;
FIG. 5 is a schematic frequency spectrum diagram of another frequency-domain filter function according to an embodiment of the present application;
fig. 6 is a schematic waveform diagram and a schematic frequency spectrum diagram of a first candidate signal after filtering according to an embodiment of the present application;
FIG. 7 is a schematic flowchart of a gearbox bearing fault signal acquisition based on a TF frequency domain filter function according to an embodiment of the present application;
FIG. 8 is a block schematic diagram of a fault signal acquisition device for a gearbox bearing according to an embodiment of the present application;
fig. 9 is a block diagram of a computer device according to an embodiment of the present disclosure.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not to be construed as limiting the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
It should be noted that, in the present application, the embodiments and features of the embodiments may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 is a system architecture of an embodiment of the present application. Referring to fig. 1, the system comprises an acceleration sensor 10, a bearing 20, a gearbox 30 and a computer device 40. The bearing 20 is located inside the gearbox 30 housing. When the bearing 20 is in a rotating state, power can be provided for the operation of the gear box 30, and the gears inside the gear box 30 are driven to rotate. The acceleration sensor 10 is located outside the housing of the gearbox 30. When the gearbox 30 is running, the acceleration sensor 10 may measure a housing vibration signal of the gearbox 30. The computer device 30 may provide some calculations related to the bearing 20, for example, may obtain a housing vibration signal of the gearbox 30 measured by the acceleration sensor 10 and extract a fault signal of the bearing 20 based on the housing vibration signal.
In a specific implementation, the gears inside the gear box 30 are located outside the bearings 20. The bearing 20 serves as a support member for the rotating shaft inside the gear case 30, and the operation of the bearing 20 serves to transmit energy for the rotation of the gears inside the gear case 30. The rotation of the gears is used to power the operation of the gearbox 30. The acceleration sensor 10 can only be installed at 1-2 observation points outside the gear box 30, and the observation points are located far from the bearing 20. After the computer device 40 acquires the housing vibration signal of the gear box 30, a failure signal of the bearing 20 is acquired.
It should be noted that the housing vibration signal of the gear box 30 includes a vibration signal of the bearing 20. The vibration signal of the bearing 20 includes a normal signal and a fault signal of the bearing 20. The noise signal filtered by the computer device 40 may be all signals except a fault signal of the bearing 20 in the case vibration signal of the gear box 30.
The bearing 20 is a bearing component for driving the gear box to rotate, and the main type of the bearing is a rolling bearing, specifically, the bearing may be a cylindrical roller bearing, a deep groove ball bearing, or a full roller cylindrical roller bearing. The gearbox 30 serves as an important mechanical component of the generator set, and may be a multi-stage gearbox in particular.
At present, the method mainly comprises the steps of carrying out denoising and extraction of a gearbox shell vibration signal measured by an acceleration sensor arranged outside a gearbox shell by methods of band-pass filtering envelope analysis, spectral kurtosis, cepstrum editing, time domain synchronous averaging and the like. However, because the coverage range of the rotation frequency and the meshing frequency in the shell vibration signal is large and discrete, the conventional band-pass filtering envelope analysis method cannot effectively remove the rotation frequency and the meshing frequency of the gearbox with high amplitude, and the signal-to-noise ratio of the obtained signal is high, so that the effect of extracting the fault signal of the bearing of the gearbox in the prior art is poor.
Based on the method, the device, the equipment and the medium for acquiring the fault signal of the bearing of the gearbox, the noise signal of the vibration signal of the shell of the gearbox except the fault signal of the bearing is determined under the condition of a frequency domain, and the fault signal of the bearing is extracted under the condition of a time domain, so that the signal-to-noise ratio of the fault signal of the bearing can be improved, and the extraction effect of the fault signal of the bearing of the multistage gearbox is improved.
Fig. 2 is a schematic flow chart of a method for acquiring a fault signal of a gearbox bearing according to an embodiment of the present application, where an execution subject of the method may be the computer device 40 described above. As shown in fig. 2, the method comprises the steps of:
201. sampling an original vibration signal of a target gearbox based on a preset sampling frequency and a preset sampling duration to obtain a first candidate signal; carrying out time domain delay on the first candidate signal to obtain a second candidate signal; the target gearbox is a multi-stage gearbox;
the fault signal of the rolling bearing in the gearbox can be extracted from the vibration signal of the gearbox. Wherein the target gearbox may be an abnormally operating multi-stage gearbox, such as the gearbox 30 described above.
In one possible implementation, the raw vibration signal may be a vibration signal of the entire housing of the gearbox in an abnormal operating condition of the internal bearings. For example, the raw vibration signal may be a housing vibration signal measured by an acceleration sensor 10 mounted outside the gearbox housing; may be a housing vibration signal collected by a computer device 40 communicatively connected to the gearbox. The raw vibration signal may comprise, among other things, a fault signal of the bearings inside the gearbox.
In a possible implementation manner, the first candidate signal may be obtained by sampling the original vibration signal based on a preset sampling condition, and may be represented as an original periodic signal U. The second candidate signal may be obtained by delaying the first candidate signal based on a preset delay time, and may be represented as a delay period V. Wherein the preset delay time may be in the range of 10-30s.
It should be noted that the original periodic signal and the delayed periodic signal are both time-domain signals. The original periodic signal may include a periodic noise signal p (t) and a random fault signal q (t). Since the delayed periodic signal is obtained based on the original periodic signal delay, the delayed periodic signal may also comprise a deterministic periodic signal p k (t) and a random signal q k (t) of (d). The random fault signal can be a fault signal of an internal bearing of the gearbox to be acquired in a periodic signal, and can be a structural vibration signal generated by impact excitation of a fault bearing. The periodic noise signal may be a noise signal other than a bearing failure signal, and may be a rotation frequency signal, a gear meshing frequency signal, a frequency doubling signal, and the like of each shaft in the gearbox.
Fig. 3 is a schematic waveform diagram and a schematic frequency spectrum diagram of a first candidate signal according to an embodiment of the present disclosure. Referring to fig. 3, a sampling frequency Fs for sampling the original vibration signal is 128KHz, and a preset sampling time period T is 100s.
202. Determining a cross-correlation parameter between the first candidate signal and the second candidate signal, and an autocorrelation parameter of the first candidate signal; the cross-correlation parameter is used for representing the frequency domain correlation of the first candidate signal and the second candidate signal, and the autocorrelation parameter is used for representing the frequency domain characteristic of the first candidate signal;
in the present application, the frequency of the frequency domain noise signal may be kept at the peak value under the cross-correlation parameter of the signal, and the frequency of the random fault signal may be reduced at the peak value under the cross-correlation parameter of the signal. Therefore, the frequency domain noise signal of the gearbox shell vibration signal can be determined by constructing a frequency domain filtering function, the frequency domain noise signal is converted into a time domain periodic noise signal, noise signals such as a rotating frequency signal, a gear meshing frequency signal and a frequency doubling signal of each shaft in the gearbox are determined as far as possible, and a bearing fault signal in the shell vibration signal is acquired based on the periodic noise signal.
In one possible implementation, the cross-correlation parameter may be a cross-power density spectrum. The cross-power density spectrum can be a density spectrum for counting the correlation degree of the original periodic signal and the delayed periodic signal in a frequency domain, and can be expressed as S UV (f) In that respect The cross-correlation parameter between the original periodic signal and the delayed periodic signal may be obtained by fourier transforming a cross-correlation function of the original periodic signal and the delayed periodic signal in the time domain.
In one possible implementation, the autocorrelation parameter may be a self-power spectrum. The self-power spectrum may be a density spectrum in a frequency domain expressing the original periodic signal, and may be represented as S UU (f)。
203. And determining a periodic noise signal in the first candidate signal according to the cross-correlation parameter and the self-correlation parameter, removing the periodic noise signal in the first candidate signal, and obtaining a random fault signal of the target gearbox bearing.
In the present application, when calculating the cross-correlation function in the time domain of the original periodic signal and the delayed periodic signal, the periodic noise signal is still in a reserved state after the delay of the original periodic signal due to the correlation. While random fault signals are attenuated after the cross-correlation operation due to their irrelevancy. Thus, based on the signal correlation, the periodic noise signal in the original periodic signal can be removed.
In one possible implementation, the transfer function may be constructed from the cross-correlation parameters and the auto-correlation parameters. And obtaining a frequency domain filter function by carrying out absolute value summation mean on the multiple groups of transfer functions. And determining a frequency domain noise signal of the original periodic signal based on a frequency domain filtering function, converting the frequency domain noise signal into a time domain periodic noise signal, removing the time domain periodic noise signal, and finally obtaining a random fault signal of the gearbox bearing.
Illustratively, fig. 4 is a schematic spectrum diagram of a frequency-domain filter function according to an embodiment of the present application. Referring to fig. 4, the frequency-domain noise signal of the original periodic signal may be determined by a frequency-domain filter function, where the frequency-domain filter function is obtained based on a sampling condition that a sampling frequency Fs is 128KHz and a preset sampling duration T is 100s.
In the method provided by the embodiment of the application, firstly, vibration signals of the whole shell of the gearbox are obtained through an acceleration sensor arranged outside a multistage gearbox body, frequency domain noise signals of partial shell vibration signals are determined by using a frequency domain filter function constructed under a frequency domain condition, the frequency domain noise signals are converted into time domain periodic noise signals to be subjected to denoising processing, and finally fault signals of bearings in the gearbox (namely random fault signals of the target gearbox bearing) are obtained. Noise signals of the vibration signals of the gearbox shell except for the bearing fault signals are determined under the condition of a frequency domain, the bearing fault signals are extracted under the condition of a time domain, the signal to noise ratio of the bearing fault signals can be improved, and therefore the extraction effect of the bearing fault signals of the multistage gearbox is improved.
In another embodiment of the present application, a specific implementation of determining a cross-correlation parameter between a first candidate signal and a second candidate signal is also provided. Exemplary, specific implementations of the aforementioned "determining a cross-correlation parameter between a first candidate signal and a second candidate signal" include: performing time domain fusion on the first candidate signal and the second candidate signal to obtain a third candidate signal; performing discrete Fourier transform processing on the third candidate signal to obtain a first discrete signal; and determining the cross-power spectrums of the first candidate signal and the second candidate signal according to the amplitude-frequency characteristics of the first discrete signal.
In one possible implementation, the cross-power spectrum of the original periodic signal and the delayed periodic signal can be obtained by a periodogram method. The original periodic signal and the delayed periodic signal may be random sequences, and each random sequence may have N observation data, where N may be an arbitrary value.
Optionally, time domain fusion may be performed on the original periodic signal U and the delayed periodic signal V of the N observation data to obtain a third candidate signal. The third candidate signal may be a sequence x with limited energy formed by splicing two random sequences of the first and second candidate signals 1 （n）。
Alternatively, the sequence x may be aligned 1 (n) performing a discrete Fourier transform to obtain a first discrete signal x 1 (k) In that respect According to the first discrete signal x 1 (k) To obtain a first discrete signal x 1 (k) The square of the amplitude. Based on the square value divided by the sequence x 1 (n) to determine the cross-power spectrum S of the original periodic signal U and the delayed periodic signal V UV (f) In that respect Wherein, when the observation data of the original periodic signal U and the delayed periodic signal V are both N, the sequence x 1 The number of observations of (N) may be 2N.
In the method provided by the embodiment of the application, the cross-power spectrums of the original periodic signal and the delayed periodic signal are estimated by using a periodogram method, and the cross-power spectrums are used as system output response spectrums in a frequency response function to provide conditions for constructing a frequency domain filtering function.
In another embodiment of the present application, a specific implementation of determining the autocorrelation parameter of the first candidate signal is also provided. Exemplary, specific implementations of the aforementioned "determining autocorrelation parameters of the first candidate signal" include: performing time domain fusion on the two first candidate signals to obtain a fourth candidate signal; performing discrete Fourier transform processing on the fourth candidate signal to obtain a second discrete signal; and determining the self-power spectrum of the first candidate signal according to the amplitude-frequency characteristics of the second discrete signal.
In one possible implementation, the self-power spectrum of the original periodic signal can be obtained by a periodogram method.
Alternatively, two primitive weeks of N observations may be takenAnd performing time domain fusion on the phase signal U to obtain a fourth candidate signal. Wherein, the fourth candidate signal can splice the random sequences of the two original periodic signals into a sequence x with limited energy 2 （n）。
Alternatively, the sequence x may be aligned 2 (n) performing a discrete Fourier transform to obtain a second discrete signal x 2 (k) .1. The According to the second discrete signal x 2 (k) To obtain a second discrete signal x 2 (k) The square of the amplitude. Based on the square value divided by the sequence x 2 (n) to determine the self-power spectrum S of the original periodic signal U UU (f) .1. The Wherein, when the original periodic signal U is N, the sequence x 2 The number of observations of (N) may be 2N.
In the method provided by the embodiment of the application, the self-power spectrum of the original periodic signal is estimated by using a periodogram method, and the self-power spectrum is used as a system input response spectrum in a frequency response function to provide conditions for constructing a frequency domain filtering function.
In another embodiment of the present application, a specific implementation of determining a periodic noise signal in a first candidate signal is also provided. Exemplary, the specific implementation of "determining the periodic noise signal in the first candidate signal according to the cross-correlation parameter and the auto-correlation parameter" referred to above includes: determining a frequency domain filtering function according to the cross-correlation parameter and the autocorrelation parameter; and determining a frequency domain noise signal of the first candidate signal according to the frequency domain filter function, and performing time domain conversion on the frequency domain noise signal to obtain a periodic noise signal.
In a possible implementation, the cross-power spectrum S can be obtained UV (f) Sum self-power spectrum S UU (f) And determining a filter function under the condition of a frequency domain. And determining a frequency domain noise signal of the original periodic signal U based on the frequency domain filtering function, and converting the frequency domain noise signal into a periodic noise signal on a time domain. Wherein the periodic noise signal may be a signal other than the bearing fault signal of the original periodic signal.
Illustratively, fig. 5 is a schematic spectrum diagram of another frequency-domain filter function according to an embodiment of the present application. Referring to fig. 5, the frequency domain filter function is obtained based on the sampling condition that the sampling frequency Fs is 1024Hz and the preset sampling duration T is 100s.
In one possible implementation, the frequency domain noise signal may be converted into a periodic noise signal in the time domain by an inverse fourier transform. Can be expressed as the following equation:
wherein Sig d Which may be a periodic noise signal in the time domain of the original periodic signal. W H (f) Which may be a frequency domain noise signal of the original periodic signal. The IFFT may be an inverse fourier transform operation. W (f) may be the spectrum of the original periodic signal. TF (f) may be a frequency domain filtering function.
Optionally, the periodic noise signal is subtracted from the original periodic signal to yield a random fault signal for the gearbox bearings.
According to the method provided by the embodiment of the application, the bearing fault signal of the gearbox is determined according to the frequency domain filter function, and the bearing fault feature is extracted from the bearing fault signal, so that the bearing fault diagnosis is completed according to the bearing fault feature.
In another embodiment of the present application, a specific implementation of determining a frequency domain filter function is also provided. Exemplary, the specific implementation of the aforementioned "determining the frequency domain filter function according to the cross-correlation parameter and the autocorrelation parameter" includes: determining a transfer function according to the cross-correlation parameter and the autocorrelation parameter; the transfer function is the ratio of the cross-correlation parameter and the autocorrelation parameter; a frequency domain filter function is determined from the transfer function.
In one possible implementation, the transfer function may be constructed by a ratio of the cross-power spectrum to the self-power spectrum. For example, the transfer function H (f) can be expressed as follows:
wherein the mutual workRate spectrum S UV (f) Cross-power spectrum S that may include an original frequency domain noise signal and a delayed frequency domain noise signal PKP And cross-power spectrum S of original random fault signal and delayed random fault signal qkq . Self-power spectrum S UU (f) Self-power spectrum S which may include original frequency domain noise signals PP And the self-power spectrum S of the original random fault signal qq . Due to the irrelevancy of the random fault signal, S qkq May be a powerless spectrum, i.e. S qkq ≈0。
Alternatively, the transfer function may be constructed based on the definition of the frequency response function. The frequency response function may be defined as the response of the output location due to the input location unit excitation force, and may be the ratio of the output response of the structure to the input excitation force. While the original periodic signal U may be the system input and the delayed periodic signal V may be the system output. Thus, due to the stochastic nature of the signals, the cross-power spectrum may be the system output response spectrum and the self-power spectrum may be the system input spectrum.
In one possible implementation, the frequency-domain filter function may be determined by weighted averaging of the absolute values of the sets of transfer functions.
Illustratively, in order to make the amplitude of the frequency-domain filter constructed based on the frequency-domain filtering function close to 1 at the frequency of the periodic noise signal and close to 0 at the frequency of the random fault signal, multiple sets of transfer function absolute values are selected for weighted averaging. The multiple groups of transfer functions are multiple groups of data under the same working condition.
In the method provided by the embodiment of the application, a transfer function is constructed by the ratio of the cross power spectrum to the self power spectrum, and the absolute value of the transfer function is used as a final frequency domain filtering function.
In another embodiment of the present application, a specific formula of the frequency domain filtering function is also provided. Illustratively, the frequency domain filter function satisfies the following equation:
wherein, TF (f) can be in frequency domainThe function of the filtering is such that,
the transfer function may be constructed for the ith set of data and N may be the number of sets.
For example, fig. 6 is a schematic waveform diagram and a schematic frequency spectrum diagram of a first candidate signal after filtering according to an embodiment of the present application. Referring to fig. 6, the signal-to-noise ratio of the filtered signal is high.
In another embodiment of the present application, FIG. 7 is a schematic flow chart of gearbox bearing fault signal acquisition based on TF frequency domain filter function. Referring to fig. 7, the acquisition method may include the following steps.
And 701, acquiring a shell vibration signal.
(1) And sampling shell vibration signals based on preset sampling conditions.
In a possible implementation mode, the sampling frequency Fs can be set to be 128KHz, the preset sampling time T is 100s, and an original shell vibration signal U is obtained from the shell vibration data of the multi-stage gearbox.
Optionally, the raw ringing signal may comprise a deterministic periodic signal p (t) and a random noise signal q (t). The random noise signal q (t) is a signal which is transmitted to the surface of the box body through a complex path by the structural vibration generated by the impact excitation of the bearing fault, namely a bearing fault signal to be acquired. The deterministic periodic signal p (t) is a harmonic component of each shaft rotation frequency and its multiple, gear meshing frequency and its multiple, etc.
(2) The original shell vibration signal is delayed.
In one possible implementation, the original shell oscillation signal U is delayed
And obtaining a delay shell oscillation signal V. The delayed ringing signal may comprise a deterministic periodic signal p k (t) and a random noise signal q k (t)。
And 702, obtaining a power spectrum of the shell vibration signal.
(1) And (5) obtaining a cross-power spectrum.
In one possible implementation, the period may be usedEstimation of cross-power spectrum S of original shell oscillation signal U and delayed shell oscillation signal V by graph method UV (f)。
Optionally, time domain fusion may be performed on N observation data in the two random sequences of the original shell vibration signal U and the delayed shell vibration signal V to obtain an energy-limited sequence. Calculating discrete Fourier transform of the fusion sequence, and taking the square of amplitude-frequency characteristics of the discrete Fourier transform and dividing the square by the length of the fusion sequence to obtain a cross-power spectrum S of an original shell oscillation signal U and a delayed shell oscillation signal V UV (f)。
(2) And obtaining a self-power spectrum.
In a possible implementation manner, a periodogram method can be adopted to estimate the self-power spectrum S of the original shell vibration signal U UU (f)。
Optionally, time domain fusion may be performed on N observation data of the two original shell vibration signal U random sequences to obtain an energy-limited sequence. Calculating discrete Fourier transform of the fusion sequence, and taking the square of the amplitude-frequency characteristic of the discrete Fourier transform and dividing the square by the length of the fusion sequence to obtain a self-power spectrum S of the original shell vibration signal U UU (f)。
703, constructing a frequency domain filter function based on the power spectrum.
(1) A transfer function H (f) is constructed.
In a possible implementation manner, the self-power spectrum S of the original shell vibration signal U may be obtained UU (f) As a system input spectrum, cross-power spectrum S of an original shell vibration signal U and a delayed shell vibration signal V UV (f) As the system output response spectrum. According to the definition of frequency response function, using cross-power spectrum S UV (f) And self-power spectrum S UU (f) The transfer function is constructed by the ratio of (a) to (b), which can be expressed as the following formula:
wherein, cross power spectrum S UV (f) May include a cross-power spectrum S of the deterministic periodic signal of the original shell vibration signal and the deterministic periodic signal of the delayed shell vibration signal PKP And a raw shellCross power spectrum S of random noise signal of vibration signal and random noise signal of delay shell vibration signal qkq . Self-power spectrum S UU (f) The self-power spectrum S of a deterministic periodic signal, which may include an original ringing signal PP And the self-power spectrum S of the random noise signal of the original shell vibration signal qq . Due to the irrelevance of random noise signals, S qkq May be a powerless spectrum, i.e. S qkq ≈0。
(2) A frequency domain filter function TF (f) is constructed.
In one possible implementation, the absolute value of the transfer function may be taken as the filter function and the phase position may be zero.
For example, to make the amplitude of the amplitude-frequency response of the filter close to 1 at each frequency of the deterministic periodic signal and close to 0 at the frequency of the random noise signal, multiple sets of data under the same working condition may be selected to calculate a transfer function, and the transfer function may be averaged to obtain a final TF function. Can be expressed as the following equation:
wherein, the first and the second end of the pipe are connected with each other,
the transfer function may be constructed for the ith set of data and N may be the number of sets.
And 704, acquiring a bearing fault signal.
In one possible implementation, the deterministic periodic signal may be determined using an inverse fourier transform operation. And subtracting the deterministic periodic signal from the original shell vibration signal to obtain a random noise signal representing a bearing fault signal.
Illustratively, it can be expressed as the following formula:
therein, sig d May be a deterministic periodic signal of the original shell vibration signal. W H (f) The periodic signal may be deterministic in the frequency domain for the original ringing signal. The IFFT may be an inverse fourier transform operation. W (f) may be the frequency spectrum of the original ringing signal. TF (f) may be a frequency domain filtering function.
In one possible implementation, the bearing fault feature may be extracted from the bearing fault signal, so as to diagnose the bearing fault according to the bearing fault feature.
In the embodiments described above, a complete flow chart for obtaining a fault signal for a gearbox bearing is described. Fig. 8 is a block diagram of a fault signal acquisition device for a gearbox bearing according to an embodiment of the present application, which may be deployed in the computer apparatus described above. Referring to fig. 8, the apparatus includes an acquisition unit 801, a determination unit 802, and a processing unit 803.
The acquisition unit 801 is used for sampling an original vibration signal of the target gearbox based on a preset sampling frequency and a preset sampling duration to obtain a first candidate signal; carrying out time domain delay on the first candidate signal to obtain a second candidate signal; the target gearbox is a multi-stage gearbox;
a determining unit 802 for determining a cross-correlation parameter between the first candidate signal and the second candidate signal, and an autocorrelation parameter of the first candidate signal; the cross-correlation parameter is used for representing the frequency domain correlation of the first candidate signal and the second candidate signal, and the autocorrelation parameter is used for representing the frequency domain characteristic of the first candidate signal;
and the processing unit 803 is configured to determine a periodic noise signal in the first candidate signal according to the cross-correlation parameter and the auto-correlation parameter, remove the periodic noise signal in the first candidate signal, and obtain a random fault signal of the target gearbox.
In a possible embodiment, the determining unit 802 is configured to perform time domain fusion on the first candidate signal and the second candidate signal to obtain a third candidate signal;
performing discrete Fourier transform processing on the third candidate signal to obtain a first discrete signal;
and determining the cross-power spectrums of the first candidate signal and the second candidate signal according to the amplitude-frequency characteristics of the first discrete signal.
In a possible embodiment, the determining unit 802 is further configured to perform time domain fusion on the two first candidate signals to obtain a fourth candidate signal;
performing discrete Fourier transform processing on the fourth candidate signal to obtain a second discrete signal;
and determining the self-power spectrum of the first candidate signal according to the amplitude-frequency characteristics of the second discrete signal.
In a possible embodiment, the processing unit 803 is configured to determine a frequency domain filter function based on the cross-correlation parameter and the auto-correlation parameter;
and determining a frequency domain noise signal of the first candidate signal according to the frequency domain filtering function, and performing time domain conversion on the frequency domain noise signal to obtain a periodic noise signal.
In a possible embodiment, the processing unit 803 is further configured to determine a transfer function based on the cross-correlation parameter and the auto-correlation parameter; the transfer function is the ratio of the cross-correlation parameter and the autocorrelation parameter;
the frequency domain filter function is determined from the transfer function.
In a possible embodiment, the processing unit 803 is further configured to satisfy the following formula for the frequency domain filter function:
；
wherein TF (f) is used to characterize a frequency domain filter function,
the transfer function constructed by the ith group of data is characterized, and N is used for characterizing the group number.
The fault signal acquisition device of gearbox bearing that this application embodiment provided obtains the vibration signal of the whole casing of gearbox through the acceleration sensor who installs in multistage gearbox box outside earlier, utilizes the frequency domain filter function who constructs under the frequency domain condition to confirm the frequency domain noise signal of partial casing vibration signal to convert frequency domain noise signal to time domain periodic noise signal and remove noise processing to it, finally obtains the fault signal of the inside bearing of gearbox (be the random fault signal of above-mentioned target gearbox bearing). Noise signals of the vibration signals of the gearbox shell except for the bearing fault signals are determined under the condition of a frequency domain, the bearing fault signals are extracted under the condition of a time domain, the signal to noise ratio of the bearing fault signals can be improved, and therefore the extraction effect of the bearing fault signals of the multistage gearbox is improved.
In one embodiment, a computer device is provided. Fig. 9 is a block diagram of a computer device according to an embodiment of the present application, and refer to fig. 9. The computing device comprises a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of, when executing the computer program:
sampling an original vibration signal of a target gearbox based on a preset sampling frequency and a preset sampling duration to obtain a first candidate signal; carrying out time domain delay on the first candidate signal to obtain a second candidate signal; the target gearbox is a multi-stage gearbox; determining a cross-correlation parameter between the first candidate signal and the second candidate signal, and an autocorrelation parameter of the first candidate signal; the cross-correlation parameter is used for representing the frequency domain correlation of the first candidate signal and the second candidate signal, and the autocorrelation parameter is used for representing the frequency domain characteristic of the first candidate signal; and determining a periodic noise signal in the first candidate signal according to the cross-correlation parameter and the self-correlation parameter, removing the periodic noise signal in the first candidate signal, and obtaining a random fault signal of the target gearbox bearing.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, the computer program can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
All possible combinations of the technical features in the above embodiments may not be described for the sake of brevity, but should be considered as being within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.
The above examples only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, and these are all within the scope of protection of the present application. Therefore, the protection scope of the present patent application shall be subject to the appended claims.
Claims (10)
1. A method of fault signal acquisition for a gearbox bearing, the method comprising:
sampling an original vibration signal of a target gearbox based on a preset sampling frequency and a preset sampling duration to obtain a first candidate signal; performing time domain delay on the first candidate signal to obtain a second candidate signal; the target gearbox is a multi-stage gearbox;
determining a cross-correlation parameter between the first candidate signal and the second candidate signal, and an autocorrelation parameter of the first candidate signal; the cross-correlation parameter is used for characterizing frequency domain correlation of the first candidate signal and the second candidate signal, and the autocorrelation parameter is used for characterizing frequency domain characteristics of the first candidate signal;
and determining a periodic noise signal in the first candidate signal according to the cross-correlation parameter and the autocorrelation parameter, removing the periodic noise signal in the first candidate signal, and obtaining a random fault signal of the target gearbox bearing.
2. The method of claim 1, wherein determining the cross-correlation parameter between the first candidate signal and the second candidate signal comprises:
performing time domain fusion on the first candidate signal and the second candidate signal to obtain a third candidate signal;
performing discrete Fourier transform processing on the third candidate signal to obtain a first discrete signal;
and determining the cross-power spectrums of the first candidate signal and the second candidate signal according to the amplitude-frequency characteristics of the first discrete signal.
3. The method of claim 1, wherein determining the autocorrelation parameters for the first candidate signal comprises:
performing time domain fusion on the two first candidate signals to obtain a fourth candidate signal;
performing discrete Fourier transform processing on the fourth candidate signal to obtain a second discrete signal;
and determining the self-power spectrum of the first candidate signal according to the amplitude-frequency characteristics of the second discrete signal.
4. The method of claim 1, wherein determining the periodic noise signal in the first candidate signal based on the cross-correlation parameter and the auto-correlation parameter comprises:
determining a frequency domain filter function according to the cross-correlation parameter and the autocorrelation parameter;
and determining a frequency domain noise signal of the first candidate signal according to the frequency domain filtering function, and performing time domain conversion on the frequency domain noise signal to obtain the periodic noise signal.
5. The method of claim 4, wherein determining a frequency domain filter function from the cross-correlation parameter and the auto-correlation parameter comprises:
determining a transfer function according to the cross-correlation parameter and the autocorrelation parameter; the transfer function is the ratio of the cross-correlation parameter and the autocorrelation parameter;
determining the frequency domain filter function from the transfer function.
6. The method of claim 5, wherein the frequency domain filter function satisfies the following equation:
wherein TF (f) is used to characterize the frequency domain filtering function,
the transfer function constructed for characterizing the ith set of data, and N is used for characterizing the set number.
7. A fault signal acquisition device for a gearbox bearing, the device comprising:
the acquisition unit is used for sampling an original vibration signal of the target gearbox based on preset sampling frequency and sampling duration to obtain a first candidate signal; performing time domain delay on the first candidate signal to obtain a second candidate signal; the target gearbox is a multi-stage gearbox;
a determining unit for determining a cross-correlation parameter between the first candidate signal and the second candidate signal, and an autocorrelation parameter of the first candidate signal; the cross-correlation parameter is used for characterizing frequency-domain correlation of the first candidate signal and the second candidate signal, and the autocorrelation parameter is used for characterizing frequency-domain characteristics of the first candidate signal;
and the processing unit is used for determining a periodic noise signal in the first candidate signal according to the cross-correlation parameter and the autocorrelation parameter, removing the periodic noise signal in the first candidate signal and obtaining a random fault signal of the target gearbox bearing.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor, when executing the program, implements the method according to any of claims 1 to 6.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 6.
10. A computer program product comprising instructions which, when executed, cause the method of any of claims 1 to 6 to be performed.
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