A human-machine collaborative interaction method for quickly identifying bearing faults

Abstract

The application discloses a man-machine cooperation interaction method for quickly identifying bearing faults, and calculates bearing characteristic frequencies through basic characteristic frequencies, speed ratios and speeds; calculates waveform spectrum and envelope spectrum by using FFT and Hilbert transform; finds peak values in frequency domain data by using a peak value finding algorithm and an amplitude limiting method; matches the peak values with the characteristic frequencies; and matches the peak value frequencies with the characteristic frequencies and their multiples based on acceptable deviations, so that bearing characteristic frequencies are automatically calculated and matched, the time for fault diagnosis is significantly shortened, the work efficiency of engineers is improved, the operation process is simplified, engineers do not need to perform complex frequency calculation, and only need to quickly obtain fault information through simple operation, human errors are reduced, and the accuracy of diagnosis is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of bearing fault detection, and particularly relates to a man-machine cooperative interaction method for quickly identifying bearing faults. BACKGROUND

[0002] At present, bearing fault diagnosis mainly relies on the calculation method of characteristic frequency. These methods can provide basic fault information, but there are the following problems in actual application:

[0003] The device usually contains multiple characteristic frequencies, and the process of finding these frequencies is tedious and time-consuming.

[0004] After finding abnormal frequencies in the spectrum, engineers also need to make complex calculations combined with related rotating speeds, which leads to low fault identification efficiency and difficulty in quickly locating problems. SUMMARY

[0005] The purpose of the application is to provide a man-machine cooperative interaction method for quickly identifying bearing faults to solve the above problems.

[0006] To achieve the above purpose, the application provides the following technical scheme: a man-machine cooperative interaction method for quickly identifying bearing faults, and the specific steps are as follows:

[0007] Step 1: existing information input, standard parts directly select corresponding models from the bearing library to obtain the characteristic frequencies of the inner ring, outer ring, rolling body and retainer; at the same time, the characteristic frequencies of the device can also be input through other ways and the corresponding rotating speed ratio ;

[0008] Step 2: calculate the bearing characteristic frequency, according to the basic characteristic frequency , the basic characteristic frequency is the characteristic frequency of the inner ring, outer ring, rolling body and retainer obtained in step 1, the rotating speed ratio , the rotating speed , the bearing characteristic frequency is calculated as follows:

[0009] Iterate through all bearings of the device and calculate the corresponding bearing characteristic frequency ; iterating through the device specifically means calculating the characteristic frequency of the bearing for all bearings contained in the monitored device in order to screen;

[0010] Step 3: calculate the characteristic spectrum, perform FFT conversion on the waveform obtained from the corresponding device to obtain the waveform spectrum ;

[0011] Perform Hilbert transform on the waveform obtained from the corresponding device to obtain the waveform envelope spectrum The two transformations are performed on the same waveform respectively to obtain the atlas for comparison.

[0012] Step four: interval peak value searching is performed on the frequency domain data, and the peak value of the specified frequency domain interval is searched, the waveform spectrum corresponds to (low frequency 1HZ-1000HZ), and the waveform envelope spectrum corresponds to a low frequency interval 1HZ-500HZ;

[0013] Step five: peak value and characteristic frequency matching, based on the spectrum parameters (spectrum resolution , spectrum range ), the acceptable deviation is calculated;

[0014] The acceptable deviation ;

[0015] Based on the acceptable deviation, the peak frequency and the characteristic frequency and the 1-5 times frequency thereof are matched , is the serial number of the characteristic frequency, is the serial number of the times frequency; when the difference value satisfies , it is the matched characteristic frequency . The matched characteristic frequency, i.e., the real characteristic frequency , is returned.

[0016] Preferably, the peak value searching method in step four adopts a peak value searching algorithm (smaller than the current value on the left and right); the peak value screening method adopts an amplitude limit, and the peak value amplitude is an effective peak value when ; wherein the peak value amplitude threshold is obtained by weighted calculation of the multi-parameters (mean value , effective value , maximum value ) corresponding to the interval spectrum amplitude. The corresponding peak value frequency is returned.

[0017] Technical effects and advantages of the present application: by automatically calculating and matching the bearing characteristic frequency, the time of fault diagnosis is significantly shortened, the work efficiency of engineers is improved, engineers do not need to perform complex frequency calculation, and only need to quickly obtain fault information through simple operation, the system automatically performs frequency matching calculation, reduces the error caused by human operation, and improves the accuracy of diagnosis. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 is a schematic diagram of the operation steps of the present application;

[0019] Figure 2 is a peak value atlas in the embodiment of the present application. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0021] The present application provides a man-machine cooperative interaction method for quickly identifying bearing failure as shown in the figure, bearing information input:

[0022] The relevant information of the bearing is input into the system. If it is a standard part, the corresponding model can be selected from the bearing library to obtain the characteristic frequencies of the inner ring, outer ring, rolling body and cage. At the same time, the system also supports inputting the characteristic frequencies of the device through other ways and the corresponding speed ratio .

[0023] Calculate the bearing characteristic frequency:

[0024] According to the basic characteristic frequency (taking 1rpm as the basis), the basic characteristic frequency is the characteristic frequency of the inner ring, outer ring, rolling body and cage obtained above, the speed ratio (the ratio compared with the power input shaft speed), the speed (power input shaft speed), calculate the bearing characteristic frequency: ;

[0025] Traverse all bearings of the device and calculate the corresponding bearing characteristic frequency .

[0026] Calculate the characteristic spectrum:

[0027] Perform FFT conversion on the waveform obtained from the device to obtain the waveform spectrum .

[0028] Perform Hilbert transform on the waveform obtained from the device to obtain the waveform envelope spectrum .

[0029] Find the interval peak value for the frequency domain data:

[0030] Find the peak value for the specified frequency domain interval, the waveform spectrum corresponding to (low frequency 1HZ-1000HZ), the waveform envelope spectrum (Corresponding to the low-frequency range of 1Hz-500Hz). The peak finding method uses a peak lookup algorithm (both left and right values ​​are less than the current value). The peak filtering method uses amplitude limiting, with the peak amplitude... ,when The time represents the effective peak value. The peak amplitude threshold is among them. The multiple parameters (mean) corresponding to the amplitude of the interval spectrum Valid value Maximum value The peak frequency is obtained by weighted calculation. .

[0031] Peak value matching with characteristic frequency:

[0032] Based on spectral parameters (spectral resolution) Spectrum range ), calculate acceptable deviation .

[0033] Acceptable deviation ;

[0034] Based on acceptable deviation matching peak frequency and characteristic frequency and their 1st to 5th harmonics , For characteristic frequency index, This is the frequency doubling number. When the difference... satisfy Then it is the matched feature frequency. Returns the frequency of the matched features, i.e., the true feature frequencies. .

[0035] Example: Bearing information entry:

[0036] Enter the relevant bearing information into the system. If it is a standard part, the corresponding model can be selected from the bearing library to obtain the characteristic frequencies of the inner ring, outer ring, rolling elements, and cage. The system also supports inputting the characteristic frequencies of the equipment through other methods. ) and their corresponding speed ratio ( ).

[0037] Calculate the bearing characteristic frequency:

[0038] Based on fundamental characteristic frequency (Based on 1 rpm) speed ratio (Ratio to the power input shaft speed), speed (Power input shaft speed), calculate the bearing characteristic frequency: ;

[0039] Iterate through all bearings in the device and calculate the corresponding bearing characteristic frequencies. .

[0040] Calculate the characteristic frequency spectrum:

[0041] FFT transform the waveform obtained by the corresponding device to obtain the waveform spectrum .

[0042] Hilbert transform the waveform obtained by the corresponding device to obtain the waveform envelope spectrum .

[0043] Interval peak finding for frequency domain data;

[0044] Find the peak value for the specified frequency domain interval, the waveform spectrum The corresponding (low frequency 1HZ-1000HZ), waveform envelope spectrum (corresponding to the low frequency interval 1HZ-500HZ). The peak finding method uses the peak finding algorithm (less than the current value on both sides). The peak screening method uses amplitude limitation, and the peak amplitude When , it is an effective peak value. The peak amplitude threshold is calculated by weighting the multi-parameter (mean , effective value , maximum value ) corresponding to the interval spectrum amplitude. Return the corresponding peak frequency .

[0045] Peak and characteristic frequency matching:

[0046] Based on the spectrum parameters (spectrum resolution , spectrum range ), calculate the acceptable deviation .

[0047] The acceptable deviation ;

[0048] Match the peak frequency and the characteristic frequency and its 1 to 5 times frequency based on the acceptable deviation , is the characteristic frequency number, is the frequency number. When the difference satisfies , it is the matched characteristic frequency . Return the matched characteristic frequency, which is the real characteristic frequency .

[0049] Calculate the characteristic frequency:

[0050] Inner ring characteristic frequency = 5 Hz;

[0051] Outer ring characteristic frequency = 10 Hz;

[0052] Rolling element characteristic frequency = 3 Hz;

[0053] Cage characteristic frequency = 4 Hz;

[0054] Input shaft = 3;

[0055] Bearing correspondence = 1;

[0056] The rotational speed ratio of these characteristic frequencies calculated for the same component in the case is All 1 / 3;

[0057] ;

[0058] Hz;

[0059] Find peak frequencies:

[0060] The spectrum obtained by applying the peak finding algorithm on the spectrum in the figure is {8.00, 10.00, 50.00};

[0061] Calculate acceptable deviations:

[0062] ;

[0063] .

[0064] Match characteristic frequencies and peaks and label fault types:

[0065] Based on the spectrum matched by the spectrum, {8.00, 10.00, 50.00} Hz, the corresponding amplitude is {0.28, 0.18, 0.25};

[0066] According to the frequency type, it is marked as ['inner ring @ 1X', 'rolling @ 2X', 'outer ring @ 3X'].

[0067] Auxiliary information:

[0068] The bearing model is: default

[0069] f1=5, f2=10, f3=3, f4=4;

[0070] R0=3, Rn=1, the ratio is 3:1;

[0071] The characteristic frequencies corresponding to the rotational speed of 300.0 rpm are [8.333333333333332, 16.666666666666664, 5.0, 6.666666666666666] Hz;

[0072] FFT Peaks:

[0073] Frequency:8.00 Hz, Magnitude: 0.28;

[0074] Frequency:10.00 Hz, Magnitude: 0.18;

[0075] Frequency: 50.00 Hz, Magnitude: 0.25;

[0076] index_characteristic_freq_list [1, 7, 10];

[0077] The matched characteristic frequencies are: [8.333333333333332, 10.0, 49.99999999999999] @ ['inner ring @ 1X', 'rolling @ 2X', 'outer ring @ 3X'].

[0078] Finally, it should be noted that: the above only for the preferred embodiments of the present application, and not for the purpose of limiting the present application, although the foregoing embodiments of the present application have been described in detail, for those skilled in the art, it still can be modified, or part of the technical features of the equivalent replacement, within the spirit and principles of the present application, any modification, equivalent replacement, improvement, etc., should be included within the scope of the present application.

Claims

1. A human-machine collaborative interaction method for quickly identifying bearing faults, characterized in that: The specific steps are as follows: Step one: existing information entry, standard parts directly select the corresponding model from the bearing library, get the characteristic frequency of inner ring, outer ring, rolling body and retainer and its corresponding speed ratio ; Step two: calculate the bearing characteristic frequency, according to the basic characteristic frequency The basic characteristic frequency is the characteristic frequency of the inner ring, outer ring, rolling body and cage obtained in step one; the speed ratio The speed Calculate the bearing characteristic frequency: ; Traversing all bearings of the device and calculating the corresponding bearing characteristic frequencies ; Step three: calculate the characteristic frequency spectrum: The FFT conversion is performed on the waveform obtained by the corresponding device to obtain the waveform spectrum ​ The Hilbert transform is performed on the waveform obtained by the corresponding device to obtain a waveform envelope spectrum ​ Step four: interval peak finding for frequency domain data, and peak finding for the specified frequency domain interval; Step five: peak matching to characteristic frequencies, based on spectral resolution Spectrum range Spectrum parameters, calculating acceptable deviations ; acceptable deviation ; Based on acceptable deviation matching peak frequency and characteristic frequency and peak frequency and characteristic frequency 1 to 5 times frequency , Characteristic frequency sequence number, Times frequency sequence number; when the difference Satisfies ; it is the matched characteristic frequency ; return the matched characteristic frequency, that is, the real characteristic frequency , wherein The acceptable deviation is; The peak frequency is.

2. The human-machine collaborative interaction method for quickly identifying bearing faults according to claim 1, characterized in that: The traversing device in step two is specifically calculating the characteristic frequency of all contained bearings of the monitored device in order to screen.

3. The human-machine co-operative interaction method for quickly identifying bearing faults of claim 1, wherein: The corresponding device waveform obtained in step three is subjected to FFT conversion to obtain a waveform spectrum The corresponding device waveform is subjected to Hilbert transform to obtain a waveform envelope spectrum The two transformations are not sequential, and the same waveform is subjected to the two transformations to obtain a spectrum for comparison.

4. The human-machine co-operative interaction method for quickly identifying bearing faults of claim 1, wherein: The peak searching method in the fourth step adopts a peak searching algorithm; the peak screening method adopts an amplitude limit, and the peak amplitude When , it is an effective peak; wherein the peak amplitude threshold value is calculated by weighting a plurality of parameters corresponding to the interval spectrum amplitude; and the corresponding peak frequency is returned, wherein μ is the mean value, θ is the effective value, X peak is the maximum value.

5. The human-machine co-operative interaction method for quickly identifying bearing faults of claim 1, wherein: The waveform spectrum in step four corresponds to the low frequency 1HZ-1000HZ, and the waveform envelope spectrum corresponds to the low frequency interval 1HZ-500HZ.

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