CN111259323A - Method for accurately positioning faults of mechanical transmission system of rocker arm of coal mining machine - Google Patents
Method for accurately positioning faults of mechanical transmission system of rocker arm of coal mining machine Download PDFInfo
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- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
- G06F17/141—Discrete Fourier transforms
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- G01M13/028—Acoustic or vibration analysis
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- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
- G06F17/148—Wavelet transforms
Abstract
The invention belongs to the technical field of fault diagnosis of a mechanical transmission system of a rocker arm of a coal mining machine, and particularly relates to a fault accurate positioning method of the mechanical transmission system of the rocker arm of the coal mining machine. The method comprises the following steps: firstly, collecting rocker arm vibration signals in normal and fault states and carrying out noise reduction processing on the rocker arm vibration signals by using a wavelet transformation method; secondly, performing FFT to obtain a spectrogram of the vibration signal; thirdly, comparing and analyzing the normal rocker arm spectrogram with the fault rocker arm spectrogram to obtain the vibration characteristic frequency of the fault part, and preliminarily positioning the fault part; fourthly, comparing and analyzing the normal and fault rocker arm continuous complex Morlet wavelet envelope demodulation spectrums to obtain the rotating frequency of the fault part; and fifthly, accurately positioning the fault position by combining self-adaptive continuous complex Morlet wavelet envelope demodulation analysis and FFT (fast Fourier transform). The method has important practical significance for ensuring the safe operation of the coal mining machine, changing the preventive regular maintenance into the predictive maintenance, producing the coal mine safely and efficiently, improving the maintenance efficiency and reducing the maintenance cost.
Description
Technical Field
The invention belongs to the technical field of fault diagnosis of a mechanical transmission system of a rocker arm of a coal mining machine, and particularly relates to a fault accurate positioning method of the mechanical transmission system of the rocker arm of the coal mining machine.
Background
The coal mining machine is one of the core equipment of the modern fully mechanized working face, has a severe working environment, is very easily influenced by huge impact loads from hard coal, gangue and the like during operation, and is continuously corroded by water vapor, coal dust and the like, and in addition, the mechanical transmission device of the coal mining machine frequently fails due to improper management, operation and maintenance of the equipment by workers. The production efficiency of coal mine enterprises is influenced slightly, and equipment damage and production interruption are caused seriously, so that great economic loss and even personal death are caused. According to statistics of failure rates of mechanical transmission devices of rocker arms of imported coal mining machines in Shendong mining areas in recent years, the failure rate of the mechanical transmission devices accounts for 34.2% of the failure rate of the coal mining machines, and the failure rate of the mechanical transmission devices tends to rise year by year. The home and abroad coal mining machines have relatively comprehensive electric and hydraulic fault diagnosis systems, but the fault diagnosis of the mechanical transmission device of the coal mining machine is still difficult because the mechanical transmission device has the characteristics of complex working condition, long transmission chain, complex structure and the like.
The failure of the mechanical transmission device of the coal mining machine can cause the abnormal changes of physical quantities such as the temperature of lubricating oil or equipment, the current of a motor, mechanical vibration and the like. Therefore, fault diagnosis is mainly performed by ferrography, temperature detection and vibration detection methods at present. The biggest restriction factor of ferrogram analysis is subjective judgment of people, judgment and sampling are mainly carried out by depending on the experience of analysts, and serious misjudgment is often caused by the fact that the spectrum making process is not standard. The temperature detection method is difficult to realize early fault diagnosis, and can not realize accurate fault positioning of the mechanical transmission device. The vibration detection method can know the running condition of equipment under the condition of no shutdown, carries out early diagnosis, accurate positioning and the like on faults, and is widely applied to fault diagnosis and fault prediction of mechanical transmission devices.
Disclosure of Invention
The invention provides a method for accurately positioning the fault of a mechanical transmission system of a rocker arm of a coal mining machine aiming at the problems. The method obtains the vibration frequency of the fault part through the comparison and analysis of the normal rocker arm spectrogram and the fault rocker arm spectrogram, and performs primary positioning on the fault; and comparing and analyzing the normal and fault rocker arm continuous complex Morlet wavelet envelope demodulation spectrums to obtain the rotating frequency of the fault part, thereby realizing the accurate positioning of the fault of the mechanical transmission system of the rocker arm of the coal mining machine.
The technical scheme adopted by the invention for solving the technical problems is as follows: the method for accurately positioning the fault of the mechanical transmission system of the rocker arm of the coal mining machine is characterized by comprising the following steps of:
collecting rocker arm vibration signals in normal and fault states, and performing noise reduction processing on the collected vibration signals by using a wavelet transformation method;
performing FFT (fast Fourier transform) on the vibration signal subjected to noise reduction to obtain a spectrogram of the vibration signal;
step three, comparing and analyzing the normal rocker arm spectrogram and the fault rocker arm spectrogram to obtain the vibration characteristic frequency of the fault part, and performing primary positioning on the fault part;
step four, comparing and analyzing the normal and fault rocker arm continuous complex Morlet wavelet envelope demodulation spectrums to obtain the fault part rotation frequency;
and step five, accurately positioning the fault position by combining self-adaptive continuous complex Morlet wavelet envelope demodulation analysis and FFT (fast Fourier transform) transformation.
The third step comprises the following specific steps:
step 301, calculating a vibration characteristic frequency including a rotation frequency and a meshing frequency of the gear, wherein a rotation frequency calculation formula is as follows: f. ofrN/60, wherein n is the rotating speed of the driving wheel, and the meshing frequency calculation formula is as follows: f. ofm＝z×frWherein z is the number of gear teeth;
step 302, comparing and analyzing the frequency spectrograms of the vibration signals of the normal rocker arm and the fault rocker arm to obtain vibration characteristic frequency with large amplitude change or frequency multiplication of the vibration characteristic frequency, namely the fault characteristic frequency;
step 303, comparing the fault characteristic frequency obtained in step 302 with the vibration characteristic frequency calculated in step 301, and if the fault characteristic frequency is a multiple relation of the characteristic frequency, determining that one or more of the gears corresponding to the frequency found in step 302 have faults.
The vibration characteristic frequency of the gear is meshing frequency and rotation frequency.
The fourth step comprises the following specific steps:
401. optimal center frequency f for simultaneously optimizing Morlet wavelet base based on minimum Shannon entropycAnd an optimal bandwidth parameter fbThe optimal matching of Morlet wavelet and fault vibration impact signal is realized, and the optimal time frequency resolution of vibration signal is realized;
402. calculating the optimal scale of Morlet envelope demodulation analysis according to the fault vibration characteristic frequency and the scale-frequency relation obtained by comparing and analyzing the normal rocker arm spectrogram and the fault rocker arm spectrogram;
403. and according to the determined optimal parameters, performing Morlet envelope demodulation spectrum comparison analysis on the vibration signals of the normal rocker arm and the fault rocker arm, thereby obtaining the rotation frequency of the fault part.
The specific steps of 402 are: the scale and frequency have the following relationship:
fi＝fc×fs/ai
wherein f isiIs the actual frequency, fcIs the center frequency, f, of the Morlet waveletsIs the sampling frequency, aiIs the scale of the wavelet transform.
The invention has the beneficial effects that:
1. the method can utilize the vibration signals of the normal rocker arm and the fault rocker arm of the coal mining machine to carry out contrastive analysis to obtain the characteristic frequency of the fault part and realize the primary positioning of the fault part;
2. the method can obtain the optimal central frequency, bandwidth and scale parameters of the Morlet wavelet, and the optimal Morlet wavelet parameters are used for carrying out envelope demodulation spectrum contrast analysis on normal and fault rocker arm vibration signals, so that the fault rotation frequency can be accurately obtained, and the problem that the Morlet wavelet optimal parameters are difficult to obtain is solved.
3. According to the fault vibration characteristic frequency obtained by the comparison and analysis of the frequency spectrograms of the normal rocker arm and the fault rocker arm and the rotating frequency of the fault part obtained by the comparison and analysis of the self-adaptive continuous complex Morlet wavelet packet demodulation spectrum, the accurate positioning of the fault part can be accurately realized.
In conclusion, the method has important practical significance for ensuring the safe operation of the coal mining machine, changing the preventive regular maintenance into the predictive maintenance, safely and efficiently producing the coal mine, improving the maintenance efficiency and reducing the maintenance cost.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
fig. 2(a) is a graph of the normal rocker arm vibration spectrum of the MG1480 shearer of the present invention;
fig. 2(b) is a graph of the vibration spectrum of a failed rocker arm of an MG1480 shearer of the present invention;
FIG. 3 is a time domain waveform diagram of a simulated signal;
FIG. 4 is a graph of simulated signal center frequency versus Shannon entropy;
FIG. 5 is a diagram of simulation signal bandwidth versus Shannon entropy;
FIG. 6 is fc＝0.5,fbWhen the ratio is 5/16.5/95, wavelet time-frequency diagram;
FIG. 7 is fc＝0.2/0.5/0.8,fbWhen the value is 16.5, a wavelet time-frequency diagram;
FIG. 8 is a graph of center frequency of MG1480 versus Shannon entropy;
FIG. 9 is a graph of MG1480 bandwidth versus Shannon entropy;
FIG. 10(a) is a Morlet envelope demodulation spectrum of a normal rocker arm;
fig. 10(b) is a Morlet envelope demodulation spectrum for a failed rocker arm.
Detailed Description
To further explain the technical means and effects of the present invention adopted to achieve the predetermined object, the following detailed description of the embodiments, structures, features and effects according to the present invention will be given with reference to the accompanying drawings and preferred embodiments.
Example one
As shown in fig. 1, a method for accurately positioning a failure of a mechanical transmission system of a rocker arm of a coal mining machine is characterized by comprising the following steps:
step one, carrying out noise reduction processing on an acquired vibration signal by utilizing a wavelet;
performing FFT (fast Fourier transform) on the vibration signal subjected to noise reduction to obtain a spectrogram of the vibration signal;
step three, comparing and analyzing the normal rocker arm spectrogram and the fault rocker arm spectrogram to obtain the vibration characteristic frequency of the fault part, and performing primary positioning on the fault part;
step 301, the fault of the gear is mainly reflected on the rotation frequency and the meshing frequency of the gear, and the calculation formula of the rotation frequency is as follows: f. ofrN/60, wherein n is the rotating speed of the driving wheel, and the meshing frequency calculation formula is as follows: f. ofm＝z×frWherein z is the number of gear teeth, frIs the frequency conversion of the gear;
step 302, comparing and analyzing the frequency spectrograms of the vibration signals of the normal rocker arm and the fault rocker arm to obtain vibration characteristic frequency with large amplitude change or frequency multiplication of the vibration characteristic frequency;
step 303, comparing the fault characteristic frequency obtained in the step 302 with the vibration characteristic frequency calculated in the step 301, and if the frequency found in the step 302 is a multiple relation of the characteristic frequency calculated in the step 301, judging that one or more gears corresponding to the frequency found in the step 302 have faults;
taking the coal mining machine MG1480 as an example, the method is specifically described as follows:
step A, calculating the rotation frequency and the meshing frequency of the gear of the coal mining machine MG1480 obtained according to the step 301, and displaying the rotation frequency and the meshing frequency in a table 1;
TABLE 1 MG1480 Gear characteristic frequencies
Step B, comparing the frequency spectrum diagrams of the rocker arm transmission system in the normal state and the fault state, as shown in FIGS. 2(a) and (B), it can be seen that the corresponding amplitude value near 472.5Hz is changed from 0.218 to 9.681 and is changed to 44.4 times of the original value, the corresponding amplitude value near 1890Hz is changed from 0.7769 to 5.05 and is changed to 6.5 times of the original value, and 1890Hz is 4 times of frequency corresponding to 472.5;
step C, comparing the frequency found in the step B with the frequency calculated in the step A to obtain the frequency in the table 1, and judging that the fault occurs in one or more gears of Z18, Z19, Z20 and Z21;
step four, simultaneously optimizing the optimal center frequency f of Morlet wavelet base based on minimum wavelet Shannon entropycAnd a bandwidth parameter fb；
In the monitoring of impulse signals, it is necessary to highlight the characteristic components and suppress the irrelevant components, i.e. to optimize the bandwidth parameters so that the Morlet wavelet basis functions have the greatest similarity to the characteristic components. Wavelet Shannon entropy is a good evaluation criterion. The wavelet Shannon entropy reflects the uniformity of the probability distribution. The most uncertain probability distribution (equiprobable distribution) has the largest entropy value, when the entropy value is the smallest, the corresponding Morlet wavelet basis function matches the feature components. The wavelet Shannon entropy calculation formula is as follows:
in the formula
pi u,vFor uncertain probability distribution, we can obtain from the wavelet transform coefficients:
the following is to verify that the method can obtain the best time-frequency resolution of the signal by using an analog signal. The analog fault impact signal is synthesized by 3 frequency signals with the frequencies of 1/10, 1/15 and 1/10, the sampling frequency is 1Hz, the number of sampling points is 1000, and the time domain waveform is shown in figure 3. Center frequency fcHas a value range of [0.05,0.7 ]]Step size of 0.05, fbHas a value range of [0.5,50 ]]The step size is 0.5. Center of a shipThe relationship curves between the frequency and the wavelet Shannon entropy and the relationship curves between the bandwidth parameter and the wavelet Shannon entropy are shown in fig. 4 and 5 respectively. When the minimum value of the wavelet Shannon entropy is taken, the corresponding Morlet wavelet basis best matches the impact component.
Take fbAre respectively 5, 16.5, 95, fc0.2, 0.5, 0.8, respectively, and the time-frequency diagrams of the Morlet continuous wavelet transform are shown in fig. 6 and 7. As can be seen from FIG. 6, fc＝0.5,fbFrequency resolution ratio f of 5-hour signalc＝0.5,fbDifference at 95 fc＝0.5,fbFrequency resolution ratio f of signal at 95 ═ timec＝0.5,fbThe difference was 16.5. As can be seen from FIG. 7, fc＝0.2,fbFrequency resolution ratio f of signal at 16.5c＝0.5,fbDifference at 16.5, fc＝0.8,fbFrequency resolution ratio f of signal at 16.5c＝0.5,fbThe difference was 16.5. Therefore, when fc＝0.5,fbWhen 16.5, the time-frequency resolution of the signal is the best. The central frequency and bandwidth parameters obtained at this time make the wavelet and the impact component reach the best matching, the parameters determined by the method are used for envelope demodulation, and the demodulated signal has the best time-frequency resolution.
Taking MG1480 as an example: the sampling frequency is 51.2KHz, and the signal sampling point is 204800. Center frequency fcIn the range of [0.1,3.0 ]]Step size of 0.1, bandwidth parameter fbIn the range of [0.1,2.0]The step size is 0.1. FIGS. 8 and 9 show the relationship between wavelet Shannon entropy and center frequency, and wavelet Shannon entropy and bandwidth, respectively, as seen when fc＝1.0，fbAt 0.7, the wavelet Shannon entropy value is the smallest and the corresponding Morlet wavelet basis matches best the impulse component of the actual signal.
Step five, calculating the optimal scale of Morlet envelope demodulation analysis according to the fault vibration characteristic frequency and the scale-frequency relation obtained by comparing and analyzing the normal rocker arm spectrogram and the fault rocker arm spectrogram;
the scale and frequency have the following relationship:
fi＝fc×fs/ai
wherein f isiIs the actual frequency, fcIs the center frequency, f, of the Morlet waveletsIs the sampling frequency, aiIs the scale of the wavelet transform.
The sampling frequency of the MG1480 of the coal mining machine is 51.2KHz, and the central frequency fc1.0. Thus, the optimal scale for the Morlet wavelet transform is 108, again according to FIG. 2.
And sixthly, performing Morlet envelope demodulation spectrum analysis according to the determined optimal parameters.
A pair of Morlet wavelet envelope demodulation spectra between a normal rocker arm and a failed rocker arm of an MG1480 shearer is shown in fig. 10(a) and 10 (b). From the data shown in Table 2, it can be seen that the critical frequencies are about 17.5Hz, 25Hz and 2 multiples thereof, and that the amplitude at 17.5Hz in the fault state is much greater than that in the normal state, up to 92.21 times. Therefore, the fault is likely to be at a rotational frequency of 17.5 Hz. Thus, failure occurred in one or both gears in Z17 and Z18;
TABLE 2 Morlet envelope solution spectral-value comparison of key frequencies of MG1480
And step seven, integrating the rotating frequency of the fault part obtained by the comparison and analysis of the self-adaptive continuous complex Morlet wavelet envelope demodulation spectrums of the normal rocker arm and the fault primary positioning obtained by FFT conversion, and realizing the accurate positioning of the fault part.
Taking MG1480 shearer as an example: judging that the fault occurs in one or more gears of Z18, Z19, Z20 and Z21 in the step C, judging that the fault occurs in one or two gears of Z17 and Z18 in the step six, and accurately positioning the fault at the common gear Z18 which is judged to possibly have the fault in the two steps compared with the step C and the step six.
The Morlet wavelet function is a square exponential decay function, the waveform of the Morlet wavelet function is similar to the fault signal of the mechanical transmission device, and the Morlet wavelet function has good time-frequency localization capability, so that the Morlet wavelet function has effectiveness as a fault diagnosis wavelet base of the mechanical transmission device of the coal mining machine in wavelet packet demodulation. Specifically, the method can perform preliminary fault location on the coal cutter rocker arm vibration signal by combining FFT (fast Fourier transform) with the meshing frequency of the gear; the invention simultaneously optimizes Morlet wavelet shape parameter f based on minimum Shannon entropycAnd fbThe signal demodulated by using the optimized parameter envelope has the optimal time-frequency resolution, so that the problem that the optimal parameter of complex Morlet transformation is difficult to obtain is solved; according to the invention, the fault vibration characteristic frequency obtained by the comparison and analysis of the spectrogram of the normal rocker arm and the fault part rotation frequency obtained by the comparison and analysis of the self-adaptive continuous complex Morlet wavelet packet demodulation spectrum can accurately realize the accurate positioning of the fault part.
The invention is beneficial to preventing the occurrence of the failure of the mechanical transmission device of the coal mining machine, ensuring the safe operation of the coal mining machine and changing the preventive regular maintenance into the predictive maintenance, thereby having important practical significance for greatly improving the after-sale service efficiency of the coal mining machine manufacturing enterprises, safely and efficiently producing coal mines, improving the maintenance efficiency and reducing the maintenance cost.
Claims (5)
1. The method for accurately positioning the fault of the mechanical transmission system of the rocker arm of the coal mining machine is characterized by comprising the following steps of:
collecting rocker arm vibration signals in normal and fault states, and performing noise reduction processing on the collected vibration signals by using a wavelet transformation method;
performing FFT (fast Fourier transform) on the vibration signal subjected to noise reduction to obtain a spectrogram of the vibration signal;
step three, comparing and analyzing the normal rocker arm spectrogram and the fault rocker arm spectrogram to obtain the vibration characteristic frequency of the fault part, and performing primary positioning on the fault part;
step four, comparing and analyzing the normal and fault rocker arm continuous complex Morlet wavelet envelope demodulation spectrums to obtain the fault part rotation frequency;
and step five, accurately positioning the fault position by combining self-adaptive continuous complex Morlet wavelet envelope demodulation analysis and FFT (fast Fourier transform) transformation.
2. The method for accurately positioning the fault of the mechanical transmission system of the rocker arm of the coal mining machine according to claim 1, is characterized in that: the third step comprises the following specific steps:
step 301, calculating a vibration characteristic frequency including a rotation frequency and a meshing frequency of the gear, wherein a rotation frequency calculation formula is as follows: f. ofrN/60, wherein n is the rotating speed of the driving wheel, and the meshing frequency calculation formula is as follows: f. ofm＝z×frWherein z is the number of gear teeth;
step 302, comparing and analyzing the frequency spectrograms of the vibration signals of the normal rocker arm and the fault rocker arm to obtain vibration characteristic frequency with large amplitude change or frequency multiplication of the vibration characteristic frequency, namely the fault characteristic frequency;
step 303, comparing the fault characteristic frequency obtained in step 302 with the vibration characteristic frequency calculated in step 301, and if the fault characteristic frequency is a multiple relation of the characteristic frequency, determining that one or more of the gears corresponding to the frequency found in step 302 have faults.
3. The method for accurately positioning the fault of the mechanical transmission system of the rocker arm of the coal mining machine as claimed in claim 2, is characterized in that: the vibration characteristic frequency of the gear is meshing frequency and rotation frequency.
4. The method for accurately positioning the fault of the mechanical transmission system of the rocker arm of the coal mining machine according to claim 1, is characterized in that: the fourth step comprises the following specific steps:
401. optimal center frequency f for simultaneously optimizing Morlet wavelet base based on minimum Shannon entropycAnd an optimal bandwidth parameter fbThe optimal matching of Morlet wavelet and fault vibration impact signal is realized, and the optimal time frequency resolution of vibration signal is realized;
402. calculating the optimal scale of Morlet envelope demodulation analysis according to the fault vibration characteristic frequency and the scale-frequency relation obtained by comparing and analyzing the normal rocker arm spectrogram and the fault rocker arm spectrogram;
403. and according to the determined optimal parameters, performing Morlet envelope demodulation spectrum comparison analysis on the vibration signals of the normal rocker arm and the fault rocker arm, thereby obtaining the rotation frequency of the fault part.
5. The method for accurately positioning the fault of the mechanical transmission system of the rocker arm of the coal mining machine as claimed in claim 4, is characterized in that: the specific steps of 402 are: the scale-frequency has the following relationship:
fi＝fc×fs/ai
wherein f isiIs the actual vibration frequency, fcIs the center frequency, f, of the Morlet waveletsIs the sampling frequency, aiIs the scale of the wavelet transform.
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| CN113589208A (en) * | 2021-07-23 | 2021-11-02 | 深圳市联影高端医疗装备创新研究院 | Frequency determination method and device of radio frequency system, magnetic resonance equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111751134A (en) * | 2020-06-22 | 2020-10-09 | 西安科技大学 | VMD and RLS-based coal mining machine vibration signal noise reduction method |
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