CN112697473A - EMD and GZC machine state monitoring method and device - Google Patents

EMD and GZC machine state monitoring method and device Download PDF

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CN112697473A
CN112697473A CN202011238853.0A CN202011238853A CN112697473A CN 112697473 A CN112697473 A CN 112697473A CN 202011238853 A CN202011238853 A CN 202011238853A CN 112697473 A CN112697473 A CN 112697473A
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豆春玲
寇兴磊
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Shandong Kerishen Intelligent Technology Co ltd
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Abstract

The invention discloses an EMD and GZC machine state monitoring method and a device, which decompose equipment vibration signals by using an EMD algorithm, remove noise components and trend items by using a nonlinear discrimination algorithm, reserve fractal signal components, interpolate extreme points by using a Newton interpolation function, fit envelopes by using a least square method, separate a frequency modulation part, estimate instantaneous frequency by using a GZC algorithm and calculate corresponding instantaneous scales, determine a vibration signal detrending result according to the analysis scales, calculate a multi-fractal spectrum of detrended signals, extract singular indexes corresponding to a left end point, a right end point and the extreme points of the multi-fractal spectrum as characteristic parameters of equipment operation states, identify the equipment operation states, deploy the algorithm to an equipment state monitoring device, can accurately distinguish the equipment operation states, and the equipment state monitoring device has good flexibility and portability, is convenient for engineering application.

Description

EMD and GZC machine state monitoring method and device
Technical Field
The invention relates to the field of equipment state monitoring and fault diagnosis, in particular to an EMD and GZC machine state monitoring method and device.
Background
The device vibration signal contains rich fractal features that can describe the operating state of the device. The box dimension, power spectrum analysis and re-standard range method can estimate the single-fractal parameters of stationary signals, and the de-trend fluctuation analysis (DFA) can estimate the single-fractal dimension of non-stationary signals. However, when the device fails, the vibration signal is usually non-stationary and has a multi-fractal characteristic, and the conventional fractal dimension estimation method generates a relatively large error. The multi-fractal detrending fluctuation analysis (MFDF) can estimate multi-fractal parameters of non-stationary signals, but the MFDF method has the problems that the analysis scale needs to be manually determined, the fitting polynomial trend order is difficult to determine, and the data segment is discontinuous. Currently, there is a document that proposes an MFDFA version (MFDFAemd) based on EMD to solve the problem of MFDFA. However, the linear filtering method adopted by mfdfame is easy to destroy the fractal structure of the original signal, and there is a negative frequency phenomenon, and these defects seriously affect the application effect of mfdfame. In summary, in the prior art, it is difficult to accurately extract the multi-fractal features of the device vibration signal, and it is difficult to accurately detect the device operating state.
Disclosure of Invention
The invention provides a method and a device for monitoring the states of an EMD machine and a GZC machine (the method provided by the invention is abbreviated as MFDFooed) aiming at the defects. The method provided by the invention is adopted to analyze the equipment vibration signal, can effectively extract the multi-fractal characteristics of the equipment vibration signal, overcomes the problems that the analysis scale of the MFDF method needs to be manually determined, the fitting polynomial trend order is difficult to determine and the data section is discontinuous, solves the phenomena of original signal fractal structure damage and negative frequency existing in the MFDF method, and has the advantages of high accuracy and precision of analysis results, high accuracy of equipment operation state identification results and the like.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: a method for monitoring the state of an EMD machine and a GZC machine is characterized in that: the method comprises the following steps:
step 1: measuring a device vibration signal x (k) by using an acceleration sensor at a sampling frequency fs, wherein k =1,2, …, N and N are lengths of the sampling signal;
step 2: decomposition by empirical mode (The Empical Mode Decomposition, EMD) algorithm decomposes the signal x (k) into the sum of n components and a trend term, i.e.
Figure 980934DEST_PATH_IMAGE001
Wherein c isi(k) Representing the i-th component, r, obtained by the EMD algorithmn(k) Represents the trend term resulting from the EMD algorithm, in this example, n = 10;
and step 3: eliminating noise components and trend terms from EMD decomposition results by adopting a nonlinear discrimination algorithm, and reserving components c containing fractal featuresf(k) F =1,2, …, p, p represents the number of residual components after filtering;
and 4, step 4: determination of cf(k) Respectively using a Newton interpolation function to the local maximum value and the local minimum value of cf(k) The local maximum and local minimum are interpolated, and c is fitted by least square methodf(k) The upper envelope u (k) and the lower envelope l (k), then cf(k) Is defined as
Figure 865713DEST_PATH_IMAGE002
The symbol | x | represents taking the absolute value of x;
and 5: repeatedly executing formula
Figure 945796DEST_PATH_IMAGE003
m times, j =1,2, …, m, until
Figure 144696DEST_PATH_IMAGE004
To obtain cf(k) Frequency modulation part FMm(k),ej(k) Represents cj(k) Envelope of cj(k)=FM(j-1)(k),c1(k)= cf(k);
Step 6: FM was calculated using the Generalized zero-crossing method (GZC)m(k) Average period of (2) to obtain cf(k) S of instantaneous dimensionf
And 7: when the scale is s, the detrending result of the vibration signal x (k) is
Figure 875891DEST_PATH_IMAGE005
And 8: will be provided withY s (k) Divided into non-overlappingN s Length of segment beingsDue to data lengthNUsually cannot be removedsSo that a segment of data remains unavailable; in order to fully utilize the length of the data, the data is segmented with the same length from the reverse direction of the data, so that 2 is obtainedN s Segment data;
and step 9: calculate variance of each piece of data:
Figure 869255DEST_PATH_IMAGE006
Figure 935169DEST_PATH_IMAGE007
step 10: calculating a q-th order function:
Figure 672181DEST_PATH_IMAGE009
step 11: changing the value of s, s = sfF =1,2, …, p, repeating the above steps 3 to 10, resulting in a variance function F about q and sq(s);
Step 12: if it is notx(k) Presence of fractal features, thenF q (s) And sizesThere is a power law relationship between:F q (s)~s H q()h (q) represents the generalized Hurst index of x (k);
when in useqWhen the value is not less than 0, the reaction time is not less than 0,H(0) determined by the logarithmic averaging procedure defined by:
Figure 257883DEST_PATH_IMAGE010
step 13: calculating a standard scale index τ (q) = qH (q) -1 for the signal x (k), in this case q is taken in the range (-5, 5);
step 14: calculating the singular index α and the multifractal spectrum f (α) of the signal x (k):
α=H(q)+q H(q),
f (alpha) = q (alpha-H (q)) +1, wherein H(q) represents the first derivative of h (q);
step 15: extracting singular indexes corresponding to a left end point, a right end point and an extreme point of the multi-fractal spectrum f (alpha), and describing the running state of the equipment by using the 3 parameters;
step 16: the method in the steps is deployed on a state monitoring device to monitor the state of equipment.
Further, the Empirical Mode Decomposition (EMD) algorithm in step 2 comprises the following steps:
1) the first screening process: finding out upper and lower local extreme points of data x (k), fitting the upper and lower local extreme points by cubic spline curve to obtain local maximum envelope and local minimum envelope of signal x (k), averaging values of corresponding points of the two envelopes to obtain an average curve m1
Then, the original signal x (k) and the average curve m are obtained1A difference of (i) thath 10=x(k)-m 1Ending the first screening process;
2) the second screening process:h 10re-regarded as the original signal, and the above step 1) is repeated to obtain the signalh 11= h 10-m 11Here parameterm 11Representsh 10Is repeated j times until the mean value of the curve is 0.2<SD<0.3 the screening process is stopped, here
Figure 687727DEST_PATH_IMAGE011
At this time, the process of the present invention,h j1= h j1(-1)-m j1in this case, it can be considered thath j1Is an Intrinsic Mode Function (IMF), and the 1 st IMF is defined asc 1=h j1
3) Subtracting from the original signalc 1Is obtained byr 1=x(k)-c 1Then will ber 1When new data is taken, the two steps are repeated, and the 2 nd IMF can be obtained;
4) repeating the operation of step 3) to obtain a series of IMFs ifr n Having become a monotonic curve, the screening process stops, and the original signal is finally decomposed into the following form:
Figure 742402DEST_PATH_IMAGE012
further, the step 3 nonlinear discrimination algorithm includes the following steps:
1) performing rearrangement operation and substitution operation on signals c (k), and using c as data obtained by rearrangement operationshuf(k) Indicating that data obtained after the substitution operation are csurr(k) Represents;
2) for c (k), cshuf(k) And csurr(k) Performing multi-fractal Detrended Fluctuation Analysis (MFDF) respectively to obtain a generalized Hurst index curve, wherein the generalized Hurst index curve of c (k) is represented by H (q); c. Cshuf(k) Generalized Hurst exponential curve of (1) using Hshuf(q) represents; c. Csurr(k) Generalized Hurst exponential curve of (1) using Hsurr(q) represents;
3) two parameters e are defined1And e2
Figure 17526DEST_PATH_IMAGE014
Figure 723314DEST_PATH_IMAGE016
If e is1And e2All less than 10%, the signal c (k) is discriminated as a noise component or a trend term, c (k) represents the signal component resulting from the EMD algorithm.
Further, the data rearrangement operation in the step 1) comprises the following steps: randomly randomizing the order of the components c (k).
Further, the data replacement operation in the step 1) comprises the following steps:
1) performing a discrete fourier transform on component c (k) to obtain the phase of component c (k);
2) replacing the original phase of component c (k) with a set of pseudo-independent identically distributed numbers located in the (-pi, pi) interval;
3) performing inverse discrete Fourier transform on the frequency domain data subjected to phase substitution to obtain data cIFFT(k) To obtain data cIFFT(k) The real part of (a).
Further, the MFDFA method in step 2) includes the following steps:
1) contour of construction x (k), k =1,2, …, NY(i):
Figure 58480DEST_PATH_IMAGE017
Figure 98986DEST_PATH_IMAGE018
x (k) represents c (k) or c in step 2) of claim 3shuf(k) Or csurr(k);
2) Signal profileY(i) Divided into non-overlappingN s Length of segment beingsDue to data lengthNUsually cannot be removedsSo that a segment of data remains unavailable; in order to fully utilize the length of the data, the data is segmented with the same length from the reverse direction of the data, so that 2 is obtainedN s Segment data;
3) fitting a polynomial trend of each section of data by using a least square method, and then calculating the variance of each section of data:
Figure 177800DEST_PATH_IMAGE019
Figure 738095DEST_PATH_IMAGE020
y v (i) Is a first of fittingvTrend of the segment data, if the fitted polynomial trend ismOrder, then note the de-trending process as (MF-) DFAm(ii) a In this example, m = 1;
4) calculate the firstqAverage of the order fluctuation function:
Figure 509742DEST_PATH_IMAGE021
5) if it is notx(k) Presence of self-similar features thenqMean value of order fluctuation functionF q (s) And time scalesThere is a power law relationship between:
F q (s)~s H q()
when in useqIf =0, the formula in step 4) diverges, at which timeH(0) Determined by the logarithmic averaging procedure defined by:
Figure 273429DEST_PATH_IMAGE010
6) taking logarithm of both sides of the formula in step 5) to obtain ln [ 2 ]F q (s)]=H(q)ln(s)+ccIs constant, whereby the slope of the straight line can be obtainedH(q)。
Further, the least square method in the step 4 comprises the following steps: for x (t), t =1,2, …, n, x (t) represents the pair c in step 4f(k) A sequence or pair c generated by interpolating the local maxima off(k) The local minima of the sequence, n represents the length of the interpolated sequence,
1) in advance ofSelecting a set of functions rk(t),k=1,2,…,m,m<n, constructioning function
f(t)=a1r1(t)+a2r2(t)+…+ amrm(t) in which rk(t) represents a second order polynomial, a third order polynomial, a hyperbolic curve, an exponential curve, a logarithmic curve or a complex function curve;
2) calculating a least squares metric
Figure 155935DEST_PATH_IMAGE022
3) Let J pair akPartial derivative of
Figure 570736DEST_PATH_IMAGE023
K =1,2, …, m, when (a)1,a2,…,am)T =(RTR)-1RTX, X=(x(1),x(2),…,x(n)) T
Figure 513284DEST_PATH_IMAGE024
Wherein R isTRepresenting the transposed matrix of R, R-1An inverse matrix representing R;
4)rk(t) respectively selecting a second-order polynomial, a third-order polynomial, a hyperbolic curve, an exponential curve, a logarithmic curve and a composite function curve for calculation, then comparing least square indexes J generated by various curve forms, and selecting a curve form r corresponding to the minimum J as the curve formk(t) form (a).
Further, the GZC method in step 6 includes the following steps:
1) determining local maximum, minimum and zero points of the signal c (k), k =1,2, …, N, c (k) = FMm(k);
2) Counting the time intervals T between two continuous local maximum points, two continuous local minimum points, two continuous rising zeros and two continuous falling zeros according to the time sequence1j,j=1,2,…,N1,N1Representing the number of time intervals, and counting adjacent local maximum points, local minimum points and adjacent local poles in time orderSmall and local maximum points, adjacent rising and falling zeros, and time intervals T between adjacent falling and rising zeros2j,j=1,2,…,N2,N2Representing the number of time intervals, and counting the time intervals T between adjacent local maximum points and descending zero points, adjacent descending zero points and local minimum points, adjacent local minimum points and ascending zero points, and adjacent ascending zero points and local maximum points according to the time sequence3j,j=1,2,…,N3,N3Calculating the average period T of the signal c (k) representing the number of time intervals
Figure 268662DEST_PATH_IMAGE026
The temporal scale of the signal c (k) is sf=T。
Based on the EMD and GZC machine state monitoring method, the device for realizing the method is characterized in that: the state monitoring device in the step 16 comprises the following parts: the system comprises a data line, an acceleration sensor, a data acquisition card, a case, a notebook computer and signal analysis software, wherein the acceleration sensor is connected with the data acquisition card through the data line, the data acquisition card is installed in the case, the case is connected with the notebook computer through the data line, the signal analysis software is installed on the notebook computer, and the signal analysis software is used for realizing the algorithm.
The method comprises the following steps:
step 1), the following steps: collecting a vibration signal;
step 2), the step of: decomposing an original signal into different component sum forms, wherein some components correspond to noise and trend terms, and some components contain fractal features;
and 3, step 3: removing noise components and trend items in the signal decomposition result by using a nonlinear discrimination algorithm, and only reserving signal components containing fractal features;
4) to 6): separating the frequency modulation part of each fractal signal component, and estimating the instantaneous frequency and the instantaneous scale of each fractal signal component by utilizing the GZC;
step 7), the steps of: selecting proper fractal signal components according to the analysis scale, and summing the selected fractal signal components to be used as a signal de-trend result corresponding to the analysis scale;
8) to 14): performing fluctuation analysis on the signal detrending result corresponding to each analysis scale to obtain a multi-fractal spectrum of the original signal;
step 15): extracting singular indexes corresponding to a left end point, a right end point and an extreme point of the multi-fractal spectrum, and taking the three parameters as characteristic parameters of the running state of the equipment;
16) step: deploying the algorithm on an equipment state monitoring device, and monitoring the equipment state;
by adopting the technical scheme, compared with the prior art, the invention has the following advantages:
1) the vibration signal is adaptively decomposed by adopting an EMD method, the noise component and the trend term are removed according to a nonlinear filtering method, the fractal structure of the original signal can be protected, and the damage of the linear filtering method to the fractal structure of the original signal is avoided;
2) a frequency modulation part for separating the signal components, and estimating the instantaneous frequency of the signal components by using the GZC, so that the instantaneous frequency can be ensured to keep a positive value, and the negative frequency phenomenon is avoided;
3) calculating corresponding instantaneous scale according to the instantaneous frequency of the signal component, and performing fluctuation analysis according to the instantaneous scale of the signal component, so that the defect of manually setting the scale is avoided;
4) the EMD method is used for automatically determining the type of the signal trend, the continuity of the signal trend is ensured, and the defects of the prior art are effectively overcome;
5) the accuracy and precision of the analysis result are high, and the accuracy of the identification result of the running state of the equipment is high.
The invention is further illustrated with reference to the following figures and examples.
Drawings
FIG. 1 is a flow chart of the method of the present invention in an embodiment of the present invention;
FIG. 2 is a schematic diagram of an apparatus state monitoring device according to an embodiment of the present invention;
fig. 3 is a multi-fractal simulation signal generated by a multi-fractal cascade model in the embodiment of the present invention;
fig. 4 is an instantaneous frequency of a multi-fractal simulation signal obtained by using the MFDFAemd method in the embodiment of the present invention, where the number of signal components is 10;
fig. 5 is an instantaneous frequency of a multi-fractal simulation signal obtained by using the mfdfoomd method in the embodiment of the present invention, where the number of signal components is 10;
FIG. 6 is a comparison graph of the multi-fractal simulation signal analysis results respectively using MFDF, MFDFEMd and MFDFEMd methods in the embodiment of the present invention;
FIG. 7 is a diagram illustrating the calculation results of two non-linear discriminant parameters, wherein the symbols "circle" and "square" represent e1 and e2, respectively;
FIG. 8 is a comparison diagram of analysis results of noisy multi-fractal simulation signals respectively using MFDF, MFDFame, and MFDFAEmd methods in the embodiment of the present invention;
FIG. 9 is a diagram illustrating correlation coefficients between each signal component and an original signal obtained by EMD according to an embodiment of the present invention;
FIG. 10 is a comparison diagram of analysis results of noisy multi-fractal simulation signals respectively using MFDFA, MFDFAEmd based on correlation filtering, and MFDFAEmd based on correlation filtering in the embodiment of the present invention;
FIG. 11 shows four vibration signals of the gearbox in the embodiment of the invention, wherein (a) - (d) respectively represent normal, light scratch, heavy scratch and broken tooth gear states;
FIG. 12 is a multi-fractal spectrum of the four gearbox vibration signals obtained using MFDFA in an embodiment of the present invention;
FIG. 13 is a multi-fractal spectrum of the four gearbox vibration signals obtained by using MFDFame in the embodiment of the present invention;
FIG. 14 is a multi-fractal spectrum of the four gearbox vibration signals obtained by using MFDFoomd in the embodiment of the present invention;
fig. 15 is a classification result of singular indexes corresponding to left, right, and extreme points of a multi-fractal spectrum obtained by MFDFA on the four gear box states in the embodiment of the present invention, where the "circle", "square", "plus", and "diamond" symbols represent normal, light, heavy, and broken gear states, respectively;
fig. 16 is a classification result of singular indexes corresponding to left end point, right end point and extreme point of a multi-fractal spectrum obtained by MFDFAemd on the states of the four gearboxes in the embodiment of the present invention, where symbols "circle", "square", "plus" and "diamond" represent normal, light scratch, heavy scratch and broken tooth gear states, respectively;
fig. 17 shows the classification results of the singular indexes corresponding to the left end point, the right end point, and the extreme point of the multi-fractal spectrum obtained from MFDFAoemd according to the embodiment of the present invention on the states of the four gearboxes, where the symbols "circle", "square", "plus", and "diamond" represent the normal, light, heavy, and broken gear states, respectively.
Detailed Description
In an embodiment, as shown in fig. 1 and fig. 2, a method for monitoring the state of an EMD and a GZC machine is characterized in that: the method comprises the following steps:
step 1: measuring a device vibration signal x (k) by using an acceleration sensor at a sampling frequency fs, wherein k =1,2, …, N and N are lengths of the sampling signal;
step 2: the signal x (k) is decomposed into the sum of n components and a trend term, i.e., using an Empirical Mode Decomposition (EMD) algorithm
Figure 689279DEST_PATH_IMAGE001
Wherein c isi(k) Representing the i-th component, r, obtained by the EMD algorithmn(k) Represents the trend term resulting from the EMD algorithm, in this example, n = 10;
and step 3: eliminating noise components and trend terms from EMD decomposition results by adopting a nonlinear discrimination algorithm, and reserving components c containing fractal featuresf(k) F =1,2, …, p, p represents the number of residual components after filtering;
and 4, step 4: determination of cf(k) Respectively using a Newton interpolation function to the local maximum value and the local minimum value of cf(k) The local maximum and local minimum are interpolated, and c is fitted by least square methodf(k) Upper envelope u (k)And a lower envelope l (k), then cf(k) Is defined as
Figure 224166DEST_PATH_IMAGE002
The symbol | x | represents taking the absolute value of x;
and 5: repeatedly executing formula
Figure 337615DEST_PATH_IMAGE003
m times, j =1,2, …, m, until
Figure 75895DEST_PATH_IMAGE004
To obtain cf(k) Frequency modulation part FMm(k),ej(k) Represents cj(k) Envelope of cj(k)=FM(j-1)(k),c1(k)= cf(k);
Step 6: FM was calculated using the Generalized zero-crossing method (GZC)m(k) Average period of (2) to obtain cf(k) S of instantaneous dimensionf
And 7: when the scale is s, the detrending result of the vibration signal x (k) is
Figure 300203DEST_PATH_IMAGE028
And 8: will be provided withY s (k) Divided into non-overlappingN s Length of segment beingsDue to data lengthNUsually cannot be removedsSo that a segment of data remains unavailable; in order to fully utilize the length of the data, the data is segmented with the same length from the reverse direction of the data, so that 2 is obtainedN s Segment data;
and step 9: calculate variance of each piece of data:
Figure 424017DEST_PATH_IMAGE006
Figure 708368DEST_PATH_IMAGE007
step 10: calculating a q-th order function:
Figure DEST_PATH_IMAGE029
step 11: changing the value of s, s = sfF =1,2, …, p, repeating the above steps 3 to 10, resulting in a variance function F about q and sq(s);
Step 12: if it is notx(k) Presence of fractal features, thenF q (s) And sizesThere is a power law relationship between:F q (s)~s H q()h (q) represents the generalized Hurst index of x (k);
when in useqWhen the value is not less than 0, the reaction time is not less than 0,H(0) determined by the logarithmic averaging procedure defined by:
Figure 432479DEST_PATH_IMAGE010
step 13: calculating a standard scale index τ (q) = qH (q) -1 for the signal x (k), in this case q is taken in the range (-5, 5);
step 14: calculating the singular index α and the multifractal spectrum f (α) of the signal x (k):
α=H(q)+q H(q),
f (alpha) = q (alpha-H (q)) +1, wherein H(q) represents the first derivative of h (q);
step 15: extracting singular indexes corresponding to a left end point, a right end point and an extreme point of the multi-fractal spectrum f (alpha), and describing the running state of the equipment by using the 3 parameters;
step 16: the method in the steps is deployed on a state monitoring device to monitor the state of equipment.
The Empirical Mode Decomposition (EMD) algorithm in step 2 comprises the following steps:
1) the first screening process: finding out upper and lower local extreme points of data x (k), fitting the upper and lower local extreme points by cubic spline curve to obtainThe envelope of local maximum and local minimum of signal x (k), and averaging the values of the corresponding points of the two envelopes to obtain an average curve m1
Then, the original signal x (k) and the average curve m are obtained1A difference of (i) thath 10=x(k)-m 1Ending the first screening process;
2) the second screening process:h 10re-regarded as the original signal, and the above step 1) is repeated to obtain the signalh 11= h 10-m 11Here parameterm 11Representsh 10Is repeated j times until the mean value of the curve is 0.2<SD<0.3 the screening process is stopped, here
Figure 460478DEST_PATH_IMAGE011
At this time, the process of the present invention,h j1= h j1(-1)-m j1in this case, it can be considered thath j1Is an Intrinsic Mode Function (IMF), and the 1 st IMF is defined asc 1=h j1
3) Subtracting from the original signalc 1Is obtained byr 1=x(k)-c 1Then will ber 1When new data is taken, the two steps are repeated, and the 2 nd IMF can be obtained;
4) repeating the operation of step 3) to obtain a series of IMFs ifr n Having become a monotonic curve, the screening process stops, and the original signal is finally decomposed into the following form:
Figure 704378DEST_PATH_IMAGE012
the step 3 of the nonlinear discriminant algorithm comprises the following steps:
1) performing rearrangement operation and substitution operation on signals c (k), and using c as data obtained by rearrangement operationshuf(k) Representing, obtaining data after alternative operationsBy csurr(k) Represents;
2) for c (k), cshuf(k) And csurr(k) Performing multi-fractal Detrended Fluctuation Analysis (MFDF) respectively to obtain a generalized Hurst index curve, wherein the generalized Hurst index curve of c (k) is represented by H (q); c. Cshuf(k) Generalized Hurst exponential curve of (1) using Hshuf(q) represents; c. Csurr(k) Generalized Hurst exponential curve of (1) using Hsurr(q) represents;
3) two parameters e are defined1And e2
Figure 894050DEST_PATH_IMAGE030
Figure 606923DEST_PATH_IMAGE016
If e is1And e2All less than 10%, the signal c (k) is discriminated as a noise component or a trend term, c (k) represents the signal component resulting from the EMD algorithm.
The data rearrangement operation in the step 1) comprises the following steps: randomly randomizing the order of the components c (k).
The data replacement operation in the step 1) comprises the following steps:
1) performing a discrete fourier transform on component c (k) to obtain the phase of component c (k);
2) replacing the original phase of component c (k) with a set of pseudo-independent identically distributed numbers located in the (-pi, pi) interval;
3) performing inverse discrete Fourier transform on the frequency domain data subjected to phase substitution to obtain data cIFFT(k) To obtain data cIFFT(k) The real part of (a).
The MFDF method in the step 2) comprises the following steps:
1) contour of construction x (k), k =1,2, …, NY(i):
Figure 173033DEST_PATH_IMAGE017
Figure 537018DEST_PATH_IMAGE018
x (k) represents c (k) or c in step 2) of claim 3shuf(k) Or csurr(k);
2) Signal profileY(i) Divided into non-overlappingN s Length of segment beingsDue to data lengthNUsually cannot be removedsSo that a segment of data remains unavailable; in order to fully utilize the length of the data, the data is segmented with the same length from the reverse direction of the data, so that 2 is obtainedN s Segment data;
3) fitting a polynomial trend of each section of data by using a least square method, and then calculating the variance of each section of data:
Figure 897593DEST_PATH_IMAGE019
Figure 596296DEST_PATH_IMAGE020
y v (i) Is a first of fittingvTrend of the segment data, if the fitted polynomial trend ismOrder, then note the de-trending process as (MF-) DFAm(ii) a In this example, m = 1;
4) calculate the firstqAverage of the order fluctuation function:
Figure 966098DEST_PATH_IMAGE021
5) if it is notx(k) Presence of self-similar features thenqMean value of order fluctuation functionF q (s) And time scalesThere is a power law relationship between:
F q (s)~s H q()
when in useqIf =0, the formula in step 4) diverges, at which timeH(0) Determined by the logarithmic averaging procedure defined by:
Figure 919010DEST_PATH_IMAGE010
6) taking logarithm of both sides of the formula in step 5) to obtain ln [ 2 ]F q (s)]=H(q)ln(s)+ccIs constant, whereby the slope of the straight line can be obtainedH(q)。
The least square method in the step 4 comprises the following steps: for x (t), t =1,2, …, n, x (t) represents the pair c in step 4f(k) A sequence or pair c generated by interpolating the local maxima off(k) The local minima of the sequence, n represents the length of the interpolated sequence,
1) a set of functions r is selected in advancek(t),k=1,2,…,m,m<n, constructioning function
f(t)=a1r1(t)+a2r2(t)+…+ amrm(t) in which rk(t) represents a second order polynomial, a third order polynomial, a hyperbolic curve, an exponential curve, a logarithmic curve or a complex function curve;
2) calculating a least squares metric
Figure 716065DEST_PATH_IMAGE022
3) Let J pair akPartial derivative of
Figure 137950DEST_PATH_IMAGE023
K =1,2, …, m, when (a)1,a2,…,am)T =(RTR)-1RTX, X=(x(1),x(2),…,x(n)) T
Figure 311442DEST_PATH_IMAGE024
Wherein R isTRepresenting the transposed matrix of R, R-1An inverse matrix representing R;
4)rk(t) respectively selecting a second-order polynomial, a third-order polynomial, a hyperbolic curve, an exponential curve, a logarithmic curve and a composite function curve for calculation, then comparing least square indexes J generated by various curve forms, and selecting a curve form r corresponding to the minimum J as the curve formk(t) form (a).
The GZC method in the step 6 comprises the following steps:
1) determining local maximum, minimum and zero points of the signal c (k), k =1,2, …, N, c (k) = FMm(k);
2) Counting the time intervals T between two continuous local maximum points, two continuous local minimum points, two continuous rising zeros and two continuous falling zeros according to the time sequence1j,j=1,2,…,N1,N1Representing the number of time intervals, and counting the time intervals T between adjacent local maximum points and local minimum points, adjacent local minimum points and local maximum points, adjacent rising zero points and falling zero points, and adjacent falling zero points and rising zero points according to the time sequence2j,j=1,2,…,N2,N2Representing the number of time intervals, and counting the time intervals T between adjacent local maximum points and descending zero points, adjacent descending zero points and local minimum points, adjacent local minimum points and ascending zero points, and adjacent ascending zero points and local maximum points according to the time sequence3j,j=1,2,…,N3,N3Calculating the average period T of the signal c (k) representing the number of time intervals
Figure 384441DEST_PATH_IMAGE026
The temporal scale of the signal c (k) is sf=T。
Based on the above-mentioned EMD and GZC machine status monitoring method, the device implementing the method, the status monitoring device in step 16 includes the following parts: the system comprises a data line, an acceleration sensor, a data acquisition card, a case, a notebook computer and signal analysis software, wherein the acceleration sensor is connected with the data acquisition card through the data line, the data acquisition card is installed in the case, the case is connected with the notebook computer through the data line, the signal analysis software is installed on the notebook computer, and the signal analysis software is used for realizing the algorithm.
The performance of the algorithm of the present invention was verified using drive gearbox vibration data.
Experiment 1 the performance of the algorithm of the present invention was verified using a multi-fractal simulation signal generated by a multi-fractal cascade model.
Firstly, a multi-fractal cascade model is adopted
Figure DEST_PATH_IMAGE031
The generated multi-fractal simulation signal verifies the performance of the MFDFA, the MFDFAemd and the MFDFAemd. In this example, p =0.375 and n =14, the resulting multi-fractal simulation signal is shown in fig. 3. The instantaneous frequency of the multi-fractal simulation signal is calculated by using the MFDFAemd, and the result is shown in fig. 4. As can be seen from fig. 4, the instantaneous frequency calculated by mfdfame has many negative frequencies, and the mfdfame analysis result has a large error because the negative frequencies have no physical significance. The instantaneous frequency of the multi-fractal simulation signal is calculated by using the MFDFAoemd, and the result is shown in fig. 5. As can be seen from fig. 5, the instantaneous frequencies calculated by MFDFAoemd are all positive frequencies, so the MFDFAoemd analysis result is in line with the actual situation. Next, a multi-fractal spectrum of the multi-fractal simulation signal is calculated using MFDFA, MFDFAemd, and MFDFAoemd, respectively, and the result is shown in fig. 6. According to the results shown in fig. 6, it can be found through calculation that the average of the absolute errors between the fractal spectrum obtained from MFDFA and the theoretical value is 0.064, the average of the relative errors is 12.21%, the average of the absolute errors between the fractal spectrum obtained from mfdfame and the theoretical value is 0.035, the average of the relative errors is 6.57%, the average of the absolute errors between the fractal spectrum obtained from MFDFAoemd and the theoretical value is 0.024, and the average of the relative errors is 5.21%, so that the fractal spectrum obtained from MFDFAoemd is reduced by 62.50%, the average of the relative errors is reduced by 57.33%, and the fractal spectrum obtained from MFDFAoemd is reduced by 62.50% compared with the average of the absolute errors from MFDFAThe absolute error average value of the multi-fractal spectrum is reduced by 31.43 percent and the relative error average value is reduced by 21.70 percent compared with the multi-fractal spectrum obtained by MFDFAEmd. Fig. 7 shows the calculation results of two non-linear discrimination parameters in the embodiment of the present invention, and it can be seen that all signal components include fractal signal components. Then, a noisy signal with a signal-to-noise ratio of 20dB is constructed by a method of adding white Gaussian noise to the multi-fractal simulation signal. The multi-fractal spectrum of the noise-containing multi-fractal simulation signal was calculated by using MFDFA, MFDFAemd, and MFDFAemd, respectively, and the result is shown in fig. 8. According to the results shown in fig. 8, the multi-fractal spectrum obtained from mfdfaamd completely deviates from the theoretical value, the mean absolute error value of the multi-fractal spectrum obtained from MFDFAemd and the theoretical value is 0.084, the mean relative error value is 11.81%, the mean absolute error value of the multi-fractal spectrum obtained from MFDFAemd and the theoretical value is 0.039, and the mean relative error value is 5.44%, so that the multi-fractal spectrum obtained from MFDFAemd is reduced by 53.57% and the mean relative error value is reduced by 53.94% compared with the mean absolute error value of the multi-fractal spectrum obtained from MFDFAemd. It follows that MFDFAoemd has better noise immunity than MFDFA and mfdfaeemd. Fig. 9 shows the correlation coefficient between each signal component and the original signal obtained by EMD in the embodiment of the present invention, and it can be seen that the 7 th component has the weakest correlation with the original signal and should be removed from the original signal. Fig. 10 is a comparison diagram of analysis results of a noisy multi-fractal simulation signal by using MFDFA, mfdfame based on correlation filtering, and mfdfame based on correlation filtering, respectively, in the embodiment of the present invention. As can be seen from fig. 10, the analysis result of the noise-containing multi-fractal simulation signal by the mfdfame based on the correlation filtering and the mfdfame based on the correlation filtering completely deviates from the theoretical value, so that the fractal structure of the original signal is easily damaged by the correlation filtering method.
Experiment 2 the performance of the algorithm of the invention was verified using gearbox experimental signals.
The invention relates to a gearbox fault simulation experiment table for gearbox vibration data. This experiment simulates the process of a tooth from normal to failure by making different degrees of scoring on the root of a tooth until the tooth is finally completely destroyed. The gear box used in the experiment is in two-stage gear transmission, the number of gear teeth from the input end to the output end is respectively 25, 40, 22 and 55, the fault gear teeth are positioned on the input shaft gear, the rotating speed of the driving motor is 2000RMP, and the vibration signal of the gear box is measured by an acceleration sensor positioned on the shell of the input end. The collected vibration signals comprise four fault states of normal, light scratch, heavy scratch and broken tooth, and the vibration signals represent the process of the gear tooth from normal to failure to a certain extent. The vibration signal sampling frequency was 16384Hz, and 20 pieces of data with a length of 10000 points were collected in each gearbox state, and these four gearbox vibration signals are shown in fig. 11. Firstly, the four gearbox vibration signals are analyzed by using an MFDF method, and the multi-fractal spectrums corresponding to the four gearbox vibration signals are obtained as shown in FIG. 12, so that the multi-fractal spectrums corresponding to the light scratches and the heavy scratches are seriously overlapped. Then, the four gearbox vibration signals are analyzed by using an MFDFame method, and the multi-fractal spectrums corresponding to the four gearbox vibration signals are obtained as shown in FIG. 13, so that the multi-fractal spectrums corresponding to the four gearbox states are seriously overlapped. Finally, the four gearbox vibration signals are analyzed by using an MFDFoomd method, and the multi-fractal spectrums corresponding to the four gearbox vibration signals are obtained as shown in fig. 14, so that the multi-fractal spectrums of the vibration signals in the tooth breaking state are obviously different from the multi-fractal spectrums corresponding to the states of the other three gearboxes, and the multi-fractal spectrums corresponding to the normal, light-scratch and heavy-scratch gear states can be clearly separated when alpha is less than 0.4. Singular indexes corresponding to a left end point, a right end point and an extreme point of a multi-fractal spectrum obtained by the MFDF, MFDFAemd and MFDFAemd methods are respectively extracted to classify the four states of the gearbox, and the results are respectively shown in FIGS. 15-17. As can be seen from fig. 15, the normal state and the tooth breakage state can be correctly distinguished by using the singular indexes corresponding to the left end point, the right end point, and the extreme point of the multi-fractal spectrum obtained by the MFDFA method, but the light scratch state and the heavy scratch state cannot be distinguished, so that the gear box state recognition rate is 50%. As can be seen from fig. 16, the normal state and the tooth breakage state can be correctly distinguished by using the singular indexes corresponding to the left end point, the right end point and the extreme point of the multi-fractal spectrum obtained by the MFDFAemd method, but the light scratch state and the heavy scratch state cannot be distinguished, so that the gear box state recognition rate is 50%. As can be seen from fig. 17, the four gear box states can be correctly distinguished by using the singular indexes corresponding to the left end point, the right end point and the extreme point of the multi-fractal spectrum obtained by the MFDFAoemd method, so that the gear box state identification rate is 100%. It can be seen that the accuracy of the state identification of the gearbox can be improved by 50% by adopting the MFDFoomed method.
From the test results, it was assumed after analysis that:
1) the vibration signal is adaptively decomposed by adopting an EMD method, the noise component and the trend term are removed according to a nonlinear filtering method, the fractal structure of the original signal can be protected, and the damage of the linear filtering method to the fractal structure of the original signal is avoided;
2) the frequency modulation part for separating the signal components estimates the instantaneous scale of the signal components by utilizing the GZC, so that the instantaneous frequency can be ensured to keep a positive value, and the negative frequency phenomenon is avoided;
3) the fluctuation analysis is carried out according to the instantaneous scale of the signal component, so that the defect of manually setting the scale is avoided;
4) the EMD method is used for automatically determining the type of the signal trend, the continuity of the signal trend is ensured, and the defects of the prior art are effectively overcome;
5) the accuracy and precision of the analysis result are high, and the accuracy of the identification result of the running state of the equipment is high.
It should be appreciated by those skilled in the art that the foregoing embodiments are merely exemplary for better understanding of the present invention, and should not be construed as limiting the scope of the present invention as long as the modifications are made according to the technical solution of the present invention.

Claims (9)

1. A method for monitoring the state of an EMD machine and a GZC machine is characterized in that: the method comprises the following steps:
step 1: measuring a device vibration signal x (k) by using an acceleration sensor at a sampling frequency fs, wherein k =1,2, …, N and N are lengths of the sampling signal;
step 2: the signal x (k) is decomposed into the sum of n components and a trend term, i.e., using an Empirical Mode Decomposition (EMD) algorithm
Figure DEST_PATH_IMAGE002
Wherein c isi(k) Representing the i-th component, r, obtained by the EMD algorithmn(k) Represents a trend term derived from the EMD algorithm;
and step 3: eliminating noise components and trend terms from EMD decomposition results by adopting a nonlinear discrimination algorithm, and reserving components c containing fractal featuresf(k) F =1,2, …, p, p represents the number of residual components after filtering;
and 4, step 4: determination of cf(k) Respectively using a Newton interpolation function to the local maximum value and the local minimum value of cf(k) The local maximum and local minimum are interpolated, and c is fitted by least square methodf(k) The upper envelope u (k) and the lower envelope l (k), then cf(k) Is defined as
Figure DEST_PATH_IMAGE004
The symbol | x | represents taking the absolute value of x;
and 5: repeatedly executing formula
Figure DEST_PATH_IMAGE006
m times, j =1,2, …, m, until
Figure DEST_PATH_IMAGE008
To obtain cf(k) Frequency modulation part FMm(k),ej(k) Represents cj(k) Envelope of cj(k)=FM(j-1)(k),c1(k)= cf(k);
Step 6: FM was calculated using the Generalized zero-crossing method (GZC)m(k) Average period of (2) to obtain cf(k) S of instantaneous dimensionf
And 7: when the scale is s, the detrending result of the vibration signal x (k) is
Figure DEST_PATH_IMAGE010
And 8: will be provided withY s (k) Divided into non-overlappingN s Length of segment beingsDue to data lengthNUsually cannot be removedsSo that a segment of data remains unavailable; in order to fully utilize the length of the data, the data is segmented with the same length from the reverse direction of the data, so that 2 is obtainedN s Segment data;
and step 9: calculate variance of each piece of data:
Figure DEST_PATH_IMAGE012
Figure DEST_PATH_IMAGE014
step 10: calculating a q-th order function:
Figure 797451DEST_PATH_IMAGE002
step 11: changing the value of s, s = sfF =1,2, …, p, repeating the above steps 3 to 10, resulting in a variance function F about q and sq(s);
Step 12: if it is notx(k) Presence of fractal features, thenF q (s) And sizesThere is a power law relationship between:F q (s)~s H q()h (q) represents the generalized Hurst index of x (k);
when in useqWhen the value is not less than 0, the reaction time is not less than 0,H(0) determined by the logarithmic averaging procedure defined by:
Figure DEST_PATH_IMAGE018
step 13: calculating a standard scale index τ (q) = qH (q) -1 for the signal x (k);
step 14: calculating the singular index α and the multifractal spectrum f (α) of the signal x (k):
α=H(q)+q H(q),
f (alpha) = q (alpha-H (q)) +1, wherein H(q) represents the first derivative of h (q);
step 15: and extracting singular indexes corresponding to a left end point, a right end point and an extreme point of the multi-fractal spectrum f (alpha), and describing the running state of the equipment by using the 3 parameters.
2. The EMD and GZC machine state monitoring method according to claim 1, wherein: the Empirical Mode Decomposition (EMD) algorithm in step 2 comprises the following steps:
1) the first screening process: finding out upper and lower local extreme points of data x (k), fitting the upper and lower local extreme points by cubic spline curve to obtain local maximum envelope and local minimum envelope of signal x (k), averaging values of corresponding points of the two envelopes to obtain an average curve m1
Then, the original signal x (k) and the average curve m are obtained1A difference of (i) thath 10=x(k)-m 1Ending the first screening process;
2) the second screening process:h 10re-regarded as the original signal, and the above step 1) is repeated to obtain the signalh 11= h 10-m 11Here parameterm 11Representsh 10Is repeated j times until the mean value of the curve is 0.2<SD<0.3 the screening process is stopped, here
Figure DEST_PATH_IMAGE020
At this time, the process of the present invention,h j1= h j1(-1)-m j1in this case, it can be considered thath j1Is an Intrinsic Mode Function (IMF), and the 1 st IMF is defined asc 1=h j1
3) Subtracting from the original signalc 1Is obtained byr 1=x(k)-c 1Then will ber 1When new data is taken, the two steps are repeated, and the 2 nd IMF can be obtained;
4) repeating the operation of step 3) to obtain a series of IMFs ifr n Having become a monotonic curve, the screening process stops, and the original signal is finally decomposed into the following form:
Figure DEST_PATH_IMAGE022
3. the EMD and GZC machine state monitoring method according to claim 1, wherein: the step 3 of the nonlinear discriminant algorithm comprises the following steps:
1) performing rearrangement operation and substitution operation on signals c (k), and using c as data obtained by rearrangement operationshuf(k) Indicating that data obtained after the substitution operation are csurr(k) Represents;
2) for c (k), cshuf(k) And csurr(k) Performing multi-fractal Detrended Fluctuation Analysis (MFDF) respectively to obtain a generalized Hurst index curve, wherein the generalized Hurst index curve of c (k) is represented by H (q); c. Cshuf(k) Generalized Hurst exponential curve of (1) using Hshuf(q) represents; c. Csurr(k) Generalized Hurst exponential curve of (1) using Hsurr(q) represents;
3) two parameters e are defined1And e2
Figure 776909DEST_PATH_IMAGE004
Figure 868230DEST_PATH_IMAGE006
If e is1And e2All less than 10%, the signal c (k) is discriminated as a noise component or a trend term, c (k) represents the signal component resulting from the EMD algorithm.
4. The EMD and GZC machine state monitoring method according to claim 3, wherein: the data rearrangement operation in the step 1) comprises the following steps: randomly randomizing the order of the components c (k).
5. The EMD and GZC machine state monitoring method according to claim 3, wherein: the data replacement operation in the step 1) comprises the following steps:
1) performing a discrete fourier transform on component c (k) to obtain the phase of component c (k);
2) replacing the original phase of component c (k) with a set of pseudo-independent identically distributed numbers located in the (-pi, pi) interval;
3) performing inverse discrete Fourier transform on the frequency domain data subjected to phase substitution to obtain data cIFFT(k) To obtain data cIFFT(k) The real part of (a).
6. The EMD and GZC machine state monitoring method according to claim 3, wherein: the MFDF method in the step 2) comprises the following steps:
1) contour of construction x (k), k =1,2, …, NY(i):
Figure DEST_PATH_IMAGE028
Figure DEST_PATH_IMAGE030
x (k) represents claim3 c (k) or c in the step 2)shuf(k) Or csurr(k);
2) Signal profileY(i) Divided into non-overlappingN s Length of segment beingsDue to data lengthNUsually cannot be removedsSo that a segment of data remains unavailable; in order to fully utilize the length of the data, the data is segmented with the same length from the reverse direction of the data, so that 2 is obtainedN s Segment data;
3) fitting a polynomial trend of each section of data by using a least square method, and then calculating the variance of each section of data:
Figure DEST_PATH_IMAGE032
Figure DEST_PATH_IMAGE034
y v (i) Is a first of fittingvTrend of the segment data, if the fitted polynomial trend ismOrder, then note the de-trending process as (MF-) DFAm(ii) a 4) Calculate the firstqAverage of the order fluctuation function:
Figure DEST_PATH_IMAGE036
5) if it is notx(k) Presence of self-similar features thenqMean value of order fluctuation functionF q (s) And time scalesThere is a power law relationship between:
F q (s)~s H q()
when in useqIf =0, the formula in step 4) diverges, at which timeH(0) Determined by the logarithmic averaging procedure defined by:
Figure 939770DEST_PATH_IMAGE018
6) taking logarithm of both sides of the formula in step 5) to obtain ln [ 2 ]F q (s)]=H(q)ln(s)+ccIs constant, whereby the slope of the straight line can be obtainedH(q)。
7. The EMD and GZC machine state monitoring method according to claim 1, wherein: the least square method in the step 4 comprises the following steps: for x (t), t =1,2, …, n, x (t) represents the pair c in step 4f(k) A sequence or pair c generated by interpolating the local maxima off(k) The local minima of the sequence, n represents the length of the interpolated sequence,
1) a set of functions r is selected in advancek(t),k=1,2,…,m,m<n, constructioning function
f(t)=a1r1(t)+a2r2(t)+…+ amrm(t) in which rk(t) represents a second order polynomial, a third order polynomial, a hyperbolic curve, an exponential curve, a logarithmic curve or a complex function curve;
2) calculating a least squares metric
Figure DEST_PATH_IMAGE038
3) Let J pair akPartial derivative of
Figure DEST_PATH_IMAGE040
K =1,2, …, m, when (a)1,a2,…,am)T =(RTR)-1RTX, X=(x(1),x(2),…,x(n)) T
Figure DEST_PATH_IMAGE042
Wherein R isTRepresenting the transposed matrix of R, R-1RepresentsAn inverse matrix of R;
4)rk(t) respectively selecting a second-order polynomial, a third-order polynomial, a hyperbolic curve, an exponential curve, a logarithmic curve and a composite function curve for calculation, then comparing least square indexes J generated by various curve forms, and selecting a curve form r corresponding to the minimum J as the curve formk(t) form (a).
8. The EMD and GZC machine state monitoring method according to claim 1, wherein: the GZC method in the step 6 comprises the following steps:
1) determining local maximum, minimum and zero points of the signal c (k), k =1,2, …, N, c (k) = FMm(k);
2) Counting the time intervals T between two continuous local maximum points, two continuous local minimum points, two continuous rising zeros and two continuous falling zeros according to the time sequence1j,j=1,2,…,N1,N1Representing the number of time intervals, and counting the time intervals T between adjacent local maximum points and local minimum points, adjacent local minimum points and local maximum points, adjacent rising zero points and falling zero points, and adjacent falling zero points and rising zero points according to the time sequence2j,j=1,2,…,N2,N2Representing the number of time intervals, and counting the time intervals T between adjacent local maximum points and descending zero points, adjacent descending zero points and local minimum points, adjacent local minimum points and ascending zero points, and adjacent ascending zero points and local maximum points according to the time sequence3j,j=1,2,…,N3,N3Calculating the average period T of the signal c (k) representing the number of time intervals
Figure 201123DEST_PATH_IMAGE008
The temporal scale of the signal c (k) is sf=T。
9. Apparatus for implementing a method for monitoring the condition of an EMD and GZC machine according to any of claims 1 to 8, characterized in that: the device comprises the following parts: the system comprises a data line, an acceleration sensor, a data acquisition card, a case, a notebook computer and signal analysis software, wherein the acceleration sensor is connected with the data acquisition card through the data line, the data acquisition card is installed in the case, the case is connected with the notebook computer through the data line, the signal analysis software is installed on the notebook computer, and the signal analysis software is used for realizing the algorithm.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116992393A (en) * 2023-09-27 2023-11-03 联通(江苏)产业互联网有限公司 Safety production monitoring method based on industrial Internet of things
CN116992393B (en) * 2023-09-27 2023-12-22 联通(江苏)产业互联网有限公司 Safety production monitoring method based on industrial Internet of things

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