CN116108333A - ICEEMDAN-based distributed optical fiber temperature measurement signal noise reduction method - Google Patents

ICEEMDAN-based distributed optical fiber temperature measurement signal noise reduction method Download PDF

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CN116108333A
CN116108333A CN202211459476.2A CN202211459476A CN116108333A CN 116108333 A CN116108333 A CN 116108333A CN 202211459476 A CN202211459476 A CN 202211459476A CN 116108333 A CN116108333 A CN 116108333A
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imf component
temperature measurement
imf
optical fiber
noise reduction
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苏怀智
徐朗
周仁练
韩彰
刘明凯
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Hohai University HHU
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
    • G01K11/00—Measuring temperature based upon physical or chemical changes not covered by groups G01K3/00, G01K5/00, G01K7/00 or G01K9/00
    • G01K11/32—Measuring temperature based upon physical or chemical changes not covered by groups G01K3/00, G01K5/00, G01K7/00 or G01K9/00 using changes in transmittance, scattering or luminescence in optical fibres

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Abstract

The invention discloses a distributed optical fiber temperature measurement signal noise reduction method based on ICEEMDAN, which comprises the following steps: s1, acquiring a distributed optical fiber temperature measurement signal of a measurement object by using a distributed optical fiber temperature measurement system; s2, decomposing the distributed optical fiber temperature measurement signals by utilizing improved self-adaptive noise complete set empirical mode decomposition to obtain IMF components; s3, removing trend items of IMF components containing temperature drift trend items to obtain residual IMF components; s4, calculating the fuzzy entropy of the residual IMF component; s5, denoising the IMF component with the fuzzy entropy being greater than or equal to a set value by utilizing a wavelet threshold method; s6, reconstructing the IMF component after noise reduction, the residual IMF component and the IMF component with fuzzy entropy smaller than a set value to obtain a temperature measurement signal. The method for reducing the noise IMF component by combining the wavelet threshold method provided by the invention has the advantages that the noise IMF component is reduced, the temperature change information is accurately extracted, the influence of signal drift on a measurement result is effectively reduced, and the operation is simple.

Description

ICEEMDAN-based distributed optical fiber temperature measurement signal noise reduction method
Technical Field
The invention relates to a distributed optical fiber temperature measurement signal noise reduction method based on ICEEMDAN, and belongs to the technical field of distributed optical fiber sensing technology signals.
Background
The distributed optical fiber temperature measurement system has unique advantages, and can acquire distributed temperature change information of a monitored object along a sensing optical fiber layout line. The method is widely applied to the fields of power cables, oil and gas pipelines, fire detection, dam leakage detection and the like. However, as the sensing distance increases, due to the influence of factors such as transmission loss and system module performance, the distributed optical fiber temperature measurement system generates noise signals, temperature drift trend of the temperature measurement signals occurs, and the useful signals containing temperature change information are influenced by the noise signals and the temperature drift, which makes detection of the temperature change signals very difficult. While it is an efficient way to boost the technical index of the system hardware devices, this is subject to technical conditions and cost constraints. In addition to improving the performance of hardware devices, noise reduction of measurement signals by signal processing methods is another effective approach.
In the practical application process, the measurement signal of the distributed optical fiber temperature measurement system has the characteristics of large data volume, non-stable change and the like. The noise reduction methods such as cumulative average, singular value decomposition, wavelet transformation and the like need to manually set parameters, and have the problems of large calculated amount, insufficient self-adaption degree, non-ideal noise reduction effect of a single noise reduction method and the like. The empirical mode decomposition (Empirical Modal Decomposition, EMD) has the problem of mode aliasing, the aggregate empirical mode decomposition (Ensemble Empirical Mode Decomposition, EEMD) has the disadvantage of noise residuals and easily changing mode numbers, the adaptive noise full aggregate empirical mode decomposition (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, CEEMDAN) is prone to generating spurious modes in the early stages of signal decomposition, and the improved adaptive noise full aggregate empirical mode decomposition (Improved Complete Ensemble Empirical Mode Decomposition With Adaptive Noise, icemdan) effectively solves the above problems and has the advantages of data driving, adaptation and no prior knowledge, and is commonly used for the analysis of nonlinear non-stationary signals. Suppressing the drift trend of the distributed optical fiber temperature measurement signal is critical to guaranteeing the accuracy of the measurement result. In addition, how to select and reconstruct the natural mode function (Intrinsic Mode Function, IMF) component obtained after the measurement signal is decomposed by icemdan is important. If the IMF component is selected and processed improperly, the noise reduction effect is not ideal, and useful information is lost.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a distributed optical fiber temperature measurement signal noise reduction method based on ICEEMDAN, decomposes a measurement signal by utilizing the self-adaptive decomposition capability of the ICEEMDAN, distinguishes a noise signal component from a temperature change signal component by calculating the fuzzy entropy of an IMF component, further processes the noise signal component by combining a wavelet threshold noise reduction method, reduces the influence of the noise signal on the temperature change signal, and accurately extracts temperature change information. In order to achieve the above objective, the present invention provides a distributed optical fiber temperature measurement signal noise reduction method based on ICEEMDAN, comprising:
s1, acquiring a distributed optical fiber temperature measurement signal of a measurement object by using a distributed optical fiber temperature measurement system;
s2, decomposing the distributed optical fiber temperature measurement signals by utilizing improved self-adaptive noise complete set empirical mode decomposition to obtain IMF components;
s3, removing trend items of IMF components containing temperature drift trend items to obtain residual IMF components;
s4, calculating the fuzzy entropy of the residual IMF component;
s5, denoising the IMF component with the fuzzy entropy being greater than or equal to the set value epsilon by utilizing a wavelet threshold method;
s6, reconstructing the IMF component after noise reduction, the residual IMF component and the IMF component with fuzzy entropy smaller than a set value epsilon to obtain a temperature measurement signal.
Preferentially, in step S3, the trend term of the IMF component including the temperature drift trend term is removed, and the remaining IMF component is obtained, including the following steps:
s31, arranging IMF components from high frequency to low frequency, and taking the last IMF component as an IMF component containing a temperature drift trend term;
the IMF component of the temperature drift trend term includes a base offset and trend term,
and taking the basic offset as a first sampling point value of the IMF component containing the temperature drift trend term to obtain the residual IMF component. Preferentially, in step S4, the fuzzy entropy of the remaining IMF component is calculated, comprising the steps of:
s41, defining the signal length of the residual IMF component as N, and constructing a vector sequence
Figure BDA0003954894570000021
Figure BDA0003954894570000022
Figure BDA0003954894570000023
in the formula ,
Figure BDA0003954894570000024
represents the ith m-dimensional vector, +.>
Figure BDA0003954894570000025
The method comprises the steps of forming m normalized continuous sampling values starting from u (i), wherein u (i+m-1) is the (i+m-1) th sampling point of the residual IMF component, and the value of m is between 1 and 5;
s42, calculating
Figure BDA0003954894570000026
Vector->
Figure BDA0003954894570000027
Distance between->
Figure BDA0003954894570000028
Figure BDA0003954894570000029
Wherein k=0, 1, …, m-1;
s43, passing through fuzzy function
Figure BDA00039548945700000210
Calculate->
Figure BDA00039548945700000211
and
Figure BDA00039548945700000212
Similarity between->
Figure BDA00039548945700000213
Figure BDA0003954894570000031
Figure BDA0003954894570000032
Wherein n and r are given constants, the value of n is between 0 and 3, and the value of r is between 0.1 and 0.3;
s44, calculating intermediate variable phi m (n，r)：
Figure BDA0003954894570000033
，
Figure BDA0003954894570000034
wherein ,
Figure BDA0003954894570000035
is->
Figure BDA0003954894570000036
Vector->
Figure BDA0003954894570000037
A distance therebetween; />
S45, calculating fuzzy entropy FuzzyEn (m, n, r) of the IMF component:
Figure BDA0003954894570000038
fuzzy entropy fuzzyn (m, N, r, N) of the residual IMF component of finite length is estimated using:
FuzzyEn(m,n,r,N)＝lnφ m (n,r)-lnφ m+1 (n,r)。
preferentially, in step S5, the IMF component with the fuzzy entropy greater than or equal to the set value epsilon is noise-reduced by using a wavelet threshold method, which includes the following steps:
s51, screening to obtain IMF components with fuzzy entropy larger than or equal to a set value epsilon;
s52, selecting a wavelet basis function and a decomposition layer number, selecting a wavelet threshold value function for noise reduction, and calculating a threshold value:
Figure BDA0003954894570000039
sigma is the noise standard deviation, and N is the signal length of the residual IMF component;
s53, facilitating the soft threshold function to wavelet coefficient W j,k Processing to obtain IMF component with i=1, …, N-m+1, j E [0, m-1 after noise reduction]。
Preferably, the soft threshold function is expressed as:
Figure BDA00039548945700000310
in the formula ：Wj,k In order to process the wavelet coefficients before processing,
Figure BDA00039548945700000311
for the processed wavelet coefficients, sgn (·) represents the sign function, j=1, 2, l, j being the maximum number of decomposition layers of the wavelet transform. k is the length of the wavelet coefficient, k=1, 2, … n j ，n j ＝N/2 J -j+1 N is the signal length.
Preferably, in step S52, the wavelet basis function selects sym6, and the number of decomposition layers is 5.
Preferably, ε is in the range of 0.05 to 0.2.
An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of any one of the methods described above when the program is executed.
A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of any of the methods described above.
The invention has the beneficial effects that:
the invention provides a distributed optical fiber temperature measurement signal noise reduction method based on ICEEMDAN, which utilizes the self-adaptive decomposition capability of ICEEMDAN to decompose a measurement signal, distinguishes a noise signal component from a temperature change signal component by calculating IMF component fuzzy entropy, further processes the noise signal component by combining a wavelet threshold noise reduction method, reduces the influence of the noise signal on the temperature change signal, accurately extracts temperature change information, effectively reduces the influence of signal drift on a measurement result and is simple to operate;
the invention adopts ICEEMDAN to decompose the measurement signal, has self-adaptability, and the decomposition result is superior to the existing EMD decomposition. The noise IMF component is subjected to noise reduction by the combined wavelet threshold method, the noise reduction result is superior to wavelet threshold noise reduction, EMD combined wavelet threshold noise reduction and ICEEMDAN noise reduction.
The invention screens the noise IMF components by utilizing the magnitude of the fuzzy entropy value of each IMF component, and can effectively distinguish the noise IMF components.
The invention can effectively reduce noise signals in the distributed optical fiber temperature measurement signals, improve the signal-to-noise ratio of the signals, reduce the root mean square error of the signals, does not change the spatial resolution of the signals, and better retains the change characteristics of the signals.
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FIG. 1 is a flow chart of the present invention;
FIG. 2 is a line graph of the distributed fiber optic temperature measurement signal acquired by the present invention for a measurement object;
FIG. 3 is a line graph of a first IMF component of an actual measured distributed fiber optic temperature measurement signal after ICEEMDAN decomposition;
FIG. 4 is a line graph of a second IMF component of an actual measured distributed fiber optic temperature measurement signal after ICEEMDAN decomposition;
FIG. 5 is a line graph of a third IMF component of an actual measured distributed fiber optic temperature measurement signal after ICEEMDAN decomposition;
FIG. 6 is a line graph of a fourth IMF component of an actual measured distributed fiber optic temperature measurement signal after ICEEMDAN decomposition;
FIG. 7 is a line graph of a fifth IMF component of an actual measured distributed fiber optic temperature measurement signal after ICEEMDAN decomposition;
FIG. 8 is a line graph of a sixth IMF component of an actual measured distributed fiber optic thermometry signal after ICEEMDAN decomposition;
FIG. 9 is a line graph of a seventh IMF component of an actual measured distributed fiber temperature measurement signal after ICEEMDAN decomposition;
FIG. 10 is a line graph of an eighth IMF component of an actual measured distributed fiber temperature measurement signal after ICEEMDAN decomposition;
FIG. 11 is a line graph of a ninth IMF component of an actual measured distributed fiber temperature measurement signal after ICEEMDAN decomposition;
FIG. 12 is a line graph of a tenth IMF component of an actual measured distributed fiber temperature measurement signal after ICEEMDAN decomposition;
FIG. 13 is a line graph of an eleventh IMF component of an actual measured distributed fiber optic temperature measurement signal after ICEEMDAN decomposition;
FIG. 14 is a line graph of a twelfth IMF component of an actual measured distributed fiber temperature measurement signal after ICEEMDAN decomposition;
FIG. 15 is a line graph of a thirteenth IMF component of an actual measured distributed fiber optic thermometry signal after ICEEMDAN decomposition;
FIG. 16 is a line graph of the fuzzy entropy distribution of the first twelve IMF components arranged in high-to-low frequency order after ICEEMDAN decomposition of the measured distributed fiber temperature measurement signal;
FIG. 17 is a line graph of the results of a measured distributed fiber optic temperature measurement signal after noise reduction by a wavelet thresholding method;
FIG. 18 is a line graph of the results of an EMD method noise reduction of an actual measured distributed fiber optic temperature measurement signal;
FIG. 19 is a line graph of the results of the measured distributed fiber optic temperature measurement signal after EMD and wavelet thresholding to reduce noise;
FIG. 20 is a line graph of the results of an actual measurement of distributed fiber optic temperature measurement signal after noise reduction by the ICEEMDAN method;
FIG. 21 is a line graph of the results of a measured distributed fiber optic temperature measurement signal after noise reduction by ICEEMDAN and wavelet thresholding;
FIG. 22 is a line graph showing the spatial resolution of the measured signal after the measured distributed fiber temperature measurement signal is noise reduced by the wavelet thresholding method, the EMD and wavelet thresholding method, the ICEEMDAN and wavelet thresholding method;
FIG. 23 is a line graph showing the trend distribution of temperature drift at the end of a measured signal after noise reduction by the wavelet thresholding method, EMD and wavelet thresholding method, ICEEMDAN and wavelet thresholding method for a measured distributed optical fiber temperature measurement signal.
Detailed Description
The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
Example 1
As shown in fig. 1, the present invention provides a distributed optical fiber temperature measurement signal noise reduction method based on icemdan, which includes:
s1, acquiring a distributed optical fiber temperature measurement signal of a measurement object by using a distributed optical fiber temperature measurement system;
s2, decomposing the distributed optical fiber temperature measurement signals by utilizing improved self-adaptive noise complete set empirical mode decomposition to obtain IMF components;
s21, definition E k (. Cndot.) represents the kth modal component after EMD decomposition, M (. Cndot.) represents distributed optical fiber thermometryLocal mean of signals E 1 (x)＝x-M(x)，w (i) Representing the ith added gaussian white noise,<·>representing and averaging;
s22, structure x (i) ＝x+β 0 E 1 (w (i) ) Calculate x (i) Is (x) (i) ) Obtaining a first residual component r 1 ＝<M(x (i) )>A selection constant beta k-1 The signal-to-noise ratio between the residual and the added noise is adjusted.
When k=1, β 0 ＝ε 0 std(x)/std(E 1 (ω (i) ))；
When k is greater than or equal to 2, beta k ＝ε 0 std(r k ) Std (·) represents standard deviation calculation, i has a value of 100-500, β k The value of (2) is between 0.1 and 0.3;
s23, calculating a first modal component IMF 1 ：
Figure BDA0003954894570000061
S24, according to r 2 ＝〈M(r 1 +β 1 E 2 (ω (i) ) -) calculating a second modality component IMF 2 ：
Figure BDA0003954894570000062
S25, for k=3, 4, …, K, cull the kth residual component respectively:
r k ＝<M(r (k-1) +β (k-1) E (k) (ω (i) ))>，
obtaining the kth modal component IMF k Is the value of (1):
Figure BDA0003954894570000063
s26, repeating the step S25 to obtain all IMF components.
S3, removing trend items of IMF components containing temperature drift trend items to obtain residual IMF components;
s4, calculating the fuzzy entropy of the residual IMF component;
s5, denoising the IMF component with the fuzzy entropy being greater than or equal to the set value epsilon by utilizing a wavelet threshold method;
s6, reconstructing the IMF component after noise reduction, the residual IMF component and the IMF component with fuzzy entropy smaller than a set value epsilon to obtain a temperature measurement signal.
Further, in step S3 of the present embodiment, the trend item of the IMF component including the temperature drift trend item is removed, and the remaining IMF component is obtained, which includes the following steps:
s31, arranging IMF components from high frequency to low frequency, and taking the last IMF component as an IMF component containing a temperature drift trend term;
the IMF component of the temperature drift trend term includes a base offset and trend term,
and taking the basic offset as a first sampling point value of the IMF component containing the temperature drift trend term to obtain the residual IMF component. Further, in step S4 in the present embodiment, the fuzzy entropy of the remaining IMF component is calculated, including the steps of:
s41, defining the signal length of the residual IMF component as N, and constructing a vector sequence
Figure BDA0003954894570000064
Figure BDA0003954894570000071
Figure BDA0003954894570000072
in the formula ,
Figure BDA0003954894570000073
represents the ith m-dimensional vector, +.>
Figure BDA0003954894570000074
The method comprises the steps of forming m normalized continuous sampling values starting from u (i), wherein u (i+m-1) is the (i+m-1) th sampling point of the residual IMF component, and the value of m is between 1 and 5;
s42, calculating
Figure BDA0003954894570000075
Vector->
Figure BDA0003954894570000076
Distance between->
Figure BDA0003954894570000077
Figure BDA0003954894570000078
Wherein k=0, 1, …, m-1;
s43, passing through fuzzy function
Figure BDA0003954894570000079
Calculate->
Figure BDA00039548945700000710
and
Figure BDA00039548945700000711
Similarity between->
Figure BDA00039548945700000712
Figure BDA00039548945700000713
Figure BDA00039548945700000714
Wherein n and r are given constants, the value of n is between 0 and 3, and the value of r is between 0.1 and 0.3;
s44, calculating intermediate variable phi m (n，r)：
Figure BDA00039548945700000715
，
Figure BDA00039548945700000716
wherein ,
Figure BDA00039548945700000717
is->
Figure BDA00039548945700000718
Vector->
Figure BDA00039548945700000719
A distance therebetween;
s45, calculating fuzzy entropy FuzzyEn (m, n, r) of the IMF component:
Figure BDA00039548945700000720
fuzzy entropy fuzzyn (m, N, r, N) of the residual IMF component of finite length is estimated using:
FuzzyEn(m,n,r,N)＝lnφ m (n,r)-lnφ m+1 (n,r)。
further, in step S5 of the present embodiment, the method of denoising the IMF component with the fuzzy entropy greater than or equal to the set value epsilon by using the wavelet threshold method includes the following steps:
s51, screening to obtain IMF components with fuzzy entropy larger than or equal to a set value epsilon;
s52, selecting a wavelet basis function and a decomposition layer number, selecting a wavelet threshold value function for noise reduction, and calculating a threshold value:
Figure BDA0003954894570000081
sigma is the standard deviation of noise, N is the signal of the remaining IMF componentA length;
s53, facilitating the soft threshold function to wavelet coefficient W j,k Processing to obtain IMF component with i=1, …, N-m+1, j E [0, m-1 after noise reduction]。
Further, the expression of the soft threshold function in this embodiment is:
Figure BDA0003954894570000082
in the formula ：Wj,k In order to process the wavelet coefficients before processing,
Figure BDA0003954894570000083
for the processed wavelet coefficients, sgn (·) represents the sign function, j=1, 2, l, j being the maximum number of decomposition layers of the wavelet transform. k is the length of the wavelet coefficient, k=1, 2, … n j ，n j ＝N/2 J -j+1 N is the signal length.
Further, in the embodiment, the wavelet basis function in step S52 is sym6, and the number of decomposition layers is 5.
Further, epsilon has a value in the range of 0.05 to 0.2 in this example.
An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of any one of the methods described above when the program is executed.
A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of any of the methods described above.
The distributed optical fiber temperature measuring system has a plurality of types which can be adopted in the prior art, and a person skilled in the art can select a proper type according to actual requirements, and the embodiment is not exemplified one by one.
Example two
The ICEEMDAN-based distributed optical fiber temperature measurement signal noise reduction method is subjected to example analysis: the fiber with the length of 60m in the middle of the temperature measuring fiber is heated by the constant temperature water tank, the heating temperature is set to 40 ℃, and the real distributed fiber temperature measuring signal is shown in figure 2.
The temperature measurement signal is decomposed by using the ICEEMDAN method to obtain 13 IMF components in total, and the IMF13 is the IMF component containing the temperature drift trend term from high frequency to low frequency as shown in figures 3 to 15. The magnitude of the fuzzy entropy of the remaining 12 IMF components is calculated as shown in fig. 16. The values of the fuzzy entropies of the IMF1, the IMF2 and the IMF3 are larger than epsilon, epsilon is taken to be 0.1, the IMF is regarded as a noise IMF component, noise reduction processing is carried out by using wavelet thresholds respectively, and finally the IMF component after noise reduction, the IMF component with the temperature drift trend removed and the residual IMF component are reconstructed to obtain a temperature measurement signal after drift correction and noise reduction, and the result is shown in figure 17. Fig. 18 is a wavelet threshold noise reduction result, fig. 19 is an EMD noise reduction result of directly removing IMF components with a blur entropy of 0.1 or more, fig. 20 is a combination noise reduction result of EMD and wavelet threshold, and fig. 21 is an icemdan noise reduction result of directly removing IMF components with a blur entropy of 0.1 or more. The signal-to-noise ratio and root mean square error of the signal after noise reduction are calculated, the larger the signal-to-noise ratio SNR is, the more obvious the noise reduction effect is, the smaller the RMSE is, the smaller the difference between the noise reduction signal and the real signal is, and the calculation formulas of the SNR and the RMSE are respectively as follows:
Figure BDA0003954894570000091
Figure BDA0003954894570000092
Figure BDA0003954894570000093
for the signal after noise reduction, x (i) is the original signal and N is the signal length.
TABLE 1 noise reduction results of different methods for optical fiber temperature measurement signals
Figure BDA0003954894570000094
As shown in fig. 17 to 21, the temperature measurement signal after the wavelet threshold noise reduction method is too smooth, distortion exists near abrupt change of the signal, and the drift trend of the temperature signal is obvious. The temperature measurement signal processed by the EMD noise reduction method has some detail loss near abrupt change of the signal, and the drift trend of the temperature measurement signal is obvious. The temperature measurement signals processed by the EMD and wavelet threshold noise reduction method are closer to real signals, but noise signals exist, and the drift trend of the temperature measurement signals is obvious. The temperature measurement signal processed by the ICEEMDAN noise reduction method also has detail loss near abrupt change of the signal, but the drift trend of the temperature signal is corrected. The ICEEMDAN and wavelet threshold method have the most satisfactory result after processing, the details of the temperature measurement signals after noise reduction are rich, the drift trend of the temperature signals is effectively corrected, and the temperature signals are closest to the real signals.
According to the results in table 1, it can be seen that the signal-to-noise ratio and root mean square error of the temperature measurement signal after noise reduction by the ICEEMDAN and wavelet threshold method provided by the invention are optimal, and the noise reduction performance is excellent. FIG. 22 shows noise reduction performance of different methods in the abrupt signal region, and the ICEEMDAN and wavelet threshold method proposed by the present invention does not reduce the spatial resolution of the temperature measurement signal. Fig. 23 shows a temperature change condition of a temperature measurement signal end, wherein the temperature drift of an original temperature measurement signal reaches 2.9 ℃ at the signal end, and the temperature drift of the temperature measurement signal processed by the ICEEMDAN and wavelet threshold method provided by the invention is reduced by 1.43 ℃ at the signal end, so that the temperature drift trend of the signal can be effectively corrected.
Therefore, the ICEEMDAN-based distributed optical fiber temperature measurement signal noise reduction method provided by the invention can effectively inhibit the influence of noise on the temperature measurement signal, does not reduce the spatial resolution of the temperature measurement signal, can effectively reserve the change characteristics of the temperature measurement signal, and has a good effect on the correction of the temperature drift trend of the temperature measurement signal.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.

Claims (9)

1. The ICEEMDAN-based distributed optical fiber temperature measurement signal noise reduction method is characterized by comprising the following steps of:
s1, acquiring a distributed optical fiber temperature measurement signal of a measurement object by using a distributed optical fiber temperature measurement system;
s2, decomposing the distributed optical fiber temperature measurement signals by utilizing improved self-adaptive noise complete set empirical mode decomposition to obtain IMF components;
s3, removing trend items of IMF components containing temperature drift trend items to obtain residual IMF components;
s4, calculating the fuzzy entropy of the residual IMF component;
s5, denoising the IMF component with the fuzzy entropy being greater than or equal to the set value epsilon by utilizing a wavelet threshold method;
s6, reconstructing the IMF component after noise reduction, the residual IMF component and the IMF component with fuzzy entropy smaller than a set value epsilon to obtain a temperature measurement signal.
2. The method for noise reduction of an icemdan-based distributed optical fiber temperature measurement signal according to claim 1, wherein in step S3, a trend term of an IMF component including a temperature drift trend term is removed to obtain a remaining IMF component, comprising the steps of:
s31, arranging IMF components from high frequency to low frequency, and taking the last IMF component as an IMF component containing a temperature drift trend term;
the IMF component of the temperature drift trend term includes a base offset and trend term,
and taking the basic offset as a first sampling point value of the IMF component containing the temperature drift trend term to obtain the residual IMF component.
3. The method for noise reduction of icemdan-based distributed optical fiber temperature measurement signals according to claim 1, wherein in step S4, the fuzzy entropy of the remaining IMF component is calculated, comprising the steps of:
s41, defining the signal length of the residual IMF component as N, and constructing a vector sequence
Figure FDA0003954894560000011
Figure FDA0003954894560000012
Figure FDA0003954894560000013
in the formula ,
Figure FDA0003954894560000014
represents the ith m-dimensional vector, +.>
Figure FDA0003954894560000015
The method comprises the steps of forming m normalized continuous sampling values starting from u (i), wherein u (i+m-1) is the (i+m-1) th sampling point of the residual IMF component, and the value of m is between 1 and 5;
s42, calculating
Figure FDA0003954894560000016
Vector->
Figure FDA0003954894560000017
Distance between->
Figure FDA0003954894560000018
Figure FDA0003954894560000019
Wherein k=0, 1, …, m-1;
s43, passing through fuzzy function
Figure FDA0003954894560000021
Calculate->
Figure FDA0003954894560000022
and
Figure FDA0003954894560000023
Similarity between->
Figure FDA0003954894560000024
Figure FDA0003954894560000025
Figure FDA0003954894560000026
Wherein n and r are given constants, the value of n is between 0 and 3, and the value of r is between 0.1 and 0.3;
s44, calculating intermediate variable phi m (n，r)：
Figure FDA0003954894560000027
，
Figure FDA0003954894560000028
wherein ,
Figure FDA0003954894560000029
is->
Figure FDA00039548945600000210
Vector->
Figure FDA00039548945600000211
A distance therebetween;
s45, calculating fuzzy entropy FuzzyEn (m, n, r) of the IMF component:
Figure FDA00039548945600000212
fuzzy entropy fuzzyn (m, N, r, N) of the residual IMF component of finite length is estimated using:
FuzzyEn(m,n,r,N)＝lnφ m (n,r)-lnφ m+1 (n,r)。
4. the method for denoising distributed optical fiber temperature measurement signals based on ICEEMDAN according to claim 3, wherein in step S5, the IMF component with the fuzzy entropy larger than or equal to the set value epsilon is denoised by using a wavelet threshold method, and the method comprises the following steps:
s51, screening to obtain IMF components with fuzzy entropy larger than or equal to a set value epsilon;
s52, selecting a wavelet basis function and a decomposition layer number, selecting a wavelet threshold value function for noise reduction, and calculating a threshold value:
Figure FDA00039548945600000213
sigma is the noise standard deviation, and N is the signal length of the residual IMF component;
s53, facilitating the soft threshold function to wavelet coefficient W j,k Processing to obtain IMF component with i=1, …, N-m+1, j E [0, m-1 after noise reduction]。
5. The method for noise reduction of icemdan-based distributed optical fiber temperature measurement signals according to claim 4, wherein the expression of the soft threshold function is:
Figure FDA0003954894560000031
in the formula ：Wj,k In order to process the wavelet coefficients before processing,
Figure FDA0003954894560000032
for the processed wavelet coefficients, sgn (·) represents the sign function, j=1, 2, l, j being the maximum number of decomposition layers of the wavelet transform. k is the length of the wavelet coefficient, k=1, 2, … n j ，n j ＝N/2 J-j+1 N is the signal length.
6. The method for noise reduction of ICEEMDAN-based distributed optical fiber temperature measurement signals according to claim 4, wherein the wavelet basis function in step S52 is sym6, and the number of decomposition layers is 5.
7. The ICEEMDAN-based distributed optical fiber temperature measurement signal noise reduction method according to claim 4, wherein epsilon has a value range of 0.05-0.2.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of any one of claims 1 to 7 when the program is executed by the processor.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any one of claims 1 to 7.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117874603A (en) * 2023-12-25 2024-04-12 南通大学 MOA resistive current extraction method based on CEEMD and fuzzy entropy
CN118312906A (en) * 2024-06-05 2024-07-09 山东海纳智能装备科技股份有限公司 Mining optical fiber temperature measurement method and device

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117874603A (en) * 2023-12-25 2024-04-12 南通大学 MOA resistive current extraction method based on CEEMD and fuzzy entropy
CN118312906A (en) * 2024-06-05 2024-07-09 山东海纳智能装备科技股份有限公司 Mining optical fiber temperature measurement method and device

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