CN117390380B - Data analysis method in oil-residue separation system - Google Patents
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Abstract
The invention relates to the technical field of data processing, in particular to a data analysis method in an oil-residue separation system, which comprises the steps of obtaining a temperature signal of the oil-residue separation system within a preset period; acquiring a target extreme point when the temperature signal is subjected to empirical mode decomposition, decomposing the temperature signal into at least one reference component signal according to the target extreme point, and performing conventional empirical mode decomposition on the temperature signal to obtain at least one actual component signal; acquiring fluctuation degrees of any component signal in all reference component signals and actual component signals; for any actual component signal, acquiring a wavelet threshold of the actual component signal according to the difference and the similarity of the corresponding fluctuation degree between the actual component signal and the target reference component signal; and carrying out self-adaptive denoising on all the actual component signals according to the wavelet threshold value of each actual component signal to obtain a denoised temperature signal, and improving the denoising effect by carrying out self-adaptive wavelet threshold denoising on the temperature signal.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a data analysis method in an oil-residue separation system.
Background
An oil and residue separation system is an apparatus for treating wastewater or liquid mixtures containing greasy dirt, the main purpose of which is to separate oil from water to reduce environmental pollution and recover useful oil. The data analysis plays an important role in the oil-slag separation system, and is helpful for optimizing the performance of the oil-slag separation system, monitoring the equipment state, improving the oil-water separation efficiency and reducing the operation cost. Specifically, in the oil and slag separation system, various system parameters including flow, temperature, pressure, chemical components of the oil-water mixture, etc. are monitored by sensors, which provide a large amount of real-time monitoring data for analyzing and controlling the oil and slag separation system.
Because the temperature has a larger influence on the separation of oil residues, the current temperature monitoring is more strict. However, when the temperature monitoring data are collected, the temperature sensor is affected by various factors, so that noise exists in the collected temperature monitoring data, and noise removal processing is needed to be carried out on the temperature monitoring data, so that accurate temperature data can be obtained.
In the prior art, a plurality of methods for denoising data are provided, wherein a wavelet transformation algorithm has a good effect on denoising data, the wavelet transformation algorithm can decompose signals into component signals with different scales so as to reveal local characteristics and frequency components of the signals, and then denoising the signals according to the change of each component signal, but when denoising the component signals, the selection of a wavelet threshold is critical, and a proper wavelet threshold can improve the denoising effect.
Therefore, how to determine a suitable wavelet threshold value when denoising temperature monitoring data of an oil-slag separation system by using a wavelet transform algorithm is a problem to be solved.
Disclosure of Invention
In view of the above, the embodiment of the invention provides a data analysis method in an oil-residue separation system, so as to solve the problem of determining a proper wavelet threshold when denoising temperature monitoring data of the oil-residue separation system by utilizing a wavelet transformation algorithm.
The embodiment of the invention provides a data analysis method in an oil-residue separation system, which comprises the following steps:
acquiring a temperature signal of an oil-residue separation system in a preset period;
obtaining all extreme points when the temperature signal is subjected to empirical mode decomposition, screening all the extreme points to obtain a target extreme point, decomposing the temperature signal into at least one IMF component signal serving as a reference component signal according to the target extreme point, and performing conventional empirical mode decomposition on the temperature signal to obtain at least one IMF component signal serving as an actual component signal;
for any one of all reference component signals and actual component signals, acquiring the fluctuation degree of the component signals according to the amplitude of each data point in the component signals and the horizontal distance between adjacent extreme points;
for any actual component signal, acquiring a target reference component signal which is equal to the frequency component of the actual component signal from all reference component signals, and acquiring a wavelet threshold of the actual component signal according to the difference and the similarity of the corresponding fluctuation degree between the actual component signal and the target reference component signal;
and carrying out self-adaptive denoising on all the actual component signals according to the wavelet threshold value of each actual component signal to obtain denoised temperature signals.
Preferably, the screening the all the extreme points to obtain a target extreme point includes:
aiming at any extreme point, according to the amplitude difference between the extreme point and the local extreme point, acquiring the local characteristic value of the extreme point;
obtaining the suspected degree of the extreme point as a target extreme point according to the difference of the local characteristic values between the extreme point and the adjacent extreme point;
acquiring a weight coefficient occupied by the extreme point serving as a target extreme point according to the suspected degree of the extreme point serving as the target extreme point;
and when the weight coefficient occupied by the extreme point serving as the target extreme point meets a preset weight coefficient threshold value, determining the extreme point as the target extreme point.
Preferably, if the extremum point is a maximum value point, the obtaining the local feature value of the extremum point according to the amplitude difference between the extremum point and the local extremum point includes:
acquiring left adjacent minimum value points and right adjacent minimum value points of the maximum value points;
calculating a first difference absolute value of the corresponding amplitude between the maximum value point and the left adjacent minimum value point, calculating a second difference absolute value of the corresponding amplitude between the maximum value point and the right adjacent minimum value point, and obtaining a first average value between the first difference absolute value and the second difference absolute value;
acquiring data points separated between the maximum value point and the left adjacent minimum value point, acquiring data points separated between the maximum value point and the right adjacent minimum value point, acquiring the absolute value of the difference value of the corresponding amplitude between the data points and the following adjacent data points according to any one of all the separated data points, and calculating a second average value of all the absolute values of the difference values;
taking the product between the first average value and the second average value as a local characteristic value of the maximum value point.
Preferably, if the extremum point is a minimum value point, the obtaining the local feature value of the extremum point according to the amplitude difference between the extremum point and the local extremum point includes:
acquiring a left adjacent maximum value point and a right adjacent maximum value point of the minimum value point;
calculating a first difference absolute value of the corresponding amplitude between the minimum value point and the left adjacent maximum value point, calculating a second difference absolute value of the corresponding amplitude between the minimum value point and the right adjacent maximum value point, and obtaining a first average value between the first difference absolute value and the second difference absolute value;
acquiring data points separated between the minimum value point and the left adjacent maximum value point, acquiring data points separated between the minimum value point and the right adjacent maximum value point, acquiring the absolute value of the difference value of the corresponding amplitude between the data points and the following adjacent data points according to any one of all the separated data points, and calculating a second average value of all the absolute values of the difference values;
and taking the product between the first average value and the second average value as the local characteristic value of the minimum value point.
Preferably, the obtaining the suspected degree of the extremum point being the target extremum point according to the difference of the local eigenvalues between the extremum point and the adjacent extremum point includes:
acquiring a left adjacent extreme point and a right adjacent extreme point of the extreme point, calculating a third difference absolute value of a corresponding local characteristic value between the extreme point and the left adjacent extreme point, and calculating a fourth difference absolute value of a corresponding local characteristic value between the extreme point and the right adjacent extreme point
And calculating the average value between the third difference absolute value and the fourth difference absolute value, and taking the product between the average value and the local characteristic value of the extreme point as the suspected degree of taking the extreme point as the target extreme point.
Preferably, the obtaining the weight coefficient occupied by the extremum point as the target extremum point according to the suspected degree of the extremum point as the target extremum point includes:
and obtaining the sum of the suspected degrees of all the extreme points serving as the target extreme points, and taking the ratio of the suspected degrees of all the extreme points serving as the target extreme points to the sum of the suspected degrees of all the extreme points serving as the target extreme points as a weight coefficient occupied by the extreme points.
Preferably, the obtaining the fluctuation degree of the component signal according to the amplitude of each data point in the component signal and the horizontal distance between adjacent extreme points includes:
acquiring the average value of the amplitude values of all the data points in the component signal, and calculating the average value of the absolute value of the difference value according to the absolute value of the difference value between the amplitude value of each data point in the component signal and the average value of the amplitude value;
calculating the average value of the horizontal distance according to the horizontal distance between every two adjacent extreme points in the component signals;
taking the product of the average value of the absolute value of the difference and the average value of the horizontal interval as the fluctuation degree of the component signal.
Preferably, the obtaining the wavelet threshold of the actual component signal according to the difference and the similarity of the corresponding fluctuation degree between the actual component signal and the target reference component signal includes:
acquiring an absolute value of a difference value between the fluctuation degree of the actual component signal and the fluctuation degree of the target reference component signal, calculating the similarity between the actual component signal and the target reference component signal by using a DTW algorithm, and acquiring the reciprocal of the similarity;
and carrying out normalization processing on the product between the absolute value and the reciprocal of the similarity to obtain a corresponding normalization result, and taking the product of a preset hyper-parameter and the normalization result as a wavelet threshold value of the actual component signal.
Preferably, before the acquiring the temperature signal of the oil-slag separation system in the preset period, the method includes:
acquiring the temperature of the oil-residue separation system based on a preset sampling frequency, and obtaining an initial temperature signal of the oil-residue separation system in a preset period;
and carrying out interpolation processing on the initial temperature signal by using a linear interpolation algorithm to obtain a temperature signal of the oil-slag separation system in a preset period.
The embodiment of the invention has at least the following beneficial effects:
the method comprises the steps of obtaining a temperature signal of an oil-residue separation system in a preset period; obtaining all extreme points when the temperature signal is subjected to empirical mode decomposition, screening all the extreme points to obtain a target extreme point, decomposing the temperature signal into at least one IMF component signal serving as a reference component signal according to the target extreme point, and performing conventional empirical mode decomposition on the temperature signal to obtain at least one IMF component signal serving as an actual component signal; for any one of all reference component signals and actual component signals, acquiring the fluctuation degree of the component signals according to the amplitude of each data point in the component signals and the horizontal distance between adjacent extreme points; for any actual component signal, acquiring a target reference component signal which is equal to the frequency component of the actual component signal from all reference component signals, and acquiring a wavelet threshold of the actual component signal according to the difference and the similarity of the corresponding fluctuation degree between the actual component signal and the target reference component signal; and carrying out self-adaptive denoising on all the actual component signals according to the wavelet threshold value of each actual component signal to obtain denoised temperature signals. When the temperature signal is decomposed by using the EMD algorithm, the extremum points of the temperature signal are screened to obtain target extremum points, so that the extremum points caused by faster noise or signal change are solved, the stability of constructing an envelope line according to the extremum points is improved, and therefore purer IMF component signals, namely reference component signals, are obtained according to the target extremum points, and then the IMF component signals, namely actual component signals, obtained by comparing the temperature signals decomposed by the traditional EMD algorithm are obtained, the self-adaptive wavelet threshold value of each actual component signal is obtained according to the fluctuation difference between the IMF component signals and the actual component signals, and then the self-adaptive wavelet threshold value is used for carrying out wavelet threshold denoising on the actual component signals, so that the temperature signal with better denoising effect is obtained.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a step flowchart of a data analysis method in an oil-residue separation system according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the present invention to achieve the preset purposes, the following detailed description refers to the specific implementation, structure, characteristics and effects of a data analysis method in an oil-residue separation system according to the present invention with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of a data analysis method in an oil-residue separation system provided by the invention with reference to the accompanying drawings.
The specific scene aimed by the invention is as follows: in the monitoring process of the temperature signals in the oil-slag separation system, denoising the acquired temperature signals so as to monitor and early warn the oil-slag separation system according to the denoised temperature signals.
Referring to fig. 1, a flowchart of a data analysis method in an oil-residue separation system according to an embodiment of the present invention is shown, the method includes the following steps:
step S101, acquiring a temperature signal of an oil-slag separation system in a preset period.
Specifically, because the temperature signal of the oil-slag separation system is subjected to denoising treatment, temperature monitoring data in the oil-slag separation system need to be acquired, and specifically, the temperature sensor is used for acquiring the temperature data in the oil-slag separation system, so that the temperature signal of the oil-slag separation system in a preset period is obtained.
Preferably, before the acquiring the temperature signal of the oil-slag separation system in the preset period, the method includes:
acquiring the temperature of the oil-residue separation system based on a preset sampling frequency, and obtaining an initial temperature signal of the oil-residue separation system in a preset period; and carrying out interpolation processing on the initial temperature signal by using a linear interpolation algorithm to obtain a temperature signal of the oil-slag separation system in a preset period.
Specifically, in the embodiment of the invention, the preset time period is set to be one hour, the sampling frequency is 0.2 seconds, the temperature of the oil-slag separation system is acquired based on the preset sampling frequency, so that an initial temperature signal of the oil-slag separation system in the preset time period is obtained, and because the acquired temperature signal possibly has partial data point missing caused by current fluctuation during data acquisition, the acquired initial temperature signal is subjected to interpolation processing by using a linear interpolation algorithm, so that the temperature signal of the oil-slag separation system in the preset time period is obtained, wherein the linear interpolation algorithm is the prior known technology and is not repeated herein.
Step S102, obtaining all extreme points when empirical mode decomposition is performed on the temperature signal, screening all the extreme points to obtain a target extreme point, decomposing the temperature signal into at least one IMF component signal as a reference component signal according to the target extreme point, and performing conventional empirical mode decomposition on the temperature signal to obtain at least one IMF component signal as an actual component signal.
Specifically, when denoising a temperature signal, because the generation of noise is random, and the noise signal is superimposed on the temperature signal, the amplitude of the acquired temperature signal is changed randomly, therefore, when denoising the temperature signal, the influence degree of the noise needs to be estimated, the larger the influence degree of the noise is, the more irrelevant data needs to be removed when denoising, and the larger the correction degree of the temperature signal is, the more accurate the temperature signal after denoising is, and the embodiment of the invention uses a wavelet transformation algorithm to denoise the temperature.
When the monitored temperature signal is denoised through a wavelet transformation algorithm, the wavelet transformation algorithm is to decompose the temperature signal through a wavelet odd function, then denoise each component signal according to the change of each decomposed component signal, and then reconstruct the denoised component signal to obtain the denoised temperature signal, so that the selection of a wavelet threshold is critical when the temperature signal is denoised. However, the wavelet transform algorithm divides signals with different frequencies, and the component signals obtained by corresponding decomposition also represent the influence degree of noise with different frequencies, so when denoising the component signals, the wavelet threshold value needs to be determined according to the change of each component signal, so that the influence degree of noise on the reconstructed temperature signal is small.
In the embodiment of the invention, in order to accurately evaluate the noise influence degree of each component signal of the temperature signal, firstly, the acquired temperature signal containing noise is subjected to empirical mode decomposition (Empirical Mode Decomposition, EMD) to obtain a plurality of IMF component signals, and the EMD algorithm is used for constructing an upper envelope and a lower envelope by searching local extremum points of the signals so as to obtain IMF component signals, and the temperature signal is in a region with relatively quick noise or signal change, so that the local extremum points can be selected inaccurately, and instability in envelope construction can be caused, therefore, when the temperature signal is subjected to empirical mode decomposition, the selection of the local extremum points of the temperature signal is optimized, and the selection of the local extremum points is adjusted so as to ensure that the IMF component signals obtained after the temperature signal is decomposed are relatively pure.
All extreme points when empirical mode decomposition is performed on a temperature signal are obtained, wherein the extreme points can be called suspected extreme points of the temperature signal, and the embodiment of the invention screens all the extreme points by analyzing local characteristics of the suspected extreme points so as to achieve the purpose of accurately selecting a target extreme point, and the specific process is as follows:
(1) And aiming at any extreme point, acquiring a local characteristic value of the extreme point according to the amplitude difference between the extreme point and the local extreme point.
Preferably, if the extremum point is a maximum value point, the obtaining the local feature value of the extremum point according to the amplitude difference between the extremum point and the local extremum point includes:
acquiring left adjacent minimum value points and right adjacent minimum value points of the maximum value points;
calculating a first difference absolute value of the corresponding amplitude between the maximum value point and the left adjacent minimum value point, calculating a second difference absolute value of the corresponding amplitude between the maximum value point and the right adjacent minimum value point, and obtaining a first average value between the first difference absolute value and the second difference absolute value;
acquiring data points separated between the maximum value point and the left adjacent minimum value point, acquiring data points separated between the maximum value point and the right adjacent minimum value point, acquiring the absolute value of the difference value of the corresponding amplitude between the data points and the following adjacent data points according to any one of all the separated data points, and calculating a second average value of all the absolute values of the difference values;
taking the product between the first average value and the second average value as a local characteristic value of the maximum value point.
In one embodiment, the calculation expression of the local eigenvalue of any maximum point is:
wherein,local feature value representing jth maximum point,/->Representing the sum of absolute values of the differences in amplitude between the jth maximum and its left and right adjacent minimum points, ">Amplitude of the ith data point among all data points representing intervals between the jth maximum and its left and right adjacent minimum points, +.>The magnitude of the (i+1) th data point among all data points indicating the interval between the jth maximum value and its left and right adjacent minimum value points, and n indicates the total number of data points indicating the interval between the jth maximum value and its left and right adjacent minimum value points.
It should be noted that, when the maximum value point has only the adjacent minimum value point on the left or right side, analysis is performed only according to the existing side;the average value of absolute values of the difference values of the amplitude values between the jth maximum value and the adjacent minimum value points is represented, and the average value represents the amplitude difference between the jth maximum value point and the adjacent minimum value points, namely the local characteristic value of the jth maximum value point is represented, and the larger the average value is, the larger the local characteristic value of the jth maximum value point is;the average value of absolute values of amplitude difference values between adjacent data points in the data points between the jth maximum value point and the adjacent minimum value points is used for representing local change characteristics of the jth maximum value point, and the larger the value is, the larger the local characteristic value of the jth maximum value point is.
Preferably, if the extremum point is a minimum value point, the obtaining the local feature value of the extremum point according to the amplitude difference between the extremum point and the local extremum point includes:
acquiring a left adjacent maximum value point and a right adjacent maximum value point of the minimum value point;
calculating a first difference absolute value of the corresponding amplitude between the minimum value point and the left adjacent maximum value point, calculating a second difference absolute value of the corresponding amplitude between the minimum value point and the right adjacent maximum value point, and obtaining a first average value between the first difference absolute value and the second difference absolute value;
acquiring data points separated between the minimum value point and the left adjacent maximum value point, acquiring data points separated between the minimum value point and the right adjacent maximum value point, acquiring the absolute value of the difference value of the corresponding amplitude between the data points and the following adjacent data points according to any one of all the separated data points, and calculating a second average value of all the absolute values of the difference values;
and taking the product between the first average value and the second average value as the local characteristic value of the minimum value point.
The calculation expression of the local feature value of the minimum value point is the same as the calculation expression of the local feature value of the maximum value point, and the local feature value of any minimum value point may be calculated with reference to the calculation expression of the local feature value of the maximum value point, which will not be described in detail here.
(2) And obtaining the suspected degree of the extreme point as the target extreme point according to the difference of the local characteristic values between the extreme point and the adjacent extreme point.
Obtaining a local characteristic value of each extreme point according to the step (1), and further obtaining the suspected degree of the extreme point as the target extreme point according to the local characteristic value of the extreme point, wherein the specific method comprises the following steps:
acquiring a left adjacent extreme point and a right adjacent extreme point of the extreme point, calculating a third difference absolute value of a corresponding local characteristic value between the extreme point and the left adjacent extreme point, and calculating a fourth difference absolute value of a corresponding local characteristic value between the extreme point and the right adjacent extreme point;
and calculating the average value between the third difference absolute value and the fourth difference absolute value, and taking the product between the average value and the local characteristic value of the extreme point as the suspected degree of taking the extreme point as the target extreme point.
In one embodiment, the calculation expression of the suspected degree that any extreme point is the target extreme point is:
wherein,indicating the suspected degree of the a-th extreme point as the target extreme point, +.>Local feature value representing the a-th extreme point, < ->Local feature value representing the a-1 st extreme point,/->The local feature value representing the a+1st extreme point,the average value of the absolute values of the difference values of the local characteristic values of the a-th extreme point and the a-1 st extreme point and the a+1 th extreme point is represented, the larger the average value is, the larger the difference between the a-th extreme point and the adjacent extreme point is, and when the left side or the right side of the a-th extreme point has no adjacent extreme point, the absolute value of the difference value of the local characteristic value between the a-th extreme point and the left side or the right side adjacent extreme point is only calculated.
It should be noted that, since the core idea of EMD decomposition is to decompose a signal into different local modes by extracting local extremal points, particularly, maximum points and minimum points, which are important features of the signal, if the difference between the features of the maximum points and the minimum points is larger, that is, the local amplitude variation between them is more obvious, it is generally more advantageous to accurately capture the local features of the signal, so that the EMD decomposition is more accurate,the greater the value of the (a) th extreme point is, the greater the possibility that the (a) th extreme point is the target extreme point is, and meanwhile, the greater the local characteristic value of the (a) th extreme point is, the greater the possibility that the (a) th extreme point is the target extreme point is.
(3) And acquiring a weight coefficient occupied by the extreme point serving as the target extreme point according to the suspected degree of the extreme point serving as the target extreme point.
Preferably, the obtaining the weight coefficient occupied by the extremum point as the target extremum point according to the suspected degree of the extremum point as the target extremum point includes:
and obtaining the sum of the suspected degrees of all the extreme points serving as the target extreme points, and taking the ratio of the suspected degrees of all the extreme points serving as the target extreme points to the sum of the suspected degrees of all the extreme points serving as the target extreme points as a weight coefficient occupied by the extreme points.
In one embodiment, the calculation expression of the weight coefficient occupied by any extremum point as the target extremum point is:
wherein,the a-th extreme point is represented as a weight coefficient occupied by the target extreme point, and m represents the total number of the extreme points.
It should be noted that the number of the substrates,the ratio of the suspected degree of the a-th extreme point serving as the target extreme point to the sum of the suspected degrees of all the extreme points serving as the target extreme point is expressed, the ratio can reflect the weight of the a-th extreme point in the total extreme point, and when the weight coefficient of the a-th extreme point is smaller, the component of the a-th extreme point representing the overall signal change characteristic is smaller, the forming reason of the a-th extreme point is the extreme point formed by the local change of the signal due to the reasons such as noise, and when the component signal is decomposed, the a-th extreme point needs to be removed, so that the component signal obtained after the decomposition is more accurate.
(4) And when the weight coefficient occupied by the extreme point serving as the target extreme point meets a preset weight coefficient threshold value, determining the extreme point as the target extreme point.
In the embodiment of the invention, the preset weight coefficient threshold value is set as the empirical value of 0.2, and when the weight coefficient occupied by any extreme point serving as the target extreme point is larger than the preset weight coefficient threshold value, the extreme point is confirmed to be the target extreme point.
Further, through the above screening of the extreme points, the target extreme points in the temperature signal are reserved, and then an upper envelope and a lower envelope are constructed according to the target extreme points, and the first IMF component signal, the second IMF component signal, … and the nth IMF component signal are gradually obtained through the EMD decomposition process, and the IMF component signal obtained through the decomposition according to the target extreme points is used as a reference component signal, wherein the reference component signal represents a purer component signal. And under the condition that extreme points of the temperature signals are not screened, carrying out traditional empirical mode decomposition on the temperature signals to obtain N IMF component signals, and taking the IMF component signals obtained under the condition as actual component signals.
It should be noted that, the method of decomposing according to the extreme points of the signals in the EMD algorithm to obtain the plurality of IMF component signals is the prior art, and will not be described herein.
Step S103, for any one of the reference component signals and the actual component signals, obtaining the fluctuation degree of the component signals according to the amplitude of each data point in the component signals and the horizontal distance between the adjacent extreme points.
Specifically, since the noise signal affects the accuracy of the original signal decomposition, and causes the problem of modal aliasing, the adaptive wavelet threshold denoising of the actual component signal can be achieved through the degree of difference of the corresponding component signals between the actual component signal and the reference component signal, so that the fluctuation degree of the component signal is obtained according to the amplitude of each data point in the component signal and the horizontal distance between adjacent extremum points for any component signal in all component signals, including the reference component signal and the actual component signal.
Preferably, the obtaining the fluctuation degree of the component signal according to the amplitude of each data point in the component signal and the horizontal distance between adjacent extreme points includes:
acquiring the average value of the amplitude values of all the data points in the component signal, and calculating the average value of the absolute value of the difference value according to the absolute value of the difference value between the amplitude value of each data point in the component signal and the average value of the amplitude value;
calculating the average value of the horizontal distance according to the horizontal distance between every two adjacent extreme points in the component signals;
taking the product of the average value of the absolute value of the difference and the average value of the horizontal interval as the fluctuation degree of the component signal.
In one embodiment, the computational expression of the degree of fluctuation of any component signal is:
wherein,representing the degree of fluctuation of the s-th component signal, r representing the number of data points in the s-th component signal,/->Representing the amplitude of the ith data point in the s-th component signal,/for>Representing the mean value of the magnitudes of all data points in the s-th component signal,/->Represents the horizontal spacing between the (y) th extreme point and the (y+1) th extreme point of the(s) th component signal,/for>The total number of extreme points in the s-th component signal is shown.
It should be noted that the number of the substrates,representing the mean value of the absolute value of the difference between the amplitude of each data point in the s-th component signal and the mean value of the amplitudes of all the data points in the s-th component signal, wherein the larger the mean value is, the larger the fluctuation degree of the s-th component signal is; />The mean value of the horizontal spacing between all two adjacent extreme points in the s-th component signal is represented, the mean value represents the variation period of the s-th component signal, and the larger the mean value is, the larger the variation period of the s-th component signal is, the larger the fluctuation degree of the corresponding s-th component signal is.
Step S104, for any actual component signal, a target reference component signal which is equal to the frequency component of the actual component signal is obtained in all the reference component signals, and the wavelet threshold of the actual component signal is obtained according to the difference and the similarity of the corresponding fluctuation degree between the actual component signal and the target reference component signal.
Specifically, the purpose of the embodiment of the present invention is to acquire the wavelet threshold of any actual component signal, so that, for any actual component signal, it is necessary to acquire a target reference component signal equal to the frequency component of the actual component signal from all the reference component signals. For example: the reference component signals are IMF1, IMF2, IMF3, and the actual component signal is IMF2, and the reference component signal IMF2 is the target reference component signal equal to the frequency component of the actual component signal IMF 2. Then, a wavelet threshold of the actual component signal is obtained based on the difference and the similarity of the corresponding fluctuation degree between the actual component signal and the target reference component signal.
Preferably, the obtaining the wavelet threshold of the actual component signal according to the difference and the similarity of the corresponding fluctuation degree between the actual component signal and the target reference component signal includes:
acquiring an absolute value of a difference value between the fluctuation degree of the actual component signal and the fluctuation degree of the target reference component signal, calculating the similarity between the actual component signal and the target reference component signal by using a DTW algorithm, and acquiring the reciprocal of the similarity;
and carrying out normalization processing on the product between the absolute value and the reciprocal of the similarity to obtain a corresponding normalization result, and taking the product of a preset hyper-parameter and the normalization result as a wavelet threshold value of the actual component signal.
In one embodiment, the wavelet threshold for any actual component signal is calculated as:
wherein,the wavelet threshold value of the w actual component signal is represented, A represents the wavelet threshold value super-parameter of the actual component signal, and the value in the embodiment of the invention is an empirical value of 5, < >>Representing a linear normalization function, ++>Represents the degree of fluctuation of the w-th actual component signal,/->Representing the degree of fluctuation of the target reference component signal of the w-th actual component signal,representing the similarity between the w-th actual component signal and the w-th target reference component signal,/->Representing dynamic time warping (Dynamic Time Warping), a technique for measuring similarity between two time series, is known in the art and will not be described in detail herein.
It should be noted that the number of the substrates,the method comprises the steps of representing the absolute value of a difference between the fluctuation degree of a w-th actual component signal in an actual component signal and the fluctuation degree of a w-th reference component signal in a reference component signal, wherein the absolute value of the difference represents the difference of the fluctuation degree between the actual component signal and a corresponding target reference component signal, and the fluctuation degree difference between the actual component signal and the reference component signal can be calculated to obtain the influence degree of noise because of the problem of modal aliasing when an original signal is decomposed due to a noise signal; />Representing the similarity between the w actual component signal and the w target reference component signal, the greater the similarity, the smaller the influence degree of the noise signal on the w actual component signal is, and the smaller the corresponding wavelet threshold value is.
Thus far, according to the method of the wavelet threshold of any one of the actual component signals described above, the wavelet threshold of each of the actual component signals can be obtained.
Step S105, carrying out self-adaptive denoising on all the actual component signals according to the wavelet threshold value of each actual component signal to obtain denoised temperature signals.
Specifically, after the wavelet threshold value of each actual component signal is obtained, each wavelet threshold value is utilized to perform adaptive wavelet threshold denoising on the corresponding actual component signal, so that each denoised actual component signal is obtained. And then, carrying out signal reconstruction on all the de-noised actual component signals, and further obtaining de-noised temperature signals so as to monitor and early warn the oil-slag separation system according to the de-noised temperature signals.
In summary, the embodiment of the invention obtains the temperature signal of the oil-residue separation system in the preset period; obtaining all extreme points when empirical mode decomposition is carried out on the temperature signal, screening all the extreme points to obtain a target extreme point, decomposing the temperature signal into at least one IMF component signal as a reference component signal according to the target extreme point, and carrying out traditional empirical mode decomposition on the temperature signal to obtain at least one IMF component signal as an actual component signal; for any one of all the reference component signals and the actual component signals, acquiring the fluctuation degree of the component signals according to the amplitude of each data point in the component signals and the horizontal distance between adjacent extreme points; for any actual component signal, acquiring a target reference component signal which is equal to the frequency component of the actual component signal from all reference component signals, and acquiring a wavelet threshold of the actual component signal according to the difference and the similarity of the corresponding fluctuation degree between the actual component signal and the target reference component signal; and carrying out self-adaptive denoising on all the actual component signals according to the wavelet threshold value of each actual component signal to obtain a denoised temperature signal. When the temperature signal is decomposed by using the EMD algorithm, the extremum points of the temperature signal are screened to obtain target extremum points, so that the extremum points caused by faster noise or signal change are solved, the stability of constructing an envelope line according to the extremum points is improved, and therefore purer IMF component signals, namely reference component signals, are obtained according to the target extremum points, and then the IMF component signals, namely actual component signals, obtained by comparing the temperature signals decomposed by the traditional EMD algorithm are obtained, the self-adaptive wavelet threshold value of each actual component signal is obtained according to the fluctuation difference between the IMF component signals and the actual component signals, and then the self-adaptive wavelet threshold value is used for carrying out wavelet threshold denoising on the actual component signals, so that the temperature signal with better denoising effect is obtained.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The foregoing description of the preferred embodiments of the present invention is not intended to be limiting, but rather, any modifications, equivalents, improvements, etc. that fall within the principles of the present invention are intended to be included within the scope of the present invention.
Claims (3)
1. A data analysis method in an oil and slag separation system, the data analysis method comprising:
acquiring a temperature signal of an oil-residue separation system in a preset period;
obtaining all extreme points when the temperature signal is subjected to empirical mode decomposition, screening all the extreme points to obtain a target extreme point, decomposing the temperature signal into at least one IMF component signal serving as a reference component signal according to the target extreme point, and performing conventional empirical mode decomposition on the temperature signal to obtain at least one IMF component signal serving as an actual component signal;
for any one of all reference component signals and actual component signals, acquiring the fluctuation degree of the component signals according to the amplitude of each data point in the component signals and the horizontal distance between adjacent extreme points;
for any actual component signal, acquiring a target reference component signal which is equal to the frequency component of the actual component signal from all reference component signals, and acquiring a wavelet threshold of the actual component signal according to the difference and the similarity of the corresponding fluctuation degree between the actual component signal and the target reference component signal;
carrying out self-adaptive denoising on all the actual component signals according to the wavelet threshold value of each actual component signal to obtain denoised temperature signals;
the step of screening all the extreme points to obtain a target extreme point comprises the following steps:
aiming at any extreme point, according to the amplitude difference between the extreme point and the local extreme point, acquiring the local characteristic value of the extreme point;
obtaining the suspected degree of the extreme point as a target extreme point according to the difference of the local characteristic values between the extreme point and the adjacent extreme point;
acquiring a weight coefficient occupied by the extreme point serving as a target extreme point according to the suspected degree of the extreme point serving as the target extreme point;
when the weight coefficient occupied by the extreme point serving as the target extreme point meets a preset weight coefficient threshold value, determining the extreme point as the target extreme point;
the obtaining the local characteristic value of the extremum point according to the amplitude difference between the extremum point and the local extremum point comprises:
acquiring left adjacent minimum value points and right adjacent minimum value points of the maximum value points;
calculating a first difference absolute value of the corresponding amplitude between the maximum value point and the left adjacent minimum value point, calculating a second difference absolute value of the corresponding amplitude between the maximum value point and the right adjacent minimum value point, and obtaining a first average value between the first difference absolute value and the second difference absolute value;
acquiring data points separated between the maximum value point and the left adjacent minimum value point, acquiring data points separated between the maximum value point and the right adjacent minimum value point, acquiring the absolute value of the difference value of the corresponding amplitude between the data points and the following adjacent data points according to any one of all the separated data points, and calculating a second average value of all the absolute values of the difference values;
taking the product between the first average value and the second average value as a local characteristic value of the maximum value point;
the obtaining the local characteristic value of the extremum point according to the amplitude difference between the extremum point and the local extremum point comprises:
acquiring a left adjacent maximum value point and a right adjacent maximum value point of the minimum value point;
calculating a first difference absolute value of the corresponding amplitude between the minimum value point and the left adjacent maximum value point, calculating a second difference absolute value of the corresponding amplitude between the minimum value point and the right adjacent maximum value point, and obtaining a first average value between the first difference absolute value and the second difference absolute value;
acquiring data points separated between the minimum value point and the left adjacent maximum value point, acquiring data points separated between the minimum value point and the right adjacent maximum value point, acquiring the absolute value of the difference value of the corresponding amplitude between the data points and the following adjacent data points according to any one of all the separated data points, and calculating a second average value of all the absolute values of the difference values;
taking the product between the first average value and the second average value as a local characteristic value of the minimum value point;
the obtaining the suspected degree of the extreme point as the target extreme point according to the difference of the local characteristic values between the extreme point and the adjacent extreme point comprises the following steps:
acquiring a left adjacent extreme point and a right adjacent extreme point of the extreme point, calculating a third difference absolute value of a corresponding local characteristic value between the extreme point and the left adjacent extreme point, and calculating a fourth difference absolute value of a corresponding local characteristic value between the extreme point and the right adjacent extreme point;
calculating the average value between the third difference absolute value and the fourth difference absolute value, and taking the product between the average value and the local characteristic value of the extreme point as the suspected degree of taking the extreme point as a target extreme point;
according to the suspected degree that the extreme point is the target extreme point, the weight coefficient occupied by the extreme point is obtained, and the method comprises the following steps:
obtaining the sum of the suspected degrees of all the extreme points serving as the target extreme points, and taking the ratio between the suspected degrees of the extreme points serving as the target extreme points and the sum of the suspected degrees of all the extreme points serving as the target extreme points as a weight coefficient occupied by the extreme points;
the step of obtaining the wavelet threshold of the actual component signal according to the difference and the similarity of the corresponding fluctuation degree between the actual component signal and the target reference component signal comprises the following steps:
acquiring an absolute value of a difference value between the fluctuation degree of the actual component signal and the fluctuation degree of the target reference component signal, calculating the similarity between the actual component signal and the target reference component signal by using a DTW algorithm, and acquiring the reciprocal of the similarity;
and carrying out normalization processing on the product between the absolute value and the reciprocal of the similarity to obtain a corresponding normalization result, and taking the product of a preset hyper-parameter and the normalization result as a wavelet threshold value of the actual component signal.
2. The data analysis method of claim 1, wherein the obtaining the fluctuation degree of the component signal according to the amplitude of each data point in the component signal and the horizontal distance between the adjacent extreme points comprises:
acquiring the average value of the amplitude values of all the data points in the component signal, and calculating the average value of the absolute value of the difference value according to the absolute value of the difference value between the amplitude value of each data point in the component signal and the average value of the amplitude value;
calculating the average value of the horizontal distance according to the horizontal distance between every two adjacent extreme points in the component signals;
taking the product of the average value of the absolute value of the difference and the average value of the horizontal interval as the fluctuation degree of the component signal.
3. The data analysis method according to claim 1, comprising, before the acquiring the temperature signal of the oil and slag separation system for the preset period of time:
acquiring the temperature of the oil-residue separation system based on a preset sampling frequency, and obtaining an initial temperature signal of the oil-residue separation system in a preset period;
and carrying out interpolation processing on the initial temperature signal by using a linear interpolation algorithm to obtain a temperature signal of the oil-slag separation system in a preset period.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103984866A (en) * | 2014-05-20 | 2014-08-13 | 浙江师范大学 | Signal denoising method based on local mean value decomposition |
| WO2018053525A1 (en) * | 2016-09-19 | 2018-03-22 | The University Of New Hampshire | Techniques for empirical mode decomposition (emd)-based noise estimation |
| AU2019101756A4 (en) * | 2019-09-06 | 2020-11-12 | Jiangsu Huakang Information Technology Co. Ltd | Method for analyzing heart rate variability signal based on extremum energy decomposition method |
| CN114970602A (en) * | 2022-04-13 | 2022-08-30 | 浙大宁波理工学院 | Signal denoising method and system based on improved empirical mode decomposition and wavelet threshold function |
| WO2022261805A1 (en) * | 2021-06-15 | 2022-12-22 | 大连理工大学 | Diesel engine gearbox fault diagnosis method |
| CN116776168A (en) * | 2023-08-22 | 2023-09-19 | 惠州帝恩科技有限公司 | Intelligent analysis method and system for production data of reagent tubes |
| CN116976684A (en) * | 2023-09-25 | 2023-10-31 | 尚古智造(山东)智能装备有限公司 | Risk model predictive control method and system for logistics conveyor |
| CN117056642A (en) * | 2023-10-10 | 2023-11-14 | 湖南科技大学 | Microwave data processing method and system |
-
2023
- 2023-12-12 CN CN202311695023.4A patent/CN117390380B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103984866A (en) * | 2014-05-20 | 2014-08-13 | 浙江师范大学 | Signal denoising method based on local mean value decomposition |
| WO2018053525A1 (en) * | 2016-09-19 | 2018-03-22 | The University Of New Hampshire | Techniques for empirical mode decomposition (emd)-based noise estimation |
| AU2019101756A4 (en) * | 2019-09-06 | 2020-11-12 | Jiangsu Huakang Information Technology Co. Ltd | Method for analyzing heart rate variability signal based on extremum energy decomposition method |
| WO2022261805A1 (en) * | 2021-06-15 | 2022-12-22 | 大连理工大学 | Diesel engine gearbox fault diagnosis method |
| CN114970602A (en) * | 2022-04-13 | 2022-08-30 | 浙大宁波理工学院 | Signal denoising method and system based on improved empirical mode decomposition and wavelet threshold function |
| CN116776168A (en) * | 2023-08-22 | 2023-09-19 | 惠州帝恩科技有限公司 | Intelligent analysis method and system for production data of reagent tubes |
| CN116976684A (en) * | 2023-09-25 | 2023-10-31 | 尚古智造(山东)智能装备有限公司 | Risk model predictive control method and system for logistics conveyor |
| CN117056642A (en) * | 2023-10-10 | 2023-11-14 | 湖南科技大学 | Microwave data processing method and system |
Non-Patent Citations (4)
| Title |
|---|
| 刘利强 ; .经验模态分解在微地震信号去噪中的应用.内蒙古煤炭经济.2020,(06),全文. * |
| 基于EMD阈值方法的轴承故障振动信号去噪;包广清;常勇;杨国金;;计算机工程与应用;20150515(10);全文 * |
| 基于改进EMD和小波阈值法的风机数据降噪研究;赵坤;郑小霞;;上海电力大学学报;20200415(02);全文 * |
| 张晖 ; 鲁明明 ; 丁华平 ; 沈庆宏 ; .基于经验模态分解法的桥梁应变信号降噪方法研究.电子测量技术.2016,(10),全文. * |
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