CN108446440A - The method for improving particle temperature measurement accuracy - Google Patents
The method for improving particle temperature measurement accuracy Download PDFInfo
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- CN108446440A CN108446440A CN201810141156.XA CN201810141156A CN108446440A CN 108446440 A CN108446440 A CN 108446440A CN 201810141156 A CN201810141156 A CN 201810141156A CN 108446440 A CN108446440 A CN 108446440A
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Abstract
The present invention relates to a kind of method improving particle temperature measurement accuracy, step is：1）Decomposition using one-dimensional wavelet analysis to particle temperature data-signal, 2）Wavelet transformation decomposition is carried out to primary granule temperature data, obtains a low frequency part and several high frequency section；3）High frequency section after particle temperature datagram wavelet decomposition is retained constant；4）The threshold value quantizing of wavelet decomposition high frequency coefficient selects a threshold value to carry out soft-threshold quantification treatment the high frequency coefficient under each decomposition scale；5）Then wavelet reconstruction is carried out again, and one-dimensional wavelet reconstruction is carried out according to the bottom low frequency coefficient of wavelet decomposition and each high frequency coefficient；6）As a result wavelet inverse transformation is carried out according to the high frequency section obtained after new low frequency part and particle temperature signal graph wavelet decomposition and obtains that treated obtains particle temperature signal graph.This method complexity is low, and smoothing function is good, and the data handled are more accurate, and the noise of signal is greatly reduced, error smaller.
Description
Technical field
The present invention relates to a kind of methods improving particle temperature measurement accuracy, are carried with wavelet analysis more particularly, to one kind
The method of high particle temperature the measuring precision.
Background technology
Particulate matter is the system of the complicated unordered accumulation of particle, in nature, engineering construction and industrial production extensively
In the presence of, particulate matter flows under external force, shows the property of fluid, forms particle stream, such as clast stream disaster,
Desert, rivers silt, snowslide and pebble bed reactor etc..Particle temperature (Granular temperature, ＜ δ v2＞ it) is used to
The active degree for indicating particle disordered motion, by reference to the analysis method of the dense gas theory of molecular motion heterogeneous,
Grain random motion and the warm-up movement of gas molecule are comparable, and particle is described due to random pulse caused by collision with particle temperature,
The size of particle temperature illustrates the power of particle speed pulsation, and particle temperature is higher, stronger, the particle temperature of particle speed pulsation
Definition be：
Wherein m is total particle number, viFor the movement velocity of i-th of particle,For the average speed of whole particles movement.
During particle temperature measures, when particulate matter movement is slower, particle temperature value at this time is relatively low easy
It is interfered by noise signal, such as fluid bed self vibration and the noise that generates, the noise that ambient light interference generates, detector itself
Noise etc., therefore how to reduce noise, improve the problem that system signal noise ratio is in the urgent need to address at present.
Wavelet transformation is an international Disciplinary Frontiers for rising upsurge in the world in recent ten years, it is the office of spatio-temporal frequency
Portionization is analyzed, and has good distinguishing simultaneously in time domain and frequency domain, can be in different scales using multiscale analysis method
The local feature of lower observation signal different accuracy has apparent advantage in terms of denoising, not only can remove noise, but also can be fine
Reservation original signal essential characteristic.
Invention content
The present invention is directed to low-speed motion particle stream poor problem of noise in particle temperature measurement process, proposes a kind of
The method for improving particle temperature measurement accuracy, this method carries out reduction noise based on the noise-removed technology of wavelet transformation, and retains
The useful information of the overwhelming majority, the signal-to-noise ratio and measurement accuracy of system are improved with this.
To achieve the above object, the technical solution adopted by the present invention is：A method of particle temperature measurement accuracy is improved,
Include the following steps：
1) decomposition using one-dimensional wavelet analysis to particle temperature data-signal, particle temperature have the signal made an uproar to be expressed as x
(k)=f (k) integer of+e (k), k=0,1 ..., n-1, x (k) are noisy particle temperature signal, and f (k) is useful
Grain temperature signal, e (k) are noise signal, and e (k) is 1 grade of white Gaussian noise, is usually expressed as high-frequency signal, and f (k) is
The particle temperature signal of low frequency either some more stable particle temperature signals, then select a wavelet function and determination
The level of decomposition, then carry out decomposition computation；
2) wavelet transformation decomposition is carried out to primary granule temperature data, obtains a low frequency part and several high frequency section；
3) high frequency section after particle temperature datagram wavelet decomposition is retained constant；
4) threshold value quantizing of wavelet decomposition high frequency coefficient, under each decomposition scale high frequency coefficient select a threshold value into
Row soft-threshold quantification treatment；
5) wavelet reconstruction and then is again carried out, is carried out according to the bottom low frequency coefficient of wavelet decomposition and each high frequency coefficient one-dimensional
Wavelet reconstruction；
6) result is carried out according to the high frequency section after obtaining new low frequency part and particle temperature signal graph wavelet decomposition
Wavelet inverse transformation obtains that treated obtains particle temperature signal graph.
The step 3) specifically includes：High frequency section after the particle temperature signal graph wavelet decomposition is retained not
Become；Linear transformation is carried out to low resolution particle temperature data-signal figure, utilizes the wavelet coefficient and numerical relation of low frequency part
Adaptive amplitude enhancing, obtains new low frequency part.
The step 4) specifically includes：Wavelet analysis carries out thresholding method, using given threshold value denoising method,
During actual denoising, threshold value can usually be obtained by empirical equation, and this threshold value is than the threshold value of acquiescence
Confidence level and accuracy want high, and the measurement accuracy of particle temperature system is improved with this.
The beneficial effects of the invention are as follows：
A kind of method complexity of raising particle temperature measurement accuracy based on wavelet transformation proposed by the present invention is low, smoothly
Better function, the data that processing all obtains are more accurate, and the noise of signal is greatly reduced, error smaller.
Description of the drawings
Fig. 1 is the particle temperature provided in an embodiment of the present invention improved based on wavelet transformation used in the measuring precision method
The algorithm flow chart of signal decomposition.
Specific implementation mode
The present invention is further illustrated with embodiment below in conjunction with the accompanying drawings.
The present invention uses Mallat algorithm decomposition textures, and Mallat algorithms are by S.Mallat and Y.Meyer in the preceding National People's Congress
It was proposed in 1986 on the basis of amount work, the conceptive multi-resolution characteristics that small echo is figuratively illustrated from space, with
The descending variation of scale, a kind of algorithm of the different characteristic of observation image that can be from thick to thin on each scale.At this
The basic principle of Mallat algorithms in invention is to be decomposed in the process to signal, and decomposition obtains low-frequency information and high frequency
Information, low-frequency information are the slow parts of variation, account for the major part of all information, and high-frequency information is the rapid part of variation, it
What is reflected is the detailed information of signal, accounts for the fraction of all information.It is that first layer decomposes above.Handle on the basis of first layer
Low-frequency information part is decomposed into two parts again：Low frequency+high frequency.Third layer is that the second layer is decomposited the low-frequency information come to be decomposed into
Low frequency+high frequency ... and so on.It is multi-resolution decomposition above, high frequency section without decomposing again.If single scale decomposes, meeting
High frequency section is decomposed in layer as low frequency part.It can be obtained in decomposition computation, on measurement accuracy scale, by
It is small in signal-to-noise ratio, the groundwork to be done be maximize eliminate these noises, while again can be as possible reservation original signal structure
Information, opposite, on big scale, because signal-to-noise ratio is big, the correlation of data structure is stronger, can not also do noise filter
Wave processing, then can retain initial data as possible, this of wavelet transformation is similar to adaptive method for processing noise, in song
In line smoothing process, it can effectively inhibit noise and ensure high undistorted property.According to uncertainty principle, for appointing
Anticipate scale, wavelets Subspace showed when be invariable for frequency window ara, scale increases, then window width when corresponding
Degree is multiplied.
Specific embodiment described herein is used only for explaining the present invention rather than limitation of the invention.In addition it also needs
It is noted that for ease of description, only some but not all contents related to the present invention are shown in the drawings, unless otherwise
Definition, all technical and scientific terms used herein and the those skilled in the art for belonging to the present invention are normally understood
Meaning is identical.Term used herein is intended merely to description specific embodiment, it is not intended that in the limitation present invention.
(1) it please refers to shown in Fig. 1, Fig. 1 is the raising systematic survey essence provided in an embodiment of the present invention based on wavelet transformation
The flow chart of the particle temperature signal decomposition of algorithm used in degree method.
(2) in the present embodiment the method for the raising particle temperature technology measurement accuracy based on wavelet transformation specifically include it is as follows
Step：
Step 1, the decomposition using one-dimensional wavelet analysis to particle temperature data-signal, particle temperature have the signal made an uproar to indicate
For the integer of x (k)=f (k)+e (k), k=0,1 ..., n-1, x (k) is noisy particle temperature signal, and f (k) is useful
Particle temperature signal, e (k) are noise signal, and e (k) is 1 grade of white Gaussian noise, is usually expressed as high-frequency signal, and f (k)
For particle temperature signal either some more stable particle temperature signals of low frequency, a wavelet function and true is then selected
Surely the level decomposed, then carry out decomposition computation；
Step 2 carries out wavelet transformation decomposition to the primary granule temperature data, obtains a low frequency part and several high
Frequency part.
Step 3, the high frequency section obtained after particle temperature signal graph wavelet decomposition is retained it is constant.In this implementation
The high frequency section obtained after particle temperature signal graph wavelet decomposition is retained in example constant；To particle temperature signal graph into
Row linear transformation is enhanced using the adaptive amplitude of the wavelet coefficient and numerical relation of low frequency part, obtains new low frequency part.
The threshold value quantizing of step 4, wavelet decomposition high frequency coefficient selects a threshold to the high frequency coefficient under each decomposition scale
Value carries out soft-threshold quantification treatment, and wavelet analysis carries out thresholding method, using given threshold value denoising method, in reality
Denoising during, threshold value can usually be obtained by empirical equation, and this threshold value is than the confidence level of the threshold value of acquiescence
It wants high with accuracy, the measurement accuracy of system is improved with this.
Step 5 and then wavelet reconstruction is carried out again, carried out according to the bottom low frequency coefficient of wavelet decomposition and each high frequency coefficient
One-dimensional wavelet reconstruction.
Step 6 is carried out according to the high frequency section after acquisition new low frequency part and particle temperature signal graph wavelet decomposition
Wavelet inverse transformation obtains treated particle temperature signal graph.
A kind of method complexity of raising particle temperature technology measurement accuracy based on wavelet transformation proposed by the present invention is low,
Smoothing function is good, low-pass filtering property is not present, the data of processing are more accurate, the error smaller of acquisition.The foregoing is merely
The preferred embodiment of the present invention is not intended to restrict the invention, and to those skilled in the art, the present invention can have various
Modifications and changes.
One of ordinary skill in the art will appreciate that realizing all or part of flow in above-described embodiment method, being can be with
Relevant software is instructed to complete by computer program, the program can be stored in a computer read/write memory medium
In, the program is when being executed, it may include such as the flow of the embodiment of above-mentioned each method.
Claims (3)
1. a kind of method improving particle temperature measurement accuracy, which is characterized in that include the following steps：
1) using decomposition of the one-dimensional wavelet analysis to particle temperature data-signal, particle temperature have the signal made an uproar be expressed as x (k)=
The integer of f (k)+e (k), k=0,1 ..., n-1, x (k) are noisy particle temperature signal, and f (k) is useful particle temperature
Signal, e (k) are noise signal, and e (k) is 1 grade of white Gaussian noise, are usually expressed as high-frequency signal, and f (k) is low frequency
Then particle temperature signal either some more stable particle temperature signals select a wavelet function and determine decomposition
Level, then carry out decomposition computation；
2) wavelet transformation decomposition is carried out to primary granule temperature data, obtains a low frequency part and several high frequency section；
3) high frequency section after particle temperature datagram wavelet decomposition is retained constant；
4) threshold value quantizing of wavelet decomposition high frequency coefficient selects a threshold value to carry out the high frequency coefficient under each decomposition scale soft
Threshold value quantizing processing；
5) wavelet reconstruction and then is again carried out, one-dimensional small echo is carried out according to the bottom low frequency coefficient of wavelet decomposition and each high frequency coefficient
Reconstruct；
6) result carries out small echo according to the high frequency section after obtaining new low frequency part and particle temperature signal graph wavelet decomposition
Inverse transformation obtains that treated obtains particle temperature signal graph.
2. the method according to claim 1 for improving particle temperature measurement accuracy, it is characterised in that：The step 3) is specific
Including：High frequency section after the particle temperature signal graph wavelet decomposition is retained constant；To low resolution particle temperature
Data-signal figure carries out linear transformation, is enhanced using the adaptive amplitude of the wavelet coefficient and numerical relation of low frequency part, obtains new
Low frequency part.
3. the method according to claim 1 for improving particle temperature measurement accuracy, it is characterised in that：The step 4) is specific
Including：Wavelet analysis carries out thresholding method, using given threshold value denoising method, in actual denoising process
In, threshold value can usually be obtained by empirical equation, and this threshold value is higher than the confidence level of the threshold value of acquiescence and accuracy, with
This improves the measurement accuracy of particle temperature system.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010145258A (en) * | 2008-12-19 | 2010-07-01 | Yazaki Corp | Gas analyzer |
| CN103076028A (en) * | 2013-01-21 | 2013-05-01 | 新疆美特智能安全工程股份有限公司 | Wavelet de-noising method of optical-phase vibration |
| CN105004278A (en) * | 2015-07-10 | 2015-10-28 | 东南大学 | Real-time base line and denoising processing method based on distributed sensing and wavelet analyzing technologies |
| CN106441288A (en) * | 2016-08-31 | 2017-02-22 | 北斗时空信息技术（北京）有限公司 | Adaptive wavelet denoising method for accelerometer |
| CN107481193A (en) * | 2017-08-21 | 2017-12-15 | 叶军 | A kind of image interpolation method based on wavelet transformation |
-
2018
- 2018-02-11 CN CN201810141156.XA patent/CN108446440A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010145258A (en) * | 2008-12-19 | 2010-07-01 | Yazaki Corp | Gas analyzer |
| CN103076028A (en) * | 2013-01-21 | 2013-05-01 | 新疆美特智能安全工程股份有限公司 | Wavelet de-noising method of optical-phase vibration |
| CN105004278A (en) * | 2015-07-10 | 2015-10-28 | 东南大学 | Real-time base line and denoising processing method based on distributed sensing and wavelet analyzing technologies |
| CN106441288A (en) * | 2016-08-31 | 2017-02-22 | 北斗时空信息技术（北京）有限公司 | Adaptive wavelet denoising method for accelerometer |
| CN107481193A (en) * | 2017-08-21 | 2017-12-15 | 叶军 | A kind of image interpolation method based on wavelet transformation |
Non-Patent Citations (5)
| Title |
|---|
| C.F.THERESA CENATE等: "Multi-scale wavelet algorithm based noise reduction in ultrasonic images at elevated temperatures", 《IEEE XPLORE》 * |
| 刘磊: "分布式光纤拉曼温度传感***及其应用研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 张健等: "基于小波变换的列车广播声压自适应调整算法", 《电声技术》 * |
| 柳刚等: "基于小波变换的雷达低信噪比信号处理技术及应用研究", 《工程勘察》 * |
| 江海峰: "分布式拉曼光纤温度传感***性能提升方法研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
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Application publication date: 20180824 |