CN110487916A - A kind of noise-reduction method suitable for the dirt ultrasound detection echo-signal that exchanges heat - Google Patents
A kind of noise-reduction method suitable for the dirt ultrasound detection echo-signal that exchanges heat Download PDFInfo
- Publication number
- CN110487916A CN110487916A CN201910786883.6A CN201910786883A CN110487916A CN 110487916 A CN110487916 A CN 110487916A CN 201910786883 A CN201910786883 A CN 201910786883A CN 110487916 A CN110487916 A CN 110487916A
- Authority
- CN
- China
- Prior art keywords
- signal
- noise
- component
- modal components
- dirt
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/04—Analysing solids
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
- G01N29/4409—Processing the detected response signal, e.g. electronic circuits specially adapted therefor by comparison
- G01N29/4427—Processing the detected response signal, e.g. electronic circuits specially adapted therefor by comparison with stored values, e.g. threshold values
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
- G01N29/50—Processing the detected response signal, e.g. electronic circuits specially adapted therefor using auto-correlation techniques or cross-correlation techniques
Landscapes
- Physics & Mathematics (AREA)
- Analytical Chemistry (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Signal Processing (AREA)
- Engineering & Computer Science (AREA)
- Acoustics & Sound (AREA)
- Investigating Or Analyzing Materials By The Use Of Ultrasonic Waves (AREA)
Abstract
The invention discloses a kind of noise-reduction method suitable for the dirt detection signal that exchanges heat, the following steps are included: S1, using VMD method decomposition goal signal obtaining multiple modal components；S2, the energy entropy analyzed the auto-correlation function characteristic curve of each component and calculate each component；S3, judge and identify noise dominant component；S4, the useful information in noise dominant component is extracted using wavelet shrinkage threshold method；S5, reconstruction signal are to obtain the signal after noise reduction.The present invention is based on improved VMD methods, effectively avoid modal overlap phenomenon；Noise dominant mode is difficult to determining problem in each modal components decomposed for VMD, the method combined using the energy entropy of analysis auto-correlation function characteristics curve and each component of calculating, accurately determine noise dominant mode, then wavelet shrinkage threshold process is carried out to it, last reconstruction signal has accomplished good noise reduction on the basis of being sufficiently reserved original signal.
Description
Technical field
The present invention relates to the Ultrasonic NDTs of heat exchange dirt, and in particular to one kind is suitable for heat exchange dirt ultrasound detection and returns
The noise-reduction method of wave signal.
Background technique
Currently, heat exchange dirt be widely present in the actual production process, seriously affect heat exchange equipment operation safety and
Economy, ultrasonic time-domain reflectometry are a kind of highly effective and safe, Yi Shixian, lossless detection technique, are detected in Heat Exchanger Fouling
In show good application prospect.
Since heat exchange dirt forming process is complicated, often there is noise jamming and a variety of noises in ultrasound detection echo, make
Accurately acquisition transition time calculating dirt thickness is obtained to have difficulties.At present in field of signal processing, for heat exchange dirt echo letter
The noise reduction of number this kind of nonlinear and non local boundary value problem, wavelet de-noising can filter out partial noise, but its threshold selection criteria disunity, and
Discomposing effect excessively relies on the selection of wavelet basis.Empirical mode decomposition is a kind of common method, but its theoretical basis is not perfect,
Easily there is modal overlap phenomenon.Existing signal processing method is all difficult to adapt to the demand of this problem, and therefore, heat exchanging dirt is super
The noise reduction process research of sound detection echo-signal has necessity and urgency.
Variation mode decomposition (Variational Mode Decomposition, VMD) is a kind of new non-recursive letter
Number decomposition method has stringent theoretical basis, and the form for solving modal components is equivalent to Wiener filtering process, inherently has
There is certain noise reduction effect.It needs to take each component envelope could identification feature frequency with empirical mode decomposition, wavelet analysis etc.
The method of rate compares, and VMD eliminates the process of envelope demodulation.
Summary of the invention
The main purpose of the present invention is to provide a kind of noise-reduction methods suitable for the dirt ultrasound detection echo-signal that exchanges heat.
The technical solution adopted by the present invention is that: a kind of noise reduction side suitable for the dirt ultrasound tim e- domain detection echo-signal that exchanges heat
Method, comprising the following steps:
S1 treats de-noising signal using the legal adopted variation modal components number K of centre frequency differenceCarry out variation mode
It decomposes, obtains K modal components；
S2 calculates the auto-correlation function and energy entropy of each modal components in step 1；
S3, according to the local minimum of auto-correlation function characteristic and search Energy-Entropy, judgement obtains noise component(s)；
S4 carries out wavelet shrinkage threshold deniosing, threshold value selection rule to the noise component(s) judged in step 3 are as follows:
(1)
Wherein,For threshold function table,For wavelet coefficient,For threshold value, m, n, k are threshold function table regulatory factor；
S5 will be reconstructed with untreated modal components after denoising, obtain signal after final noise reduction.
Further, the step S1 specifically:
S11, initialization,,It is 0 with n, whereinFor K modal components,For K modal components
Centre frequency,For Lagrange multiplier operator, n is initial modal components number；
S12,, execute entire circulation；
S13 updates、, whereinFor modal components,For the corresponding centre frequency of modal components:
(2)
In formula,It is equal toRespectivelyFourier transformation,For the confined equilibrium parameter of data fidelity；
(3)
In formula,It is equal to；
S14,, step S13 is repeated, until；
S15, according to, update,For fidelity coefficient, 0 is generally taken；
S16 gives discrimination precisionIf meeting iteration stopping condition,
Then terminate entirely to recycle, output result obtains K narrowband IMF component, otherwise repeatedly step S12-S15.
Further, the step S1 decomposes signal using VMD algorithm, is then analyzed using auto-correlation function
Noise component(s) is judged with the energy entropy joint identification for calculating each component, and noise component(s) is dropped using wavelet shrinkage threshold method
It makes an uproar and extracts useful information, finally reconstruct obtains signal after noise reduction.
Advantages of the present invention:
The present invention is based on improved VMD method, combines to be recognized accurately with energy entropy using auto-correlation function analysis and make an uproar
Sound component, have certain novelty, can simple, accurate, real-time heat exchanging dirt ultrasound detection echo-signal dropped
It makes an uproar processing, and obtains preferable effect, concept feasible is stronger, realizes relatively simple convenience.While eliminating noise, can have
Faint effective information in the reservation heat exchange dirt ultrasound detection signal dirt echo of effect, be capable of real non-destructive carries out heat exchange dirt
Dirt detection provides effective way for further increasing for industrial production efficiency, creates certain industrial value.Meanwhile this research
Other field is studied, such as in terms of the processing of seismic wave, voice signal, also has preferable reference and reference.
Other than objects, features and advantages described above, there are also other objects, features and advantages by the present invention.
Below with reference to figure, the present invention is described in further detail.
Detailed description of the invention
The attached drawing constituted part of this application is used to provide further understanding of the present invention, schematic reality of the invention
It applies example and its explanation is used to explain the present invention, do not constitute improper limitations of the present invention.
Fig. 1 is flow diagram of the invention；
Fig. 2 is the waveform diagram of original signals with noise of the invention；
Fig. 3 is the waveform diagram of each component after VMD of the invention is decomposed；
Fig. 4 is the auto-correlation function performance diagram of each component after VMD of the invention is decomposed；
Fig. 5 is the waveform diagram of noise reduction back echo signal of the invention.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.
With reference to Fig. 1, as shown in Figure 1, a kind of noise-reduction method suitable for the dirt ultrasound tim e- domain detection echo-signal that exchanges heat, packet
Include following steps:
S1 treats de-noising signal using the legal adopted variation modal components number K of centre frequency differenceCarry out variation mode
It decomposes, obtains K modal components；
S2 calculates the auto-correlation function and energy entropy of each modal components in step 1；
S3, according to the local minimum of auto-correlation function characteristic and search Energy-Entropy, judgement obtains noise component(s)；
S4 carries out wavelet shrinkage threshold deniosing, threshold value selection rule to the noise component(s) judged in step 3 are as follows:
(1)
Wherein,For threshold function table,For wavelet coefficient,For threshold value, m, n, k are threshold function table regulatory factor；
S5 will be reconstructed with untreated modal components after denoising, obtain signal after final noise reduction.
The step S1 specifically:
S11, initialization,,It is 0 with n, whereinFor K modal components,For K mode point
The centre frequency of amount,For Lagrange multiplier operator, n is initial modal components number；
S12,, execute entire circulation；
S13 updates、, whereinFor modal components,For the corresponding centre frequency of modal components:
(2)
In formula,It is equal toRespectivelyFourier transformation,For the confined equilibrium parameter of data fidelity；
(3)
In formula,It is equal to；
S14,, step S13 is repeated, until；
S15, according to, update,For fidelity coefficient, 0 is generally taken；
S16 gives discrimination precisionIf meeting iteration stopping condition
, then terminate entirely to recycle, output result obtains K narrowband IMF component, otherwise repeatedly step S12-S15.
The step S1 decomposes signal using VMD algorithm, then analyzes and calculate each point using auto-correlation function
The energy entropy joint identification of amount judges noise component(s), carries out noise reduction to noise component(s) using wavelet shrinkage threshold method and has extracted
With information, finally reconstruct obtains signal after noise reduction.
Method and experiment suitable for the dirt ultrasound detection echo-signal noise reduction that exchanges heat further include:
(1) heat exchange dirt ultrasound detection echo-signal is obtained, is built based on COMSOL Multiphysics numbered analog simulation software
Vertical four layers of fluid structure interaction mode of filling liquid dirt pipeline 3D finite element, using global definition, each material parameter is as shown in table 1, generates
Without noise cancellation signal, the white Gaussian noise for recycling Matlab that 10dB is added into no noise cancellation signal simulates original signals with noise, such as Fig. 2 institute
Show.
1 dirt pipeline model material parameter table of table
| Material | Length mm | Outer diameter mm | Wall thickness mm | Young's modulus GPa | Density kg/m3 | Poisson's ratio |
| Carbon steel pipe | 300 | 25 | 1.5 | 195 | 8400 | 0.28 |
| Calcium carbonate | 300 | - | 0.5 | 10 | 2200 | 0.28 |
(2) VMD decomposition is carried out to the signal of acquisition, as shown in figure 3, being decomposed into 4 layers.
(3) the auto-correlation function characteristic curve of each component is solved, as shown in Figure 4.
(4) the energy entropy of each component is calculated, the results are shown in Table 2.
The energy entropy of each component of table 2
| IMF1 | IMF2 | IMF3 | IMF4 | |
| Energy-Entropy | 0.3657 | 0.3638 | 0.3097 | 0.2243 |
(6) according to auto-correlation function characteristic curve, normalized function value is 1 at zero point, and the component that elsewhere is 0 is noise
Dominant component；According to energy entropy table, first Energy-Entropy component corresponding when taking local minimum and its component later
For noise component(s), can obtain IMF4 is noise component(s), is handled using wavelet shrinkage threshold method it, and finally reconstruct is dropped
Signal after making an uproar, as shown in Figure 5.
The present invention carries out VMD decomposition to echo-signal first, by the auto-correlation function and Energy-Entropy that calculate each component
Value judges and identifies noise dominant component, carries out useful information extraction to noise dominant component using wavelet shrinkage threshold value, most
Reconstruct obtains signal after noise reduction afterwards.
The present invention is based on improved VMD methods, are analyzed using auto-correlation function and combine and can accurately identify with energy entropy
Noise component(s) out, have certain novelty, can simple, accurate, real-time heat exchanging dirt ultrasound detection echo-signal into
Row noise reduction process, and preferable effect is obtained, concept feasible is stronger, realizes relatively simple convenience.While eliminating noise,
It can effectively retain faint effective information in heat exchange dirt ultrasound detection signal dirt echo, be capable of real non-destructive and change
Hot dirt detection, provides effective way for further increasing for industrial production efficiency, creates certain industrial value.Meanwhile this
Other field is studied in research, such as in terms of the processing of seismic wave, voice signal, also has preferable reference and reference.
The foregoing is merely presently preferred embodiments of the present invention, is not intended to limit the invention, it is all in spirit of the invention and
Within principle, any modification, equivalent replacement, improvement and so on be should all be included in the protection scope of the present invention.
Claims (3)
1. a kind of noise-reduction method suitable for the dirt ultrasound tim e- domain detection echo-signal that exchanges heat, feature exist
In, comprising the following steps:
S1 treats de-noising signal using the legal adopted variation modal components number K of centre frequency differenceCarry out variation mode
It decomposes, obtains K modal components；
S2 calculates the auto-correlation function and energy entropy of each modal components in step 1；
S3, according to the local minimum of auto-correlation function characteristic and search Energy-Entropy, judgement obtains noise component(s)；
S4 carries out wavelet shrinkage threshold deniosing, threshold value selection rule to the noise component(s) judged in step 3 are as follows:
(1)
Wherein,For threshold function table,For wavelet coefficient,For threshold value, m, n, k are threshold function table regulatory factor；
S5 will be reconstructed with untreated modal components after denoising, obtain signal after final noise reduction.
2. the drop of the dirt ultrasound tim e- domain detection echo-signal according to claim 1 that is suitable for exchanging heat
Method for de-noising, which is characterized in that the step S1 specifically:
S11, initialization,,It is 0 with n, whereinFor K modal components,For K modal components
Centre frequency,For Lagrange multiplier operator, n is initial modal components number；
S12,, execute entire circulation；
S13 updates、, whereinFor modal components,For the corresponding centre frequency of modal components:
(2)
In formula,It is equal toRespectivelyFourier transformation,For the confined equilibrium parameter of data fidelity；
(3)
In formula,It is equal to；
S14,, step S13 is repeated, until；
S15, according to, update,For fidelity coefficient, 0 is generally taken；
S16 gives discrimination precisionIf meeting iteration stopping condition,
Then terminate entirely to recycle, output result obtains K narrowband IMF component, otherwise repeatedly step S12-S15.
3. the drop of the dirt ultrasound tim e- domain detection echo-signal according to claim 1 that is suitable for exchanging heat
Method for de-noising, which is characterized in that the step S1 decomposes signal using VMD algorithm, then utilizes auto-correlation function
Analyze and calculate each component energy entropy joint identification judges noise component(s), using wavelet shrinkage threshold method to noise component(s) into
Row noise reduction extracts useful information, and finally reconstruct obtains signal after noise reduction.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910786883.6A CN110487916A (en) | 2019-08-24 | 2019-08-24 | A kind of noise-reduction method suitable for the dirt ultrasound detection echo-signal that exchanges heat |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910786883.6A CN110487916A (en) | 2019-08-24 | 2019-08-24 | A kind of noise-reduction method suitable for the dirt ultrasound detection echo-signal that exchanges heat |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110487916A true CN110487916A (en) | 2019-11-22 |
Family
ID=68553745
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910786883.6A Pending CN110487916A (en) | 2019-08-24 | 2019-08-24 | A kind of noise-reduction method suitable for the dirt ultrasound detection echo-signal that exchanges heat |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110487916A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110333285A (en) * | 2019-07-04 | 2019-10-15 | 大连海洋大学 | Ultrasonic Lamb waves Defect signal recognition method based on variation mode decomposition |
| CN113221692A (en) * | 2021-04-29 | 2021-08-06 | 长春工业大学 | Continuous variational modal decomposition DWT denoising method for optical fiber sensing |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107515424A (en) * | 2017-07-26 | 2017-12-26 | 山东科技大学 | A kind of microseismic signals noise reduction filtering method based on VMD and wavelet packet |
| CN108491355A (en) * | 2018-02-05 | 2018-09-04 | 南京邮电大学 | A kind of ultrasonic signal noise-reduction method based on CEEMD and wavelet packet |
-
2019
- 2019-08-24 CN CN201910786883.6A patent/CN110487916A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107515424A (en) * | 2017-07-26 | 2017-12-26 | 山东科技大学 | A kind of microseismic signals noise reduction filtering method based on VMD and wavelet packet |
| CN108491355A (en) * | 2018-02-05 | 2018-09-04 | 南京邮电大学 | A kind of ultrasonic signal noise-reduction method based on CEEMD and wavelet packet |
Non-Patent Citations (2)
| Title |
|---|
| 孙灵芳 等: "基于改进CEEMD 的薄层污垢超声检测信号去噪", 《仪器仪表学报》 * |
| 杜必强 等: "变分模态分解和熵理论在超声信号降噪中的应用", 《中国工程机械学报》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110333285A (en) * | 2019-07-04 | 2019-10-15 | 大连海洋大学 | Ultrasonic Lamb waves Defect signal recognition method based on variation mode decomposition |
| CN110333285B (en) * | 2019-07-04 | 2021-07-27 | 大连海洋大学 | Ultrasonic lamb wave defect signal identification method based on variational modal decomposition |
| CN113221692A (en) * | 2021-04-29 | 2021-08-06 | 长春工业大学 | Continuous variational modal decomposition DWT denoising method for optical fiber sensing |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108954020B (en) | Pipeline positioning method | |
| Pearson et al. | Improved acoustic emission source location during fatigue and impact events in metallic and composite structures | |
| CN104807534B (en) | Equipment eigentone self study recognition methods based on on-line vibration data | |
| CN110388570B (en) | VMD-based self-adaptive noise reduction method and application thereof in water supply pipeline leakage positioning | |
| CN106814396A (en) | A kind of noise reduction filtering method of the mine microquake signal based on VMD | |
| CN110487916A (en) | A kind of noise-reduction method suitable for the dirt ultrasound detection echo-signal that exchanges heat | |
| CN105700020A (en) | Random noise suppression method and apparatus for seismic data | |
| CN105125204A (en) | Electrocardiosignal denoising method based on ESMD (extreme-point symmetric mode decomposition) method | |
| CN110136745A (en) | A kind of vehicle whistle recognition methods based on convolutional neural networks | |
| CN107886078A (en) | A kind of Threshold Denoising method based on layered self-adapting threshold function table | |
| CN111947045A (en) | GVMD parameter optimization and singular value decomposition-based fluid pipeline leakage positioning method | |
| CN104680495A (en) | Adaptive denoising method for ultrasonic image | |
| CN109598152A (en) | Hardware Trojan horse inspection optimization method based on EMD noise reduction data prediction | |
| CN115628863A (en) | Fluid pipeline leakage detection method based on parameter optimization variational modal decomposition | |
| CN111580151A (en) | SSNet model-based earthquake event time-of-arrival identification method | |
| CN112013286A (en) | Method and device for positioning pipeline leakage point, storage medium and terminal | |
| CN109543599A (en) | A kind of method of transformer fault traveling wave noise reduction | |
| CN110207689A (en) | A kind of pulsar signal denoising and discrimination method based on Wavelet Entropy | |
| CN105913382A (en) | High-fidelity anisotropy filtering method for threshold searching optimization | |
| Yan et al. | A bearing fault feature extraction method based on optimized singular spectrum decomposition and linear predictor | |
| CN105674065A (en) | Variational mode decomposition-based method for locating leakage point of pipeline by acoustic emission | |
| CN104978716A (en) | SAR image noise reduction method based on linear minimum mean square error estimation | |
| CN117609702A (en) | Pipeline leakage acoustic emission signal denoising method, system, equipment and medium | |
| CN103473451A (en) | Ultrasonic professional dictionary construction and use method | |
| CN105676292A (en) | 3D earthquake data de-noising method based on 2D curvelet transform |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20191122 |