CN110161123A - A kind of new defect inspection method based on magnetic striction wave guide - Google Patents
A kind of new defect inspection method based on magnetic striction wave guide Download PDFInfo
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Abstract
The invention discloses a kind of new defect inspection methods based on magnetic striction wave guide, building is synchronous first squeezes wavelet transformation, using the synchronous wavelet transformation that squeezes of building to magnetic striction wave guide signal progress defects detection, the time-frequency map of flaw indication is obtained, the time-frequency characteristics of defect are gone out according to time-frequency characteristics energy Precise Representation.The present invention is compared with the magnetic striction wave guide detection method based on wavelet transformation and based on Short Time Fourier Transform, also there is anti-acoustic capability to magnetic striction wave guide signal, time-frequency precision is high, algorithm stability is strong, defect location precision is high, has extraordinary application prospect in the defects detection based on magnetic striction wave guide.
Description
Technical field
The present invention relates to defect guided wave detecting method, in particular to a kind of new defects detection based on magnetic striction wave guide
Method.
Background technique
Since guided wave has the characteristics that low signal-to-noise ratio, frequency dispersion and multi-modal, always for magnetic striction wave guide signal processing
It is one of the research topic that people pay much attention to.And carrying out time frequency analysis to guided wave signals is the key of flaw indication processing
The problem of.And traditional time frequency analysis, such as Short Time Fourier Transform, the methods of wavelet transformation.Frequency division when with to guided wave signals
The research of analysis problem is goed deep into, and increasingly it is impossible to meet require for conventional method.Due to the presence of Heisenberg's uncertainty principle, pass
System method is that the time-frequency precision in magnetic striction wave guide time frequency analysis domain can not reach the time point for most preferably wanting promotion signal
Resolution must just reduce the frequency resolution of signal, and the feature decision at this point for defect is totally unfavorable[1].In addition, right
In different window functions carry out Short Time Fourier Transform, or for different morther wavelets carry out wavelet transformation, acquired signal
Does is time-frequency feature difference very big, and how to select window function or morther wavelet, how to determine the width of window function? these
All lack foundation, thus, it is just limited by very large in practical applications.
Therefore, insufficient in the obtained time-frequency precision of processing guided wave signals for previous traditional Time-Frequency Analysis Method and can not
Defect characteristic is described, while the problem of influence defect location, there is an urgent need to seek it is new can more accurately describe defect characteristic, from
And the method that can effectively find defect.Research on Time Frequency is being carried out to guided wave signals, is there is Nicolas[2]Become Deng using small echo
It changes and guided wave signals is handled, separate mode signals, but is bad for the near-lying mode state signal processing of guided wave in time-frequency.
Rodriguez[3]Carry out wavelet transformation and Wigner-Ville transformation Deng the guided wave signals to pipeline beam hanger, experimental verification this
The feasibility of two methods, the results showed that the identification to signal can be enhanced.Wu Bin etc.[4]Pass through pseudo- Wigner-Ville transformation pair
The carry out time frequency analysis of guided wave signals, the recognition capability of flaw indication are improved.However, with development in science and technology, to signal
Time-frequency required precision is continuously improved, and previous conventional method there will naturally be deficiency, such as Wigner-Ville transformation has void
Pseudocomponent, the selection of the morther wavelet in wavelet transformation defects detection is influenced it is very big, moreover, different morther wavelets is often selected
It when handling same defect, as a result differs greatly, and how to select suitable morther wavelet also without any foundation.Synchronous extruding is small
Wave conversion method is a kind of completely new Time-Frequency Analysis Method proposed by Daubechies in 2011, is that one kind can be based on difference
Morther wavelet, which synchronizes, squeezes the algorithm that higher time-frequency precision is realized in transformation[5]。Gaurav[6]When being carried out using synchronous extruding algorithm
Frequency analysis, demonstrating this method is a kind of effective Time-Frequency Analysis Method.Li[7]The synchronous wavelet algorithm that squeezes is applied in gear-box
Fault diagnosis in, demonstrate this method can by the time-frequency map after gear-box vibration signal processing readability enhance, thus
Be conducive to the identification to the failure of gear-box.Wang[8]Demonstrating this method has the function of noise reduction.Zhang Zhiyu[9]Utilize the party
Method is extracted the instantaneous frequency of seismic signal, and the earthquake instantaneous attribute for demonstrating this method extraction has that noise immunity is strong, reliability
High feature.Liu Han[10]Microseism signal is had detected using the synchronous method for squeezing wavelet transformation, the results showed that synchronous extruding is small
Wave conversion method has preferable application effect, and is better than S-transformation and bandpass filtering.It explains quick etc.[11]Small echo is squeezed using synchronous
In electric system time-varying harmonic, this method still can accurately extract each mode in noise circumstance for transformation, verifying
Validity of this method in time-varying harmonic.Liu Jingliang[12]It is squeezed in Time variable structure non-destructive tests using synchronous
Small wave converting method, obtained damage criterion can relatively accurately identify the damage position and changing damage of structure.Happy friend's happiness
Deng[13]Using the synchronous method for squeezing wavelet transformation in reservoir prediction, pass through comparison continuous wavelet transform and Three parameter wavelet
The methods of transformation demonstrates the synchronous validity and superiority for squeezing wavelet method.Bear Xin etc.[14]The synchronous small echo that squeezes of research becomes
It changes and demonstrates the synchronous frequency for squeezing transform method energy accurate description signal in the performance comparison of Hillbert-huang transformation
The distribution for constituting, and concentrating the time-frequency energy obtained.
Currently, although the synchronous application for squeezing wavelet transformation makes some progress, however, field of non destructive testing still
It is a blank, is even more without reference to based on this, the present invention is based on synchronous extruding small echos in magnetic striction wave guide defects detection
The unique features of transformation propose a kind of new defect inspection method based on magnetic striction wave guide, which are stretched with mangneto
In contracting guided wave traditional small wave converting method and Short Time Fourier Transform method compare and analyze, the experiment show hair
It is bright that there is more preferably stability and superiority.
Summary of the invention
Not high based on time-frequency precision present in drawbacks described above detection, algorithm stability is not strong, and defect location precision is not high
The shortcomings that, the technical problem to be solved by the present invention is to squeeze wavelet transformation using synchronous, provide a kind of new based on magnetostriction
The defect inspection method of guided wave can greatly improve guided wave signals time frequency analysis precision and positioning accuracy, and guarantee algorithmic stability
Property.
The present invention adopts the following technical solutions to achieve above-mentioned purpose.A kind of new defects detection based on magnetic striction wave guide
Method, first building are synchronous to squeeze wavelet transformation, using building it is synchronous squeeze wavelet transformation to magnetic striction wave guide signal into
Row defects detection obtains the time-frequency map of flaw indication, the time-frequency characteristics of defect is gone out according to time-frequency characteristics energy Precise Representation, specifically
Process is as follows:
1) continuous wavelet transform is carried out to the flaw indication that magnetic striction wave guide detects, the small echo for obtaining flaw indication becomes
The coefficient changed；
2) phase inversion is carried out to the wavelet coefficient of resulting flaw indication；
Wherein: a, b ∈ R and a ≠ 0, a are scale factor, b is known as shift factor, ωf(a, b) is wavelet coefficient transformation
Phase, WTf(a, b) is wavelet coefficient,
3) it utilizesThe when m- ruler of flaw indication can be constructed
Spend the conversion in domain to time-frequency domain；
In formula: ψ (aj) it is the changing morther wavelet of scale factor, ωmFor the phase obtained after phase inversion, (ψj)mFor structure
Build the obtained frequency energy of mapping function；
4) synchronous squeeze obtains the synchronous extruding transformation magnitude of discrete representation flaw indication:
In formula: (Δ a)i=ai-ai-1, ωx(a, b) is instantaneous frequency, ωlFor center frequency, WTf(a, b) is wavelet systems
Number；
5) it constructs the synchronous of flaw indication and squeezes wavelet transformation time-frequency map, describe flaw indication time-frequency characteristics, positioning point
Analysis.
The present invention in the defects detection of magnetic striction wave guide have biggish potentiality, its advantages be mainly reflected in
Lower aspect:
1, in the magnetic striction wave guide detection method based on wavelet transformation and based on Short Time Fourier Transform, there is selection
Different morther wavelets carry out wavelet transformation window function different with selection or set different time width length to flaw indication processing difference compared with
Big problem.And the present invention can choose different morther wavelets and synchronize the defect for squeezing wavelet transformation and obtaining to flaw indication
Time-frequency map is consistent.
2, the magnetic striction wave guide detection method based on Short Time Fourier Transform and based on wavelet transformation, time-frequency precision is not
Height can not more be accurately positioned defect using it, and the present invention can be very good to solve the problems, such as this.
3, the present invention also there is anti-acoustic capability to magnetic striction wave guide signal, with based on wavelet transformation and in short-term Fourier change
The magnetic striction wave guide detection method changed is compared, and advantage is more obvious.The invention is in the defects detection based on magnetic striction wave guide
In have apparent advantage, traditional Short Time Fourier Transform method, small wave converting method etc. are based on magnetic striction wave guide
Time-frequency precision present in defects detection is not high, and algorithm stability is not strong, the not high disadvantage of defect location precision.And the present invention has
There is time-frequency precision high, algorithm stability is strong, defect location advantage with high accuracy, in the defects detection based on magnetic striction wave guide
With extraordinary application prospect.
Detailed description of the invention
Fig. 1 is the new defect inspection method flow chart based on magnetic striction wave guide of the invention；
Fig. 2 is the time-frequency figure of the magnetic striction wave guide obtained based on Short Time Fourier Transform；
Fig. 3 is the time-frequency figure of the magnetic striction wave guide signal obtained based on wavelet transformation；
Fig. 4 is the time-frequency figure for the magnetic striction wave guide signal that the present invention obtains.
Specific embodiment
Below in conjunction with attached drawing, algorithm principle and experimental applications example, the invention will be further described.Referring to Fig. 1 to Fig. 4.
1, continuous wavelet transform (CWT) is carried out to the flaw indication that magnetic striction wave guide detects, obtains flaw indication
The coefficient of wavelet transformation:
In formula: a, b ∈ R and a ≠ 0, a are scale factor, b is known as shift factor, < f (t), ψa,b(t) > in two functions
Product；
Signal, which carries out wavelet transformation, to be needed to meet:
2, phase inversion is carried out to the wavelet coefficient of obtained flaw indication, and constructs the when m- scale of flaw indication
Mapping function of the domain to time-frequency domain:
In actually detected flaw indication, when | WTf(a, b) | when ≈ 0, the continuous wavelet coefficient of obtained flaw indication
Mutually will be extremely unstable.In processing, a signal threshold parameter greater than 0 need to be chosen, so as to ignore | WTf(a,b)
| it is as follows to establish threshold values formula for the point of ≈ γ:
In formula: MAD is average deviation,For 1:nvThe wavelet coefficient size of a middle optimal scale.Wherein nv≥
32, usually take 32 or 64.
The partial derivative of flaw indication wavelet coefficient is calculated as follows:
In formula: Fn -1,FnFor the inverse direct transform of discrete fourier, ⊙ is the product of corresponding element.
Pass throughTo construct the when m- ruler of flaw indication
Spend the conversion in domain to time-frequency domain.
ψ (a in formulaj) it is the changing morther wavelet of scale factor, ωmFor the phase obtained after phase inversion, (ψj)mFor structure
Build the obtained frequency energy of mapping function.
3, synchronous squeeze obtains the T of flaw indicationf(ωl, b):
In actually calculating, due to a, b, ω be it is discrete, the obtained synchronous transformation magnitude that squeezes can use discrete type table
Show:
In formula: (Δ a)i=ai-ai-1, ωx(a, b) is instantaneous frequency, ωlFor center frequency, WTf(a, b) is wavelet systems
Number；
4, engineer application example:
Choose experiment gained longitudinal mode guided wave signals (driving frequency 100KHz), for verifying effectiveness of the invention and
Stability, here, the present invention and small wave converting method and Short Time Fourier Transform method have been carried out comparative analysis.
Fig. 2, Fig. 3, Fig. 4 be respectively Guided waves flaw indication by Short Time Fourier Transform, wavelet transformation with it is synchronous
Squeeze the time-frequency map that wavelet transformation obtains.
The result from figure is it is found that the flaw indication spectrum signature that synchronous extruding wavelet transformation obtains can be represented significantly
The time-frequency precision of the time-frequency characteristics of defect, magnetic striction wave guide signal is significantly improved, this also means that, it can be stretched according to mangneto
The time-frequency characteristics of contracting guided wave signals carry out more accurate positioning analysis to defect.This is because based on Short Time Fourier Transform
In magnetic striction wave guide detection method, windowing process is needed, and how to select the type of window function and the width of window function, at present
There is no any foundation, choose different window functions or the different time width length of setting to magnetic striction wave guide signal processing, often result
It differs greatly, thus, the adaptive ability of this method is excessively poor, and time frequency resolution is poor, and positioning there is uncertainty.Equally
How ground selects morther wavelet to be also a lack of foundation in the magnetic striction wave guide defect inspection method based on wavelet transformation, selection
Different morther wavelets handle flaw indication, and obtained result is also multifarious, in this way, just bringing to defect recognition tired
Difficulty, meanwhile, there is also uncertainties for defect location.And the time-frequency figure (such as Fig. 4) for the magnetic striction wave guide signal that the present invention obtains
The deficiency of both methods is overcome well, can carry out defects detection with any morther wavelet, obtained result is consistent,
This guarantees the accuracys of result, while improving time frequency resolution, and more accurate positioning can be carried out to defect.
The present invention is by carrying out the obtained wavelet coefficient of continuous wavelet transform to signal defect, then carries out what phase inversion obtained
T/F area image, the image be squeeze reset after obtain, the time-frequency characteristics of image obviously sharpen, to defect when
Frequency character representation is obvious, to being remarkably improved in the positioning accuracy of defect.The present invention and traditional wavelet transformation and in short-term in Fu
Leaf transformation is compared, and is had preferable stability, i.e., will not be generated larger difference with the difference of morther wavelet, time-frequency spectrum, in addition,
The time-frequency precision of signal significantly improves.It chooses the time-frequency spectrum that different morther wavelets progress wavelet transformations obtain to differ greatly, and right
The standard of its selection of unlike signal is not also identical.And it chooses the different time width length of different window function or setting and carries out Fu in short-term
In obtained time-frequency characteristics its difference of leaf transformation it is also fairly obvious, and the present invention not only can solve this problem, but also
Obtained time-frequency spectrum precision is also significantly improved.
Bibliography
1. Wang Fei magnetostriction longitudinal mode guided wave signals processing key technology research [D] Zhejiang University, 2011.
2.Nicolas Leymaric,Baste Syephane.Guided waves and ultrasonic
characterization of 3-dimensinal composites[J].Review of Progress in
Quantitive Nondestructive Evaluation 2000,28:1175-1181.
3.Roriguez M A,San Emeterio JL,Lazaro J C.Ultrasonic flaw detective
in NDE of highly scattering materials using wavelet and Wigner-Ville
transform processing[J].Utrasonics,2004,42(I):847-851.
4. application [J] the non-destructive testing of the time-frequency rearrangement method such as Wu Bin in the processing of pipeline guided wave signals, 2006,28:
(7)337-340.
5.Daubechies I,Lu JF,Wu HT,Synchrosqueezed wavelet transforms:an
empirical mode decomposition-like tool[J].Applied and Computational Harmonic
Analysis, 2011,2 (30): 243-261.
6.Gaurav.The synchrosqueezing algorithm for time-varying spectral
analysis:robustness properities and new paleoclimate applications[J].Applied
and computational harmonic analysis,2011,93(5):1079-1094.
7.Chuan Li,Ming Liang.Time–frequency signal analysis for gearbox
fault diagnosis using a generalized synchrosqueezing transform[J].Mechanical
Systems and Signal Processing,2012,26,205-217.
8.Wang P,Gao J,Wang Z.Time-Frequency Analysis of Seis-mic Data Using
SynchrosqueezingTransform[J].IEEE Geo-science&Remote Sensing Letters,2014,11
(12):2042-2044.
9. the synchronous wavelet transformation that squeezes of Zhang Zhiyu, Liu Yanxia, Li Xiangyue extracts seismic signal instantaneous attribute [J] computer
Measurement and control, 2018,26 (10): 260-263.
10. Liu Han, Zhang Jianzhong, Huang Zhonglai squeeze change detection microseism signal [J] chinese scientific papers using synchronous,
2015,10(21):2472-2476.
11. explaining quick, Wang Bin, Wang Wenbo, Zhang Liangli, Cheng Yongzhi is based on the synchronous electric system time-varying for squeezing wavelet transformation
Harmonic detecting [J] electrotechnics journal, 2017,32 (S1): 50-57.
12. the good equal of Liu Jing based on synchronous Time variable structure damnification recognition method research [J] the vibration for squeezing wavelet transformation with
Impact, 2017 (21): 15-22+40.
13. happy friend's happiness, Cai Junxiong, Li Bin, synchronous application study [J] for squeezing wavelet transformation in reservoir prediction of Zeng Mian
Advances in Geophysics, 2018,33 (06): 2498-2506.
Bear Xin, Zhan Rui, Wang little Jing synchronous compression small echo and Hilbert-Huang transform performance comparison [J] vibration test with
Diagnosis, 2015,35 (06): 1103-1109+1201-120.
Claims (1)
1. a kind of new defect inspection method based on magnetic striction wave guide, which is characterized in that building is synchronous first squeezes small echo
Transformation obtains the time-frequency of signal using the synchronous wavelet transformation that squeezes of building to magnetic striction wave guide signal progress defects detection
Map goes out the time-frequency characteristics of defect according to time-frequency characteristics energy Precise Representation, and detailed process is as follows:
1) continuous wavelet transform is carried out to the flaw indication that magnetic striction wave guide detects, obtains the wavelet transformation of flaw indication
Coefficient；
2) phase inversion is carried out to the wavelet coefficient of resulting flaw indication；
Wherein: a, b ∈ R and a ≠ 0, a are scale factor, b is known as shift factor, ωf(a, b) is the phase of wavelet coefficient transformation, WTf
(a, b) is wavelet coefficient,
3) it utilizesThe when m- scale domain of flaw indication can be constructed
To the conversion of time-frequency domain；
In formula: ψ (aj) it is the changing morther wavelet of scale factor, ωmFor the phase obtained after phase inversion, (ψj)mIt is reflected for building
Penetrate the obtained frequency energy of function；
4) synchronous squeeze obtains the synchronous extruding transformation magnitude of discrete representation flaw indication:
In formula: (Δ a)i=ai-ai-1, ωx(a, b) is instantaneous frequency, ωlFor center frequency, WTf(a, b) is wavelet coefficient；
It constructs the synchronous of flaw indication and squeezes wavelet transformation time-frequency map, describe flaw indication time-frequency characteristics, positioning analysis.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110865123A (en) * | 2019-11-14 | 2020-03-06 | 天津大学 | Method for calculating laser surface acoustic wave frequency dispersion curve |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106290587A (en) * | 2016-08-22 | 2017-01-04 | 中国石油化工股份有限公司 | The storage tank bottom plate ultrasonic guided wave detection signals noise reduction algorithm decomposed based on SVD |
| CN107576726A (en) * | 2017-08-14 | 2018-01-12 | 东莞理工学院 | Damage differentiation and damage propatagtion recognition methods for Guided waves |
| CN108154081A (en) * | 2016-11-30 | 2018-06-12 | 东北林业大学 | Based on instantaneous frequency stability SWT logistics equipment vibration signal noise-reduction methods |
| CN108776031A (en) * | 2018-03-21 | 2018-11-09 | 南京航空航天大学 | A kind of rotary machinery fault diagnosis method based on improved synchronous extruding transformation |
| CN108983051A (en) * | 2018-07-25 | 2018-12-11 | 武汉科技大学 | Based on the synchronous shelf depreciation kind identification method for squeezing wavelet transformation |
| CN109521421A (en) * | 2018-01-27 | 2019-03-26 | 河南工业大学 | A kind of Ground Penetrating Radar thin layer object recognition and detection method |
-
2019
- 2019-06-21 CN CN201910543623.6A patent/CN110161123A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106290587A (en) * | 2016-08-22 | 2017-01-04 | 中国石油化工股份有限公司 | The storage tank bottom plate ultrasonic guided wave detection signals noise reduction algorithm decomposed based on SVD |
| CN108154081A (en) * | 2016-11-30 | 2018-06-12 | 东北林业大学 | Based on instantaneous frequency stability SWT logistics equipment vibration signal noise-reduction methods |
| CN107576726A (en) * | 2017-08-14 | 2018-01-12 | 东莞理工学院 | Damage differentiation and damage propatagtion recognition methods for Guided waves |
| CN109521421A (en) * | 2018-01-27 | 2019-03-26 | 河南工业大学 | A kind of Ground Penetrating Radar thin layer object recognition and detection method |
| CN108776031A (en) * | 2018-03-21 | 2018-11-09 | 南京航空航天大学 | A kind of rotary machinery fault diagnosis method based on improved synchronous extruding transformation |
| CN108983051A (en) * | 2018-07-25 | 2018-12-11 | 武汉科技大学 | Based on the synchronous shelf depreciation kind identification method for squeezing wavelet transformation |
Non-Patent Citations (7)
| Title |
|---|
| AMEZQUITA-SANCHEZ JUAN P等: "Synchrosqueezed wavelet transform-fractality model for locating, detecting, and quantifying damage in smart highrise building structures", 《SMART MATERIALS AND STRUCTURES》 * |
| MIHALEC MARKO等: "Synchrosqueezed wavelet transform for damping identification", 《MECHANICAL SYSTEMS AND SIGNAL PROCESSING》 * |
| 刘景良等: "基于同步挤压小波变换的时变结构损伤识别方法研究", 《振动与冲击》 * |
| 曾文钧等: "磁致伸缩导波检测技术研究综述", 《2018 远东无损检测新技术论坛论文集》 * |
| 沈微等: "基于同步挤压小波变换的振动信号自适应降噪方法", 《振动与冲击》 * |
| 王正君: "《水泥混凝土板超声检测技术研究》", 30 November 2010, 黑龙江大学出版社 * |
| 郑国军: "磁致伸缩导波管道无损检测数字信号处理关键技术", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110865123A (en) * | 2019-11-14 | 2020-03-06 | 天津大学 | Method for calculating laser surface acoustic wave frequency dispersion curve |
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