CN109682676A - A kind of feature extracting method of the acoustic emission signal of fiber tension failure - Google Patents
A kind of feature extracting method of the acoustic emission signal of fiber tension failure Download PDFInfo
- Publication number
- CN109682676A CN109682676A CN201811644043.8A CN201811644043A CN109682676A CN 109682676 A CN109682676 A CN 109682676A CN 201811644043 A CN201811644043 A CN 201811644043A CN 109682676 A CN109682676 A CN 109682676A
- Authority
- CN
- China
- Prior art keywords
- acoustic emission
- emission signal
- tension failure
- fiber tension
- signal
- 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
- 239000000835 fiber Substances 0.000 title claims abstract description 102
- 238000000034 method Methods 0.000 title claims abstract description 31
- 239000011159 matrix material Substances 0.000 claims abstract description 16
- 238000010606 normalization Methods 0.000 claims abstract description 9
- 238000004458 analytical method Methods 0.000 claims abstract description 8
- 238000004364 calculation method Methods 0.000 claims abstract description 6
- 230000009467 reduction Effects 0.000 claims description 18
- 230000009466 transformation Effects 0.000 claims description 18
- 238000000354 decomposition reaction Methods 0.000 claims description 12
- 238000001914 filtration Methods 0.000 claims description 9
- 239000002033 PVDF binder Substances 0.000 claims description 6
- 229920002981 polyvinylidene fluoride Polymers 0.000 claims description 6
- 238000001228 spectrum Methods 0.000 claims description 3
- 239000000284 extract Substances 0.000 abstract description 3
- 230000008569 process Effects 0.000 abstract description 3
- 238000011160 research Methods 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 4
- 238000000513 principal component analysis Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012847 principal component analysis method Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000012706 support-vector machine Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N3/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N3/02—Details
- G01N3/06—Special adaptations of indicating or recording means
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
- G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N2203/0014—Type of force applied
- G01N2203/0016—Tensile or compressive
- G01N2203/0017—Tensile
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N2203/0058—Kind of property studied
- G01N2203/006—Crack, flaws, fracture or rupture
- G01N2203/0067—Fracture or rupture
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N2203/02—Details not specific for a particular testing method
- G01N2203/026—Specifications of the specimen
- G01N2203/0262—Shape of the specimen
- G01N2203/0278—Thin specimens
- G01N2203/028—One dimensional, e.g. filaments, wires, ropes or cables
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N2203/02—Details not specific for a particular testing method
- G01N2203/06—Indicating or recording means; Sensing means
- G01N2203/0658—Indicating or recording means; Sensing means using acoustic or ultrasonic detectors
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Immunology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Pathology (AREA)
- Analytical Chemistry (AREA)
- Computational Linguistics (AREA)
- Signal Processing (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Human Computer Interaction (AREA)
- Acoustics & Sound (AREA)
- Multimedia (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
- Investigating Or Analyzing Materials By The Use Of Ultrasonic Waves (AREA)
Abstract
The present invention discloses a kind of feature extracting method of the acoustic emission signal of fiber tension failure, it is therefore intended that implementation of the fiber to yarn in the more efficiently research yarn Tensile Fracture Process of searching comprising following steps: do normalized to preset signals；Calculate the covariance matrix of the preset signals after normalization；Using every a line of covariance matrix as a transversal vector, and calculate the variance of each transversal vector；Judge whether variance is greater than preset threshold, if so, calculating the average value of transversal vector；The frequency of the transversal vector and average value that carry out mean value calculation in former transversal vector is obtained, and the transversal vector based on acquisition and frequency obtain the eigenmatrix of fiber tension failure acoustic emission signal.The present invention extracts the eigenmatrix of fiber tension failure acoustic emission signal by principal component analytical method to preset signals, reduces the complexity of calculating, facilitates project analysis to a certain extent.
Description
Technical field
The present invention relates to field of signal processing, specifically, the present invention relates to a kind of sound emission of fiber tension failure letters
Number feature extracting method.
Background technique
With the development of country, high-performance emerges one after another with multifunctional fibre, and the application range of fiber can not only expire
The daily demand of sufficient people is also deep into more and more industries.Generally, it is greatly multiple fiber that common yarn, which has,
Mixed yarn, different fiber compositions, then the various performances that yarn is showed are also different.Assess the traditional side of yarn property
Method is concentrated mainly on terms of mechanics, such index is more macroscopical, fuzzy, can accurately not judge different fibers to yam linear
The influence of energy.Therefore there is an urgent need to a kind of new means to study in yarn Tensile Fracture Process, influence of the fiber to yarn.
Summary of the invention
In order to find in more efficiently research yarn Tensile Fracture Process fiber to the implementation of yarn, it is contemplated that fine
Dimension tension failure is a kind of transient signal of non-stationary, and Hilbert-Huang transform (Hilbert-Huang Transform, letter
Claim HHT) it is a kind of NEW ADAPTIVE time frequency processing method, by empirical mode decomposition (Empirical Mode
Decomposition, abbreviation EMD) and Hilbert transform (Hilbert Transform, abbreviation Hilbert) two parts group
At, wherein EMD is core, but EMD is a kind of effective treating method for non-stationary signal, but can have mode
Aliasing.It is proposed that EEMD is set empirical mode decomposition (Ensemble Empirical Mode later
Decomposition, abbreviation EEMD) during signal decomposition it is continuously added white noise, make uniform in entire time frequency space
It is distributed additional white noise, independent test then is carried out to signal, there is good antidecomposition ability.Meanwhile principal component analysis
(Principal Component Analysis, abbreviation PCA) there may be the variables of correlation by one group by orthogonal transformation
One group of linearly incoherent variable is converted to, this group of variable after conversion is principal component.In actual subject, in order to analyze comprehensively
Problem often proposes much variables related with this, because each variable reflects the certain of this project to varying degrees
Information.Principal component analysis is to be introduced by K. Pearson (Karl Pearson) to nonrandom variable first, thereafter H. Hotelling
The method is generalized to the situation of random vector.The size of information is usually measured with sum of squares of deviations or variance.For this purpose, this hair
Bright embodiment provides a kind of feature extracting method of the acoustic emission signal of fiber tension failure comprising following steps:
Normalized is done to preset signals, the preset signals are generated based on fiber tension failure acoustic emission signal；
Calculate the covariance matrix of the preset signals after normalization；
Using every a line of the covariance matrix as a transversal vector, and calculate the variance of each transversal vector；
Judge whether the variance is greater than preset threshold, if so, calculating the average value of the transversal vector；
The frequency of the transversal vector and the average value that carry out mean value calculation in former transversal vector is obtained, and based on acquisition
The transversal vector and the frequency obtain the eigenmatrix of fiber tension failure acoustic emission signal.
Preferably, the preset threshold is 0.1.
Preferably, the transversal vector based on acquisition and the frequency obtain fiber tension failure acoustic emission signal
After eigenmatrix, include the following steps:
Obtain the eigenmatrix of the fiber tension failure acoustic emission signal of different fibers and based on least square supporting vector
Machine establishes identification model.
Preferably, the eigenmatrix of the fiber tension failure acoustic emission signal for obtaining different fibers and based on minimum two
Multiply support vector machines to establish after identification model, include the following steps:
Fiber tension failure acoustic emission signal to be identified is obtained,
Identification model based on foundation identifies the classification of the fiber tension failure acoustic emission signal to be identified.
Preferably, the preset signals are included the following steps: based on the generation of fiber tension failure acoustic emission signal
Fiber tension failure acoustic emission signal is acquired, the fiber tension failure acoustic emission signal is dropped using wavelet transformation
It makes an uproar；The fiber tension failure acoustic emission signal includes from starting to stretch all sound for terminating to fibrous fracture of fiber；
Useful signal is intercepted from fiber tension failure acoustic emission signal described after noise reduction, and generates institute using HHT transformation
State useful signal about T/F-energy three-dimensional distribution map namely preset signals.
Preferably, the acquisition fiber tension failure acoustic emission signal includes the following steps:
Fiber tension failure acoustic emission signal is acquired using PVDF piezoelectric transducer under full squelch.
Preferably, the useful signal that intercepts from fiber tension failure acoustic emission signal described after noise reduction includes following step
It is rapid:
High-pass filtering filtering is carried out to the fiber tension failure acoustic emission signal after noise reduction；
Judge whether the shape factor of the fiber tension failure acoustic emission signal after filtering is greater than the first preset threshold, if
It is greater than, then intercepted and judges whether the root-mean-square value of the intercept signal obtained is located at default codomain and whether kurtosis index is big
In the second preset threshold, if so, the intercept signal is useful signal.
Preferably, first preset threshold is 1.1；Default codomain is greater than 5 less than 40；Second preset threshold is 10.
Preferably, the use wavelet transformation is to be using wavelet basis to the fiber tension failure acoustic emission signal noise reduction
Sym6 function, the wavelet transformation that Decomposition order is 5 is to the fiber tension failure acoustic emission signal noise reduction.
Preferably, it is described using HHT transformation generate the useful signal about T/F-energy distributed in three dimensions
Figure namely preset signals, include the following steps:
EEMD decomposition is carried out to the useful signal, and to white noise is incorporated in the N number of IMF obtained after decomposing, wherein N
For the natural number greater than 1；
Using the integrated mean value of each IMF after each involvement white noise as final signal, threshold value is carried out to final signal
Related coefficient judgement then carries out Hilbert transformation to corresponding IMF when threshold value related coefficient is greater than 5%；
Summarize all IMF components Hilbert spectrum generate the useful signal about T/F-ability
Three-dimensional distribution map namely preset signals.
Compared with prior art, a kind of feature extracting method of the acoustic emission signal of fiber tension failure of the embodiment of the present invention
It has the following beneficial effects:
A kind of fibrous fracture acoustic emission analysis method based on Hilbert-Huang transform of the embodiment of the present invention passes through principal component
Analysis method extracts the eigenmatrix of fiber tension failure acoustic emission signal to preset signals, reduces the complexity of calculating,
Project analysis is facilitated to a certain extent.
The additional aspect of the present invention and advantage will be set forth in part in the description, these will become from the following description
Obviously, or practice through the invention is recognized.
Detailed description of the invention
Above-mentioned and/or additional aspect and advantage of the invention will become from the following description of the accompanying drawings of embodiments
Obviously and it is readily appreciated that, in which:
Fig. 1 is a kind of stream of the feature extracting method of the acoustic emission signal of fiber tension failure provided in an embodiment of the present invention
Journey schematic diagram；
Fig. 2 is in a kind of feature extracting method of the acoustic emission signal of fiber tension failure provided in an embodiment of the present invention
Calculate the flow diagram of the covariance matrix of the preset signals after normalization.
Specific embodiment
The embodiment of the present invention is described below in detail, examples of the embodiments are shown in the accompanying drawings, wherein from beginning to end
Same or similar label indicates same or similar element or element with the same or similar functions.Below with reference to attached
The embodiment of figure description is exemplary, and for explaining only the invention, and is not construed as limiting the claims.
Referring to Fig. 1, Fig. 1 shows a kind of feature extraction of the acoustic emission signal of fiber tension failure of the embodiment of the present invention
The flow diagram of method, as shown in Figure 1, a kind of feature extraction of the acoustic emission signal of fiber tension failure of the embodiment of the present invention
Method includes the following steps:
Step S101: normalized is done to preset signals, the preset signals are believed based on the sound emission of fiber tension failure
Number generate.
Illustratively, it is assumed that preset signals XiThe N-dimensional vector of (i=1 ... 150), then normalized processing formula is as follows:
Wherein, xiFor the preset signals after normalized,For the average value of preset signals, SiFor preset signals
Variance.
In some embodiments, preset signals are included the following steps: based on the generation of fiber tension failure acoustic emission signal
Fiber tension failure acoustic emission signal is acquired, using wavelet transformation to fiber tension failure acoustic emission signal noise reduction；
Fiber tension failure acoustic emission signal includes from starting to stretch all sound for terminating to fibrous fracture of fiber；
From intercepting useful signal after noise reduction in fiber tension failure acoustic emission signal, and effectively letter is generated using HHT transformation
Number about T/F-energy three-dimensional distribution map namely preset signals.
Specifically, acquisition fiber tension failure acoustic emission signal includes the following steps:
Fiber tension failure acoustic emission signal is acquired using PVDF piezoelectric transducer under full squelch.Wherein, PVDF
Piezoelectric transducer is polyvinylidene fluoride (abbreviation PVDF) piezoelectric film sensor.
It in some embodiments, include as follows from useful signal is intercepted after noise reduction in fiber tension failure acoustic emission signal
Step:
High-pass filtering filtering is carried out to the fiber tension failure acoustic emission signal after noise reduction；
Judge whether the shape factor of the fiber tension failure acoustic emission signal after filtering is greater than the first preset threshold, if
It is greater than, then intercepted and judges whether the root-mean-square value of the intercept signal obtained is located at default codomain and whether kurtosis index is big
In the second preset threshold, if so, intercept signal is useful signal.
Preferably, the first preset threshold is 1.1；Default codomain is greater than 5 less than 40；Second preset threshold is 10.
It in some embodiments, the use of wavelet transformation is using small to the fiber tension failure acoustic emission signal noise reduction
Wave base is sym6 function, and the wavelet transformation that Decomposition order is 5 is to fiber tension failure acoustic emission signal noise reduction.
Sym6 function is small echo tool function, can be to the fiber tension failure sound emission after noise reduction using sym6 function small echo
Signal is decomposed.
In some embodiments, it is converted using HHT and generates dividing about T/F-energy three-dimensional for useful signal
Butut namely preset signals, include the following steps:
EEMD decomposition is carried out to useful signal, and to white noise is incorporated in the N number of IMF obtained after decomposing, wherein N is big
In 1 natural number；
Using the integrated mean value of each IMF after each involvement white noise as final signal, threshold value is carried out to final signal
Related coefficient judgement then carries out Hilbert transformation to corresponding IMF when threshold value related coefficient is greater than 5%；
Summarize all IMF components Hilbert spectrum i.e. generation useful signal about T/F-ability three-dimensional
Distribution map namely preset signals.
Intrinsic mode function (Intrinsic Mode Function, abbreviation IMF) is that have given using EEMD method
Effect signal decomposition obtains.
Step S103: the covariance matrix of the preset signals after normalization is calculated.
Referring to Fig. 2, Fig. 2 shows the flow diagram for the covariance matrix for calculating the preset signals after normalizing,
As shown in Fig. 2, the covariance matrix for calculating the preset signals after normalization includes the following steps:
Step S1031: the correlation matrix of the preset signals after normalization is calculated.
Illustratively, the correlation matrix formula for calculating the preset signals after normalization is as follows:
Wherein, rij(i, j=1,2 ..., p) is the preset signals x after normalizationiWith xjRelated coefficient, rij=rji,
Its calculation formula is as follows:
Step S1032: covariance matrix is calculated according to correlation matrix.
Illustratively, it is assumed that covariance matrix Rij, then its calculation is as follows:
Step S105: using every a line of covariance matrix as a transversal vector, and the variance of each transversal vector is calculated；
Judge whether variance is greater than preset threshold, if so, calculating the average value of transversal vector.
In some embodiments, if variance is not more than preset threshold, give up.
Preferably, preset threshold 0.1.
In some embodiments, when judging variance greater than preset threshold, user can also go to calculate laterally as needed
The weighted average of amount, the embodiment of the present invention are without limitation.
Step S107: obtaining the frequency of the transversal vector and the average value that carry out mean value calculation in former transversal vector, and
The transversal vector and the frequency based on acquisition obtain the eigenmatrix of fiber tension failure acoustic emission signal.
In some embodiments, the transversal vector based on acquisition and the frequency obtain fiber tension failure sound emission
After the eigenmatrix of signal, include the following steps:
Obtain the eigenmatrix of the fiber tension failure acoustic emission signal of different fibers and based on least square supporting vector
Machine establishes identification model.
Specifically, one group of training sample is given when classifying to the fiber tension failure acoustic emission signal of different fibers,
Each label is two classes, and a least square method supporting vector machine training algorithm establishes a model, distributes new example
For a kind of or other classes, non-probability binary linearity classification is become.
In this way, obtaining the eigenmatrix of the fiber tension failure acoustic emission signal of different fibers and being based on least square branch
It holds vector machine to establish after identification model, so that it may identify the classification of fiber tension failure acoustic emission signal to be identified, specifically
Include the following steps:
Fiber tension failure acoustic emission signal to be identified is obtained,
Identification model based on foundation identifies the classification of fiber tension failure acoustic emission signal to be identified.
Compared with prior art, a kind of feature extracting method of the acoustic emission signal of fiber tension failure of the embodiment of the present invention
It has the following beneficial effects:
A kind of fibrous fracture acoustic emission analysis method based on Hilbert-Huang transform of the embodiment of the present invention passes through principal component
Analysis method extracts the eigenmatrix of fiber tension failure acoustic emission signal to preset signals, reduces the complexity of calculating,
Project analysis is facilitated to a certain extent.
The above is only some embodiments of the invention, it is noted that for the ordinary skill people of the art
For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also answered
It is considered as protection scope of the present invention.
Claims (10)
1. a kind of feature extracting method of the acoustic emission signal of fiber tension failure, which is characterized in that the fiber tension failure
The feature extracting method of acoustic emission signal include the following steps:
Normalized is done to preset signals, the preset signals are generated based on fiber tension failure acoustic emission signal；
Calculate the covariance matrix of the preset signals after normalization；
Using every a line of the covariance matrix as a transversal vector, and calculate the variance of each transversal vector；Judgement
Whether the variance is greater than preset threshold, if so, calculating the average value of the transversal vector；
The frequency of the transversal vector and the average value that carry out mean value calculation in former transversal vector is obtained, and based on described in acquisition
Transversal vector and the frequency obtain the eigenmatrix of fiber tension failure acoustic emission signal.
2. the feature extracting method of the acoustic emission signal of fiber tension failure as described in claim 1, which is characterized in that described
Preset threshold is 0.1.
3. the feature extracting method of the acoustic emission signal of fiber tension failure as described in claim 1, which is characterized in that described
After the transversal vector and the frequency based on acquisition obtain the eigenmatrix of fiber tension failure acoustic emission signal, including such as
Lower step:
It obtains the eigenmatrix of the fiber tension failure acoustic emission signal of different fibers and is built based on least square method supporting vector machine
Vertical identification model.
4. the feature extracting method of the acoustic emission signal of fiber tension failure as claimed in claim 3, which is characterized in that described
It obtains the eigenmatrix of the fiber tension failure acoustic emission signal of different fibers and is established based on least square method supporting vector machine and known
After other model, include the following steps:
Fiber tension failure acoustic emission signal to be identified is obtained,
Identification model based on foundation identifies the classification of the fiber tension failure acoustic emission signal to be identified.
5. the feature extracting method of the acoustic emission signal of fiber tension failure as described in claim 1, which is characterized in that described
Preset signals are based on the generation of fiber tension failure acoustic emission signal and include the following steps:
Fiber tension failure acoustic emission signal is acquired, using wavelet transformation to the fiber tension failure acoustic emission signal noise reduction；
The fiber tension failure acoustic emission signal includes from starting to stretch all sound for terminating to fibrous fracture of fiber；
Useful signal is intercepted from fiber tension failure acoustic emission signal described after noise reduction, and is had using described in HHT transformation generation
Imitate signal about T/F-energy three-dimensional distribution map namely preset signals.
6. the feature extracting method of the acoustic emission signal of fiber tension failure as claimed in claim 5, which is characterized in that described
Acquisition fiber tension failure acoustic emission signal includes the following steps:
Fiber tension failure acoustic emission signal is acquired using PVDF piezoelectric transducer under full squelch.
7. the feature extracting method of the acoustic emission signal of fiber tension failure as claimed in claim 5, which is characterized in that described
Useful signal is intercepted from fiber tension failure acoustic emission signal described after noise reduction to include the following steps:
High-pass filtering filtering is carried out to the fiber tension failure acoustic emission signal after noise reduction；
Judge whether the shape factor of the fiber tension failure acoustic emission signal after filtering is greater than the first preset threshold, if greatly
In then being intercepted and judge whether the root-mean-square value of the intercept signal obtained is located at default codomain and whether kurtosis index is greater than
Second preset threshold, if so, the intercept signal is useful signal.
8. the feature extracting method of the acoustic emission signal of fiber tension failure as claimed in claim 7, which is characterized in that described
First preset threshold is 1.1；Default codomain is greater than 5 less than 40；Second preset threshold is 10.
9. the fibrous fracture acoustic emission analysis method based on Hilbert-Huang transform as claimed in claim 5, which is characterized in that
It is described using wavelet transformation be to the fiber tension failure acoustic emission signal noise reduction be sym6 function, decomposition layer using wavelet basis
The wavelet transformation that number is 5 is to the fiber tension failure acoustic emission signal noise reduction.
10. the fibrous fracture acoustic emission analysis method based on Hilbert-Huang transform, feature exist as claimed in claim 5
In, it is described using HHT transformation generate the useful signal about T/F-energy three-dimensional distribution map, namely default letter
Number, include the following steps:
EEMD decomposition is carried out to the useful signal, and to white noise is incorporated in the N number of IMF obtained after decomposing, wherein N is big
In 1 natural number；
Using the integrated mean value of each IMF after each involvement white noise as final signal, it is related that threshold value is carried out to final signal
Coefficient judgement then carries out Hilbert transformation to corresponding IMF when threshold value related coefficient is greater than 5%；
Summarize all IMF components Hilbert spectrum generate the useful signal about T/F-ability three-dimensional
Distribution map namely preset signals.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811644043.8A CN109682676A (en) | 2018-12-29 | 2018-12-29 | A kind of feature extracting method of the acoustic emission signal of fiber tension failure |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811644043.8A CN109682676A (en) | 2018-12-29 | 2018-12-29 | A kind of feature extracting method of the acoustic emission signal of fiber tension failure |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109682676A true CN109682676A (en) | 2019-04-26 |
Family
ID=66191424
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811644043.8A Pending CN109682676A (en) | 2018-12-29 | 2018-12-29 | A kind of feature extracting method of the acoustic emission signal of fiber tension failure |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109682676A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110196283A (en) * | 2019-05-27 | 2019-09-03 | 东南大学 | A kind of timber structure damage sound emission lossless detection method based on instantaneous frequency |
| CN110376289A (en) * | 2019-07-11 | 2019-10-25 | 南京航空航天大学 | A kind of composite fiber braiding layer damnification recognition method based on sound emission means |
| CN114036655A (en) * | 2021-10-18 | 2022-02-11 | 安徽新华学院 | Rotary machine fault diagnosis method and system based on improved HTT algorithm |
| CN114123977A (en) * | 2021-11-26 | 2022-03-01 | 南京鼓楼医院 | White noise generation method based on controllable fracture junction |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102004549A (en) * | 2010-11-22 | 2011-04-06 | 北京理工大学 | Automatic lip language identification system suitable for Chinese language |
| CN102506995A (en) * | 2011-11-22 | 2012-06-20 | 中国建筑材料科学研究总院 | Vibration signal processing method based on HHT (Hilbert-Huang Transformation) and related analyses |
| CN106095966A (en) * | 2016-06-15 | 2016-11-09 | 成都品果科技有限公司 | A kind of user's extendible label for labelling method and system |
| CN107153096A (en) * | 2017-06-23 | 2017-09-12 | 中国石油大学(北京) | Shale reservoir into seam aptitude tests method and device |
| CN107702990A (en) * | 2017-11-06 | 2018-02-16 | 西安科技大学 | A kind of sound emission extensometer and its test method |
| US20180054674A1 (en) * | 2016-08-19 | 2018-02-22 | Hyundai Motor Company | Portable noise measuring device and noise measuring method |
| EP3300077A1 (en) * | 2016-09-23 | 2018-03-28 | Samsung Electronics Co., Ltd. | Electronic device and control method thereof |
| CN108776795A (en) * | 2018-06-20 | 2018-11-09 | 邯郸学院 | Method for identifying ID, device and terminal device |
| CN108875279A (en) * | 2018-07-27 | 2018-11-23 | 中国计量大学 | Bearing sound emission signal characteristic extracting method based on EMD and shape filtering |
| EP3419021A1 (en) * | 2017-06-20 | 2018-12-26 | Thomson Licensing | Device and method for distinguishing natural and artificial sound |
-
2018
- 2018-12-29 CN CN201811644043.8A patent/CN109682676A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102004549A (en) * | 2010-11-22 | 2011-04-06 | 北京理工大学 | Automatic lip language identification system suitable for Chinese language |
| CN102506995A (en) * | 2011-11-22 | 2012-06-20 | 中国建筑材料科学研究总院 | Vibration signal processing method based on HHT (Hilbert-Huang Transformation) and related analyses |
| CN106095966A (en) * | 2016-06-15 | 2016-11-09 | 成都品果科技有限公司 | A kind of user's extendible label for labelling method and system |
| US20180054674A1 (en) * | 2016-08-19 | 2018-02-22 | Hyundai Motor Company | Portable noise measuring device and noise measuring method |
| EP3300077A1 (en) * | 2016-09-23 | 2018-03-28 | Samsung Electronics Co., Ltd. | Electronic device and control method thereof |
| EP3419021A1 (en) * | 2017-06-20 | 2018-12-26 | Thomson Licensing | Device and method for distinguishing natural and artificial sound |
| CN107153096A (en) * | 2017-06-23 | 2017-09-12 | 中国石油大学(北京) | Shale reservoir into seam aptitude tests method and device |
| CN107702990A (en) * | 2017-11-06 | 2018-02-16 | 西安科技大学 | A kind of sound emission extensometer and its test method |
| CN108776795A (en) * | 2018-06-20 | 2018-11-09 | 邯郸学院 | Method for identifying ID, device and terminal device |
| CN108875279A (en) * | 2018-07-27 | 2018-11-23 | 中国计量大学 | Bearing sound emission signal characteristic extracting method based on EMD and shape filtering |
Non-Patent Citations (5)
| Title |
|---|
| 佟德纯: "《时序分析与工程应用》", 31 March 1990, 上海交通大学出版社 * |
| 张陆佳，林兰天，陈春敏，申炎仃，高琮: "基于主成分分析的纤维拉伸断裂声发射信号识别", 《纺织学报》 * |
| 程号楠: "针对含噪暂态电能质量扰动信号的检测方法研究", 《中国农机化学报》 * |
| 葛贞笛: "基于声发射的非接触式机械密封膜厚检测技术", 《工程科技Ⅱ辑》 * |
| 薛亚静: "基于AE技术和HHT的涤纶机织物拉伸断裂过程研究", 《河北科技大学学报》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110196283A (en) * | 2019-05-27 | 2019-09-03 | 东南大学 | A kind of timber structure damage sound emission lossless detection method based on instantaneous frequency |
| CN110376289A (en) * | 2019-07-11 | 2019-10-25 | 南京航空航天大学 | A kind of composite fiber braiding layer damnification recognition method based on sound emission means |
| CN114036655A (en) * | 2021-10-18 | 2022-02-11 | 安徽新华学院 | Rotary machine fault diagnosis method and system based on improved HTT algorithm |
| CN114123977A (en) * | 2021-11-26 | 2022-03-01 | 南京鼓楼医院 | White noise generation method based on controllable fracture junction |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109682676A (en) | A kind of feature extracting method of the acoustic emission signal of fiber tension failure | |
| CN1258733C (en) | Method for recognizing human its using Daubechies wavelet transform | |
| CN106653032B (en) | Based on the animal sounds detection method of multiband Energy distribution under low signal-to-noise ratio environment | |
| CN109949823B (en) | DWPT-MFCC and GMM-based in-vehicle abnormal sound identification method | |
| CN105788603A (en) | Audio identification method and system based on empirical mode decomposition | |
| US20030236661A1 (en) | System and method for noise-robust feature extraction | |
| CN105841797A (en) | Window motor abnormal noise detection method and apparatus based on MFCC and SVM | |
| NZ331431A (en) | Speech processing via voice recognition | |
| Wang et al. | Acoustic feature extraction and optimization of crack detection for eggshell | |
| CN111553207B (en) | Statistical distribution-based ship radiation noise characteristic recombination method | |
| CN109241849B (en) | Feature decomposition selection and fault diagnosis method for main engine of intelligent power plant steam turbine | |
| CN103323532B (en) | Fish identification method and system based on psychoacoustics parameters | |
| CN103854660B (en) | A kind of four Mike's sound enhancement methods based on independent component analysis | |
| CN101515454B (en) | Signal characteristic extracting methods for automatic classification of voice, music and noise | |
| Sharan et al. | Subband time-frequency image texture features for robust audio surveillance | |
| Zhang et al. | Fault diagnosis method based on MFCC fusion and SVM | |
| CN112786054A (en) | Intelligent interview evaluation method, device and equipment based on voice and storage medium | |
| CN111968651A (en) | WT (WT) -based voiceprint recognition method and system | |
| CN109682677A (en) | A kind of fibrous fracture acoustic emission analysis method based on Hilbert-Huang transform | |
| Mokhov | Choosing best algorithm combinations for speech processing tasks in machine learning using MARF | |
| CN109682678A (en) | A kind of analysis method of fibrous fracture sound | |
| Tu et al. | Discriminative feature analysis based on the crossing level for leakage classification in water pipelines | |
| CN116504253A (en) | Bird voice recognition method and system based on frequency dynamic convolution model | |
| CN113782051B (en) | Broadcast effect classification method and system, electronic equipment and storage medium | |
| Song et al. | Underwater Noise Classification based on Support Vector Machine |
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: 20190426 |