CN107328868A - A kind of Analysis of Acoustic Emission Signal method of quick identification ceramic coating failure type - Google Patents
A kind of Analysis of Acoustic Emission Signal method of quick identification ceramic coating failure type Download PDFInfo
- Publication number
- CN107328868A CN107328868A CN201710500277.4A CN201710500277A CN107328868A CN 107328868 A CN107328868 A CN 107328868A CN 201710500277 A CN201710500277 A CN 201710500277A CN 107328868 A CN107328868 A CN 107328868A
- Authority
- CN
- China
- Prior art keywords
- mrow
- signal
- acoustic emission
- ceramic coating
- msub
- 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.)
- Granted
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
- G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
- G01N29/4454—Signal recognition, e.g. specific values or portions, signal events, signatures
-
- 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
- G01N29/14—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 using acoustic emission techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2291/00—Indexing codes associated with group G01N29/00
- G01N2291/02—Indexing codes associated with the analysed material
- G01N2291/023—Solids
- G01N2291/0232—Glass, ceramics, concrete or stone
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Data Mining & Analysis (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Pathology (AREA)
- Acoustics & Sound (AREA)
- Theoretical Computer Science (AREA)
- Signal Processing (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Investigating Or Analyzing Materials By The Use Of Ultrasonic Waves (AREA)
Abstract
The present invention relates to a kind of Analysis of Acoustic Emission Signal method of quick identification ceramic coating failure type.The acoustical signal produced first during collection coating test, extract signal characteristic parameter, clustering is carried out to acoustic emission signal, then the signal chosen near each cluster centre carries out wavelet transformation to it, and energy spectrum coefficient is extracted as the characteristic parameter of pattern-recognition, coating damage type is identified according to Energy distribution.The acoustie emission event number of all kinds of damages is finally counted, the data obtained is combined with plus load displacement curve, the critical load and corresponding signal frequency of coating failure process is obtained, failure procedure of the ceramic coating under plus load effect is fully understanded.The present invention provides direct foundation for the failure Mechanism of ceramic coating, and the life prediction to coating normal service is significant.
Description
Technical field
The present invention relates to a kind of Analysis of Acoustic Emission Signal method of quick identification ceramic coating failure type.
Background technology
Ceramic coating refers to sprayed coating of the coating material for ceramics, has been widely used in high-temperature superconductor, electronics industry, aviation
The every field such as space flight.It can effectively reduce influence of the working environment to base material, so that the performance of optimization component.
At present, the mechanical property and its damage development of ceramic coating are an important research topics.
Acoustic emission testing technology is as a kind of Dynamic Non-Destruction Measurement method, and it has been widely applied to various types of section bar
In the damage check of material.Although the damage mode of ceramic coating and damage process are sufficiently complex, during damage all
Acoustic emission signal can be produced, therefore ceramic coating failure procedure is monitored in real time by acoustic emission, is research coating
The effective means of damage development and failure mechanism.The processing and analysis of acoustic emission signal are the keys of acoustic emission, how to be done
The qualitative and quantitative information of acoustic emission source is obtained for research material to effective identification of acoustic emission signal, and from these signals
Failure mechanism there is important influence.
As a kind of Dynamic Monitoring, research of the sound emission to coating failure is significant, meanwhile, follow-up letter
Number analysis with processing it is also extremely important.Chinese patent (application number：CN200810031180.4 a kind of thermal barrier coating) is disclosed to damage
The Acoustic Emission Real-Time Monitoring method of its failure procedure is injured, by carrying out wavelet transformation to thermal barrier coating acoustic emission signal, is extracted
Wavelet Energy Spectrum coefficient identifies two kinds of failure moulds of thermal barrier coating surface vertical crack and Interface Crack as signal characteristic parameter
Formula.But, this method needs to carry out wavelet transformation to each damage acoustic emission signal and energy spectrum coefficient is calculated, and then takes people
Work knows method for distinguishing and carries out pattern-recognition to signal one by one.Due to often producing a large amount of acoustical signals in process of the test, therefore
Need to put into substantial amounts of time and efforts during analysis.Chinese patent (application number：CN201310341961.4 one) is disclosed
Thermal barrier coating acoustic emission signal automatic identifying method is planted, is characterized in carrying out wavelet package transforms to collected acoustical signal and extracts
Energy spectrum coefficient, then builds BP artificial neural networks, and finally identify the corresponding damage of acoustic emission signal by repeatedly training
Type, this method improve efficiently the problem of workload is big on the basis of previous patent, but still need to each
Individual signal waveform does the extraction of wavelet transformation and energy spectrum coefficient, and process is complex.
The content of the invention
It is an object of the invention to solve above-mentioned to there is problem there is provided a kind of sound of quick identification ceramic coating failure type
Transmission signal analysis method, this method can simplify existing signal analysis method on the basis of distortionless, improve signal analysis
Efficiency.
To achieve the above object, the technical scheme is that：A kind of sound hair of quick identification ceramic coating failure type
Penetrate signal analysis method,
The acoustic emission signal of S1, collection ceramic coating in damage failure procedure；
S2, extraction FEATURE PARAMETERS OF ACOUSTIC EMISSION are simultaneously normalized；
S3, the signal characteristic parameter to being obtained in step S2, carry out clustering, obtain sorted signal and corresponding
Cluster centre；
Several nearest signals of S4, each cluster centre of selection carry out wavelet transformation, extract Wavelet Energy Spectrum coefficient conduct
The FEATURE PARAMETERS OF ACOUSTIC EMISSION of ceramic coating damage mode is recognized, coating damage type is identified.
In an embodiment of the present invention, it is the method progress by using average variance criterion in the step S2
The normalized of FEATURE PARAMETERS OF ACOUSTIC EMISSION, its principle is to transform data to average for 0, standard deviation for 1 standard just
State distribution transformation, is implemented as follows,
For the sample with n acoustic emission signal, the calculation formula of average variance criterion is：
Wherein, xiFor primary signal parameter, xi' it is signal parameter after standardization；For the average of primary signal parameter, σ is
Standard deviation.
In an embodiment of the present invention, the FEATURE PARAMETERS OF ACOUSTIC EMISSION includes the duration of acoustic emission signal, shaken
The characteristic parameter of width, signal intensity.
In an embodiment of the present invention, the step S3 to implement process as follows,
Euclidean distance conduct is chosen as the input vector of pattern-recognition according to the signal characteristic parameter obtained in step S2
Similarity measure between acoustic emission signal, the corresponding profile value of different value of K is calculated using formula (2), institute is determined according to profile value
Choosing classification number,
Wherein, a (l) represent l-th point with its in similar other point between average distance, b (l, k) be one to
Amount, represents the average distance of and different classes of middle each point at l-th point；It is that can determine that cluster is effective that s values, which are met more than predetermined threshold value,；
According to the classification number of determination, it is that can obtain sorted signal and corresponding cluster centre to carry out clustering.
In an embodiment of the present invention, the predetermined threshold value of the s values is 0.6.
In an embodiment of the present invention, to implement process as follows by the step S4,
The cluster centre obtained according to step S3, choose several nearest signals of each cluster centre according to formula (3)-
(6) calculate and extract Wavelet Energy Spectrum coefficient as the FEATURE PARAMETERS OF ACOUSTIC EMISSION of identification ceramic coating damage mode,
Acoustical signal available waveforms function f (t) is represented：
F (t)=f0(t)+f1(t)+...+fn(t) (3)
Wherein, f0(t),f1(t),...,fn(t) it is the at different levels of decomposed signal；Energy at different levels is：
Gross energy calculation formula is：
Energy coefficient solution formula at different levels is under wavelet scale：
R(i)(t)=E(i)(t)/E(T)(t) (6)
Each frequency range Wavelet Energy Spectrum coefficient is drawn according to above formula, the wherein wavelet scale corresponding to maximum is the feature of signal
Yardstick, corresponding frequency range is the characteristic spectra of acoustical signal.
In an embodiment of the present invention, also need to carry out wavelet decomposition scales before the calculating for carrying out extraction Wavelet Energy Spectrum coefficient
Calculating, formula is：
Wherein, fsFor sample frequency, LfFor filter length, N is sampling length.
Compared to prior art, the invention has the advantages that：The present invention is the failure Mechanism of ceramic coating
There is provided direct foundation, simplify existing signal analysis method on the basis of distortionless, improve the efficiency of signal analysis to applying
The life prediction of layer normal service is significant.
Brief description of the drawings
Fig. 1 is analysis method schematic flow sheet of the present invention.
Fig. 2 is the graph of a relation between profile value s in the present invention and cluster classification number k.
Fig. 3 is distribution map of the three class acoustic emission signals in different parameters space.
Fig. 4 is five layers of wavelet decomposition schematic diagram of signal.
Fig. 5 is the wavelets and scaling function function graft of db8 small echos.
Fig. 6 is the ceramic coating damage typical feature energy spectrum diagram of three kinds of acoustic emission signal in the present invention：(a) coating elastoplasticity
Deform (b) synusia crackle (c) Interface Crack.
Fig. 7 is the Ring-down count of different failure modes and compression distance in ceramic coating impression failure procedure in the present invention
Relation schematic diagram.
Embodiment
1-7, technical scheme is specifically described below in conjunction with the accompanying drawings.
A kind of Analysis of Acoustic Emission Signal method of quick identification ceramic coating failure type of the present invention,
The acoustic emission signal of S1, collection ceramic coating in damage failure procedure；
S2, extract FEATURE PARAMETERS OF ACOUSTIC EMISSION (including the spy such as duration, amplitude, the signal intensity of acoustic emission signal
Levy parameter) and be normalized；
S3, the signal characteristic parameter to being obtained in step S2, carry out clustering, obtain sorted signal and corresponding
Cluster centre；
Several nearest signals of S4, each cluster centre of selection carry out wavelet transformation, extract Wavelet Energy Spectrum coefficient conduct
The FEATURE PARAMETERS OF ACOUSTIC EMISSION of ceramic coating damage mode is recognized, coating damage type is identified.
It is that FEATURE PARAMETERS OF ACOUSTIC EMISSION is carried out by using the method for average variance criterion in the step S2
Normalized, its principle is to transform data to average for 0, and standard deviation converts for 1 standardized normal distribution, implement as
Under,
For the sample with n acoustic emission signal, the calculation formula of average variance criterion is：
Wherein, xiFor primary signal parameter, xi' it is signal parameter after standardization；For the average of primary signal parameter, σ is
Standard deviation.
The step S3 to implement process as follows,
Euclidean distance conduct is chosen as the input vector of pattern-recognition according to the signal characteristic parameter obtained in step S2
Similarity measure between acoustic emission signal, the corresponding profile value of different value of K is calculated using formula (2), institute is determined according to profile value
Choosing classification number,
Wherein, a (l) represent l-th point with its in similar other point between average distance, b (l, k) be one to
Amount, represents the average distance of and different classes of middle each point at l-th point；It is that can determine that cluster is effective that s values, which are met more than predetermined threshold value,；
The predetermined threshold value of the s values is 0.6；
According to the classification number of determination, it is that can obtain sorted signal and corresponding cluster centre to carry out clustering.
It is as follows that the step S4 implements process,
The cluster centre obtained according to step S3, choose several nearest signals of each cluster centre according to formula (3)-
(6) calculate and extract Wavelet Energy Spectrum coefficient as the FEATURE PARAMETERS OF ACOUSTIC EMISSION of identification ceramic coating damage mode,
Acoustical signal available waveforms function f (t) is represented：
F (t)=f0(t)+f1(t)+...+fn(t) (3)
Wherein, f0(t),f1(t),...,fn(t) it is the at different levels of decomposed signal；Energy at different levels is：
Gross energy calculation formula is：
Energy coefficient solution formula at different levels is under wavelet scale：
R(i)(t)=E(i)(t)/E(T)(t) (6)
Each frequency range Wavelet Energy Spectrum coefficient is drawn according to above formula, the wherein wavelet scale corresponding to maximum is the feature of signal
Yardstick, corresponding frequency range is the characteristic spectra of acoustical signal.
The calculating of progress wavelet decomposition scales is also needed before the calculating for carrying out extraction Wavelet Energy Spectrum coefficient, formula is：
Wherein, fsFor sample frequency, LfFor filter length, N is sampling length.
It is below the specific implementation process of the present invention.
A kind of Analysis of Acoustic Emission Signal method of quick identification ceramic coating failure type of the present invention, realizes step such as
Under：
(1) acoustic emission signal of the collection ceramic coating in damage failure procedure；
(2) characteristic parameter such as duration, amplitude, signal intensity of acoustic emission signal collected in extraction step (1),
And normalized is done to parameters using the method for average variance criterion；
(3) Euclidean distance conduct is chosen as the input vector of pattern-recognition according to the signal parameter obtained in step (2)
Similarity measure between acoustic emission signal, the corresponding profile value of different value of K is calculated using formula (2), institute is determined according to profile value
Choosing classification number；
(4) the k values determined according to step (3) obtain sorted signal and correspondingly to acoustic emission signal progress clustering
Cluster centre；
(5) the nearest several signals of each cluster centre are chosen and carry out wavelet transformation, are counted according to the formula (3)~(6)
Calculate and extract Wavelet Energy Spectrum coefficient as the FEATURE PARAMETERS OF ACOUSTIC EMISSION of identification ceramic coating damage mode, wherein selected
Wavelet decomposition scales are drawn by formula (7).
The principle of clustering is as follows in the present invention：
Acoustic emission signal clustering method based on unsupervised pattern-recognition, be in damage mode and each signal classification all
In the case of unknown, by choosing suitable signal characteristic parameter, according to a certain specific method for measuring similarity between parameter
The similar acoustic emission signal of classification is divided into a class with clustering algorithm.Its basic step is：
1) selected acoustic emission signal parameter is normalized and all parameters is converted between [0,1]
Number；
2) classification number k is pre-defined, and k sample is chosen according to certain rule and is used as initial cluster centre；
3) distance of each input vector and cluster centre is calculated, input vector is distributed to and its cluster centre distance
A minimum class, draws an initial classification schemes, and recalculate cluster centre according to average of all categories；
4) sample is reclassified according to new cluster centre；
5) iterative cycles this 3), 4) process, when cluster centre convergence, cluster terminates.
Wherein, the method that normalized uses average variance criterion, its principle is to transform data to average to be
0, standard deviation converts for 1 standardized normal distribution.
For the sample with n acoustic emission signal, the calculation formula of average variance criterion is：
Wherein, xiFor primary signal parameter, xi' it is signal parameter after standardization；For the average of primary signal parameter, σ is
Standard deviation
Classification number k values are determined that k-means clusters the determination of k value profile value by its respective profile value：
Wherein, a (l) represent l-th point with its in similar other point between average distance, b (l, k) be one to
Amount, represents the average distance of and different classes of middle each point at l-th point.
Get between the bigger explanation class of s values and class and more open, Clustering Effect is better.Generally, when s value is more than 0.6
It is assured that cluster is effective.
The principle of wavelet analysis is as follows in the present invention：
Wavelet analysis is a kind of new time frequency analysis, transform method, the feature with multiresolution, in time domain and frequency domain all
With good resolution ratio, the principle of wavelet transformation is that data are decomposed into a series of Wavelet Component, i.e., small wave scale.It is each small
Wave energy level is due to its specific frequency range.
Generally, acoustical signal available waveforms function f (t) is represented：
F (t)=f0(t)+f1(t)+...+fn(t) (3)
Wherein, f0(t),f1(t),...,fn(t) it is the at different levels of decomposed signal；Energy at different levels is：
Gross energy calculation formula is：
Energy coefficient solution formula at different levels is under wavelet scale：
R(i)(t)=E(i)(t)/E(T)(t) (6)
Each frequency range Wavelet Energy Spectrum coefficient is drawn according to above formula, the wherein wavelet scale corresponding to maximum is the feature of signal
Yardstick, corresponding frequency range is the characteristic spectra of acoustical signal.
The calculating of progress wavelet decomposition scales is also needed before the calculating for carrying out extraction Wavelet Energy Spectrum coefficient, formula is：
Wherein, fsFor sample frequency, LfFor filter length, N is sampling length.
Embodiment：
Pattern-recognition is carried out to coating failure process by taking 8YSZ thermal barrier coating indentation tests as an example：
Thermal barrier coating sample is prepared using air plasma spraying method, and matrix is Inconel718 high temperature alloys, is glued
Knot layer is NiCoCrAlY, and ceramic layer is 8YSZ, and tack coat and ceramic layer thickness are respectively 100 μm and 300 μm.
Fig. 1 is the acoustic emission signal of sample damage in signal analysis flow chart, collection Indentation Process, extracts signal characteristic ginseng
Simultaneously it is normalized according to formula (1) for number, then chooses Euclidean distance and is used as similarity measure between acoustic emission signal
And clustering is carried out to acoustical signal.The corresponding profile value of different value of K can be obtained according to formula (2) in this example, Fig. 2 is required wheel
Relation between exterior feature value s and cluster classification number k.As k=3, s=0.6736, therefore signal can be broadly divided into three classes.Fig. 3
For distribution map of the three class signals in different parameters space after signal clustering.The small of signal can be tried to achieve according to formula (7)
Wave Decomposition yardstick is 5 grades, and three nearest class signals of selected distance cluster centre carry out five to it respectively in cluster analysis result
Layer wavelet decomposition (shown in Fig. 4), and each frequency band Wavelet Energy Spectrum coefficient is calculated using formula (3)~(6), know as pattern
Another characteristic parameter, identifies coating damage type.Wherein first kind signal waveform amplitude is relatively low, and main band is A5 energy
Level, its corresponding energy coefficient is 0.5519；Equations of The Second Kind basic frequency of signal band is D5 energy levels, and its corresponding energy coefficient is
0.5017；3rd class basic frequency of signal band is D4 energy levels, and its corresponding energy coefficient is 0.4201.Fig. 5 is this example in signal waveform
Selected wavelet basis function --- the wavelets and scaling function functional arrangement of db8 small echos in analysis.Fig. 6 is thermal barrier coating in this example
Three kinds of typical sound emission signal characteristic energy spectrum diagrams under indentation load effect, main band is respectively A5 (0-156.25kHz), D5
(156.25-312.5kHz) and D4 (312.5-625kHz), correspondence coating elastic-plastic deformation, synusia cracking and Interface Cracking.Fig. 7
Acoustic emission signal event number and compression distance for each damage mode in thermal barrier coating impression failure procedure in the present invention
Graph of a relation, with the increase of loading of pressing in, elastic-plastic deformation first occurs for coat inside, synusia crackle then occurs and interface is split
Line.
The present invention uses the technical scheme of the above, with advantages below：Effectively analyze being done to signal waveform and handle it
Before (extraction for including wavelet transformation and energy spectrum coefficient) clustering first is done to signal, all signals have been done one clearly point
Class.Then the nearest several signals of chosen distance cluster centre do waveform processing, and letter is effectively simplified under the conditions of distortionless
Number waveform processing procedures, reduce the workload of signal analysis, improve the analysis efficiency of signal.The chief value of the present invention exists
In：A kind of simple and quick signal analysis method is proposed, is provided for the failure Mechanism and life prediction of ceramic coating
Direct foundation.
Above is presently preferred embodiments of the present invention, all changes made according to technical solution of the present invention, produced function is made
During with scope without departing from technical solution of the present invention, protection scope of the present invention is belonged to.
Claims (7)
1. a kind of Analysis of Acoustic Emission Signal method of quick identification ceramic coating failure type, it is characterised in that：
The acoustic emission signal of S1, collection ceramic coating in damage failure procedure；
S2, extraction FEATURE PARAMETERS OF ACOUSTIC EMISSION are simultaneously normalized；
S3, the signal characteristic parameter to being obtained in step S2, carry out clustering, obtain sorted signal and corresponding cluster
Center；
Several nearest signals of S4, each cluster centre of selection carry out wavelet transformation, extract Wavelet Energy Spectrum coefficient and are used as identification
The FEATURE PARAMETERS OF ACOUSTIC EMISSION of ceramic coating damage mode, identifies coating damage type.
2. a kind of Analysis of Acoustic Emission Signal method of quick identification ceramic coating failure type according to claim 1, its
It is characterised by：It is that FEATURE PARAMETERS OF ACOUSTIC EMISSION is carried out by using the method for average variance criterion in the step S2
Normalized, its principle is to transform data to average for 0, and standard deviation converts for 1 standardized normal distribution, implements
It is as follows,
For the sample with n acoustic emission signal, the calculation formula of average variance criterion is：
<mrow>
<msubsup>
<mi>x</mi>
<mi>i</mi>
<mo>&prime;</mo>
</msubsup>
<mo>=</mo>
<mfrac>
<mrow>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
</mrow>
<mi>&sigma;</mi>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, xiFor primary signal parameter, x 'iFor signal parameter after standardization；For the average of primary signal parameter, σ is standard
Difference.
3. a kind of Analysis of Acoustic Emission Signal method of quick identification ceramic coating failure type according to claim 1 or 2,
It is characterized in that：Duration of the FEATURE PARAMETERS OF ACOUSTIC EMISSION including acoustic emission signal, amplitude, the feature of signal intensity
Parameter.
4. a kind of Analysis of Acoustic Emission Signal method of quick identification ceramic coating failure type according to claim 1, its
It is characterised by：The step S3 to implement process as follows,
Euclidean distance is chosen according to the signal characteristic parameter obtained in step S2 as the input vector of pattern-recognition to send out as sound
Similarity measure between signal is penetrated, the corresponding profile value of different value of K is calculated using formula (2), is divided according to selected by being determined profile value
Class number,
<mrow>
<mi>s</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mi>n</mi>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mn>1</mn>
<mi>n</mi>
</munderover>
<mfrac>
<mrow>
<mo>&lsqb;</mo>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
<mrow>
<mo>(</mo>
<mi>b</mi>
<mo>(</mo>
<mrow>
<mi>l</mi>
<mo>,</mo>
<mi>k</mi>
</mrow>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mi>a</mi>
<mrow>
<mo>(</mo>
<mi>l</mi>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
</mrow>
<mrow>
<mi>max</mi>
<mo>&lsqb;</mo>
<mi>a</mi>
<mrow>
<mo>(</mo>
<mi>l</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>min</mi>
<mrow>
<mo>(</mo>
<mi>b</mi>
<mo>(</mo>
<mrow>
<mi>l</mi>
<mo>,</mo>
<mi>k</mi>
</mrow>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
</mrow>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, a (l) represents the average distance between l-th point of other points with its in similar, and b (l, k) is a vector, table
Show the average distance of and different classes of middle each point at l-th point；It is that can determine that cluster is effective that s values, which are met more than predetermined threshold value,；
According to the classification number of determination, it is that can obtain sorted signal and corresponding cluster centre to carry out clustering.
5. a kind of Analysis of Acoustic Emission Signal method of quick identification ceramic coating failure type according to claim 1, its
It is characterised by：The predetermined threshold value of the s values is 0.6.
6. a kind of Analysis of Acoustic Emission Signal method of quick identification ceramic coating failure type according to claim 1, its
It is characterised by：It is as follows that the step S4 implements process,
The cluster centre obtained according to step S3, chooses several nearest signals of each cluster centre according to formula (3)-(6)
Calculate and extract Wavelet Energy Spectrum coefficient as the FEATURE PARAMETERS OF ACOUSTIC EMISSION of identification ceramic coating damage mode,
Acoustical signal available waveforms function f (t) is represented：
F (t)=f0(t)+f1(t)+...+fn(t) (3)
Wherein, f0(t),f1(t),...,fn(t) it is the at different levels of decomposed signal；Energy at different levels is：
<mrow>
<msup>
<mi>E</mi>
<mrow>
<mo>(</mo>
<mi>j</mi>
<mo>)</mo>
</mrow>
</msup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msubsup>
<mo>&Sigma;</mo>
<mrow>
<mi>&tau;</mi>
<mo>=</mo>
<msub>
<mi>t</mi>
<mn>0</mn>
</msub>
</mrow>
<mi>t</mi>
</msubsup>
<msup>
<mrow>
<mo>(</mo>
<msup>
<mi>f</mi>
<mrow>
<mo>(</mo>
<mi>j</mi>
<mo>)</mo>
</mrow>
</msup>
<mo>(</mo>
<mi>&tau;</mi>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>4</mn>
<mo>)</mo>
</mrow>
</mrow>
Gross energy calculation formula is：
<mrow>
<msup>
<mi>E</mi>
<mrow>
<mo>(</mo>
<mi>T</mi>
<mo>)</mo>
</mrow>
</msup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msubsup>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>0</mn>
</mrow>
<mi>n</mi>
</msubsup>
<msup>
<mi>E</mi>
<mrow>
<mo>(</mo>
<mi>j</mi>
<mo>)</mo>
</mrow>
</msup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>5</mn>
<mo>)</mo>
</mrow>
</mrow>
Energy coefficient solution formula at different levels is under wavelet scale：
R(i)(t)=E(i)(t)/E(T)(t) (6)
Each frequency range Wavelet Energy Spectrum coefficient is drawn according to above formula, the wherein wavelet scale corresponding to maximum is the feature chi of signal
Degree, corresponding frequency range is the characteristic spectra of acoustical signal.
7. a kind of Analysis of Acoustic Emission Signal method of quick identification ceramic coating failure type according to claim 6, its
It is characterised by：The calculating of progress wavelet decomposition scales is also needed before the calculating for carrying out extraction Wavelet Energy Spectrum coefficient, formula is：
<mrow>
<msub>
<mi>J</mi>
<mrow>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
<mo>=</mo>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
<mo>&lsqb;</mo>
<mi>int</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>log</mi>
<mn>2</mn>
</msub>
<mfrac>
<msub>
<mi>f</mi>
<mi>s</mi>
</msub>
<mn>20</mn>
</mfrac>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>int</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>log</mi>
<mn>2</mn>
</msub>
<mfrac>
<mi>N</mi>
<msub>
<mi>L</mi>
<mi>f</mi>
</msub>
</mfrac>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>7</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, fsFor sample frequency, LfFor filter length, N is sampling length.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710500277.4A CN107328868B (en) | 2017-06-27 | 2017-06-27 | A kind of Analysis of Acoustic Emission Signal method of quick identification ceramic coating failure type |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710500277.4A CN107328868B (en) | 2017-06-27 | 2017-06-27 | A kind of Analysis of Acoustic Emission Signal method of quick identification ceramic coating failure type |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107328868A true CN107328868A (en) | 2017-11-07 |
| CN107328868B CN107328868B (en) | 2019-06-07 |
Family
ID=60197721
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710500277.4A Active CN107328868B (en) | 2017-06-27 | 2017-06-27 | A kind of Analysis of Acoustic Emission Signal method of quick identification ceramic coating failure type |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107328868B (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109507299A (en) * | 2018-10-15 | 2019-03-22 | 浙江大学 | High pressure compound material hydrogen cylinder Processing Method of Acoustic Emission Signals |
| CN110297040A (en) * | 2019-06-10 | 2019-10-01 | 华北水利水电大学 | A kind of sound emission type judgement method rising wave band acquisition parameter based on sound emission |
| CN110824424A (en) * | 2019-11-19 | 2020-02-21 | 金陵科技学院 | Acoustic emission source positioning method based on wavelet transformation |
| CN110967251A (en) * | 2019-12-02 | 2020-04-07 | 湘潭大学 | Method for identifying damage mode of wind power blade |
| CN111433599A (en) * | 2017-11-27 | 2020-07-17 | 阿克森斯公司 | Method and system for determining process attributes using active sonographers |
| WO2020156348A1 (en) * | 2019-01-31 | 2020-08-06 | 青岛理工大学 | Structural damage identification method based on ensemble empirical mode decomposition and convolution neural network |
| CN112924556A (en) * | 2021-01-24 | 2021-06-08 | 西北工业大学 | Machine learning-based ceramic matrix composite material acoustic emission online damage pattern recognition method |
| CN116482219A (en) * | 2023-03-14 | 2023-07-25 | 防灾科技学院 | Layered pipeline damage position analysis method |
| CN116825243A (en) * | 2023-05-09 | 2023-09-29 | 安徽工程大学 | Multi-source data-based thermal barrier coating service life prediction method and system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090301198A1 (en) * | 2006-06-30 | 2009-12-10 | Carnegie Mellon University | Methods, Apparatuses, and Systems for Damage Detection |
| CN103033567A (en) * | 2012-12-31 | 2013-04-10 | 江苏大学 | Pipeline defect signal identification method based on guided wave |
| CN105067707A (en) * | 2015-08-03 | 2015-11-18 | 北京航空航天大学 | Damage monitoring method of composite material structure, and apparatus and system thereof |
| CN105510213A (en) * | 2015-12-09 | 2016-04-20 | 中国科学院上海硅酸盐研究所 | System and method for testing failure of thermal barrier coating |
| CN105929024A (en) * | 2016-04-21 | 2016-09-07 | 安徽省（水利部淮河水利委员会）水利科学研究院 | Intelligent detection and quantitative recognition method for defect of concrete |
-
2017
- 2017-06-27 CN CN201710500277.4A patent/CN107328868B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090301198A1 (en) * | 2006-06-30 | 2009-12-10 | Carnegie Mellon University | Methods, Apparatuses, and Systems for Damage Detection |
| CN103033567A (en) * | 2012-12-31 | 2013-04-10 | 江苏大学 | Pipeline defect signal identification method based on guided wave |
| CN105067707A (en) * | 2015-08-03 | 2015-11-18 | 北京航空航天大学 | Damage monitoring method of composite material structure, and apparatus and system thereof |
| CN105510213A (en) * | 2015-12-09 | 2016-04-20 | 中国科学院上海硅酸盐研究所 | System and method for testing failure of thermal barrier coating |
| CN105929024A (en) * | 2016-04-21 | 2016-09-07 | 安徽省（水利部淮河水利委员会）水利科学研究院 | Intelligent detection and quantitative recognition method for defect of concrete |
Non-Patent Citations (1)
| Title |
|---|
| 栗丽 等: "复合材料损伤失效的声发射检测研究进展", 《材料导报》 * |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11598754B2 (en) | 2017-11-27 | 2023-03-07 | Acosense Ab | Method and system for determining process properties using active acoustic spectroscopy |
| CN111433599A (en) * | 2017-11-27 | 2020-07-17 | 阿克森斯公司 | Method and system for determining process attributes using active sonographers |
| CN109507299A (en) * | 2018-10-15 | 2019-03-22 | 浙江大学 | High pressure compound material hydrogen cylinder Processing Method of Acoustic Emission Signals |
| WO2020156348A1 (en) * | 2019-01-31 | 2020-08-06 | 青岛理工大学 | Structural damage identification method based on ensemble empirical mode decomposition and convolution neural network |
| CN110297040A (en) * | 2019-06-10 | 2019-10-01 | 华北水利水电大学 | A kind of sound emission type judgement method rising wave band acquisition parameter based on sound emission |
| CN110824424A (en) * | 2019-11-19 | 2020-02-21 | 金陵科技学院 | Acoustic emission source positioning method based on wavelet transformation |
| CN110967251A (en) * | 2019-12-02 | 2020-04-07 | 湘潭大学 | Method for identifying damage mode of wind power blade |
| CN112924556A (en) * | 2021-01-24 | 2021-06-08 | 西北工业大学 | Machine learning-based ceramic matrix composite material acoustic emission online damage pattern recognition method |
| CN112924556B (en) * | 2021-01-24 | 2022-05-13 | 西北工业大学 | Machine learning-based ceramic matrix composite material acoustic emission online damage pattern recognition method |
| CN116482219A (en) * | 2023-03-14 | 2023-07-25 | 防灾科技学院 | Layered pipeline damage position analysis method |
| CN116482219B (en) * | 2023-03-14 | 2024-01-02 | 防灾科技学院 | Layered pipeline damage position analysis method |
| CN116825243A (en) * | 2023-05-09 | 2023-09-29 | 安徽工程大学 | Multi-source data-based thermal barrier coating service life prediction method and system |
| CN116825243B (en) * | 2023-05-09 | 2024-01-16 | 安徽工程大学 | Multi-source data-based thermal barrier coating service life prediction method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107328868B (en) | 2019-06-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107328868A (en) | A kind of Analysis of Acoustic Emission Signal method of quick identification ceramic coating failure type | |
| CN104712542B (en) | A kind of reciprocating compressor sensitive features based on Internet of Things are extracted and method for diagnosing faults | |
| CN106769052B (en) | A kind of mechanical system rolling bearing intelligent failure diagnosis method based on clustering | |
| CN109213121B (en) | Method for diagnosing clamping cylinder fault of fan braking system | |
| CN105334269A (en) | Pipeline defect type determination method based on neural network and guided wave characteristic database | |
| CN111238843B (en) | Fan health evaluation method based on rapid spectrum kurtosis analysis | |
| CN112257530B (en) | Rolling bearing fault diagnosis method based on blind signal separation and support vector machine | |
| CN102778358A (en) | Failure prediction model establishing method and system as well as fan monitoring pre-warning system and method | |
| CN108830328B (en) | Microseismic signal SMOTE identification method and monitoring system fusing spatial knowledge | |
| CN105679313A (en) | Audio recognition alarm system and method | |
| CN111042917B (en) | Common rail fuel injector weak fault diagnosis method based on GOA-MCKD and hierarchical discrete entropy | |
| CN110376289A (en) | A kind of composite fiber braiding layer damnification recognition method based on sound emission means | |
| CN110020637A (en) | A kind of analog circuit intermittent fault diagnostic method based on more granularities cascade forest | |
| CN106874950A (en) | A kind of method for identifying and classifying of transient power quality recorder data | |
| CN113763986B (en) | Abnormal sound detection method for air conditioner indoor unit based on sound classification model | |
| CN112924556B (en) | Machine learning-based ceramic matrix composite material acoustic emission online damage pattern recognition method | |
| CN108920426A (en) | A kind of method for diagnosing faults based on power equal operator and DS evidence theory | |
| CN107832729A (en) | A kind of bearing rust intelligent diagnosing method | |
| CN105844055A (en) | Damage monitoring method based on guided wave dynamic enhanced fission-convergence probability model | |
| CN111024527A (en) | Crack propagation monitoring method based on multi-sensor data fusion | |
| Satour et al. | Wavelet based clustering of acoustic emission hits to characterize damage mechanisms in composites | |
| CN115754022A (en) | Steel truss welding node damage degree identification method based on acoustic emission | |
| CN105354170A (en) | CEEMD and singular value decomposition based recognition method | |
| CN109507299A (en) | High pressure compound material hydrogen cylinder Processing Method of Acoustic Emission Signals | |
| CN114487129A (en) | Flexible material damage identification method based on acoustic emission technology |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |