CN107328868B - 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
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- CN107328868B CN107328868B CN201710500277.4A CN201710500277A CN107328868B CN 107328868 B CN107328868 B CN 107328868B CN 201710500277 A CN201710500277 A CN 201710500277A CN 107328868 B CN107328868 B CN 107328868B
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- 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
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- 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
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- 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
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- 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
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- 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
Abstract
The present invention relates to a kind of Analysis of Acoustic Emission Signal methods of quickly identification ceramic coating failure type.The acoustical signal generated during acquisition coating test first, 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 characteristic parameter of the energy spectrum coefficient as pattern-recognition is extracted, coating damage type is identified according to Energy distribution.The acoustie emission event number for finally counting all kinds of damages combines the data obtained with plus load-displacement curve, obtains the critical load and corresponding signal frequency of coating failure process, fully understands failure procedure of the ceramic coating under plus load effect.The present invention provides direct foundation for the failure Mechanism of ceramic coating, is of great significance to the life prediction of coating normal service.
Description
Technical field
The present invention relates to a kind of Analysis of Acoustic Emission Signal methods of quickly identification ceramic coating failure type.
Background technique
Ceramic coating refers to that coating material is the spray coating of ceramics, has been widely used in high-temperature superconductor, electronics industry, aviation
The every field such as space flight.Influence of the working environment to base material can be effectively reduced in it, thus the service performance of optimization component.
Currently, the mechanical property and its damage development of ceramic coating are an important research topics.
For acoustic emission testing technology as a kind of Dynamic Non-Destruction Measurement method, it has been widely applied to various types of profile
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 generated, therefore real-time monitoring is carried out to ceramic coating failure procedure 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 key that acoustic emissions, 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 have important influence.
As a kind of Dynamic Monitoring, sound emission is of great significance to the research of coating failure, meanwhile, subsequent letter
Number analysis with processing it is also extremely important.Chinese patent (application number: CN200810031180.4) discloses a kind of thermal barrier coating damage
The Acoustic Emission Real-Time Monitoring method for injuring its failure procedure is extracted by carrying out wavelet transformation to thermal barrier coating acoustic emission signal
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 calculates, and then takes people
Work knows method for distinguishing one by one and carries out pattern-recognition to signal.Due to often generating a large amount of acoustical signals during test,
It needs to put into a large amount of time and efforts in analytic process.Chinese patent (application number: CN201310341961.4) discloses one
Kind thermal barrier coating acoustic emission signal automatic identifying method, its main feature is that carrying out wavelet package transforms to collected acoustical signal and extracting
Then energy spectrum coefficient constructs BP artificial neural network, and finally identifies the corresponding damage of acoustic emission signal by repeatedly training
The problem of type, this method improves efficiently heavy workload on the basis of previous patent, however, there remains to each
A signal waveform does the extraction of wavelet transformation and energy spectrum coefficient, and process is complex.
Summary of the invention
It is an object of the invention to solve the problems, such as it is above-mentioned exist, a kind of sound of quickly identification ceramic coating failure type is provided
Emit 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 quickly identification ceramic coating failure type
Signal analysis method is penetrated,
The acoustic emission signal of S1, acquisition ceramic coating in damage failure procedure；
S2, it extracts FEATURE PARAMETERS OF ACOUSTIC EMISSION and is normalized；
S3, to signal characteristic parameter obtained in step S2, carry out clustering, obtain sorted signal and corresponding
Cluster centre；
S4, nearest several signals progress wavelet transformation of each cluster centre is chosen, extracts the conduct of Wavelet Energy Spectrum coefficient
The FEATURE PARAMETERS OF ACOUSTIC EMISSION for identifying ceramic coating damage mode, identifies coating damage type.
It in an embodiment of the present invention, in the step S2, is carried out by using the method for average variance criterion
The normalized of FEATURE PARAMETERS OF ACOUSTIC EMISSION, principle be transform data to mean value be 0, standard deviation be 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 are as follows:
Wherein, xiFor original signal parameter, xi' it is signal parameter after standardization；For the mean value of original 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, vibration
The characteristic parameter of width, signal strength.
In an embodiment of the present invention, the step S3's the specific implementation process is as follows,
Input vector according to signal characteristic parameter obtained in step S2 as pattern-recognition chooses Euclidean distance conduct
Similarity measure between acoustic emission signal, the corresponding profile value of different value of K is calculated using formula (2), determines institute according to profile value
Choosing classification number,
Wherein, a (l) indicate first point it is similar with its in other points between average distance, b (l, k) be one to
Amount indicates the average distance of with different classes of middle each point at first point；It is that can determine that cluster is effective that s value, which meets and is greater than preset threshold,；
According to determining classification number, carrying out clustering can be obtained sorted signal and corresponding cluster centre.
In an embodiment of the present invention, the preset threshold of the s value is 0.6.
In an embodiment of the present invention, the step S4 the specific implementation process is as follows,
According to the cluster centre that step S3 is obtained, several nearest signals of each cluster centre are chosen according to formula (3)-
(6) FEATURE PARAMETERS OF ACOUSTIC EMISSION of the Wavelet Energy Spectrum coefficient as identification ceramic coating damage mode is calculated and extracts,
Acoustical signal available waveforms function f (t) is indicated:
F (t)=f0(t)+f1(t)+...+fn(t) (3)
Wherein, f0(t),f1(t),...,fnIt (t) is the at different levels of decomposed signal；Energy at different levels are as follows:
Gross energy calculation formula are as follows:
Energy coefficient solution formula at different levels under wavelet scale are as follows:
R(i)(t)=E(i)(t)/E(T)(t) (6)
Each frequency range Wavelet Energy Spectrum coefficient is obtained according to above formula, and wherein wavelet scale corresponding to maximum value is the feature of signal
Scale, corresponding frequency range are the characteristic spectra of acoustical signal.
In an embodiment of the present invention, it also needs to carry out wavelet decomposition scales before extracting the calculating of Wavelet Energy Spectrum coefficient
Calculating, formula are as follows:
Wherein, fsFor sample frequency, LfFor filter length, N is sampling length.
Compared to the prior art, the invention has the following advantages: the present invention is the failure Mechanism of ceramic coating
Direct foundation is provided, simplifies existing signal analysis method on the basis of distortionless, improves the efficiency of signal analysis to painting
The life prediction of layer normal service is of great significance.
Detailed description of the invention
Fig. 1 is analysis method flow diagram of the present invention.
Fig. 2 is the relational graph in the present invention between profile value s and cluster classification number k.
Fig. 3 is distribution map of the three classes acoustic emission signal 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 echo.
Fig. 6 is that ceramic coating damages three kinds of acoustic emission signal typical feature energy spectrum diagrams: (a) coating elastoplasticity in the present invention
Deform (b) synusia crackle (c) Interface Crack.
Fig. 7 is the Ring-down count and compression distance of different failure modes in ceramic coating impression failure procedure in the present invention
Relation schematic diagram.
Specific embodiment
Technical solution of the present invention is specifically described in 1-7 with reference to the accompanying drawing.
One kind of the invention quickly identifies the Analysis of Acoustic Emission Signal method of ceramic coating failure type,
The acoustic emission signal of S1, acquisition ceramic coating in damage failure procedure；
S2, the FEATURE PARAMETERS OF ACOUSTIC EMISSION (spies such as duration, amplitude, signal strength including acoustic emission signal are extracted
Sign parameter) and be normalized；
S3, to signal characteristic parameter obtained in step S2, carry out clustering, obtain sorted signal and corresponding
Cluster centre；
S4, nearest several signals progress wavelet transformation of each cluster centre is chosen, extracts the conduct of Wavelet Energy Spectrum coefficient
The FEATURE PARAMETERS OF ACOUSTIC EMISSION for identifying ceramic coating damage mode, identifies coating damage type.
It is that FEATURE PARAMETERS OF ACOUSTIC EMISSION is carried out by using the method for average variance criterion in the step S2
Normalized, principle be transform data to mean value be 0, standard deviation be 1 standardized normal distribution convert, specific implementation such as
Under,
For the sample with n acoustic emission signal, the calculation formula of average variance criterion are as follows:
Wherein, xiFor original signal parameter, xi' it is signal parameter after standardization；For the mean value of original signal parameter, σ is
Standard deviation.
The step S3's the specific implementation process is as follows,
Input vector according to signal characteristic parameter obtained in step S2 as pattern-recognition chooses Euclidean distance conduct
Similarity measure between acoustic emission signal, the corresponding profile value of different value of K is calculated using formula (2), determines institute according to profile value
Choosing classification number,
Wherein, a (l) indicate first point it is similar with its in other points between average distance, b (l, k) be one to
Amount indicates the average distance of with different classes of middle each point at first point；It is that can determine that cluster is effective that s value, which meets and is greater than preset threshold,；
The preset threshold of the s value is 0.6；
According to determining classification number, carrying out clustering can be obtained sorted signal and corresponding cluster centre.
The step S4 the specific implementation process is as follows,
According to the cluster centre that step S3 is obtained, several nearest signals of each cluster centre are chosen according to formula (3)-
(6) FEATURE PARAMETERS OF ACOUSTIC EMISSION of the Wavelet Energy Spectrum coefficient as identification ceramic coating damage mode is calculated and extracts,
Acoustical signal available waveforms function f (t) is indicated:
F (t)=f0(t)+f1(t)+...+fn(t) (3)
Wherein, f0(t),f1(t),...,fnIt (t) is the at different levels of decomposed signal；Energy at different levels are as follows:
Gross energy calculation formula are as follows:
Energy coefficient solution formula at different levels under wavelet scale are as follows:
R(i)(t)=E(i)(t)/E(T)(t) (6)
Each frequency range Wavelet Energy Spectrum coefficient is obtained according to above formula, and wherein wavelet scale corresponding to maximum value is the feature of signal
Scale, corresponding frequency range are the characteristic spectra of acoustical signal.
The calculating of progress wavelet decomposition scales, formula are also needed before extracting the calculating of Wavelet Energy Spectrum coefficient are as follows:
Wherein, fsFor sample frequency, LfFor filter length, N is sampling length.
The following are specific implementation process of the invention.
One kind of the invention quickly identifies the Analysis of Acoustic Emission Signal method of ceramic coating failure type, realizes step such as
Under:
(1) acoustic emission signal of the acquisition ceramic coating in damage failure procedure；
(2) characteristic parameters such as duration, amplitude, signal strength of collected acoustic emission signal in extraction step (1),
And normalized is done to parameters using the method for average variance criterion；
(3) signal parameter according to obtained in step (2) chooses Euclidean distance conduct as the input vector of pattern-recognition
Similarity measure between acoustic emission signal, the corresponding profile value of different value of K is calculated using formula (2), determines institute according to profile value
Choosing classification number；
(4) the k value determined according to step (3) carries out clustering to acoustic emission signal and obtains sorted signal and correspondence
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)
FEATURE PARAMETERS OF ACOUSTIC EMISSION of the Wavelet Energy Spectrum coefficient as identification ceramic coating damage mode is calculated and extracts, wherein selected
Wavelet decomposition scales are obtained 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 damage mode and each signal classification all
In the case where unknown, by choosing suitable signal characteristic parameter, according to specific method for measuring similarity a certain between parameter
The similar acoustic emission signal of classification is divided into one kind with clustering algorithm.Its basic step are as follows:
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 as initial cluster centre according to certain rule；
3) each input vector is calculated at a distance from cluster centre, and input vector is distributed to and its cluster centre distance
The smallest one kind obtains an initial classification schemes, and recalculates cluster centre according to mean value of all categories；
4) sample is reclassified according to new cluster centre；
5) iterative cycles this 3), 4) process, when cluster centre is restrained, end of clustering.
Wherein, the method that normalized uses average variance criterion, principle is to transform data to mean value to be
0, the standardized normal distribution that standard deviation is 1 converts.
For the sample with n acoustic emission signal, the calculation formula of average variance criterion are as follows:
Wherein, xiFor original signal parameter, xi' it is signal parameter after standardization；For the mean value of original signal parameter, σ is
Standard deviation
Classification number k value is determined that k-means clusters the determination of k value profile value by its respective profile value:
Wherein, a (l) indicate first point it is similar with its in other points between average distance, b (l, k) be one to
Amount indicates the average distance of with different classes of middle each point at first point.
S value is bigger to illustrate to get between class and class and more opens, and Clustering Effect is better.Under normal circumstances, when the value of s is greater 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, 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 grade is due to its specific frequency range.
In general, acoustical signal available waveforms function f (t) is indicated:
F (t)=f0(t)+f1(t)+...+fn(t) (3)
Wherein, f0(t),f1(t),...,fnIt (t) is the at different levels of decomposed signal；Energy at different levels are as follows:
Gross energy calculation formula are as follows:
Energy coefficient solution formula at different levels under wavelet scale are as follows:
R(i)(t)=E(i)(t)/E(T)(t) (6)
Each frequency range Wavelet Energy Spectrum coefficient is obtained according to above formula, and wherein wavelet scale corresponding to maximum value is the feature of signal
Scale, corresponding frequency range are the characteristic spectra of acoustical signal.
The calculating of progress wavelet decomposition scales, formula are also needed before extracting the calculating of Wavelet Energy Spectrum coefficient are as follows:
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 test as an example:
Thermal barrier coating sample is prepared using air plasma spraying method, and matrix is Inconel718 high temperature alloy, is glued
Knot layer is NiCoCrAlY, and ceramic layer 8YSZ, adhesive layer and ceramic layer thickness are respectively 100 μm and 300 μm.
Fig. 1 is signal analysis flow chart diagram, acquires the acoustic emission signal of sample damage in Indentation Process, extracts signal characteristic ginseng
Number is simultaneously normalized it according to formula (1), then chooses Euclidean distance 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 found out according to formula (2) in this example, Fig. 2 is required wheel
Relationship 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 classes signal in different parameters space after signal clustering.The small of signal can be acquired according to formula (7)
Wave Decomposition scale is 5 grades, and the nearest three classes signal of selected distance cluster centre carries 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 mode
Another characteristic parameter identifies coating damage type.Wherein first kind signal waveform amplitude is relatively low, and main band is A5 energy
Grade, corresponding energy coefficient are 0.5519；Second class basic frequency of signal band is D5 energy level, and corresponding energy coefficient is
0.5017；Third class basic frequency of signal band is D4 energy level, and 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 echo 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), corresponding coating elastic-plastic deformation, synusia cracking and Interface Cracking.Fig. 7
For the acoustic emission signal event number and compression distance of each damage mode in thermal barrier coating impression failure procedure in the present invention
Relational graph, 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 use more than technical solution, have the advantage that and effectively analyze and handle it being done to signal waveform
Before (extraction including wavelet transformation and energy spectrum coefficient) clustering first is done to signal, one specific point has been done to all signals
Class.Then it selects several signals nearest apart from cluster centre to do waveform processing, 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.Chief value of the 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.
The above are preferred embodiments of the present invention, all any changes made according to the technical solution of the present invention, and generated function is made
When with range without departing from technical solution of the present invention, all belong to the scope of protection of the present invention.
Claims (6)
1. a kind of Analysis of Acoustic Emission Signal method of quickly identification ceramic coating failure type, it is characterised in that:
The acoustic emission signal of S1, acquisition ceramic coating in damage failure procedure；
S2, it extracts FEATURE PARAMETERS OF ACOUSTIC EMISSION and is normalized；
S3, to signal characteristic parameter obtained in step S2, carry out clustering, obtain sorted signal and corresponding cluster
Center；
S4, nearest several signals progress wavelet transformation of each cluster centre is chosen, extracts Wavelet Energy Spectrum coefficient as identification
The FEATURE PARAMETERS OF ACOUSTIC EMISSION of ceramic coating damage mode identifies coating damage type；
The step S3's the specific implementation process is as follows,
Euclidean distance is chosen as sound as the input vector of pattern-recognition according to signal characteristic parameter obtained in step S2 to send out
Similarity measure between signal is penetrated, the corresponding profile value of different value of K is calculated using formula (2), determines selected point according to profile value
Class number,
Wherein, a (l) indicate first point it is similar with its in other points between average distance, b (l, k) be a vector, table
Show the average distance of with different classes of middle each point at first point；It is that can determine that cluster is effective that s value, which meets and is greater than preset threshold,；
According to determining classification number, carrying out clustering can be obtained sorted signal and corresponding cluster centre.
2. a kind of Analysis of Acoustic Emission Signal method of quickly identification ceramic coating failure type according to claim 1,
It is characterized in that: being that FEATURE PARAMETERS OF ACOUSTIC EMISSION is carried out by using the method for average variance criterion in the step S2
Normalized, principle be transform data to mean value be 0, standard deviation be 1 standardized normal distribution convert, specific implementation
It is as follows,
For the sample with n acoustic emission signal, the calculation formula of average variance criterion are as follows:
Wherein, xiFor original signal parameter, xi' it is signal parameter after standardization；For the mean value of original signal parameter, σ is standard
Difference.
3. a kind of Analysis of Acoustic Emission Signal method of quickly identification ceramic coating failure type according to claim 1 or 2,
It is characterized by: the FEATURE PARAMETERS OF ACOUSTIC EMISSION include duration of acoustic emission signal, amplitude, signal strength feature
Parameter.
4. a kind of Analysis of Acoustic Emission Signal method of quickly identification ceramic coating failure type according to claim 1,
Be characterized in that: the preset threshold of the s value is 0.6.
5. a kind of Analysis of Acoustic Emission Signal method of quickly identification ceramic coating failure type according to claim 1,
Be characterized in that: the step S4 the specific implementation process is as follows,
According to the cluster centre that step S3 is obtained, several nearest signals of each cluster centre are chosen according to formula (3)-(6)
FEATURE PARAMETERS OF ACOUSTIC EMISSION of the Wavelet Energy Spectrum coefficient as identification ceramic coating damage mode is calculated and extracts,
Acoustical signal available waveforms function f (t) is indicated:
F (t)=f0(t)+f1(t)+...+fn(t) (3)
Wherein, f0(t),f1(t),...,fnIt (t) is the at different levels of decomposed signal；Energy at different levels are as follows:
Gross energy calculation formula are as follows:
Energy coefficient solution formula at different levels under wavelet scale are as follows:
R(i)(t)=E(i)(t)/E(T)(t) (6)
Each frequency range Wavelet Energy Spectrum coefficient is obtained according to above formula, and wherein wavelet scale corresponding to maximum value is the feature ruler of signal
Degree, corresponding frequency range is the characteristic spectra of acoustical signal.
6. a kind of Analysis of Acoustic Emission Signal method of quickly identification ceramic coating failure type according to claim 5,
It is characterized in that: also needing to carry out the calculating of wavelet decomposition scales, formula before extracting the calculating of Wavelet Energy Spectrum coefficient are as follows:
Wherein, fsFor sample frequency, LfFor filter length, N is sampling length.
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| 复合材料损伤失效的声发射检测研究进展;栗丽 等;《材料导报》;20130930;第27卷(第9期);第20页第2栏第1段-第21页第1栏第3段 |
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