CN110222650A - A kind of acoustie emission event classification method based on sound emission all band acquisition parameter - Google Patents
A kind of acoustie emission event classification method based on sound emission all band acquisition parameter Download PDFInfo
- Publication number
- CN110222650A CN110222650A CN201910498019.6A CN201910498019A CN110222650A CN 110222650 A CN110222650 A CN 110222650A CN 201910498019 A CN201910498019 A CN 201910498019A CN 110222650 A CN110222650 A CN 110222650A
- Authority
- CN
- China
- Prior art keywords
- emission
- acoustie
- wave
- type
- sound emission
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
Landscapes
- Investigating Or Analyzing Materials By The Use Of Ultrasonic Waves (AREA)
Abstract
The present invention provides a kind of acoustie emission event classification method based on sound emission all band acquisition parameter, several acoustic emission waves are obtained including acquisition, calculate each acoustic emission wave average frequency and antigradient, sound emission all band type parameter is calculated again, and directly acoustie emission event type is divided according to calculated result.Using technical solution of the present invention, by setting up simple sound emission all band type parameter, corresponding acoustie emission event type is divided according to the size of sound emission all band type parameter quantized values, simplify acoustie emission event Type division method, it is convenient for calculating and analyze, it lays a good foundation for the extent of damage and the trend of being damaged of Correct Analysis and prediction solid material, acoustie emission event type judgement method provided by the invention, it is easy to operate, the required hardware device to come into operation is less, testing efficiency is substantially increased, testing cost is reduced.
Description
Technical field
The present invention relates to acoustic emission fields, particularly relate to a kind of sound emission based on sound emission all band acquisition parameter
Event category method.
Background technique
Sound emission be solid interior by external force when internal sabotage issue ultrasonic wave, for solid interior destroy
The early warning of dynamic monitoring and the final damage inactivation of solid, this technology can be widely applied to the relevant geology of the various rock failure mechanisms of rock
Disaster monitoring and early warning are also widely used in the fatigue damage detection evaluation of bridge and metal pressure container etc..It is sent out using sound
Inject the destruction of row solid interior dynamic monitoring and the final damage inactivation of solid early warning when, if to the type of acoustic emission wave into
The reasonable classification of row, so that it may the degree of injury of solid material and the risk that is damaged be made correctly according to the type of sound emission
Prediction.The division of existing sound emission type is typically necessary the amplitude used when acoustic emission wave initially reaches, this amplitude
Referred to as first motion, the direction of amplitude are known as first motion polarity, and a kind of method collects sound hair by the probe of four or more spatial distributions
First motion amplitude size is penetrated, Moment tensor inversion operation is then carried out, sound emission type is determined according to inversion result and experimental conditions, separately
A kind of method is 4 or more channel sound emission first motion polarity being distributed by statistical space, determines sound emission class according to statistical value
Type.Both of which has the following deficiencies:
1) 4 or more the acoustic emission probes with spatial distribution are required, theoretical calculation basis is otherwise just lost；
2) all greatly influenced by the identification of sound emission first motion, first motion identification again because by noise, the acquisition performance etc. of popping one's head in because
There are extreme difficulties for the influence of element, to influence the accuracy of final sound emission type judgement.
Summary of the invention
In order to solve the above technical problems, the present invention provides a kind of sound emission things based on sound emission all band acquisition parameter
Part classification method.
The present invention is achieved by the following technical programs.
The present invention provides a kind of acoustie emission event classification method based on sound emission all band acquisition parameter, including it is following
Step:
Step 1: within different acquisition times, by sound emission acquisition system from same inside of solid material
Multiple acoustic emission waves, the acoustic emission wave is made of several continuous wave crests, and each wave crest shakes with corresponding wave crest
Width, choosing the wherein most peak amplitude value of frequency of occurrence, as threshold value, defining the maximum value in all peak amplitudes is most
Large amplitude Amax is defined in acoustic emission wave and is occurred at the time of first peak amplitude is greater than threshold value being initial time, definition sound
Occur at the time of peak amplitude reaches peak swing Amax in transmitted wave for peak value moment, the peak value moment and initial time it
Difference is defined as rise time RT；In acoustic emission wave, when continuously there are multiple peak amplitudes greater than threshold value, then by first
Wave crest occur until the last one wave crest when occurring timing definition experienced be duration T；Be defined on the duration T with
The wave crest quantity of interior appearance is wave crest number PC；Defining the ratio between wave crest number PC and duration T is average frequency AF, i.e.,
Defining the ratio between rise time RT and peak swing Amax is antigradient RA, it may be assumed that
Step 2: sound emission all band type parameter WP corresponding to each acoustic emission wave is calculated by following relationship
(this parameter just has the meaning of antigradient.):
Step 3: the calculated result of sound emission all band type parameter WP according to step 2 will as WP < 1
The corresponding acoustie emission event of the wave is classified as shearing-type acoustie emission event, and as WP > 1, the corresponding acoustie emission event of the wave is returned
Class is tension type acoustie emission event.
The acoustie emission event classification method based on sound emission all band acquisition parameter is further comprising the steps of:
Step 1: calculating separately average frequency AF and antigradient RA corresponding to each acoustic emission wave；
Step 2: all average frequency AF being compared, obtaining maximum value therein is IFmax, to all antigradient RA
It is compared, obtaining maximum value therein is RAmax；
Step 3: acoustic emission wave is calculated by following relationship and normalizes type parameter WPN:
Step 4: when the normalization of the acoustic emission wave described in the step 3 type parameter WPN is less than 1, then the sound hair corresponding to it
Penetrating event classification is shearing-type acoustie emission event, when the normalization of the acoustic emission wave described in the step 3 type parameter WPN is greater than 1, then
Acoustie emission event corresponding to it is classified as tension type acoustie emission event.
The acoustie emission event classification method based on sound emission all band acquisition parameter is further comprising the steps of:
When the normalization of the acoustic emission wave described in the step 3 type parameter WPN meets following relationship, then the sound corresponding to it
Transmitting event classification is mixed type acoustie emission event:
0.8≤WPN≤1.2。
The acoustie emission event classification method based on sound emission all band acquisition parameter is further comprising the steps of:
According to the classification results to acoustie emission event type, when acoustie emission event is shearing-type acoustie emission event, institute
Corresponding solid material will by failure by shear, when acoustie emission event is tension type acoustie emission event, corresponding to solid
Material will be destroyed by tension.
The solid material is rock.
The beneficial effects of the present invention are: technical solution of the present invention is used, by setting up simple sound emission all band
Type parameter divides corresponding sound emission type according to the size of sound emission all band type parameter quantized values, letter
Change sound emission Type division method, be convenient for calculating and analyzed, be easily mastered those of ordinary skill, and is easy basis
The type of acoustic emission wave makes correctly analysis and prediction to the extent of damage of solid material and the trend of being damaged.The present invention provides
Acoustie emission event type judgement method, easy to operate, the required hardware device to come into operation is less, substantially increase test effect
Rate reduces testing cost.
Detailed description of the invention
Fig. 1 is solid material acoustic emission wave waveform and its parameters schematic diagram；
Fig. 2 is the flow chart of sound emission type judgement method of the present invention；
Fig. 3 is the method schematic diagram that the present invention judges sound emission type using rectangular coordinate system；
Fig. 4 is the Evolution States figure of sound emission at any time in Rock Failure under Uniaxial Compression in the embodiment of the present invention 1；
Fig. 5 is the evolution shape of tension sound emission percentage at any time in Rock Failure under Uniaxial Compression in the embodiment of the present invention 1
State figure；
Fig. 6 be in the embodiment of the present invention 1 all types of sound emissions of Rock Under Uniaxial Compression compression peaks section in the distribution shape in rock sample space
State；
Fig. 7 be in the embodiment of the present invention 1 the tension type sound emission of Rock Under Uniaxial Compression compressed residual section in the distribution shape in rock sample space
State.
Specific embodiment
Be described further below technical solution of the present invention, but claimed range be not limited to it is described.
As shown in Figure 1, the present invention relates to sound emission type judgement method, from the parameter of acoustic emission waveform signal
Acquisition, common acoustic emission wave waveform is as shown in Figure 1, acoustic emission wave parameters have: maximum amplitude, threshold value, peak counting,
Rise time, peak counting, original frequency, in which:
(1) peak swing Amax: the maximum displacement of position, unit when particle is offset to static when it refers to wave-type vibration
dB。
(2) threshold value:, by being manually set, the acoustic emission wave is made of several continuous wave crests for it, each wave crest tool
Have a corresponding peak amplitude, choose wherein the most peak amplitude value of frequency of occurrence as threshold value.
(3) rise time RT: it refers to that acoustic emission waveform reaches maximum value by more than first threshold value to amplitude of wave form
Time interval, unit μ s.
(4) duration T: refer to that acoustic emission waveform first time crosses threshold value to being finally down to the threshold time experienced
Interval, unit μ s.
(5) peak counting PC: it refers under the threshold value of setting, the signal oscillating number meter within sustained segment rise time
Number, unit is secondary；
(6) average frequency AF: refer to the acoustic emission wave number of oscillation within the duration, i.e. PC/T, unit kHz.
As shown in Fig. 2, the present invention provides a kind of acoustie emission event classification sides based on sound emission all band acquisition parameter
Method, comprising the following steps:
Step 1: within different acquisition times, by sound emission acquisition system from same inside of solid material
Multiple acoustic emission waves, the preferably described solid material is rock, and the acoustic emission wave is made of several continuous wave crests, each
Wave crest has corresponding peak amplitude, and choosing the wherein most peak amplitude value of frequency of occurrence, as threshold value, definition is all
Maximum value in peak amplitude is peak swing Amax, defines in acoustic emission wave and first peak amplitude occurs greater than threshold value
Moment is initial time, defines and occurs at the time of peak amplitude reaches peak swing Amax being peak value moment, institute in acoustic emission wave
The difference for stating peak value moment and initial time is defined as rise time RT；In acoustic emission wave, when continuously there are multiple peak amplitudes
When greater than threshold value, then first wave crest is occurred until timing definition experienced is when continuing when the last one wave crest occurs
Between T；Being defined on the wave crest quantity occurred within the duration T is wave crest number PC；Define wave crest number PC and duration T it
Than for average frequency AF, i.e.,
Defining the ratio between rise time RT and peak swing Amax is antigradient RA, it may be assumed that
Step 2: sound emission all band type parameter WP corresponding to each acoustic emission wave is calculated by following relationship:
Step 3: the calculated result of sound emission all band type parameter WP according to step 2 will as WP < 1
The corresponding acoustie emission event of the wave is classified as shearing-type acoustie emission event, and as WP > 1, the corresponding acoustie emission event of the wave is returned
Class is tension type acoustie emission event.
In addition, the sound emission type judgement method based on sound emission all band acquisition parameter is further comprising the steps of:
Step 1: calculating separately average frequency AF and antigradient RA corresponding to each acoustic emission wave；
Step 2: all average frequency AF being compared, obtaining maximum value therein is IFmax, to all antigradient RA
It is compared, obtaining maximum value therein is RAmax；
Step 3: acoustic emission wave is calculated by following relationship and normalizes type parameter WPN:
Step 4: when the normalization of the acoustic emission wave described in the step 3 type parameter WPN is less than 1, then the sound hair corresponding to it
Penetrating event classification is shearing-type acoustie emission event, when the normalization of the acoustic emission wave described in the step 3 type parameter WPN is greater than 1, then
Acoustie emission event corresponding to it is classified as tension type acoustie emission event.
Further, when the normalization of the acoustic emission wave described in the step 3 type parameter WPN meets following relationship, then its
Corresponding acoustie emission event is classified as mixed type acoustie emission event:
0.8≤WPN≤1.2。
In addition, the sound emission type judgement method based on sound emission all band acquisition parameter is further comprising the steps of:
According to the classification results to acoustie emission event type, when acoustie emission event is shearing-type acoustie emission event, institute
Corresponding solid material will by failure by shear, when acoustie emission event is tension type acoustie emission event, corresponding to solid
Material will be destroyed by tension.
It is complete according to sound emission by setting up simple sound emission all band type parameter using technical solution of the present invention
The size of Band Class parameter quantized values divides corresponding sound emission type, simplifies sound emission Type division side
Method is convenient for calculating and analyze, is easily mastered those of ordinary skill, and is easy the type according to acoustic emission wave to solid
The extent of damage of material and the trend of being damaged make correctly analysis and prediction.Sound emission type provided by the invention judgement side
Method, easy to operate, the required hardware device to come into operation is less, substantially increases testing efficiency, reduces testing cost.
Embodiment 1:
In the embodiment of the present invention 1, the RA=rise time/peak swing calculated using acoustic emission parameters is as horizontal seat
Mark, the average frequency AF=wave crest number PC/ duration as ordinate, then the stress shape of the scatter diagram of AE and damage field
State has good corresponding relationship, if as shown in figure 3, i.e. more close to the AE point of RA coordinate, it is meant that and loading method is shearing,
If the AE point close to AF is more, mean that loading method is tension.The present invention converts this qualitative mode to quantitative
Index, be named as full section parameter sound emission type WP, enable WP=AF/RA, whether be greater than 1 according to WP value, it is available each
The type of a sound emission, in conjunction with Fig. 3 it is found that sound emission is tension type if WP is greater than 1, WP < 1 item sound emission is shearing-type.Have
When in order to analyze needs, the range of a mixed type sound emission, the i.e. sound emission by WP value within the scope of 0.8-1.2 can be divided
It is divided into mixed type sound emission.In order to keep the judgement of AE type more clear, place can be normalized respectively to AF and RA
Reason.Normalized step is that the maximum value AFmax and RAmax of AF or RA are found out in all data monitored, will be every
The AF value or RA value of a point are divided by maximum value, then calculate WP, i.e. WPN=(AF/AFmax)/(RA/RAmax).It monitors at the scene
When, need to obtain the maximum AF and RA of same type of material by uniaxial compression acoustic emission experiment in laboratory in advance, then again dynamically
It is normalized when monitoring, if can also directly be calculated without experimental data.
The use of sound emission all band type parameter is divided into two kinds of situations:
(1) when having experiment to obtain AF and RA maximum value to monitoring object, when monitoring, which is normalized, is calculated WPN, right
Sound emission type carries out dynamic discriminant；
(2) without experimental data when, when can monitor at the scene, WP is directly used, it, can be to being supervised after monitoring a period of time
The data measured are normalized, and obtain WPN, then judge the state of monitoring object.
Acoustie emission event classification method provided by the invention based on sound emission all band acquisition parameter has the following characteristics that
(1) only need the acoustic emission monitor(ing) data in a channel that can be judged；
(2) when there are multiple channels, the waveform comparison in each channel is first carried out, frequency spectrum consistent channel similar with waveform
Data carry out the judgement of sound emission type, as a result more reliable.
Fig. 4 shows the Evolution States of sound emission at any time in Rock Failure under Uniaxial Compression, in Fig. 4: when axis of abscissas is
Between, left axis of ordinates is the stress axis being normalized with uniaxial compressive strength, and right reference axis is frequency.Sound emission type by
Color indicates that sound emission of the WP value less than 0.8 is shearing-type sound emission, is shown as Dark grey in Fig. 4；WP value is greater than 1.2 sound
It is emitted as tension type sound emission, is shown as black in Fig. 4；Sound emission of the WP value between 0.8-1.2 is mixed type sound emission,
It is shown as light grey in Fig. 4.
Fig. 5 shows the Evolution States of tension sound emission percentage at any time in Rock Failure under Uniaxial Compression, horizontal in Fig. 5
Reference axis is the time, and left axis of ordinates is the stress axis being normalized with uniaxial compressive strength, and right reference axis is tension type
Sound emission T-AE's will be divided into 100 sections total time in the percentage fixed in the period, figure, count tension sound in each period
The shared percentage of transmitting.
1) dynamic monitoring is carried out to loaded object, passes through the percentage and quantity of the sound emission of tension type and shearing-type sound emission
State judges its load locating stage and future developing trend, i.e., now how far away from damage inactivation, future is to stablize also
It is that will continue to develop until destroying.Applicant carried out the acoustic emission monitor(ing) research under siltstone uniaxial compression, inhomogeneities
The Evolution States of type sound emission at any time are as shown in Figure 4, Figure 5.As it can be seen that load initial stage sound emission negligible amounts, sound emission type
Based on tension type, about 80% is accounted for, but more discrete, it is OA sections as shown；Certain period, with continuing to increase for load, shearing-type
Type gradually increases, and the ratio of tension type slowly declines, and mainly in the AB section of diagram and BC sections, wherein more concentrates for AB sections, BC
Section is more discrete；Rock is destroyed at DE sections namely stress dropping section, and DE sections occur under the percentage of preceding tension type sound emission
Drop to a minimum value, about 50%, show CD sections；The percentage of tension type sound emission is restored to one higher again later
Level, show as concentrate increase trend.The percentage of above-mentioned each stage tension type sound emission, the rock with literature research
Each stage damage development mechanism of uniaxial compression is consistent, and is also consistent with the rock load phase of other sound emission type classifications.As it can be seen that
The sound emission type that WP value is distinguished, can be used for the judgement of loaded object load phase and future developing trend.
2) dynamic monitoring is carried out to loaded object, the quantity according to sound emission tension type sound emission and shearing-type sound emission is more
Less and ratio, judge that type the crackle for generating sound emission is, and then the stress of discriminatory analysis damage zone, to loaded right
It is assessed as carrying out damage or fatigue state.As shown in fig. 6, the sound emission monitored is drawn on rock sample sky by different type
Between, the shearing-type sound emission quantity of peak segment increases, and middle part concentrates quantity more, it more tails off to both ends quantity and more dispersed,
The distribution space for foring cone cell shows that rock integrally enters shear state, there is the risk that will be destroyed.Fig. 7 is then shown
The position of subsequent burst after showing rock main fracture, it is clear that rupture is biased to rock sample with side, and based on destroying and mainly being destroyed with tension.
Claims (5)
1. a kind of acoustie emission event classification method based on sound emission all band acquisition parameter, it is characterised in that: including following step
It is rapid:
Step 1: within different acquisition times, by sound emission acquisition system from the more of same inside of solid material
A acoustic emission wave, the acoustic emission wave are made of several continuous wave crests, and each wave crest has corresponding peak amplitude, choosing
Take the peak amplitude value that wherein frequency of occurrence is most as threshold value, defining the maximum value in all peak amplitudes is peak swing
Amax defines in acoustic emission wave and occurs at the time of first peak amplitude is greater than threshold value being initial time, defines acoustic emission wave
In occur at the time of peak amplitude reaches peak swing Amax for peak value moment, the difference definition of the peak value moment and initial time
For rise time RT；In acoustic emission wave, when continuously there are multiple peak amplitudes greater than threshold value, then first wave crest is gone out
Now until the last one wave crest when occurring timing definition experienced be duration T；It is defined within the duration T and occurs
Wave crest quantity be wave crest number PC；Defining the ratio between wave crest number PC and duration T is average frequency AF, i.e.,
Defining the ratio between rise time RT and peak swing Amax is antigradient RA, it may be assumed that
Step 2: sound emission all band type parameter WP corresponding to each acoustic emission wave is calculated by following relationship:
Step 3: the calculated result of sound emission all band type parameter WP according to step 2, as WP < 1, by the wave
Corresponding acoustie emission event is classified as shearing-type acoustie emission event, and as WP > 1, the corresponding acoustie emission event of the wave is classified as
Tension type acoustie emission event.
2. the acoustie emission event classification method as described in claim 1 based on sound emission all band acquisition parameter, it is characterised in that:
The acoustie emission event classification method based on sound emission all band acquisition parameter is further comprising the steps of:
Step 1: calculating separately average frequency AF and antigradient RA corresponding to each acoustic emission wave；
Step 2: all average frequency AF being compared, obtaining maximum value therein is IFmax, all antigradient RA are carried out
Compare, obtaining maximum value therein is RAmax；
Step 3: acoustic emission wave is calculated by following relationship and normalizes type parameter WPN:
Step 4: when the normalization of the acoustic emission wave described in the step 3 type parameter WPN is less than 1, then the sound emission thing corresponding to it
Part is classified as shearing-type acoustie emission event, when the normalization of the acoustic emission wave described in the step 3 type parameter WPN is greater than 1, then its institute
Corresponding acoustie emission event is classified as tension type acoustie emission event.
3. the acoustie emission event classification method as claimed in claim 3 based on sound emission all band acquisition parameter, it is characterised in that:
The acoustie emission event classification method based on sound emission all band acquisition parameter is further comprising the steps of:
When the normalization of the acoustic emission wave described in the step 3 type parameter WPN meets following relationship, then the sound emission corresponding to it
Event classification is mixed type acoustie emission event:
0.8≤WPN≤1.2。
4. the acoustie emission event classification method based on sound emission all band acquisition parameter as described in any one of claims 1 to 3,
Be characterized in that: the acoustie emission event classification method based on sound emission all band acquisition parameter is further comprising the steps of:
According to the classification results to acoustie emission event type, when acoustie emission event is shearing-type acoustie emission event, corresponding to
Solid material will by failure by shear, when acoustie emission event is tension type acoustie emission event, corresponding to solid material
It will be destroyed by tension.
5. the acoustie emission event classification method based on sound emission all band acquisition parameter as described in any one of claims 1 to 3,
Be characterized in that: the solid material is rock.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910498019.6A CN110222650A (en) | 2019-06-10 | 2019-06-10 | A kind of acoustie emission event classification method based on sound emission all band acquisition parameter |
| PCT/CN2019/114711 WO2020248477A1 (en) | 2019-06-10 | 2019-10-31 | Acoustic emission event classification method based on acoustic emission full-waveband collection parameter |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910498019.6A CN110222650A (en) | 2019-06-10 | 2019-06-10 | A kind of acoustie emission event classification method based on sound emission all band acquisition parameter |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110222650A true CN110222650A (en) | 2019-09-10 |
Family
ID=67816018
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910498019.6A Pending CN110222650A (en) | 2019-06-10 | 2019-06-10 | A kind of acoustie emission event classification method based on sound emission all band acquisition parameter |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN110222650A (en) |
| WO (1) | WO2020248477A1 (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110703313A (en) * | 2019-10-11 | 2020-01-17 | 重庆大学 | Acoustic emission event magnitude acquisition method and system considering sensor sensitivity and readable storage medium |
| CN111390306A (en) * | 2020-04-22 | 2020-07-10 | 东北林业大学 | Electric spark machining state detection method based on acoustic emission technology |
| CN111812211A (en) * | 2020-07-09 | 2020-10-23 | 武汉理工大学 | RA-AF-E rock material brittle fracture crack classification method based on acoustic emission parameters |
| CN111999171A (en) * | 2020-08-21 | 2020-11-27 | 青岛理工大学 | Hard rock joint surface sudden dislocation and instability early warning method based on acoustic emission monitoring |
| WO2020248476A1 (en) * | 2019-06-10 | 2020-12-17 | 华北水利水电大学 | Acoustic emission type determination method based on acoustic emission ascending wave band collection parameter |
| WO2020248477A1 (en) * | 2019-06-10 | 2020-12-17 | 华北水利水电大学 | Acoustic emission event classification method based on acoustic emission full-waveband collection parameter |
| CN112964787A (en) * | 2020-12-18 | 2021-06-15 | 中国计量大学 | Brittle material crack type detection method based on acoustic emission |
| CN113063857A (en) * | 2020-06-15 | 2021-07-02 | 中国科学院武汉岩土力学研究所 | Acoustic emission identification method for rock structural surface tension-shear failure in direct shear test |
| CN113358469A (en) * | 2021-06-03 | 2021-09-07 | 中国矿业大学 | Acoustic emission signal visualization processing method for rock fracture classification |
| CN114526451A (en) * | 2022-02-21 | 2022-05-24 | 南京邮电大学 | Underground space rock mass pipeline water seepage acoustic emission fluctuation level identification method and device |
Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS5548558A (en) * | 1978-09-29 | 1980-04-07 | Kazuaki Iwata | Damage detector for cutting tool |
| JPH07318457A (en) * | 1994-05-27 | 1995-12-08 | Koyo Seiko Co Ltd | Abnormality diagnostic device for bearing |
| US20050244016A1 (en) * | 1997-03-17 | 2005-11-03 | American Technology Corporation | Parametric loudspeaker with electro-acoustical diaphragm transducer |
| CN102012403A (en) * | 2010-11-01 | 2011-04-13 | 北京市市政工程研究院 | Judging method of incompactness defect in node of concrete structure by detection by ultrasonic method |
| CN102539539A (en) * | 2012-01-19 | 2012-07-04 | 清华大学 | Cavitation erosion detection method based on acoustic emission |
| EP2486397A1 (en) * | 2009-10-09 | 2012-08-15 | Sumitomo Chemical Co., Ltd | Method for inspecting corrosion under insulation |
| CN103063563A (en) * | 2013-01-10 | 2013-04-24 | 湘潭大学 | Testing device for simulation and real-time detection of high-temperature deposition corrosion of thermal barrier coatings |
| JP2013170942A (en) * | 2012-02-21 | 2013-09-02 | Ntt Facilities Inc | Abnormality detection system |
| CN103439413A (en) * | 2013-08-07 | 2013-12-11 | 湘潭大学 | Acoustic emission signal analytical method for automatically identifying damage mode of thermal barrier coating |
| CN203432784U (en) * | 2012-01-31 | 2014-02-12 | 西门子公司 | State monitoring device used for rotary mechanical components |
| US20140260634A1 (en) * | 2013-03-15 | 2014-09-18 | Digital Wind Systems | Method and apparatus for monitoring wind turbine blades during operation |
| CN104133000A (en) * | 2014-07-02 | 2014-11-05 | 上海大学 | Material corrosion detection apparatus and method |
| CN105334269A (en) * | 2015-10-19 | 2016-02-17 | 江苏大学 | Pipeline defect type determination method based on neural network and guided wave characteristic database |
| CN106248801A (en) * | 2016-09-06 | 2016-12-21 | 哈尔滨工业大学 | A kind of Rail crack detection method based on many acoustie emission events probability |
| CN106338763A (en) * | 2016-08-23 | 2017-01-18 | 中国石油天然气股份有限公司 | Method and device for determining seismic section display data |
| CN107144876A (en) * | 2017-05-24 | 2017-09-08 | 辽宁大学 | It is a kind of based on when window coal mine rock burst earthquake magnitude and seismic source location method |
| CN108386323A (en) * | 2018-02-02 | 2018-08-10 | 长沙理工大学 | A kind of fan blade damage sync detection device and method |
| CN108907895A (en) * | 2018-04-13 | 2018-11-30 | 上海交通大学 | A kind of milling cutter breakage on-line monitoring method |
| CN108982247A (en) * | 2018-07-03 | 2018-12-11 | 华北水利水电大学 | Test deep rock mass II type fracture toughness value test macro and method |
| CN109506615A (en) * | 2018-10-31 | 2019-03-22 | 同济大学 | Long term monitoring methods based on beam bridge cross section neutral axis safety index |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102008063948B4 (en) * | 2008-12-19 | 2011-02-24 | Tsg Technische Service Gesellschaft Mbh | Improved sound emission test on LPG tanks |
| CN107886071A (en) * | 2017-11-10 | 2018-04-06 | 南京航空航天大学 | A kind of processing method of fibre reinforced composites damage acoustic emission signal |
| CN109507299A (en) * | 2018-10-15 | 2019-03-22 | 浙江大学 | High pressure compound material hydrogen cylinder Processing Method of Acoustic Emission Signals |
| CN110222650A (en) * | 2019-06-10 | 2019-09-10 | 华北水利水电大学 | A kind of acoustie emission event classification method based on sound emission all band acquisition parameter |
-
2019
- 2019-06-10 CN CN201910498019.6A patent/CN110222650A/en active Pending
- 2019-10-31 WO PCT/CN2019/114711 patent/WO2020248477A1/en active Application Filing
Patent Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS5548558A (en) * | 1978-09-29 | 1980-04-07 | Kazuaki Iwata | Damage detector for cutting tool |
| JPH07318457A (en) * | 1994-05-27 | 1995-12-08 | Koyo Seiko Co Ltd | Abnormality diagnostic device for bearing |
| US20050244016A1 (en) * | 1997-03-17 | 2005-11-03 | American Technology Corporation | Parametric loudspeaker with electro-acoustical diaphragm transducer |
| EP2486397A1 (en) * | 2009-10-09 | 2012-08-15 | Sumitomo Chemical Co., Ltd | Method for inspecting corrosion under insulation |
| CN102012403A (en) * | 2010-11-01 | 2011-04-13 | 北京市市政工程研究院 | Judging method of incompactness defect in node of concrete structure by detection by ultrasonic method |
| CN102539539A (en) * | 2012-01-19 | 2012-07-04 | 清华大学 | Cavitation erosion detection method based on acoustic emission |
| CN203432784U (en) * | 2012-01-31 | 2014-02-12 | 西门子公司 | State monitoring device used for rotary mechanical components |
| JP2013170942A (en) * | 2012-02-21 | 2013-09-02 | Ntt Facilities Inc | Abnormality detection system |
| CN103063563A (en) * | 2013-01-10 | 2013-04-24 | 湘潭大学 | Testing device for simulation and real-time detection of high-temperature deposition corrosion of thermal barrier coatings |
| US20140260634A1 (en) * | 2013-03-15 | 2014-09-18 | Digital Wind Systems | Method and apparatus for monitoring wind turbine blades during operation |
| CN103439413A (en) * | 2013-08-07 | 2013-12-11 | 湘潭大学 | Acoustic emission signal analytical method for automatically identifying damage mode of thermal barrier coating |
| CN104133000A (en) * | 2014-07-02 | 2014-11-05 | 上海大学 | Material corrosion detection apparatus and method |
| CN105334269A (en) * | 2015-10-19 | 2016-02-17 | 江苏大学 | Pipeline defect type determination method based on neural network and guided wave characteristic database |
| CN106338763A (en) * | 2016-08-23 | 2017-01-18 | 中国石油天然气股份有限公司 | Method and device for determining seismic section display data |
| CN106248801A (en) * | 2016-09-06 | 2016-12-21 | 哈尔滨工业大学 | A kind of Rail crack detection method based on many acoustie emission events probability |
| CN107144876A (en) * | 2017-05-24 | 2017-09-08 | 辽宁大学 | It is a kind of based on when window coal mine rock burst earthquake magnitude and seismic source location method |
| CN108386323A (en) * | 2018-02-02 | 2018-08-10 | 长沙理工大学 | A kind of fan blade damage sync detection device and method |
| CN108907895A (en) * | 2018-04-13 | 2018-11-30 | 上海交通大学 | A kind of milling cutter breakage on-line monitoring method |
| CN108982247A (en) * | 2018-07-03 | 2018-12-11 | 华北水利水电大学 | Test deep rock mass II type fracture toughness value test macro and method |
| CN109506615A (en) * | 2018-10-31 | 2019-03-22 | 同济大学 | Long term monitoring methods based on beam bridge cross section neutral axis safety index |
Non-Patent Citations (7)
| Title |
|---|
| KAROLINA HOZAK: "Investigation of the mechanical behavior of MAX phases by acoustic emission technique", 《MATERIALS SCIENCE & ENGINEERING》 * |
| KENTARO OHNO: ""Crack classification in concrete based on acoustic emission"", 《CONSTRUCTION&BUILDING MATERIALS》 * |
| ZABIHALLAH MORADIAN: "Detection of Cracking Levels in Brittle Rocks by Parametric", 《ROCK MECH ROCK ENG》 * |
| 何满潮: ""不同卸载速率下岩爆破坏特征试验分析"", 《岩土力学》 * |
| 罗明迪: "岩石蠕变破坏声发射特性研究现状与展望", 《山西建筑》 * |
| 赵菲: "煤岩体岩爆模拟试验中声发射时频演化规律分析", 《华北水利水电大学学报（自然科学版）》 * |
| 邓旭彪: "基于声发射时空演化的岩石全应力-应变曲线阶段特征分析", 《岩石力学与工程学报》 * |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020248476A1 (en) * | 2019-06-10 | 2020-12-17 | 华北水利水电大学 | Acoustic emission type determination method based on acoustic emission ascending wave band collection parameter |
| WO2020248477A1 (en) * | 2019-06-10 | 2020-12-17 | 华北水利水电大学 | Acoustic emission event classification method based on acoustic emission full-waveband collection parameter |
| CN110703313A (en) * | 2019-10-11 | 2020-01-17 | 重庆大学 | Acoustic emission event magnitude acquisition method and system considering sensor sensitivity and readable storage medium |
| CN111390306A (en) * | 2020-04-22 | 2020-07-10 | 东北林业大学 | Electric spark machining state detection method based on acoustic emission technology |
| CN113063857A (en) * | 2020-06-15 | 2021-07-02 | 中国科学院武汉岩土力学研究所 | Acoustic emission identification method for rock structural surface tension-shear failure in direct shear test |
| CN113063857B (en) * | 2020-06-15 | 2022-02-18 | 中国科学院武汉岩土力学研究所 | Acoustic emission identification method for rock structural surface tension-shear failure in direct shear test |
| CN111812211A (en) * | 2020-07-09 | 2020-10-23 | 武汉理工大学 | RA-AF-E rock material brittle fracture crack classification method based on acoustic emission parameters |
| CN111999171A (en) * | 2020-08-21 | 2020-11-27 | 青岛理工大学 | Hard rock joint surface sudden dislocation and instability early warning method based on acoustic emission monitoring |
| CN112964787A (en) * | 2020-12-18 | 2021-06-15 | 中国计量大学 | Brittle material crack type detection method based on acoustic emission |
| CN112964787B (en) * | 2020-12-18 | 2022-07-12 | 中国计量大学 | Brittle material crack type detection method based on acoustic emission |
| CN113358469A (en) * | 2021-06-03 | 2021-09-07 | 中国矿业大学 | Acoustic emission signal visualization processing method for rock fracture classification |
| CN113358469B (en) * | 2021-06-03 | 2022-07-05 | 中国矿业大学 | Acoustic emission signal visualization processing method for rock fracture classification |
| CN114526451A (en) * | 2022-02-21 | 2022-05-24 | 南京邮电大学 | Underground space rock mass pipeline water seepage acoustic emission fluctuation level identification method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2020248477A1 (en) | 2020-12-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110222650A (en) | A kind of acoustie emission event classification method based on sound emission all band acquisition parameter | |
| CN110297040A (en) | A kind of sound emission type judgement method rising wave band acquisition parameter based on sound emission | |
| Dong et al. | Discriminant models of blasts and seismic events in mine seismology | |
| Behnia et al. | Advanced structural health monitoring of concrete structures with the aid of acoustic emission | |
| Shateri et al. | On acoustic emission for damage detection and failure prediction in fiber reinforced polymer rods using pattern recognition analysis | |
| Jayaram et al. | Statistical tests of the joint distribution of spectral acceleration values | |
| Ju et al. | Machine‐learning‐based methods for crack classification using acoustic emission technique | |
| Ramasso et al. | Unsupervised consensus clustering of acoustic emission time-series for robust damage sequence estimation in composites | |
| Mao et al. | Monitoring of single-particle fragmentation process under static loading using acoustic emission | |
| JP2012505406A5 (en) | ||
| Barsoum et al. | ACOUSTIC EMISSION MONITORING AND FATIGUE LIFE PREDICTION IN AXIALLY LOADED NOTCHED STEEL SPECIMENS. | |
| US7552035B2 (en) | Method to use a receiver operator characteristics curve for model comparison in machine condition monitoring | |
| KR20200080223A (en) | Acoustic testing of batteries on portable devices | |
| Coste et al. | Sound propagation in a constrained lattice of beads: High-frequency behavior and dispersion relation | |
| Coelho et al. | Data-driven machine learning for pattern recognition and detection of loosening torque in bolted joints | |
| Miguel et al. | Probabilistic machine learning for detection of tightening torque in bolted joints | |
| Tasar et al. | Is Turkey in a middle income trap | |
| Lei et al. | Temporal‐frequency distribution and multi‐fractal characterization of acoustic emission of rock materials containing two parallel pre‐existing flaws | |
| Talwar et al. | A signal detection analysis of auditory-frequency discrimination in the rat | |
| Bell | When is my grid cognitively complex and when is it simple? Some approaches to deciding | |
| Pollock | A pod model for acoustic emission—Discussion and status | |
| Ignatieva et al. | Point process models for quasi-periodic volcanic earthquakes | |
| Bliese et al. | Discontinuous growth modeling of adaptation to sleep setting changes: Individual differences and age | |
| Agletdinov et al. | Application of recurrence quantification analysis of acoustic emission time series to analysis of a plastic flow of metals | |
| Hulbert et al. | Estimating the physical state of a laboratory slow slipping fault from seismic signals |
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: 20190910 |