CN114813964A - Method for deciding crack initiation damage of brittle material structural part by adopting time domain information - Google Patents
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Abstract
The invention discloses a method for deciding crack initiation damage of a brittle material structural part by adopting time domain information, which comprises the following steps: and sequentially carrying out data cleaning, data sharpening, feature extraction and fusion judgment decision on time domain information, wherein the time domain information comprises multi-channel real-time temperature and strain data and acoustic emission data. By adopting the method, a set of explosive piece cracking damage fusion judgment engineering software and an explosive piece force thermal response intelligent monitor model machine are developed and successfully applied to intelligent judgment of damage in the strength test of the explosive material.
Description
Technical Field
The invention relates to the technical field of damage judgment of brittle materials, in particular to a method for deciding crack initiation damage of a brittle material structural member by adopting time domain information.
Background
Low-elasticity brittle materials such as ceramic matrix composite materials, alloy materials and the like are widely applied to the fields of high-end equipment development such as aerospace, wind power generation, high-speed railways and the like, and civil buildings such as bridge venues and the like, and structural safety accidents not only can bring huge economic damage, but also can bring very serious social influence, so that the technology for evaluating the structural integrity and the structural reliability is very important. The final damage of the structure is a long-term evolution result under long-term composite multi-factor load loading, the structure is gradually damaged until the damage is accompanied by a large amount of acoustic emission characteristic information and strain characteristic information from the research of structural mechanics, and the structure failure decision is hopefully realized by monitoring the strain information and the acoustic emission information of the key weak part of the structure in real time. Most of the existing structural damage decision methods obtain the stress-strain characteristics of weak parts through a constitutive model of a structure, and then evaluate the damage of the structural part through methods such as numerical simulation and the like. At present, no technology for judging structural member cracking damage by adopting a strain and acoustic emission multi-parameter structure response time domain information fusion decision is adopted.
Disclosure of Invention
In order to solve the technical problem, the invention provides a method for intelligently judging cracking damage of a material or a structure by adopting time domain information. The technology for intelligently deciding the cracking damage of the brittle material is obtained by researching an information fusion processing method of state and damage monitoring data, is not only used for researching the structural mechanical property of a brittle material structural member, but also can provide a decision-making function for structural damage alarm.
In order to achieve the technical effects, the invention provides the following technical scheme:
a method for determining cracking damage of a brittle material structural member by adopting time domain information comprises the following steps: and sequentially carrying out data cleaning, data sharpening, feature extraction and fusion judgment decision on time domain information, wherein the time domain information comprises multi-channel real-time temperature and strain data and acoustic emission data.
The further technical scheme is that the data cleaning method specifically comprises the following steps: processing the temperature and strain data of the multiple channels into corresponding effective data at sampling moments according to a set acquisition frequency; the method comprises the following specific steps: according to sampling intervals, firstly, redundant value elimination and default value estimation of corresponding sampling points are carried out, and each sampling time is ensured to have only one effective data; and secondly, performing sensor disconnection detection processing on each channel, and if the channel is judged to be disconnected, performing estimation processing on disconnection data to ensure that the disconnection data does not influence the accuracy of information characteristics.
The further technical scheme is that the data cleaning method specifically comprises the following steps: the method comprises the following steps of processing multi-channel acoustic emission data into acoustic emission information which is used for eliminating noise information and represents structural response, and specifically comprises the following steps: and judging whether low-amplitude acoustic emission information exists all the time in the monitoring process, and if the low-amplitude noise acoustic emission information exists, filtering the acoustic emission information according to an amplitude thresholding processing method.
The further technical scheme is that the data sharpening method specifically comprises the following steps: for multi-channel temperature and strain data, sharpening the cleaned data by adopting a backward difference method to obtain change information of multi-parameter temperature strain, wherein the processing method comprises the following steps: the difference value of the current time data and the previous time data of the corresponding channel is used as the sharpening data of the time, and the calculation formula is as follows:
the further technical scheme is that the data sharpening method specifically comprises the following steps: for acoustic emission data, filtering and sharpening acoustic emission impact at the moment is realized according to the principle that at least two acoustic emission sensors simultaneously receive primary stress waves in a certain time interval for the cleaned data, and the processing method comprises the following steps: firstly, obtaining all acoustic emission impacts of all channels from a previous time interval to the current time interval of the continuous quantity, calculating the time difference of the same stress wave signal at the receiving time of different acoustic emission sensors according to the wave speed of the stress wave and the distance between the acoustic emission sensors, and filtering according to the requirement that at least 2 channels receive the acoustic emission impacts in the time range to obtain acoustic emission sharpening information.
The further technical scheme is that the feature extraction method specifically comprises the following steps: for the multichannel temperature and strain data, obtaining the temperature crack initiation characteristics and the damage crack initiation characteristics at the corresponding moment by adopting a thresholding processing method for the sharpened data, wherein the processing method comprises the following steps: mining historical data, and counting to obtain crack initiation damage thresholds of various parameters at each measuring point of the structure; and processing the sharpening value exceeding the threshold value into a characteristic value of the crack initiation damage, wherein the formula is as follows:
the further technical scheme is that for acoustic emission data and sharpened acoustic emission information, acoustic emission characteristics are obtained by processing according to the number of optimal acoustic emission filtering channels.
The further technical scheme is that the fusion decision specifically comprises the following steps:
1) establishing a characteristic time vector of each channel of each parameter in a period of continuous time before the current moment by using the crack initiation characteristic time dispersion degree prior knowledge of each sensor of each measuring point of the structure;
2) combining the characteristic time vectors of the various sensors according to the prior knowledge of the time dispersion degree of the data of the various measuring points of the various sensors to obtain the characteristic time vectors of the various sensors;
3) merging the characteristic time vectors of the various sensors to obtain the characteristic time merged vectors of the various sensors;
4) clustering the combined feature time vectors according to a Kmeans clustering algorithm to obtain each feature cluster;
5) and determining to obtain the crack initiation damage in the characteristic cluster according to a logic AND method of the absence of temperature characteristics, the presence of strain characteristics and the presence of acoustic emission characteristics.
Compared with the prior art, the invention has the following beneficial effects: by adopting the method, a set of explosive member cracking damage fusion judgment engineering software and an explosive member force thermal response intelligent monitor prototype are successfully developed, the intelligent judgment of damage in the strength test of the explosive material is successfully realized, and better application benefit is obtained. The explosive piece belongs to a brittle low-viscosity material, and any material with the mechanical properties of brittleness or low viscosity is expected to realize damage judgment by adopting the method.
Drawings
FIG. 1 is a flow chart of time domain information for determining cracking damage of a brittle material;
FIG. 2 is a schematic diagram illustrating backward differential sharpening of continuous magnitude force thermal response information; a
FIG. 3 is a schematic diagram illustrating sharpening of acoustic emission information;
FIG. 4 is a schematic diagram of continuous quantity information feature extraction;
FIG. 5 is a schematic view of acoustic emission feature extraction;
FIG. 6 is a schematic diagram of a multi-parameter feature crack initiation damage fusion decision.
Detailed Description
The invention will be further explained and explained with reference to the drawings and the embodiments.
Example 1
Fig. 1 is a block diagram of a general flow of a crack initiation damage determination method, which includes: monitoring the collection of acoustic emission information and strain information from damage; cleaning the collected data, eliminating the influence caused by nonlinear errors such as sensor damage and the like, and generating high-quality data with consistent format and the like; preprocessing and sharpening the data by cleaning to realize characteristic highlighting representing cracking damage; discretizing the continuous features by the salient, so that the characteristics of the physical process of the crack initiation damage are better met; clustering the obtained acoustic emission characteristics and strain characteristics according to the information synchronism characteristics at the crack initiation time, and dividing a plurality of crack initiation damage process signal clusters; and fusion crack initiation judgment is realized according to the strain mutation and high-amplitude acoustic emission of the weak position always accompanied at the crack initiation damage moment.
FIG. 2 realizes the sharpening of the strain data after cleaning and realizes the highlighting of the characteristic cracking damage to the strain characteristics.
FIG. 3 realizes acoustic emission sharpening after cleaning, and realizes improvement of confidence of characterization of crack initiation damage on acoustic emission characteristics.
FIG. 4 is a diagram for extracting corresponding variable data features after sharpening is performed, and corresponding variable features representing cracking damage are obtained;
FIG. 5 is used for extracting acoustic emission data features after sharpening is carried out, and acoustic emission features representing cracking damage pairs are obtained;
fig. 6 realizes merging, clustering, and fusion determination of the damage feature pairs to obtain a fusion determination result.
The embodiment provides a method for determining cracking damage of a brittle material structural member by adopting time domain information, which comprises the following steps: and sequentially carrying out data cleaning, data sharpening, feature extraction and fusion judgment decision on time domain information, wherein the time domain information comprises multi-channel real-time temperature and strain data and acoustic emission data.
The data cleaning method specifically comprises the following steps: processing the temperature and strain data of the multiple channels into corresponding effective data at sampling moments according to a set acquisition frequency; the method specifically comprises the following steps: according to sampling intervals, firstly, redundant value elimination and default value estimation of corresponding sampling points are carried out, and each sampling time is ensured to have only one effective data; and secondly, performing sensor disconnection detection processing on each channel, and if the channel is judged to be disconnected, performing estimation processing on disconnection data to ensure that the disconnection data does not influence the accuracy of information characteristics. This step is the second step of the general flow diagram of fig. 1.
The acoustic emission data processing is the acoustic emission information of the characteristic structure response of eliminating the noise information, and specifically comprises the following steps: and judging whether low-amplitude acoustic emission information exists all the time in the monitoring process, and if the low-amplitude noise acoustic emission information exists, filtering the acoustic emission information according to an amplitude thresholding processing method. This step is the sharpening process of the acoustic emission data of fig. 3.
The data sharpening method specifically comprises the following steps: for multi-channel temperature and strain data, sharpening the cleaned data by adopting a backward difference method to obtain change information of multi-parameter temperature strain, wherein the processing method comprises the following steps: the difference value of the current time data and the previous time data of the corresponding channel is used as the sharpening data of the time, and the calculation formula is as follows:
this step is the strain data sharpening process of fig. 2.
For acoustic emission data, filtering and sharpening acoustic emission impact at the moment is realized according to the principle that at least two acoustic emission sensors simultaneously receive primary stress waves in a certain time interval for the cleaned data, and the processing method comprises the following steps: firstly, obtaining all acoustic emission impacts of all channels from a previous time interval to the current time interval of the continuous quantity, calculating the time difference of the same stress wave signal at the receiving time of different acoustic emission sensors according to the wave speed of the stress wave and the distance between the acoustic emission sensors, and filtering according to the requirement that at least 2 channels receive the acoustic emission impacts in the time range to obtain acoustic emission sharpening information. This step is the acoustic emission crack initiation damage feature extraction of fig. 5.
The feature extraction method specifically comprises the following steps: for the multichannel temperature and strain data, obtaining the temperature crack initiation characteristics and the damage crack initiation characteristics at the corresponding moment by adopting a thresholding processing method for the sharpened data, wherein the processing method comprises the following steps: mining the historical data, and counting to obtain crack initiation damage threshold values of various parameters at each measuring point of the structure; and processing the sharpening value exceeding the threshold value into a characteristic value of the crack initiation damage, wherein the formula is as follows:
this step is the strain-initiated crack damage feature extraction of fig. 4.
And for acoustic emission data, processing the sharpened acoustic emission information according to the optimal number of acoustic emission filtering channels to obtain acoustic emission characteristics.
The fusion decision specifically comprises the following steps:
1) establishing a characteristic time vector of each channel of each parameter in a period of continuous time before the current moment by using the crack initiation characteristic time dispersion degree prior knowledge of each sensor of each measuring point of the structure;
2) combining the characteristic time vectors of the various sensors according to the prior knowledge of the time dispersion degree of the data of the various measuring points of the various sensors to obtain the characteristic time vectors of the various sensors;
3) merging the characteristic time vectors of the various sensors to obtain the characteristic time merged vectors of the various sensors;
4) clustering the combined feature time vectors according to a Kmeans clustering algorithm to obtain each feature cluster;
5) and determining to obtain the crack initiation damage in the characteristic cluster according to a logic AND method of the absence of temperature characteristics, the presence of strain characteristics and the presence of acoustic emission characteristics. The step is the characteristic merging clustering and crack initiation damage fusion judgment of the figure 6
Although the present invention has been described herein with reference to the illustrated embodiments thereof, which are intended to be preferred embodiments of the present invention, it is to be understood that the invention is not limited thereto, and that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure.
Claims (8)
1. A method for determining cracking damage of a brittle material structural member by adopting time domain information is characterized by comprising the following steps: and sequentially carrying out data cleaning, data sharpening, feature extraction and fusion judgment decision on time domain information, wherein the time domain information comprises multi-channel real-time temperature and strain data and acoustic emission data.
2. The method for determining the crack initiation damage of the brittle material structural member by using the time domain information as claimed in claim 1, wherein the data cleaning method specifically comprises: processing the temperature and strain data of the multiple channels into corresponding effective data at sampling moments according to a set acquisition frequency; the method specifically comprises the following steps: according to sampling intervals, firstly, removing redundant values and estimating default values of corresponding sampling points, and ensuring that each sampling moment has only one effective data; and secondly, performing sensor disconnection detection processing on each channel, and if the channel is judged to be disconnected, performing estimation processing on disconnection data to ensure that the disconnection data does not influence the accuracy of information characteristics.
3. The method for determining the crack initiation damage of the brittle material structural member by using the time domain information as claimed in claim 1, wherein the data cleaning method specifically comprises: the method comprises the following steps of processing multi-channel acoustic emission data into acoustic emission information which is used for eliminating noise information and represents structural response, and specifically comprises the following steps: and judging whether low-amplitude acoustic emission information exists all the time in the monitoring process, and if the low-amplitude noise acoustic emission information exists, filtering the acoustic emission information according to an amplitude thresholding processing method.
4. The method for determining the crack initiation damage of the brittle material structural member by using the time domain information as claimed in claim 1, wherein the data sharpening method specifically comprises: for multi-channel temperature and strain data, sharpening the cleaned data by adopting a backward difference method to obtain change information of multi-parameter temperature strain, wherein the processing method comprises the following steps: the difference value of the current time data and the previous time data of the corresponding channel is used as the sharpening data of the time, and the calculation formula is as follows:
5. the method for determining the crack initiation damage of the brittle material structural member by using the time-domain information according to claim 1, wherein the data sharpening method specifically comprises the following steps: for acoustic emission data, filtering and sharpening acoustic emission impact at the moment is realized according to the principle that at least two acoustic emission sensors simultaneously receive primary stress waves in a certain time interval for the cleaned data, and the processing method comprises the following steps: firstly, obtaining all acoustic emission impacts of all channels from a previous time interval to the current time interval of the continuous quantity, calculating the time difference of the same stress wave signal at the receiving time of different acoustic emission sensors according to the wave speed of the stress wave and the distance between the acoustic emission sensors, and filtering according to the requirement that at least 2 channels receive the acoustic emission impacts in the time range to obtain acoustic emission sharpening information.
6. The method for deciding the crack initiation damage of the brittle material structural member by adopting the time domain information according to claim 1, wherein the characteristic extraction method specifically comprises the following steps: for the multichannel temperature and strain data, obtaining the temperature crack initiation characteristics and the damage crack initiation characteristics at the corresponding moment by adopting a thresholding processing method for the sharpened data, wherein the processing method comprises the following steps: mining historical data, and counting to obtain crack initiation damage thresholds of various parameters at each measuring point of the structure; and processing the sharpening value exceeding the threshold value into a characteristic value of the crack initiation damage, wherein the formula is as follows:
7. the method for deciding the crack initiation damage of the brittle material structural member by adopting the time domain information as claimed in claim 1, wherein for the acoustic emission data, the sharpened acoustic emission information is processed according to the optimal number of acoustic emission filtering channels to obtain the acoustic emission characteristics.
8. The method for deciding on the initiation of the crack damage of the brittle material structural member by using the time domain information as claimed in claim 1, wherein the fusion decision specifically comprises the following steps:
1) establishing a characteristic time vector of each channel of each parameter in a period of continuous time before the current moment by using the crack initiation characteristic time dispersion degree prior knowledge of each sensor of each measuring point of the structure;
2) combining the characteristic time vectors of the various sensors according to the prior knowledge of the time dispersion degree of the data of the various measuring points of the various sensors to obtain the characteristic time vectors of the various sensors;
3) merging the characteristic time vectors of the various sensors to obtain the characteristic time merged vectors of the various sensors;
4) clustering the combined characteristic time vectors according to a Kmeans clustering algorithm to obtain each characteristic cluster;
5) and determining to obtain the crack initiation damage in the characteristic cluster according to a logic AND method of the absence of temperature characteristics, the presence of strain characteristics and the presence of acoustic emission characteristics.
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Citations (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6213958B1 (en) * | 1996-08-29 | 2001-04-10 | Alan A. Winder | Method and apparatus for the acoustic emission monitoring detection, localization, and classification of metabolic bone disease |
| CN101793590A (en) * | 2010-02-10 | 2010-08-04 | 南京航空航天大学 | Structural impact damage diagnostic method based on blackboard cooperation |
| CN102680263A (en) * | 2012-05-17 | 2012-09-19 | 天津大学 | Combined parameter monitoring system |
| CN102890119A (en) * | 2012-09-29 | 2013-01-23 | 北京航空航天大学 | Characterization and quantitative evaluation system for fatigue damage state of in-service AZ31 magnesium alloy structural member based on WPT (Wavelet Packet Transform) strategy and DF (Dual-Frequency) strategy |
| WO2013158933A1 (en) * | 2012-04-18 | 2013-10-24 | Drexel University | Integration of digital image correlation with acoustic emissions |
| 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 |
| CN106370730A (en) * | 2016-08-25 | 2017-02-01 | 中国科学院武汉岩土力学研究所 | Method of precisely measuring damage threshold value of brittle materials on the basis of acoustic emission technology |
| US20170168024A1 (en) * | 2015-12-15 | 2017-06-15 | General Electric Company | Monitoring systems and methods for electrical machines |
| CN107870204A (en) * | 2016-09-22 | 2018-04-03 | 淮阴师范学院 | A kind of high composite structure damage monitoring method of accuracy |
| CN107905778A (en) * | 2017-10-19 | 2018-04-13 | 中国石油大学(华东) | Supercritical CO2The enhanced geothermal system experimental provision of fluid fracturing and method |
| CN109374682A (en) * | 2018-11-26 | 2019-02-22 | 中国工程物理研究院化工材料研究所 | A kind of monitoring device of fragile material crack initiation time |
| CN109540710A (en) * | 2018-12-06 | 2019-03-29 | 中国工程物理研究院化工材料研究所 | A kind of integrated many reference amounts multichannel strength of materials TT&C system |
| CN110118829A (en) * | 2019-06-27 | 2019-08-13 | 中国工程物理研究院化工材料研究所 | A kind of test macro and method of the amplitude-frequency characteristic of acoustic emission sensor |
| CN209400457U (en) * | 2018-11-26 | 2019-09-17 | 中国工程物理研究院化工材料研究所 | A kind of monitoring device of fragile material crack initiation time |
| CN110376289A (en) * | 2019-07-11 | 2019-10-25 | 南京航空航天大学 | A kind of composite fiber braiding layer damnification recognition method based on sound emission means |
| US20200110041A1 (en) * | 2018-10-08 | 2020-04-09 | University Of South Carolina | Integrated and Automated Video/Structural Health Monitoring System |
| FR3094480A1 (en) * | 2019-03-29 | 2020-10-02 | Frec'n'sys | Acoustic wave sensor and interrogation thereof |
| CN111829887A (en) * | 2019-04-22 | 2020-10-27 | 中国石油化工股份有限公司 | Rock fracturing simulation experiment method based on high-pressure mercury injection |
| CN112684012A (en) * | 2020-12-02 | 2021-04-20 | 青岛科技大学 | Equipment key force-bearing structural part fault diagnosis method based on multi-parameter information fusion |
| CN112986390A (en) * | 2020-06-15 | 2021-06-18 | 北京科技大学 | Rock full-stress-strain damage monitoring system and method based on sound wave dry coupling |
| CN113218766A (en) * | 2021-05-27 | 2021-08-06 | 江西理工大学 | Rock crack initiation stress and damage stress identification method based on moment tensor analysis |
-
2022
- 2022-04-24 CN CN202210455022.1A patent/CN114813964B/en active Active
Patent Citations (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6213958B1 (en) * | 1996-08-29 | 2001-04-10 | Alan A. Winder | Method and apparatus for the acoustic emission monitoring detection, localization, and classification of metabolic bone disease |
| CN101793590A (en) * | 2010-02-10 | 2010-08-04 | 南京航空航天大学 | Structural impact damage diagnostic method based on blackboard cooperation |
| WO2013158933A1 (en) * | 2012-04-18 | 2013-10-24 | Drexel University | Integration of digital image correlation with acoustic emissions |
| CN102680263A (en) * | 2012-05-17 | 2012-09-19 | 天津大学 | Combined parameter monitoring system |
| CN102890119A (en) * | 2012-09-29 | 2013-01-23 | 北京航空航天大学 | Characterization and quantitative evaluation system for fatigue damage state of in-service AZ31 magnesium alloy structural member based on WPT (Wavelet Packet Transform) strategy and DF (Dual-Frequency) strategy |
| 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 |
| US20170168024A1 (en) * | 2015-12-15 | 2017-06-15 | General Electric Company | Monitoring systems and methods for electrical machines |
| CN107024654A (en) * | 2015-12-15 | 2017-08-08 | 通用电气公司 | Monitoring system and method for motor |
| CN106370730A (en) * | 2016-08-25 | 2017-02-01 | 中国科学院武汉岩土力学研究所 | Method of precisely measuring damage threshold value of brittle materials on the basis of acoustic emission technology |
| CN107870204A (en) * | 2016-09-22 | 2018-04-03 | 淮阴师范学院 | A kind of high composite structure damage monitoring method of accuracy |
| CN107905778A (en) * | 2017-10-19 | 2018-04-13 | 中国石油大学(华东) | Supercritical CO2The enhanced geothermal system experimental provision of fluid fracturing and method |
| US20200110041A1 (en) * | 2018-10-08 | 2020-04-09 | University Of South Carolina | Integrated and Automated Video/Structural Health Monitoring System |
| CN109374682A (en) * | 2018-11-26 | 2019-02-22 | 中国工程物理研究院化工材料研究所 | A kind of monitoring device of fragile material crack initiation time |
| CN209400457U (en) * | 2018-11-26 | 2019-09-17 | 中国工程物理研究院化工材料研究所 | A kind of monitoring device of fragile material crack initiation time |
| CN109540710A (en) * | 2018-12-06 | 2019-03-29 | 中国工程物理研究院化工材料研究所 | A kind of integrated many reference amounts multichannel strength of materials TT&C system |
| FR3094480A1 (en) * | 2019-03-29 | 2020-10-02 | Frec'n'sys | Acoustic wave sensor and interrogation thereof |
| CN111829887A (en) * | 2019-04-22 | 2020-10-27 | 中国石油化工股份有限公司 | Rock fracturing simulation experiment method based on high-pressure mercury injection |
| CN110118829A (en) * | 2019-06-27 | 2019-08-13 | 中国工程物理研究院化工材料研究所 | A kind of test macro and method of the amplitude-frequency characteristic of acoustic emission sensor |
| CN110376289A (en) * | 2019-07-11 | 2019-10-25 | 南京航空航天大学 | A kind of composite fiber braiding layer damnification recognition method based on sound emission means |
| CN112986390A (en) * | 2020-06-15 | 2021-06-18 | 北京科技大学 | Rock full-stress-strain damage monitoring system and method based on sound wave dry coupling |
| CN112684012A (en) * | 2020-12-02 | 2021-04-20 | 青岛科技大学 | Equipment key force-bearing structural part fault diagnosis method based on multi-parameter information fusion |
| CN113218766A (en) * | 2021-05-27 | 2021-08-06 | 江西理工大学 | Rock crack initiation stress and damage stress identification method based on moment tensor analysis |
Non-Patent Citations (6)
| Title |
|---|
| DAI XIAOGAN: "Impact response characteristics of a cyclotetramethylene tetranitramine based polymer-bonded explosives under different temperatures", no. 114, pages 114906 - 1 * |
| SAID JAZOULI: "Application of time‑stress equivalence to nonlinear creep of polycarbonare", no. 24, pages 463 - 467 * |
| 司刚强;李伟;徐海丰;许志军;: "基于DIC和声发射的TC4合金疲劳损伤状态监测方法", no. 03, pages 131 - 138 * |
| 梁晓辉等: "温等静压机卸压***的建模与仿真", vol. 42, no. 21, pages 152 - 155 * |
| 温茂萍等: "基于光纤光栅的PBX 声发射监测方法", vol. 28, no. 5, pages 449 - 453 * |
| 薛云亮;李庶林;林峰;徐宏斌;: "类岩石材料声发射参数与应力和应变耦合本构关系", vol. 33, no. 06, pages 664 - 667 * |
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