CN114813964B - Method for deciding cracking damage of brittle material structural member by adopting time domain information - Google Patents
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- 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
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Abstract
The application discloses a method for deciding cracking damage of a brittle material structural member by adopting time domain information, which comprises the following steps: and carrying out data cleaning, data sharpening, feature extraction and fusion decision making on the time domain information in sequence, wherein the time domain information comprises multichannel real-time temperature and strain data and acoustic emission data. By adopting the method, a set of explosive piece cracking damage fusion judging engineering software and an explosive piece force thermal response intelligent monitor prototype are developed, and are successfully applied to intelligent judgment of damage in an intensity test of explosive materials.
Description
Technical Field
The application relates to the technical field of brittle material damage judgment, in particular to a method for deciding cracking damage of a brittle material structural member by adopting time domain information.
Background
The ceramic matrix composite, alloy material and other low-viscosity elastic brittle materials are widely applied to the fields of aerospace, wind power generation, development of high-end equipment such as high-speed railways and other civil buildings such as bridge venues, and structural safety accidents not only bring about huge economic damage, but also bring about 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 the result of long-term evolution under long-term composite multi-factor load, the structure is gradually damaged until the damage is accompanied with a large amount of acoustic emission characteristic information and strain characteristic information from the structural mechanics angle research, and the structural failure decision is hopefully realized by monitoring the strain information and acoustic emission information of the key weak part of the structure in real time. The existing structural damage decision method mostly obtains stress strain characteristics of weak parts through a constitutive model of a structure, and then evaluates damage of a structural member through numerical simulation and other methods. At present, no technology for carrying out crack damage judgment on structural members 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 problems, the application 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 through an information fusion processing method of the research state and the damage monitoring data, is not only used for researching the structural mechanical properties of the brittle material structural member, but also can provide a decision function for structural damage alarm.
In order to achieve the technical effects, the application provides the following technical scheme:
a method for deciding cracking damage of a brittle material structural member by adopting time domain information comprises the following steps: and carrying out data cleaning, data sharpening, feature extraction and fusion decision making on the time domain information in sequence, wherein the time domain information comprises multichannel 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 sampling moments according to the set acquisition frequency, wherein effective data exist in the sampling moments; the method comprises the following steps: firstly, carrying out redundant value rejection and deficiency value estimation of corresponding sampling points according to sampling intervals, and ensuring that each sampling time has only one effective data; and secondly, carrying out sensor disconnection detection processing on each channel, and if the sensor disconnection is judged, carrying out estimation processing on disconnection data to ensure that the accuracy of information characteristics is not influenced by the disconnection data.
The further technical scheme is that the data cleaning method specifically comprises the following steps: the acoustic emission data processing of the multiple channels is acoustic emission information of the response of the characterization structure for eliminating noise information, and the acoustic emission information is specifically: and judging whether low-amplitude acoustic emission information exists all the time in the monitoring process, and if so, filtering the acoustic emission information according to an amplitude thresholding method.
The further technical scheme is that the data sharpening method specifically comprises the following steps: sharpening the cleaned data by adopting a backward differential method for the temperature and strain data of multiple channels to obtain multiple parameter temperaturesThe processing method of the change information of the degree strain comprises the following steps: the difference value between the current time data and the previous time data of the corresponding channel is adopted 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, the principle that at least two acoustic emission sensors simultaneously receive primary stress waves in a certain time interval is adopted for the cleaned data, so that acoustic emission impact at the moment is filtered and sharpened, and the processing method is as follows: firstly, all acoustic emission impacts of all channels from a last time interval to the time interval are obtained, the time difference of the same stress wave signal at the receiving moments of different acoustic emission sensors is calculated according to the stress wave speed and the interval between the acoustic emission sensors, and acoustic emission sharpening information is obtained by filtering according to the requirement that at least 2 channels receive the acoustic emission impacts in a time range.
The further technical scheme is that the feature extraction method specifically comprises the following steps: and (3) for the temperature and strain data of the multiple channels, a thresholding treatment method is adopted for the sharpened data to obtain temperature cracking characteristics and damage cracking characteristics at corresponding moments, and the treatment method comprises the following steps: digging historical data, and counting to obtain the cracking damage threshold value of each measuring point of each parameter in the structure; and processing the sharpening value exceeding the threshold value into a crack damage characteristic value, wherein the formula is as follows:
according to the further technical scheme, for acoustic emission data, the sharpened acoustic emission information is processed according to the number of the optimal acoustic emission filtering channels, so that acoustic emission characteristics are obtained.
The further technical scheme is that the fusion decision comprises the following steps:
1) The characteristic time vector of each channel of each parameter in a period of continuous time before the moment of the moment is established by using the priori knowledge of the crack initiation characteristic time dispersion degree 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) Combining the characteristic time vectors of various sensors to obtain characteristic time combined vectors of various sensors;
4) Clustering the combined characteristic time vectors according to a Kmeans clustering algorithm to obtain each characteristic cluster;
5) And (3) deciding to obtain the cracking damage in the feature cluster according to a logic AND method of absence of temperature features, presence of strain features and presence of acoustic emission features.
Compared with the prior art, the application has the following beneficial effects: by adopting the method, a set of explosive piece cracking damage fusion judging engineering software and an explosive piece force thermal response intelligent monitor prototype have been developed successfully, the intelligent judgment of damage in the strength test of explosive materials is realized successfully, and good application benefits are obtained. The explosive piece belongs to a brittle low-viscosity material, and all materials with brittle or low-viscosity mechanical properties are expected to realize damage judgment by adopting the method.
Drawings
FIG. 1 is a flow chart for deciding cracking damage of a brittle material by time domain information;
FIG. 2 is a schematic diagram of backward differential sharpening of continuous force thermal response information; a step of
FIG. 3 is a schematic diagram of acoustic emission information sharpening;
FIG. 4 is a continuous quantity information feature extraction schematic;
FIG. 5 is an acoustic emission feature extraction schematic;
FIG. 6 is a schematic diagram of a multi-parameter feature initiation damage fusion decision.
Detailed Description
The application is further illustrated and described below with reference to the drawings and specific embodiments.
Example 1
FIG. 1 is a block diagram of a total flow of a method for determining a crack initiation damage, the method comprising: monitoring acoustic emission information and strain information acquisition; the collected data are cleaned, nonlinear errors such as sensor damage and the like are removed, and high-quality data such as consistent format and the like are generated; preprocessing and sharpening the cleaning data to realize the characteristic highlighting for representing the cracking damage; discretizing the salient pair serialization characteristic to better accord with the physical process characteristic of cracking damage; clustering the obtained acoustic emission characteristics and strain characteristics according to the initiation time to obtain information synchronism characteristics, and dividing a plurality of initiation damage process signal clusters; and realizing fusion cracking judgment according to the strain mutation and high-amplitude acoustic emission which always accompany weak positions at the cracking damage moment.
Fig. 2 achieves strain data sharpening after cleaning, and achieves strain characteristic highlighting characterizing crack initiation damage.
FIG. 3 achieves acoustic emission sharpening after cleaning, and achieves improved confidence of characterization of crack initiation damage to acoustic emission features.
FIG. 4 is a diagram showing the extraction of the corresponding variable data characteristics after sharpening, and the characteristic crack initiation damage and the corresponding variable characteristics are obtained;
FIG. 5 is a graph of acoustic emission data feature extraction after sharpening to obtain acoustic emission features characterizing crack initiation damage;
fig. 6 achieves merging, clustering and fusion judgment of the damage feature pairs, and obtains a fusion judgment result.
The embodiment provides a method for deciding cracking damage of a brittle material structural member by adopting time domain information, which comprises the following steps: and carrying out data cleaning, data sharpening, feature extraction and fusion decision making on the time domain information in sequence, wherein the time domain information comprises multichannel 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 sampling moments according to the set acquisition frequency, wherein effective data exist in the sampling moments; the method comprises the following steps: firstly, carrying out redundant value rejection and deficiency value estimation of corresponding sampling points according to sampling intervals, and ensuring that each sampling time has only one effective data; and secondly, carrying out sensor disconnection detection processing on each channel, and if the sensor disconnection is judged, carrying out estimation processing on disconnection data to ensure that the accuracy of information characteristics is not influenced by the disconnection data. This step is the second step of the overall flow diagram of fig. 1.
Acoustic emission data processing is acoustic emission information of characteristic structural response of eliminating 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 so, filtering the acoustic emission information according to an amplitude thresholding method. This step is the acoustic emission data sharpening process of fig. 3.
The data sharpening method specifically comprises the following steps: sharpening the cleaned data by adopting a backward differential method for the temperature and strain data of multiple channels to obtain the change information of the multi-parameter temperature strain, wherein the processing method comprises the following steps: the difference value between the current time data and the previous time data of the corresponding channel is adopted 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, the principle that at least two acoustic emission sensors simultaneously receive primary stress waves in a certain time interval is adopted for the cleaned data, so that acoustic emission impact at the moment is filtered and sharpened, and the processing method is as follows: firstly, all acoustic emission impacts of all channels from a last time interval to the time interval are obtained, the time difference of the same stress wave signal at the receiving moments of different acoustic emission sensors is calculated according to the stress wave speed and the interval between the acoustic emission sensors, and acoustic emission sharpening information is obtained by filtering according to the requirement that at least 2 channels receive the acoustic emission impacts in a time range. This step is the acoustic emission initiation damage feature extraction of fig. 5.
The feature extraction method specifically comprises the following steps: and (3) for the temperature and strain data of the multiple channels, a thresholding treatment method is adopted for the sharpened data to obtain temperature cracking characteristics and damage cracking characteristics at corresponding moments, and the treatment method comprises the following steps: digging historical data, and counting to obtain the cracking damage threshold value of each measuring point of each parameter in the structure; to process the sharpening value exceeding the threshold value asThe characteristic value of the crack damage is as follows:this step is the strain-initiation damage feature extraction of fig. 4.
And processing the acoustic emission data according to the number of the optimal acoustic emission filtering channels for the sharpened acoustic emission information to obtain acoustic emission characteristics.
The fusion decision comprises the following steps:
1) The characteristic time vector of each channel of each parameter in a period of continuous time before the moment of the moment is established by using the priori knowledge of the crack initiation characteristic time dispersion degree 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) Combining the characteristic time vectors of various sensors to obtain characteristic time combined vectors of various sensors;
4) Clustering the combined characteristic time vectors according to a Kmeans clustering algorithm to obtain each characteristic cluster;
5) And (3) deciding to obtain the cracking damage in the feature cluster according to a logic AND method of absence of temperature features, presence of strain features and presence of acoustic emission features. This step is the feature-merged cluster and crack-initiation lesion-fusion decision of FIG. 6
Although the application has been described herein with reference to the above-described illustrative embodiments thereof, the foregoing embodiments are merely preferred embodiments of the present application, and it should be understood that the embodiments of the present application are not limited to the above-described embodiments, and that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the scope and spirit of the principles of this disclosure.
Claims (4)
1. The method for deciding the cracking damage of the brittle material structural member by adopting the time domain information is characterized by comprising the following steps: sequentially performing data cleaning, data sharpening, feature extraction and fusion decision making on time domain information, wherein the time domain information comprises multichannel 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 sampling moments according to the set acquisition frequency, wherein effective data exist in the sampling moments; the method comprises the following steps: firstly, carrying out redundant value rejection and deficiency value estimation of corresponding sampling points according to sampling intervals, and ensuring that each sampling time has only one effective data; secondly, carrying out sensor disconnection detection processing on each channel, and if the disconnection is judged, carrying out estimation processing on disconnection data to ensure that the disconnection data does not influence the accuracy of information characteristics;
the data sharpening method specifically comprises the following steps: sharpening the cleaned data by adopting a backward differential method for the temperature and strain data of multiple channels to obtain the change information of the multi-parameter temperature strain, wherein the processing method comprises the following steps: the difference value between the current time data and the previous time data of the corresponding channel is adopted as the sharpening data of the time, and the calculation formula is as follows:
the feature extraction method specifically comprises the following steps: and (3) for the temperature and strain data of the multiple channels, a thresholding treatment method is adopted for the sharpened data to obtain temperature cracking characteristics and damage cracking characteristics at corresponding moments, and the treatment method comprises the following steps: digging historical data, and counting to obtain the cracking damage threshold value of each measuring point of each parameter in the structure; and processing the sharpening value exceeding the threshold value into a crack damage characteristic value, wherein the formula is as follows:
the fusion decision comprises the following specific steps:
1) The characteristic time vector of each channel of each parameter in a period of continuous time before the moment of the moment is established by using the priori knowledge of the crack initiation characteristic time dispersion degree 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) Combining the characteristic time vectors of various sensors to obtain characteristic time combined vectors of various sensors;
4) Clustering the combined characteristic time vectors according to a Kmeans clustering algorithm to obtain each characteristic cluster;
5) And (3) deciding to obtain the cracking damage in the feature cluster according to a logic AND method of absence of temperature features, presence of strain features and presence of acoustic emission features.
2. The method for determining cracking damage of a brittle material structure by using time domain information according to claim 1, wherein the data cleaning method specifically comprises the following steps: the acoustic emission data processing of the multiple channels is acoustic emission information of the response of the characterization structure for eliminating noise information, and the acoustic emission information is specifically: and judging whether low-amplitude acoustic emission information exists all the time in the monitoring process, and if so, filtering the acoustic emission information according to an amplitude thresholding method.
3. The method for determining cracking damage of a brittle material structure by using time domain information according to claim 1, wherein the data sharpening method specifically comprises: for acoustic emission data, the principle that at least two acoustic emission sensors simultaneously receive primary stress waves in a certain time interval is adopted for the cleaned data, so that acoustic emission impact at the moment is filtered and sharpened, and the processing method is as follows: firstly, all acoustic emission impacts of all channels from a last time interval to the time interval are obtained, the time difference of the same stress wave signal at the receiving moments of different acoustic emission sensors is calculated according to the stress wave speed and the interval between the acoustic emission sensors, and acoustic emission sharpening information is obtained by filtering according to the requirement that at least 2 channels receive the acoustic emission impacts in a time range.
4. The method for deciding cracking damage of a brittle material structure by using time domain information according to claim 1, wherein for acoustic emission data, acoustic emission characteristics are obtained by processing the sharpened acoustic emission information according to the optimal number of acoustic emission filtering channels.
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