CN111999391A - Acoustic emission monitoring device and fatigue damage analysis method for material structure by using same - Google Patents
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- G—PHYSICS
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Abstract
The invention relates to the technical field of nondestructive testing, in particular to an acoustic emission monitoring device which comprises an acoustic emission sensor, a switch controller, a preamplifier, an AD converter, a D matrix generation program, a probability space decomposer and a damage state track generator. The invention is applicable to the assessment of the random damage response of mechanical, civil and other similar load bearing structures to solid structures under alternating stress.
Description
Technical Field
The invention relates to the technical field of nondestructive testing, in particular to an acoustic emission monitoring device and a fatigue damage analysis method for a material structure.
Background
Acoustic emission devices and techniques are now widely used for characterization and evaluation of material and structural properties. The most complex of concern in these applications is the randomness of the occurrence of various damage events as the load increases and the service life of the material and structure increases. At present, the characteristic parameters of the signals acquired by the acoustic emission sensor can be applied to related applications, such as amplitude, energy, frequency, duration, rise time, signal count (ring count), event number, waveform, and the like. For example, the count, amplitude, and energy of acoustic emission events are related to crack propagation; the frequency spectrum of the acoustic emissions is related to various failure mechanisms of the composite material; counting acoustic emission events may indicate the intensity of the damage. These characteristic parameters are used to reveal the correlation between random damage and the change in material properties under the action of applied stress.
In this case, the damage is complementary to the structural material, which is the key to the above-mentioned relationship. The presence of damage results in a redistribution of stresses within the material. The redistributed stresses in turn exacerbate the damage process, further weakening the integrity of the structural material, which in turn results in a change in its stress strength. The interaction between damage and stress eventually becomes a series of consecutive interconnected processes, eventually leading to material and structure fracture.
In these processes, the damage produced by materials and structures under alternating stress is highly random, which complicates the characterization and evaluation of the mechanical properties of materials and structures. Therefore, there is a need for an apparatus and method capable of characterizing the evolution process of the damage state of materials and structures.
Disclosure of Invention
The invention aims to overcome the defects of the technology and provides an acoustic emission monitoring device and a fatigue damage analysis method for a material structure.
In order to achieve the purpose, the invention adopts the following technical scheme: an acoustic emission monitoring device comprises an acoustic emission sensor, a preamplifier, an AD converter, a D matrix generation program, a probability space decomposer and a damage state track generator; the acoustic emission sensor is arranged on the outer surface of the material structure and used for receiving acoustic emission signals;
the preamplifier is used for amplifying the acoustic emission signal;
the AD converter is used for converting the obtained amplified acoustic emission signal into a digital signal;
the D matrix generation program is used for extracting characteristic parameters of the acquired digital signal data to construct a two-dimensional multivariate random damage variable D matrix capable of representing micro-damage energy scale characteristics and time sequence characteristics;
the probability space decomposer is used for calculating the probability space of the D matrix;
the damage state track generator is used for calculating probability entropy and generating a damage state track.
A fatigue damage analysis method for a material structure by using the acoustic emission monitoring device comprises the following steps:
step one, applying a hierarchical test to a loaded structure;
secondly, placing at least one acoustic emission sensor on the surface of the structure, and collecting mass acoustic emission signals of the acoustic emission sensor in the damage accumulation process;
extracting characteristic parameters of the acoustic emission signal data from the acquired acoustic emission signal data to construct a multivariate random damage variable D matrix capable of representing micro-damage energy scale characteristics and time sequence characteristics;
step four, calculating the probability space of the D matrix;
calculating the probability entropy of the probability space and correlating the probability entropy with the applied stress to obtain an entropy-stress relation;
and step six, evaluating the damage state of the material structure according to the entropy value of the entropy-stress relation.
Preferably, in the first step, the test is based on single-axis quasi-static stretching of the material structure after a certain amount of fatigue damage.
Preferably, in step three, the generating the D matrix includes generating the D matrix according to a plurality of parameters, wherein the parameters include: one of an applied load, displacement, stress, time series. In the present invention, applied stress is used as the parameter.
Preferably, in step four, the probability space of the D matrix includes the calculation of the probability space of the D matrix related to a plurality of parameters, including: one of an applied load, displacement, stress, time series. In the present invention, applied stress is used as the parameter. Preferably, in step five, the step of calculating probability entropy of the probability space includes calculating probability entropy of the probability space related to a plurality of parameters, the parameters including: one of an applied load, displacement, stress, time series. In the present invention, applied stress is used as the parameter.
Preferably, in step six, the step of generating the damage state trajectory includes generating the damage state trajectory in relation to a plurality of parameters, including: one of an applied load, displacement, stress, time series. In the present invention, applied stress is used as the parameter.
Preferably, the D matrix is a two-dimensional data matrix of characteristic parameters of the acoustic emission signals for characterizing the damage state of the material and the structure.
Preferably, in the D matrix data, rows in the two-dimensional data matrix are scale standards of the analyzed characteristic parameters, and the analyzed parameters have amplitude, energy and time; the columns are observation indices of the analyzed feature parameters. Considering that the external load plays the most dominant role in the evolution of material damage throughout the fracture process of the selected specimens of the present invention, the stress is selected as the observation index in the present invention.
Preferably, the probability space is a row normalization of the D matrix data, and the probability entropy is calculated row by row from the probability space data.
Preferably, the probability entropy is approximate.
The invention has the advantages that 1) the correlation of damage and damage of materials and structures under dynamic and static loads is considered; 2) the inherent damage difference caused by dynamic loading is indirectly measured through the static loading response of the material structure; 3) the influence of noise, vibration and the like in a fatigue test is overcome, and the internal damage difference of the material subjected to fatigue loading is also reserved; 4) effectively evaluating the damage state evolution by means of probability entropy
The invention is applicable to the assessment of random damage response of mechanical, civil and other similar load bearing structures to solid structures under alternating stress.
Drawings
FIG. 1 is a schematic view of the operation of an acoustic emission system of the present invention.
FIG. 2 is a schematic view of the damage state monitoring and analyzing apparatus of the present invention.
FIG. 3 is a D matrix constructed based on Q235 steel material extraction acoustic emission characteristic parameters.
Fig. 4 is a damage state trace curve obtained by static stretching after 0 fatigue cycle based on the Q235 steel material.
FIG. 5 shows that the steel material based on Q235 is 2 x 106And (4) obtaining a damage state track curve by static stretching after secondary fatigue circulation.
FIG. 6 shows that the steel material of the invention is based on Q235 at 3X 106And (4) obtaining a damage state track curve by static stretching after secondary fatigue circulation.
FIG. 7 shows that the steel material based on Q235 is 4 x 106And (4) obtaining a damage state track curve by static stretching after secondary fatigue circulation.
FIG. 8 shows that the steel material based on Q235 is 5 × 106And (4) obtaining a damage state track curve by static stretching after secondary fatigue circulation.
FIG. 9 shows that the steel material based on Q235 is at 6X 106And (4) obtaining a damage state track curve by static stretching after secondary fatigue circulation.
Fig. 10 is a comparison of the lesion state trace curves of fig. 4-9 in accordance with the present invention.
Detailed Description
Spatially relative terms such as "above … …", "above … …", "above … …", "above", and the like may be used herein for ease of description to describe the spatial relationship of one feature or characteristic to another feature or characteristic as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, devices described as "above" or "on" other devices or configurations would then be oriented "below" or "under" the other devices or configurations. Thus, the exemplary term "above … …" can include both an orientation of "above … …" and "below … …". The device may be otherwise variously oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.
The present invention includes devices and methods that can be used to monitor and evaluate Acoustic Emission (AE) signals to reveal the importance and statistical mechanisms of correlation between characteristic parameters of such signals and random damage events. According to the method, the statistical information of the strain wave triggering the acoustic emission signals is obtained by arranging the acoustic emission sensors, and the result is transmitted in a concise mode through a limited data acquisition system. The invention is suitable for the characterization and evaluation of materials, the safety monitoring of engineering structures and the like.
FIG. 1 is a schematic diagram of the general operation of an acoustic emission system: when the structure 11 is subjected to an external load, causing a permanent damage 16, the structure will emit strain waves containing energy information, i.e. acoustic emission signals 12. An acoustic emission sensor 13 is attached to the outer surface of the structure 11 to receive the acoustic emission signal 12. The signal 12 is amplified by a preamplifier 14 and then fed back to the AE system 15 for post-processing.
Fig. 2 presents a schematic view of the monitoring and analysis of the damage state, including: AE sensor 13, switch controller/preamplifier/AD converter (SCAAD)21, D matrix (M-D) generation program 22, probability space decomposer (PSR)23, and damage state Trajectory (TDS) generator 24. The purpose of the AE sensor 13 is to collect acoustic emission signals of materials and structures during damage. The purpose of the SCAAD 21 is to control the switching of the device, to amplify the acoustic emission signal and to convert the output voltage signal into a digital signal. The purpose of the M-D generation program 22 is to construct a multivariate random damage variable D matrix capable of characterizing micro-damage energy scale features and time series features for the acquired massive acoustic emission signals. The purpose of the PSR23 is to compute the probability space of the determined D matrix. The TDS generator 24 is configured to calculate a probability entropy, which is further associated with the applied stress to obtain an entropy-stress curve, i.e., a damage state trajectory curve.
For the purposes of the present invention, damage is an irreversible event, such as the nucleation, coalescence, deformation, fracture of a microcrack, or the detection of different degrees of damage to a material structure by an AE sensor. These damage events can be generated experimentally to reveal the damage mechanisms of the material structure, i.e., the evaluation and analysis of the material structure, and in fact, most cases damage events are unexpected and unpredictable, including failure of structural materials such as petroleum pipelines, bridges, aircraft wings, and landing gear, as well as many other load-bearing structures.
The apparatus of the present invention triggers the switch controller/amplifier/AD converter 21 to record the damage of the structure under a predetermined trigger criteria. The device is triggered by an acoustic emission signal that has a threshold value (e.g., amplitude) and then is recorded as an element of a column of the data matrix based on a characteristic parameter of the AE signal. The above process is repeated as the data acquisition progresses, so that a D matrix of AE signal characteristic parameters will be constructed by the D matrix generation program 23. The matrix may be composed of parameters characterizing the amplitude, energy, rise time and duration of the acoustic emission signal, depending on the needs of the study. For example, when a matrix is built from the amplitudes of the acoustic emission signals, an amplitude matrix is generated; when the energy of the acoustic emission signal is used, an energy matrix is generated.
When the probability space decomposer 23 normalizes each row of the D matrix, the data matrix becomes an approximation of the corresponding probability space that is used to reveal the probability of occurrence of an acoustic emission event under a particular characteristic parameter.
Probability entropy is calculated using the probability space. The change in entropy is related to the change in lesion status. This scalar is the composite average of all recorded microscopic random injury event statistics. When applied stress is available, the plot of probability entropy versus applied stress may be defined as a damage state Trajectory (TDS) curve generated by the TDS generator 24. In TDS curves, changes in entropy can reveal macroscopic properties, and in particular can take into account the effects of solid microstructure changes.
The invention gives the following examples, denoted by X: and the amplitude, energy, duration, rising time and other characteristic parameters of the detected acoustic emission signals.
The range of X is defined bymax-XminMeaning that X is divided into n sub-intervals, each sub-interval being at Xmin+ Δ X (i-1) to | Xmin+ Δ X (i)Then, each subinterval is called a scale vector under the scale standard with the characteristic parameter X as the scale standard. Namely:
[Amin，Amin+(Amax-Amin)/n]，[Amin+(Amax-Amin)/n，Amin+2( 「Amin:+(n-1)(Amin-Amin)/n，Amax]
considering that the external load plays the most important driving role in the evolution of material damage in the whole fracture process of the sample, the stress is selected as the scale index in the research, and the observation vector number m can be jointly determined by the stress value range and the selected observation value length.
Finally, the scale standard is used as a column vector, the observation index is used as a row vector, and the scale and the observation vector are integrated, so that a plurality of random damage variables can be obtained: d matrix
Wherein the column vector { X1，X2ΛXjΛXn}，
As micro-lesion scale vector
Yi＝{αi1，αi2ΛαinThe observed vector of the micro-damage is obtained; wherein m is the number of observation index vectors of the characteristic parameter X, and n is the number of standard vectors of the D matrix scale. Element alphaijAnd representing the acoustic emission event count which simultaneously falls into the corresponding observation standard and scale index interval, namely the number of material micro-damage events.
The D matrix will increase or accumulate as the load increases or the experimental time lengthens. Each row of the D matrix is a measurement interval, each row of the D matrix data is summed, and each value of the row is divided by the summed value to obtain a normalized D matrix such that it is an approximate probability space of the acoustic emission event feature parameter X.
Assigning a multivariate random variable D matrix as a lesionThe field is physically represented as the state of damage to the material under an external load. That is, the material damage state is a physical quantity. The damage state can be represented by a probability entropy, i.e. the entropy of the probability space of the characteristic parameters of the observed acoustic signal. The concrete expression is as follows: the larger the entropy increase s is, the higher the probability of generating a certain or limited scale damage is, namely, the relative biased distribution of the multi-scale random damage is generated, and the more the event probability is, the more the result is; on the contrary, the smaller the entropy is decreased, the less uncertainty of random damage is reduced, and the damage state of the material is more stable. The probability entropy to quantify this impairment state can be described as
Wherein n is the number of vectors of the D matrix scale, PjIs the probability that a random damage event falls into the jth observation subinterval.
Under the action of external load, the damage process of the material gradually and cumulatively evolves in time and stress sequence, and the final damage result can be regarded as a highly-coupled result of each previous transient damage in multiple dimensions. Therefore, according to the significance of entropy, the damage probability entropy is further defined as a scalar for representing the evolution state of the material damage, and entropy values of all statistical observation points based on the stress sequence are connected into a continuous curve, so that the process integration of each transient damage in the material damage evolution is realized. Is defined as: a damage state trajectory curve, i.e., a TDS curve, of a material to substantially reflect a macroscopic description of microscopic damage characteristics within the material.
The invention evaluates the macroscopic properties of materials and structures while overcoming complex factors such as different structural features, different dimensions, and the random response of these structures to applied stress by establishing models of interacting stress and damage fields. To describe the lesion field, the lesion state is defined by all microscopic categories of irreversible lesion events occurring within one volume of structural units. In order to quantify the damage state, a multivariate random variable D matrix is constructed according to the characteristic parameters of the acoustic emission signals, and then probability entropy can be used for further generalization.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. An acoustic emission monitoring device, characterized by: the device comprises an acoustic emission sensor, a preamplifier, an AD converter, a D matrix generation program, a probability space decomposer and a damage state track generator; the acoustic emission sensor is arranged on the outer surface of the material structure and used for receiving acoustic emission signals;
the preamplifier is used for amplifying the acoustic emission signal;
the AD converter is used for converting the obtained amplified acoustic emission signal into a digital signal;
the D matrix generation program is used for extracting characteristic parameters of the acquired digital signal data to construct a two-dimensional multivariate random damage variable D matrix capable of representing micro-damage energy scale characteristics and time sequence characteristics;
the probability space decomposer is used for calculating the probability space of the D matrix;
the damage state track generator is used for calculating probability entropy and generating a damage state track.
2. A fatigue damage analysis method for a material structure by using the acoustic emission monitoring device is characterized in that: the method comprises the following steps:
step one, applying a hierarchical test to a loaded structure;
secondly, placing at least one acoustic emission sensor on the surface of the structure, and collecting mass acoustic emission signals of the acoustic emission sensor in the damage accumulation process;
extracting characteristic parameters of the acoustic emission signal data from the acquired acoustic emission signal data to construct a multivariate random damage variable D matrix capable of representing micro-damage energy scale characteristics and time sequence characteristics;
step four, calculating the probability space of the D matrix;
calculating the probability entropy of the probability space and correlating the probability entropy with the applied stress to obtain an entropy-stress relation;
and step six, evaluating the damage state of the material structure according to the entropy value of the entropy-stress relation.
3. The method of claim 2, wherein: in the first step, the test is based on uniaxial quasi-static stretching of the material structure after a certain amount of fatigue damage.
4. The method of claim 2, wherein: in step three, generating the D matrix includes generating the D matrix according to a plurality of parameters, where the parameters include: one of an applied load, displacement, stress, time series.
5. The method of claim 2, wherein: in step four, the probability space of the D matrix includes the calculation of the probability space of the D matrix with respect to a plurality of parameters, including: one of an applied load, displacement, stress, time series.
6. The method of claim 2, wherein: in step five, the step of calculating the probability entropy of the probability space includes calculating the probability entropy of the probability space related to a plurality of parameters, including: one of an applied load, displacement, stress, time series.
7. The method of claim 2, wherein: in step six, the step of generating the damage state trajectory includes generating the damage state trajectory related to a plurality of parameters, including: one of an applied load, displacement, stress, time series.
8. The method of claim 2, wherein: the D matrix is a two-dimensional data matrix of characteristic parameters of acoustic emission signals for representing the damage states of the materials and the structures.
9. The method of claim 8, wherein: in the D matrix data, the rows in the two-dimensional data matrix are the scale standard of the analyzed characteristic parameters, and the analyzed parameters comprise amplitude, energy and time; the columns are observation indices of the analyzed feature parameters.
10. The method of claim 2, wherein: the probability space is a row normalization of the D matrix data, the probability entropy is calculated row by row from the probability space data, and the probability entropy is approximate.
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