CN110208377A - A kind of more characteristic parameters damage degree assessment method based on Lamb wave - Google Patents
A kind of more characteristic parameters damage degree assessment method based on Lamb wave Download PDFInfo
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Abstract
The invention discloses a kind of more characteristic parameters damage degree assessment methods based on Lamb wave in damage detection technology field, it aims to solve the problem that in the prior art using handheld device artificial detection structural damage is inefficient, timeliness is not strong, the excessively high technical problem of testing cost is carried out using highly sensitive ultrasonic detection equipment.Described method includes following steps: acquisition obtains the Lamb wave structural response signal of geodesic structure Injured level to be checked；Extract the characteristic parameter of Lamb wave structural response signal；Training sample set and test sample collection are extracted from Lamb wave structural response signal；Lesion assessment model is established based on genetic-BP neural networks；The characteristic parameter of Lamb wave structural response signal is concentrated to be trained lesion assessment model using training sample；The characteristic parameter of Lamb wave structural response signal is concentrated to input trained lesion assessment model test sample, according to model output value evaluation structure degree of injury.
Description
Technical field
The present invention relates to a kind of more characteristic parameters damage degree assessment method based on Lamb wave, belongs to damage detection technology
Field.
Background technique
Engineering structure is mostly large scale structure in practical applications, and reliability requirement is high, and working environment is more severe, for
Its damage being likely to occur in longtime running is assessed just in time seems particularly important, to ensure damaging original state just
It can find in time, to avoid that even more serious consequence occurs.
Existing structural damage detection technology includes two classes, i.e., carries out artificial detection using handheld device and using highly sensitive
Degree ultrasonic detection equipment detects automatically.Artificial detection is carried out using handheld device, it is desirable that testing staff's business water with higher
Gentle practical experience abundant, and large scale structure is difficult to detect its damage in time, detection efficiency is not high, and timeliness is not
By force.It is detected using highly sensitive ultrasonic detection equipment, although can satisfy detection efficiency and timeliness, often price mistake
In high.
Summary of the invention
It is an object of the invention to overcome deficiency in the prior art, a kind of more characteristic parameters based on Lamb wave are provided
Damage degree assessment method includes the following steps:
Acquisition obtains the Lamb wave structural response signal of geodesic structure Injured level to be checked；
Extract the characteristic parameter of Lamb wave structural response signal；
Training sample set and test sample collection are extracted from Lamb wave structural response signal；
Lesion assessment model is established based on genetic-BP neural networks；
The characteristic parameter of Lamb wave structural response signal is concentrated to be trained lesion assessment model using training sample；
The characteristic parameter of Lamb wave structural response signal is concentrated to input trained lesion assessment model, root test sample
According to model output value evaluation structure degree of injury.
Further, acquisition obtains the Lamb wave structural response signal of geodesic structure Injured level to be checked, comprising:
According to structural region size to be detected, several groups piezoelectric patches is laid, constitutes excitation/sensor array；
Host computer modulates Lamb wave pumping signal, and piezoelectric patches is acted on after power amplifier amplifies, and the Lamb wave swashs
Encouraging signal includes 5 wave crest Lamb wave pumping signals；
Charge amplifier amplifies the Lamb wave structural response signal that piezoelectric patches acquires；
Host computer acquires amplified Lamb wave structural response signal.
Further, the characteristic parameter of Lamb wave structural response signal is extracted, comprising:
Lamb wave structural response signal is decomposed using wavelet package transforms, obtains and is based on Lamb wave structural response signal
Wavelet packet tree construction；
Extract each node of wavelet packet tree construction on characteristic parameter, the characteristic parameter include: time domain waveform feature Bx,
Sharp peaks characteristic information Bf, energy frequency domain distribution Ef, energy percentage E.
Further, Lamb wave structural response signal is decomposed using wavelet package transforms, comprising: selection and Lamb wave
The consistent wavelet basis function of waveform decomposes Lamb wave structural response signal.
Further, Lamb wave structural response signal is decomposed using wavelet package transforms, comprising:
According to sampling number set by acquisition Lamb wave structural response signal, the Decomposition order of wavelet packet tree is chosen；
Lamb wave structural response signal is decomposed by Decomposition order, temporal frequency figure and the wavelet packet tree for obtaining wavelet packet tree are every
The wavelet packet coefficient figure of a node.
Further, time domain waveform feature Bx, including following calculation formula:
In formula, Bx (l, j) is the time domain waveform feature of j-th of node of l layer,For on wavelet packet tree construction l layer
The wavelet packet coefficient of j-th of node, N are the length of wavelet packet coefficient；
Sharp peaks characteristic information Bf, including following calculation formula:
In formula, Bf (l, j) is the sharp peaks characteristic information of j-th of node of l layer；
Energy frequency domain distribution Ef, including following calculation formula:
In formula, Ef (l, j) is the energy frequency domain distribution of j-th of node of l layer；
Energy percentage E, including following calculation formula:
In formula, E (l, j) is that the signal frequency domain energy of j-th of node of l layer accounts for the percentage of l layer signal frequency domain gross energy.
Further, training sample set and test sample collection are extracted from Lamb wave structural response signal, comprising:
The characteristic parameter of Lamb wave structural response signal is normalized；
Training sample set and test sample collection are extracted from the Lamb wave structural response signal after normalized；
Training sample set and test sample concentrate the ratio of Lamb wave structural response signal quantity value range be [4,
9]。
Further, lesion assessment model is established based on genetic-BP neural networks, comprising:
According to the quantity of structure Injured level, the number of nodes of network output layer is determined；
According to the quantity for extracting characteristic parameter, the number of nodes of network input layer is determined；
The number of nodes of input layer and output layer is substituted into preset formula, calculates the number of nodes for obtaining network hidden layer；
According to the number of nodes of network output layer, input layer and hidden layer, the topological structure of lesion assessment model is determined.
Further, using training sample concentrate Lamb wave structural response signal characteristic parameter to lesion assessment model into
Row training, comprising:
Default lesion assessment model parameter, the parameter includes genetic algebra, crossover probability, mutation probability；
The characteristic parameter input lesion assessment model of Lamb wave structural response signal is concentrated to be trained training sample；
With the minimum target of error, the parameter is adjusted according to the error change curve in training process；
When error is less than preset threshold, trained damage model is extracted；
The value range of preset threshold is [0.01,0.03].
Compared with prior art, advantageous effects of the invention: Lamb wave has, the rate of decay is slow, propagation distance is remote
The characteristics of, and it is more sensitive to the microlesion of structure；Lamb wave structural response signal is analyzed using wavelet package transforms, extracts letter
With the biggish characteristic parameter of quinolin-eta number in time-frequency domain, finally damage is assessed using neural network, having makes
With the advantage that at low cost, detection efficiency is high, timeliness is strong, while reducing to practitioner's technical requirements.
Detailed description of the invention
Fig. 1 is test specimen structure and sensing/excitation array schematic layout pattern in the embodiment of the present invention；
Fig. 2 is the method for the present invention flow chart；
Fig. 3 is the time-frequency figure that through-hole damages in the embodiment of the present invention；
Fig. 4 is the Energy distribution chart of percentage comparison that through-hole damages in the embodiment of the present invention；
Fig. 5 is the wavelet packet coefficient figure that through-hole damages in the embodiment of the present invention；
Fig. 6 is wavelet packet decomposition diagram in the embodiment of the present invention.
Specific embodiment
The invention will be further described below in conjunction with the accompanying drawings.Following embodiment is only used for clearly illustrating the present invention
Technical solution, and not intended to limit the protection scope of the present invention.
As shown in Figure 1, being test specimen structure and sensing/excitation array schematic layout pattern, this hair in the embodiment of the present invention
Test specimen used by bright embodiment is epoxy resin structural composite panel, having a size of 1000mm × 500mm × 3mm, the material
Density be 1960kg/m3, Young's modulus of elasticity 20GPa, Poisson's coefficient ratio is 0.17.By 32 pieces of piezoelectric patches by laterally
120mm, longitudinal direction 120mm spacing be evenly arranged, longitudinal each column arranges that 4 pieces of piezoelectric patches are a group, and laterally every row arranges 8 pieces
Piezoelectric patches amounts to 8 groups, constitutes excitation/sensing linear array.Circle center hole between adjacent rows piezoelectric patches is typical logical
Hole damage, diameter is incremented by successively from left to right, respectively 0mm, 1.5mm, 2.5mm, 3.5mm, 4.5mm, 5.5mm, 6.5mm.
Since circle center hole damage is convenient for simulation through-hole damage, can be simulated between longitudinally adjacent two pieces of piezoelectric patches, while in original
Damage is gradually expanded on the basis of having；It is nondestructive state between the adjacent rows piezoelectric patches of the leftmost side.Piezoelectric patches can not only be used for driver,
To excite Lamb wave signal into test specimen structure；But also as sensor, the Lamb wave structure to acquire test specimen structure is rung
Induction signal.Longitudinal each adjacent two piezoelectric patches constitutes one group, one of them is as driver, another is as sensor, the two
Cooperate with data acquisition；One-shot measurement is carried out using the excitation/sensing linear array, 3 × 8=24 group data can be collected.
As shown in Fig. 2, be the method for the present invention flow chart, a kind of more characteristic parameters degree of injury assessment side based on Lamb wave
Method includes the following steps:
Step 1, acquisition obtain the Lamb wave structural response signal of test specimen structure Injured level.
5 wave crest Lamb wave pumping signals are modulated using computer as host computer, is issued by data collecting card, is put through power
Big device amplification pumping signal acts on piezoelectric patches, and piezoelectric patches is motivated the excitation Lamb wave signal into test specimen structure, then leads to
It crosses with another piezoelectric patches acquisition Lamb wave structural response signal is organized, amplifies by charge amplifier, finally complete on computers
The acquisition of Lamb wave structural response signal.It has been proved by practice that 5 wave crest Lamb waves are chosen, pumping signal and reception response signal
Between overlapping degree it is minimum, be conducive to improve Evaluation accuracy.
More specifically, firstly, acquiring in test specimen 7 kinds of Injured levels respectively by one group of piezoelectric patches and a kind not damaged, often
Kind acquisition twice, obtains 16 groups of Lamb wave structural response signals；Then, remaining 23 groups of piezoelectric patches repeat the process, amount to acquisition
384 groups of Lamb wave structural response signals.
Step 2 extracts the characteristic parameter of Lamb wave structural response signal using wavelet package transforms.
(1) it chooses and is divided with the consistent 384 groups of Lamb wave structural response signals of wavelet basis function of 5 wave crest Lamb wave waveforms
Solution obtains the wavelet packet tree construction based on Lamb wave structural response signal.Wavelet basis function is not unique, it has been proved by practice that
Optimal Evaluated effect can be obtained with the excitation consistent wavelet basis function of Lamb wave waveform by choosing.
More specifically, firstly, the sampling number according to set by acquisition Lamb wave structural response signal, chooses wavelet packet tree
Decomposition order, the sampling number refer to constitute Lamb wave structural response signal waveform diagram data point quantity, this implementation
Sampling number is 1000 in example, and to ensure Evaluated effect, the Decomposition order that wavelet packet tree is chosen in the present embodiment is 3, such as Fig. 6 institute
Show, is wavelet packet decomposition diagram in the embodiment of the present invention, Lamb wave structural response signal F (t) is through three layers points of wavelet package transforms
Xie Hou, third layer number of nodes are 8, and each node data point quantity is 1000 ÷ 8=125, due to damage of composite materials
Length is typically not greater than 128 data points, thus 125 data points of each node after waveform interception, can either obtain waveform letter
The energy feature information of number all frequency ranges, while retaining the waveform time domain characteristic information of damage；Then, using wavelet package transforms pair
Lamb wave structural response signal carry out three layers it is complete decompose, obtain the temporal frequency figure and each node of wavelet packet tree of wavelet packet tree
Wavelet packet coefficient figure is the time-frequency figure, Energy distribution that through-hole damages in the embodiment of the present invention respectively as shown in Fig. 3, Fig. 4 and Fig. 5
Chart of percentage comparison, wavelet packet coefficient figure.
(2) characteristic parameter on each node of wavelet packet tree construction is extracted, the characteristic parameter includes time domain waveform feature
Bx, sharp peaks characteristic information Bf, energy frequency domain distribution Ef, energy percentage E, wherein time domain waveform feature Bx, including calculate as follows
Formula:
In formula, Bx (l, j) is the time domain waveform feature of j-th of node of l layer,For on wavelet packet tree construction l layer
The wavelet packet coefficient of j-th of node, N are the length of wavelet packet coefficient；
Sharp peaks characteristic information Bf, including following calculation formula:
In formula, Bf (l, j) is the sharp peaks characteristic information of j-th of node of l layer；
Energy frequency domain distribution Ef, including following calculation formula:
In formula, Ef (l, j) is the energy frequency domain distribution of j-th of node of l layer；
Energy percentage E, including following calculation formula:
In formula, E (l, j) is that the signal frequency domain energy of j-th of node of l layer accounts for the percentage of l layer signal frequency domain gross energy.
Step 3 extracts training sample set and test sample collection from Lamb wave structural response signal.
More specifically, firstly, the characteristic parameter of 384 groups of Lamb wave structural response signals is normalized；Then,
Lamb wave structural response signal after normalized is divided into training sample set and test sample collection；To ensure training effect,
It is [4,9] that training sample set and test sample, which concentrate the value range of the ratio of Lamb wave structural response signal quantity,.This implementation
In example, 80 groups of Lamb wave structural response signals are chosen as training sample set, choose 10 groups of Lamb wave structural response signal conducts
Test sample collection.
Step 4 establishes lesion assessment model based on genetic-BP neural networks.
More specifically, firstly, determining the number of nodes of network output layer, this implementation according to the quantity of structure Injured level
In example, six kinds of degree of injury are shared, diameter is respectively 1.5mm, 2.5mm, 3.5mm, 4.5mm, 5.5mm, 6.5mm, thus is determined
The number of nodes of network output layer is 6, the corresponding degree of injury of each node, and center of circle bore dia is corresponding with network output valve
Relationship such as table 1；
Table 1:
Then, the number of nodes of network input layer, Lamb wave knot in the present embodiment are determined according to the quantity for extracting characteristic parameter
For structure response signal F (t) after three layers of wavelet package transforms are decomposed, third layer number of nodes is 8, each Node extraction Bx, Bf,
Ef, E totally 4 characteristic parameters, thus determine that the number of nodes of network input layer is 8 × 4=32；
Then, the number of nodes of input layer and output layer is substituted into preset formula, calculates the number of nodes for obtaining network hidden layer
It is 25, preset formula is as follows:
In formula, n2For the number of nodes of hidden layer, n1For the number of nodes of input layer, l is the number of nodes of output layer, and α is constant,
Its value range is [1,10]；
Finally, determining the topology knot of lesion assessment model according to the number of nodes of network output layer, input layer and hidden layer
Structure.
Step 5 concentrates the characteristic parameter of Lamb wave structural response signal to carry out lesion assessment model using training sample
Training.
Default lesion assessment model parameter, the parameter includes genetic algebra, crossover probability, mutation probability；
The characteristic parameter input lesion assessment model of Lamb wave structural response signal is concentrated to be trained training sample, with
The minimum target of error goes to adjust the parameter according to the error change curve in training process；
When error is less than preset threshold, extract trained damage model, the value range of preset threshold be [0.01,
0.03]。
Test sample is concentrated the characteristic parameter of Lamb wave structural response signal to input trained lesion assessment by step 6
Model, according to model output value evaluation structure degree of injury, if certain degree of injury output valve closer to 1, other degree of injury
Output valve then shows that its degree of injury meets the correspondence degree of injury provided closer to 0, on the contrary then show to fail correctly to judge
Such degree of injury, test sample concentrate Injured level recognition effect as shown in table 2.
Table 2:
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, without departing from the technical principles of the invention, several improvement and deformations can also be made, these improvement and deformations
Also it should be regarded as protection scope of the present invention.
Claims (9)
1. a kind of more characteristic parameters damage degree assessment method based on Lamb wave, characterized in that include the following steps:
Acquisition obtains the Lamb wave structural response signal of geodesic structure Injured level to be checked；
Extract the characteristic parameter of Lamb wave structural response signal；
Training sample set and test sample collection are extracted from Lamb wave structural response signal；
Lesion assessment model is established based on genetic-BP neural networks；
The characteristic parameter of Lamb wave structural response signal is concentrated to be trained lesion assessment model using training sample；
The characteristic parameter of Lamb wave structural response signal is concentrated to input trained lesion assessment model test sample, according to mould
Type output valve evaluation structure degree of injury.
2. the more characteristic parameters damage degree assessment method according to claim 1 based on Lamb wave, characterized in that acquisition
Obtain the Lamb wave structural response signal of geodesic structure Injured level to be checked, comprising:
According to structural region size to be detected, several groups piezoelectric patches is laid, constitutes excitation/sensor array；
Host computer modulates Lamb wave pumping signal, and piezoelectric patches, the Lamb wave excitation letter are acted on after power amplifier amplifies
Number include 5 wave crest Lamb wave pumping signals；
Charge amplifier amplifies the Lamb wave structural response signal that piezoelectric patches acquires；
Host computer acquires amplified Lamb wave structural response signal.
3. the more characteristic parameters damage degree assessment method according to claim 1 based on Lamb wave, characterized in that extract
The characteristic parameter of Lamb wave structural response signal, comprising:
Lamb wave structural response signal is decomposed using wavelet package transforms, is obtained based on the small of Lamb wave structural response signal
Wave packet tree construction；
The characteristic parameter on each node of wavelet packet tree construction is extracted, the characteristic parameter includes: time domain waveform feature Bx, peak value
Characteristic information Bf, energy frequency domain distribution Ef, energy percentage E.
4. the more characteristic parameters damage degree assessment method according to claim 3 based on Lamb wave, characterized in that utilize
Wavelet package transforms decompose Lamb wave structural response signal, comprising: choose and the consistent wavelet basis function of Lamb wave waveform
Lamb wave structural response signal is decomposed.
5. the more characteristic parameters damage degree assessment method according to claim 3 based on Lamb wave, characterized in that utilize
Wavelet package transforms decompose Lamb wave structural response signal, comprising:
According to sampling number set by acquisition Lamb wave structural response signal, the Decomposition order of wavelet packet tree is chosen；
Lamb wave structural response signal is decomposed by Decomposition order, obtains the temporal frequency figure and each section of wavelet packet tree of wavelet packet tree
The wavelet packet coefficient figure of point.
6. the more characteristic parameters damage degree assessment method according to claim 5 based on Lamb wave, characterized in that
Time domain waveform feature Bx, including following calculation formula:
In formula, Bx (l, j) is the time domain waveform feature of j-th of node of l layer,For jth on wavelet packet tree construction l layer
The wavelet packet coefficient of a node, N are the length of wavelet packet coefficient；
Sharp peaks characteristic information Bf, including following calculation formula:
In formula, Bf (l, j) is the sharp peaks characteristic information of j-th of node of l layer；
Energy frequency domain distribution Ef, including following calculation formula:
In formula, Ef (l, j) is the energy frequency domain distribution of j-th of node of l layer；
Energy percentage E, including following calculation formula:
In formula, E (l, j) is that the signal frequency domain energy of j-th of node of l layer accounts for the percentage of l layer signal frequency domain gross energy.
7. the more characteristic parameters damage degree assessment method according to claim 1 based on Lamb wave, characterized in that from
Training sample set and test sample collection are extracted in Lamb wave structural response signal, comprising:
The characteristic parameter of Lamb wave structural response signal is normalized；
Training sample set and test sample collection are extracted from the Lamb wave structural response signal after normalized；
It is [4,9] that training sample set and test sample, which concentrate the value range of the ratio of Lamb wave structural response signal quantity,.
8. the more characteristic parameters damage degree assessment method according to claim 1 based on Lamb wave, characterized in that be based on
Genetic-BP neural networks establish lesion assessment model, comprising:
According to the quantity of structure Injured level, the number of nodes of network output layer is determined；
According to the quantity for extracting characteristic parameter, the number of nodes of network input layer is determined；
The number of nodes of input layer and output layer is substituted into preset formula, calculates the number of nodes for obtaining network hidden layer；
According to the number of nodes of network output layer, input layer and hidden layer, the topological structure of lesion assessment model is determined.
9. the more characteristic parameters damage degree assessment method according to claim 1 based on Lamb wave, characterized in that utilize
Training sample concentrates the characteristic parameter of Lamb wave structural response signal to be trained lesion assessment model, comprising:
Default lesion assessment model parameter, the parameter includes genetic algebra, crossover probability, mutation probability；
The characteristic parameter input lesion assessment model of Lamb wave structural response signal is concentrated to be trained training sample；
With the minimum target of error, the parameter is adjusted according to the error change curve in training process；
When error is less than preset threshold, trained damage model is extracted；
The value range of preset threshold is [0.01,0.03].
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111175379A (en) * | 2020-01-21 | 2020-05-19 | 南京邮电大学 | Lamb wave plate structure health monitoring method based on synchronous compression wavelet transform |
| CN111307944A (en) * | 2020-03-15 | 2020-06-19 | 中国飞机强度研究所 | Quantitative monitoring method and system for structural damage of composite material |
| CN112924543A (en) * | 2021-01-20 | 2021-06-08 | 中电投工程研究检测评定中心有限公司 | Prediction method and system for safe damage state of steel structural member |
| CN113933387A (en) * | 2021-09-08 | 2022-01-14 | 南京邮电大学 | Composite material structure damage monitoring method and system |
| CN113935231A (en) * | 2021-09-14 | 2022-01-14 | 南京邮电大学 | Large-scale structural damage monitoring and evaluating method based on sparse array |
| CN117191952A (en) * | 2023-11-06 | 2023-12-08 | 中冶建筑研究总院有限公司 | Fatigue damage identification and life prediction method based on acoustic emission signal wavelet packet decomposition frequency band energy spectrum |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1811367A (en) * | 2006-03-03 | 2006-08-02 | 西安交通大学 | Integrated supporting vector machine mixed intelligent diagnosing method for mechanical fault |
| CN103868690A (en) * | 2014-02-28 | 2014-06-18 | 中国人民解放军63680部队 | Rolling bearing state automatic early warning method based on extraction and selection of multiple characteristics |
| CN105160359A (en) * | 2015-09-17 | 2015-12-16 | 华南理工大学 | Complex structure damage cooperative identification method based on ultrasonic guided-wave |
| CN105225223A (en) * | 2015-08-27 | 2016-01-06 | 南京市计量监督检测院 | Based on the damage Detection of Smart Composite Structure method of wavelet analysis and BP neural network |
| CN106841403A (en) * | 2017-01-23 | 2017-06-13 | 天津大学 | A kind of acoustics glass defect detection method based on neutral net |
| CN107121501A (en) * | 2017-04-25 | 2017-09-01 | 天津大学 | A kind of turbine rotor defect classification method |
| CN108318589A (en) * | 2018-02-02 | 2018-07-24 | 北京交通大学 | Fatigue crack length intelligence assessment method based on Gaussian process and monitoring method |
| CN109158954A (en) * | 2018-09-10 | 2019-01-08 | 杭州电子科技大学 | A kind of ultrasonic cutting-tool wear state recognition methods and system based on acoustical signal processing |
-
2019
- 2019-06-19 CN CN201910530070.0A patent/CN110208377A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1811367A (en) * | 2006-03-03 | 2006-08-02 | 西安交通大学 | Integrated supporting vector machine mixed intelligent diagnosing method for mechanical fault |
| CN103868690A (en) * | 2014-02-28 | 2014-06-18 | 中国人民解放军63680部队 | Rolling bearing state automatic early warning method based on extraction and selection of multiple characteristics |
| CN105225223A (en) * | 2015-08-27 | 2016-01-06 | 南京市计量监督检测院 | Based on the damage Detection of Smart Composite Structure method of wavelet analysis and BP neural network |
| CN105160359A (en) * | 2015-09-17 | 2015-12-16 | 华南理工大学 | Complex structure damage cooperative identification method based on ultrasonic guided-wave |
| CN106841403A (en) * | 2017-01-23 | 2017-06-13 | 天津大学 | A kind of acoustics glass defect detection method based on neutral net |
| CN107121501A (en) * | 2017-04-25 | 2017-09-01 | 天津大学 | A kind of turbine rotor defect classification method |
| CN108318589A (en) * | 2018-02-02 | 2018-07-24 | 北京交通大学 | Fatigue crack length intelligence assessment method based on Gaussian process and monitoring method |
| CN109158954A (en) * | 2018-09-10 | 2019-01-08 | 杭州电子科技大学 | A kind of ultrasonic cutting-tool wear state recognition methods and system based on acoustical signal processing |
Non-Patent Citations (2)
| Title |
|---|
| 费鸿禄 等: "隧道掘进爆破振动对地表影响的小波包分析", 《爆炸与冲击》 * |
| 郭华玲 等: "激光超声缺陷统计特征神经网络识别技术研究", 《应用激光》 * |
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| CN111307944A (en) * | 2020-03-15 | 2020-06-19 | 中国飞机强度研究所 | Quantitative monitoring method and system for structural damage of composite material |
| CN112924543A (en) * | 2021-01-20 | 2021-06-08 | 中电投工程研究检测评定中心有限公司 | Prediction method and system for safe damage state of steel structural member |
| CN113933387A (en) * | 2021-09-08 | 2022-01-14 | 南京邮电大学 | Composite material structure damage monitoring method and system |
| CN113933387B (en) * | 2021-09-08 | 2024-05-14 | 南京邮电大学 | Method and system for monitoring damage of composite material structure |
| CN113935231A (en) * | 2021-09-14 | 2022-01-14 | 南京邮电大学 | Large-scale structural damage monitoring and evaluating method based on sparse array |
| CN117191952A (en) * | 2023-11-06 | 2023-12-08 | 中冶建筑研究总院有限公司 | Fatigue damage identification and life prediction method based on acoustic emission signal wavelet packet decomposition frequency band energy spectrum |
| CN117191952B (en) * | 2023-11-06 | 2024-01-23 | 中冶建筑研究总院有限公司 | Fatigue damage identification and life prediction method based on acoustic emission signal wavelet packet decomposition frequency band energy spectrum |
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