CN107014668A - A kind of fatigue crack integrated monitoring based on piezoelectricity and smart coat sensor - Google Patents

A kind of fatigue crack integrated monitoring based on piezoelectricity and smart coat sensor Download PDF

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CN107014668A
CN107014668A CN201710192852.9A CN201710192852A CN107014668A CN 107014668 A CN107014668 A CN 107014668A CN 201710192852 A CN201710192852 A CN 201710192852A CN 107014668 A CN107014668 A CN 107014668A
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smart coat
sensor
piezoelectric transducer
monitoring
crack
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何晶靖
董邦林
王邓江
张卫方
刘晓鹏
阳劲松
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Beihang University
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N3/00Investigating strength properties of solid materials by application of mechanical stress
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
    • G01N27/02Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance
    • G01N27/04Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance
    • G01N27/041Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance of a solid body
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
    • G01N27/02Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance
    • G01N27/04Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance
    • G01N27/20Investigating the presence of flaws
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00Investigating 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
    • G01N29/04Analysing solids
    • G01N29/041Analysing solids on the surface of the material, e.g. using Lamb, Rayleigh or shear waves
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/20Metals
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2203/00Investigating strength properties of solid materials by application of mechanical stress
    • G01N2203/0058Kind of property studied
    • G01N2203/0069Fatigue, creep, strain-stress relations or elastic constants
    • G01N2203/0073Fatigue
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2291/00Indexing codes associated with group G01N29/00
    • G01N2291/02Indexing codes associated with the analysed material
    • G01N2291/023Solids
    • G01N2291/0234Metals, e.g. steel
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2291/00Indexing codes associated with group G01N29/00
    • G01N2291/02Indexing codes associated with the analysed material
    • G01N2291/023Solids
    • G01N2291/0237Thin materials, e.g. paper, membranes, thin films

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Abstract

The invention discloses a kind of based on the method for piezoelectricity and smart coat sensor to structural fatigue crackle comprehensive monitoring, this method obtains the damage quantitative model and real-time crack detection results between Lamb wave and damage according to the crack monitoring principle of piezoelectric transducer and smart coat sensor respectively, with reference to the data type of different sensors damage monitoring result, corresponding crackle detection probability model is set up.For piezoelectric transducer, according to the actual fatigue crack propagation test result of aluminium alloy plate structure, damage quantitative model is set up based on Lamb wave signal, uses Bayesian updating method correction model parameter to obtain more accurately damage quantitative model.Pass through the POD models and the POD models of smart coat Sensor monitoring result of comparative analysis piezoelectric transducer monitoring result, the monitoring capability of comprehensive descision piezoelectricity and smart coat the sensor fatigue crack under different length.

Description

A kind of fatigue crack integrated monitoring based on piezoelectricity and smart coat sensor
Technical field
The invention belongs to structural health monitoring field, it is related to structure damage monitorings such as metal, composites, and in particular to A kind of comprehensive monitoring determination methods for being used for piezoelectricity and smart coat sensor to aluminium alloy sheet crack Propagation situation.
Background technology
Structural health monitoring technology is obtained for extensive use, structural health in civil engineering and aeronautical engineering field Monitoring technology is exactly to pass through smart sensor's network on-line real time monitoring structural health.At present, in structural health monitoring skill The sensor main used in art will have piezoelectric transducer, fibre optical sensor, MEMS sensor, smart coat sensor etc..Wherein Piezoelectric transducer is the piezo-electric effect by itself, excitation and reception guided wave signals, and is realized by signal processing technology to knot Structure damage monitoring;Smart coat sensor is made up of driving layer, sensing layer and protective layer, and intelligence is expanded to by structural crack Coating sensor cloth patch position causes the change of sensing layer resistance value, so as to realize the monitoring to structural crack.
There are some researches show, for the different sensors equipment of monitoring structural health conditions, because the monitoring principle of sensor is different, The data type and processing mode of its signal are also not quite similar, each to damage monitoring advantageous.At present, according to different sensors pair The signal of structure damage monitoring collection, its data type is broadly divided into Signal response data and Hit/miss data. Signal response data refer to by the electric signal or vibration signal that detect (such as:Voltage, electric current, frequency, strain etc.) Consecutive variations be used to characterize the change of structural damage (such as:Crack length, damaged area etc.).For discrete response signal, The data of sensor collection can directly react its testing result (hit=1, miss=0), and 1 expression damage is detected, 0 table Show that damage is not detected at.The ability of piezoelectricity and smart coat sensor to damage is compared by crackle detection probability.Crackle is examined Go out probability, i.e. POD (Probability of Detection) model is a kind of characteristic manner of sensor detectability.POD Model result is not only relevant with lesion size, also by material, physical dimension, damage type, the monitoring ring for being such as monitored structure The influence of the uncertain factor such as border and monitoring personnel.
Found in pertinent literature and experiment, when monitoring crack Propagation missing inspection can occur for smart coat sensor Situation, so-called missing inspection then refers to that sensor is not detected by when crackle has already passed through the monitored area of smart coat sensor Phenomenon.
The content of the invention
The present invention fully takes into account the missing inspection problem of smart coat sensor to solve the above problems, and proposes a kind of base In piezoelectricity and the fatigue crack integrated monitoring of smart coat sensor.The present invention is the damage monitoring by piezoelectric transducer Principle, sets up structural crack quantitative model monitoring crack Propagation situation, with reference to the monitoring result of smart coat sensor, point Corresponding POD models are not set up, draw POD curves.The present invention is the POD moulds set up by the monitoring result of two kinds of sensors Type, Integrated comparative piezoelectricity and smart coat sensor are selected long in different fatigue crackle the detection probability of crack Propagation More it is capable of the sensor type of the accurate measurements structural fatigue Crack Extensions under degree.
Comprehensive descision is carried out to crack Propagation monitoring result present invention employs two kinds of sensors, passes through fatigue test Machine is loaded to testpieces, testpieces is produced crack Propagation, and two kinds of sensors are obtained by the analyzing and processing to monitoring information Corresponding POD models are obtained, the comprehensive descision to crack Propagation of two kinds of sensors is realized, overcomes two kinds of sensors itself Deficiency.
The present invention is the monitoring result for being based respectively on piezoelectricity and smart coat sensor to fatigue crack, it is proposed that information is melted Conjunction method, realizes the comprehensive descision of On Crack Propagation.Using the fatigue crack propagation test of aluminium alloy sheet as platform, on thin plate Arrange that smart coat and piezoelectric transducer are monitored to crack Propagation, specifically divide following steps simultaneously:
Step 1:Selection experiment part.
Step 2:Cloth pressing electric transducer and smart coat sensor on aluminium alloy sheet.
Step 3:Determine the pumping signal of piezoelectric transducer.
Step 4:The aluminium alloy sheet that smart coat and piezoelectric transducer are posted by more than is enterprising installed in fatigue tester Row fatigue crack propagation test.Before fatigue tester loading, the initial signal of piezoelectricity and smart coat sensor is gathered respectively.
Step 5:The crack length under different circulation cycles is recorded in real time by light microscope, in average loading stress water The flat Lamb wave signal for stopping collection piezoelectric transducer reception during CYCLIC LOADING.Meanwhile, in each smart coat sensor alarm When, record the data of smart coat sensor and the Lamb wave signal of piezoelectric transducer.
Step 6:Complete after experiment, the signal that sensor is gathered is handled.Mainly analyze and process piezoelectric transducer The Lamb wave signal of collection, is filtered to the signal, interception Lamb wave signal S0The time window of pattern.Because Lamb wave is being passed Broadcast with Crack Extension in path, it is that amplitude reduces, propagation path changes i.e. phase place change etc. that decay, which occurs, for signal energy, is extracted Damage characteristic normalization amplitude X, phase angle variations Y, set up crack lengthWith the function model f () of damaging diagnostic parameter.
Step 7:Repeat step 2-6, the Lamb wave signal gathered for piezoelectric transducer is analyzed and processed, and extracts damage The function model set up in characteristic parameter, verification step 5, and model parameter is corrected, obtain revised model g (·)。
Step 8:According to the piezoelectric transducer crack monitoring model and the history number of smart coat sensor determined in step 7 According to the corresponding POD models of selection draw POD curves, judge monitoring of two kinds of sensors to aluminium alloy structure crack Propagation Ability.
The advantage of the invention is that:
Propose it is a kind of based on damage quantitative model of the piezoelectric transducer to crack Propagation, with reference to smart coat and pressure The test data that electric transducer is monitored to aluminium alloy plate fatigue crack, obtains the POD models of respective sensor, judges two kinds of sensings Monitoring capability of the device to different crack lengths.By comprehensive intelligent coating and the POD models of piezoelectric transducer, two kinds of sensings are judged Monitoring capability of the device under different crack lengths.
Brief description of the drawings
Fig. 1 is testpieces specification schematic diagram;
Fig. 2 sensor arrangement schematic diagrames;
Fig. 3 piezoelectric transducer pumping signals;
The Lamb wave signal that Fig. 4 piezoelectric transducers are received;
Under Fig. 5 difference crack lengths, S0Modeling curve;
The POD curves of Fig. 6 smart coats and piezoelectric transducer crack monitoring.
Embodiment
Below in conjunction with drawings and examples, the present invention is described in further detail.
The invention reside in provide a kind of integrated monitoring of multisensor to crack Propagation.By smart coat and Piezoelectric transducer is monitored to the crack Propagation of aluminium alloy sheet, research and application result, draws the POD of two kinds of sensors Model curve.
Above-mentioned aluminium alloy sheet is as shown in figure 1, its specification:600*300*2(mm).In order to make knot during fatigue test Structure can carry out crack Propagation along assigned direction quickly, and the circle of a diameter 10mm is provided with the center of aluminium alloy sheet Hole, and the prefabricated long 3mm, wide 0.2mm crackle respectively in the both direction parallel to short side.
The aluminium alloy sheet (as shown in Figure 2) for being disposed with sensor is loaded into before fatigue tester is tested, it is necessary to Piezoelectricity and smart coat sensor are debugged, and obtain initial signal.Wherein, the debugging of sensor includes test sensor Whether the excitation and collection of signal are normal, and the pumping signal that piezoelectric transducer is selected in this method implementation process is that five crests are sinusoidal Modulated signal, centre frequency 160KHz, as shown in Figure 3.In view of the Dispersion of Lamb wave, in the Lamb wave signal collected In, it is T2 according to the flight time (ToF, Time-of-Flight) of distance and Lamb wave between the piezoelectric transducer of arrangement, such as Shown in Fig. 4, Lamb wave S is calculated0Pattern group velocity, intercepts S under different crack lengths0Mode time window.Because Lamb wave is being propagated During reflection and the scattering phenomenon of signal occur when running into damage, energy will dissipate.Lamb wave S is used in this method selection0 The change of the amplitude of pattern and phase characterizes the spread scenarios of fatigue crack.In the time window intercepted, extract sign and split The normalization amplitude X of the line spread scenarios and change Y at phase angle, and set up corresponding function model.
The Crack Extension quantitative model set up by the historical data and piezoelectric transducer of smart coat sensor, builds respectively Corresponding POD models are found, comparative analysis obtains monitoring effect of two kinds of sensors to different crack lengths.
A kind of fatigue crack integrated monitoring based on piezoelectricity and smart coat sensor of the present invention, including it is following several Individual step:
Step 1:Selection experiment part.In checking this method, it is contemplated that the propagation characteristic of Lamb wave and the expansion of fatigue crack Exhibition, selects aluminium alloy sheet, specification:600*300*2 (mm), is provided with Φ 10mm circular hole, simultaneously at the center of aluminium alloy sheet Hole both sides perpendicular to each prefabricated 3mm length of long side direction, crackle wide 0.2mm.
Step 2:Cloth pressing electric transducer and smart coat sensor on thin plate, wherein smart coat sensor from along The centre distance that the tip of precrack is risen between 16 sensors of arrangement, two piezoelectric ceramic pieces is 200mm, as shown in Figure 2.
Step 3:The pumping signal of piezoelectric transducer is determined, can according to the dispersion curve that Lamb wave is propagated in aluminium alloy plate Know, the Lamb wave S when the thick product of frequency is smaller0Pattern group velocity is larger and stably, the periodicity one of each driving pulse of pumping signal As select 3.5~13.5 cycles, periodicity can not be excessive, can not be very few.Because the periodicity of signal is more, it is different Crosstalk will occur for the ripple bag of pattern, be unfavorable for the analysis of signal.The periodicity of pumping signal is very few, the energy entrained by signal It is fewer, and bandwidth is wider, signal is easily disturbed.Here five cycle sinusoidal modulation wave signals, centre frequency 160KHz are selected.
Step 4:The aluminium alloy sheet that smart coat and piezoelectric transducer are posted by more than is enterprising installed in fatigue tester Row fatigue crack propagation test.Before fatigue tester loading, the initial signal of piezoelectricity and smart coat sensor is gathered respectively. Set the loading parameter of fatigue tester, including stress ratio, maximum stress, loading frequency, Loaded contact analysis etc..
Step 5:The crack length under different circulation cycles is recorded in real time by light microscope, in average loading stress water It is flat to stop gathering the Lamb wave signal that piezoelectric transducer is received in the case of CYCLIC LOADING.Meanwhile, in each smart coat sensing When device is alarmed, the data of smart coat sensor and the Lamb wave signal of piezoelectric transducer are recorded.
Step 6:After off-test, the collection signal of smart coat and piezoelectric transducer is handled.Passed for piezoelectricity The Lamb wave signal that sensor is received carries out narrow-band filtering, and S is intercepted from filtered signal0The corresponding time window of ripple bag is (see figure 5) impairment parameter, and according to below equation is extracted from time window.A in formula 3iAnd A0I-th is represented in Fig. 5 dotted line frames respectively The crest value and reference signal of curve are P in the crest value that crack length is zero, formula 4iAnd P0I-th curve is represented respectively At the time of corresponding to crest value and the crest value of reference signal.Set up tired by the measured value and damaging diagnostic parameter of Crack Extension The damage quantitative model of labor crackle.
Y (i)=Pi-P0 (4)
Wherein:X represents to normalize amplitude in above formula, and Y represents phase angle variations, αi(i=0,1,2,3) model ginseng is represented Number,The crack length of model monitoring.
Step 7:Using the testpieces of same size, repeat step 2-6 six times, the function model set up in verification step 5, And model parameter is modified using the MCMC methods of samplings in Bayes statistical method, obtain revised model.
Wherein:α ' in above formulai(i=0,1,2,3) it is model parameter after updating.
Step 8:According to model formation (7), (8) and (9), the POD models of piezoelectric transducer are set up, POD curves are drawn.
Wherein:Lna is the logarithmic form of actual crack length in formula (7),It is the logarithmic form of model monitoring result, γ is that obedience average is zero, and standard deviation is σγError variance, α, β are model parameters.In formula (8)Crack monitoring threshold value.
The monitoring result of smart coat sensor in test of many times is handled, each smart coat Sensor monitoring As a result as a Hit/miss data type, when Crack Extension is between two sensors, previous smart coat sensor Alarm, then previous smart coat sensor detects crackle, and otherwise the sensor does not detect crackle.According to testing result, Obtain the sample average and sample variance of smart coat sensor crack monitoring.Smart coat sensor is obtained by formula (10) to split The POD models of line monitoring, wherein m is sample average, and σ is sample standard deviation.Finally, according to modeling rendering smart coat sensor POD curves.
Wherein:M, σ are the sample average and standard deviation of smart coat Sensor monitoring result respectively in above formula.
According to the two of painted on top POD models, prison of two kinds of sensors to aluminium alloy structure crack Propagation is judged Survey ability, as shown in Figure 6.

Claims (1)

1. a kind of fatigue crack integrated monitoring based on piezoelectricity and smart coat sensor, is split with the fatigue of aluminium alloy sheet Line expanding test is platform, arranges that smart coat and piezoelectric transducer are monitored to crack Propagation simultaneously on thin plate, Specifically include following steps:
Step 1:Selection experiment part;
Step 2:Cloth pressing electric transducer and smart coat sensor on aluminium alloy sheet;
Step 3:Determine the pumping signal of piezoelectric transducer;
Step 4:The aluminium alloy sheet for posting smart coat and piezoelectric transducer above is carried out on fatigue tester tired Labor crack expansion test, before fatigue tester loading, gathers the initial signal of piezoelectricity and smart coat sensor respectively;
Step 5:Record the crack length under different circulation cycles in real time by light microscope, stop in average loading stress level The Lamb wave signal that piezoelectric transducer is received only is gathered during CYCLIC LOADING;Meanwhile, in each smart coat sensor alarm, note Record the data of smart coat sensor and the Lamb wave signal of piezoelectric transducer;
Step 6:Complete after experiment, the signal that sensor is gathered is handled, the Lamb wave signal gathered to piezoelectric transducer It is filtered, interception Lamb wave signal S0The time window of pattern, extracts damage characteristic normalization amplitude X, phase angle variations Y, builds Vertical crack length a and damaging diagnostic parameter function model f ():
Step 7:Repeat step 2-6, the Lamb wave signal gathered for piezoelectric transducer is analyzed and processed, and extracts damage characteristic The function model set up in parameter, verification step 6, and model parameter is corrected, obtain revised model g ():
Step 8:According to the piezoelectric transducer crack monitoring model and the historical data of smart coat sensor determined in step 7, Respectively according to the POD models (3) of the data type of piezoelectric transducer and smart coat Sensor monitoring result selection, (4), draw POD curves, judge monitoring capability of two kinds of sensors to aluminium alloy structure crack Propagation;
In above formula (3)It is the logarithmic form of model monitoring result,Lna smart coats in crack monitoring threshold value, formula (4) The logarithmic form of Sensor monitoring result, m, σ are the sample average and standard deviation of smart coat Sensor monitoring result respectively.
CN201710192852.9A 2016-04-22 2017-03-28 A kind of fatigue crack integrated monitoring based on piezoelectricity and smart coat sensor Pending CN107014668A (en)

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CN107588788A (en) * 2017-08-29 2018-01-16 北京航空航天大学 Optical fiber and smart coat data fusion implementation method based on entropy weight step analysis
CN107607055A (en) * 2017-09-08 2018-01-19 北京航空航天大学 A kind of implementation method of the hardware system based on optical fiber and smart coat sensor
CN108318261A (en) * 2018-01-08 2018-07-24 中车青岛四方机车车辆股份有限公司 The monitoring method and device of vehicle structure
CN108645727A (en) * 2018-05-10 2018-10-12 北京航空航天大学 Crack Damage quantitative detecting method based on piezoelectric excitation-optical fiber grating sensing
CN109187857A (en) * 2018-08-22 2019-01-11 中国飞机强度研究所 A kind of crackle monitoring device and method based on silver powder coating sensor
CN109612806A (en) * 2019-02-25 2019-04-12 北京航空航天大学 A kind of efficient test material preparation and test method suitable for the test of surface crack defect detection probability
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CN111024527A (en) * 2019-12-06 2020-04-17 西安理工大学 Crack propagation monitoring method based on multi-sensor data fusion
CN111611654A (en) * 2020-04-16 2020-09-01 清华大学 Fatigue prediction method, device and equipment for riveted structure and storage medium
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CN112903953A (en) * 2021-01-21 2021-06-04 北京航空航天大学 Metal plate structure damage type identification system and method
CN112903952A (en) * 2021-01-21 2021-06-04 北京航空航天大学 Metal plate structure damage evaluation system and method
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CN107588788A (en) * 2017-08-29 2018-01-16 北京航空航天大学 Optical fiber and smart coat data fusion implementation method based on entropy weight step analysis
CN107607055A (en) * 2017-09-08 2018-01-19 北京航空航天大学 A kind of implementation method of the hardware system based on optical fiber and smart coat sensor
CN108318261A (en) * 2018-01-08 2018-07-24 中车青岛四方机车车辆股份有限公司 The monitoring method and device of vehicle structure
CN110390115A (en) * 2018-04-17 2019-10-29 江苏必得科技股份有限公司 Train part Crack Damage prediction technique and device
CN108645727A (en) * 2018-05-10 2018-10-12 北京航空航天大学 Crack Damage quantitative detecting method based on piezoelectric excitation-optical fiber grating sensing
CN112334765A (en) * 2018-06-22 2021-02-05 杰富意钢铁株式会社 Pressure accumulator life estimating device and pressure accumulator life prolonging method
US11982644B2 (en) 2018-06-22 2024-05-14 Jfe Steel Corporation Life estimation apparatus for accumulator and life extension method for pressure accumulator
CN109187857A (en) * 2018-08-22 2019-01-11 中国飞机强度研究所 A kind of crackle monitoring device and method based on silver powder coating sensor
CN109632958A (en) * 2018-12-24 2019-04-16 北京航空航天大学 A kind of Lamb wave damage detecting method considering crackle orientation
CN109655493A (en) * 2019-01-21 2019-04-19 广东省特种设备检测研究院珠海检测院 A kind of crane crack Propagation monitoring device and method
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