CN112179990A - Carbon fiber composite material fatigue damage probability imaging method based on ToF damage factor - Google Patents
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Abstract
The invention relates to a carbon fiber composite material fatigue damage probability imaging method based on a ToF damage factor, and belongs to the technical field of engineering structure health monitoring. Firstly, uniformly dividing a monitored area in a piezoelectric sensing network into N small grids, and regarding each small grid as a pixel point; then calculating the ToF damage factor of each excitation-sensing channel; secondly, calculating the damage existence probability of each pixel point in each excitation-sensing channel according to the ToF damage factor; and finally, overlapping the damage probability of each pixel point in each excitation-sensing channel, mapping the damage existence probability of each pixel point into a pixel value and imaging, wherein the position with higher damage probability in an imaging image is the position where damage possibly exists. Compared with the traditional damage probability imaging method, the damage probability imaging method can accurately identify and position the damage, effectively improve the visualization effect of damage imaging, and obtain the approximate shape and size of the damage.
Description
Technical Field
The invention relates to a carbon fiber composite material fatigue damage probability imaging method based on a ToF damage factor, and belongs to the technical field of engineering structure health monitoring.
Background
Compared with metal materials, the carbon fiber composite material has the advantages of high specific stiffness, high specific strength, high temperature resistance, corrosion resistance, fatigue resistance, light weight and the like, and is widely applied to the field of aerospace. However, various defects and damages are continuously generated in the composite material during material processing, part manufacturing and service, wherein fatigue damage is a common and typical kind of damage. The fatigue damage is that the inside of a material forms tiny damage at the initial stage under the continuous load circulation action, the internal damage gradually expands along with the repeated action of the load until the material breaks, but the forming process of the internal damage is difficult to detect, and once the fatigue damage expands to a certain degree, serious economic loss and personal safety can be caused. Therefore, it is very important to find the information of the existence, position, etc. of the fatigue damage in time.
The structure health monitoring technology can monitor the structure in real time and on line, and is widely applied to the aerospace field. At present, various methods for monitoring the structural health are available, such as an electromechanical impedance method, an optical fiber sensing monitoring method, an intelligent coating method, various damage imaging methods based on Lamb waves and the like. Among them, the Lamb wave-based damage imaging method is widely studied by domestic and foreign scholars because the information such as the position and size of the damage in the structure can be intuitively reflected.
Among various damage imaging methods, the damage probability imaging method is deeply researched by many scholars because the damage probability imaging method is simple to operate, requires a small amount of data, performs imaging by using damage factors and weight distribution functions of various sensing channels, does not need the propagation velocity of guided waves, and is suitable for complex structures. Although the existing damage probability imaging method can effectively identify damage, the visualization effect and the damage positioning accuracy are poor, the imaging definition is not high, and the damage existence probability of the damage position without damage is generally higher.
Disclosure of Invention
The invention aims to provide a carbon fiber composite material fatigue damage probability imaging method based on a ToF damage factor, which is used for solving the problems.
The invention designs a new damage factor on the basis of the traditional damage probability imaging method, and the proposed ToF damage factor is that the damage probability imaging method is improved by utilizing the relation between Lamb wave flight time under the condition that damage exists in each sensing channel and the flight time under the structural health condition, so that the accuracy of damage positioning is improved, the visual effect of damage imaging is effectively improved, the rough shape and size of the damage are preliminarily obtained, the defects of the existing damage probability imaging method are effectively made up, and valuable reference is provided for further realizing the fatigue damage quantitative analysis of the carbon fiber composite material.
The technical scheme of the invention is as follows: a carbon fiber composite material fatigue damage probability imaging method based on ToF damage factors comprises the following specific steps:
step 1: the method comprises the steps of uniformly dividing a monitored area in a piezoelectric sensing network into N small grids, and regarding each small grid as a pixel point.
Step 2: and calculating the ToF damage factor of each excitation-sensing channel.
Step 3: and calculating the damage existence probability of each pixel point in each excitation-sensing channel according to the ToF damage factor.
Step 4: and overlapping the damage existence probability of each pixel point in each excitation-sensing channel, mapping the damage existence probability of each pixel point into a pixel value and imaging, wherein the position with high damage probability in an imaging picture is the position where damage exists.
The Step1 is specifically as follows:
step1.1: and respectively arranging a group of one-dimensional linear piezoelectric sensor arrays consisting of K piezoelectric sensors at the upper edge and the lower edge of the carbon fiber composite material plate.
Step1.2: the sensor array located on the upper edge portion of the carbon fiber composite material plate serves as an exciter, the number of the sensor array is 1-K from right to left, the sensor array located on the lower portion serves as a receiver, and the number of the sensor array is K-2K from left to right.
Step1.3: the actuators 1-K respectively and alternately excite Lamb wave signals, and the receivers respectively receive signals excited by the actuators, for example, the actuator 1 excites Lamb wave signals, the signals are respectively received by the receivers K-2K after being propagated in a monitored structure, the actuator 2 excites Lamb wave signals after being received, the signals are received by the receivers K-2K after being propagated in the structure, and so on, the actuators 3 to K respectively excite Lamb wave signals, and the receivers K-2K respectively receive to form K2M excitation-sensing channels.
Step1.4: the monitored area is evenly divided into N small grids with the same size, and each small grid is regarded as a pixel point.
The Step2 is specifically as follows: and improving the damage probability imaging method by utilizing the relationship between the Lamb wave flight time under the condition that the damage of each sensing channel exists and the flight time under the structural health condition.
Step2.1: under the structural health condition, performing Hilbert transform on the excitation signal and the response signal of each excitation-sensing channel and taking an absolute value to obtain a Hilbert transform module value graph, wherein the time corresponding to the maximum value of the coefficient module values of the excitation signal and the response signal in the graph is the emission time T of the excitation signalbe(i)And the arrival time T of the response signalbr(i)The time of flight of the health signal is
Step2.2: and under the condition that the structure has damage, subtracting the health reference signal of the corresponding channel from the response signal of each excitation-sensing channel to obtain the damage scattering signal of each excitation-sensing channel.
Step2.3: under the condition that the structure has damage, performing Hilbert transform on the excitation signal and the damage scattering signal of each excitation-sensing channel and taking the absolute value to obtain a Hilbert transform module value graph, wherein the moments corresponding to the maximum values of the coefficient module values of the excitation signal and the damage scattering signal in the graph are respectively the sending moments T of the excitation signalce(i)And the arrival time T of the damage scattered signaldr(i)The time of flight of the impairment scatter signal is
Step2.4: the TOF damage factor is obtained according to the flight time calculated by Step2.1-Step2.3, and the expression is as follows:
in the formula (1), the reaction mixture is,
the flight time of the health reference signal and the flight time of the damage scattering signal of the ith excitation-sensing path are respectively.
The Step3 is specifically as follows:
calculating the damage existence probability of each pixel point in each excitation-sensing channel according to the ToF damage factor in Step2, and assuming that M excitation-sensing channels are shared in the sensing network, the damage existence probability P of any pixel point (x, y) in the ith excitation-sensing channeli(x, y) is:
Pi(x,y)＝DITi·Wi[Ri(x,y)] (2)
in the formula (2), DITiThe ToF damage factor for the ith excitation-sensing channel indicates the degree of difference between the characteristic monitoring signal and the healthy reference signal. Wi[Ri(x, y) is a weight distribution function of the ith excitation-sensing channel and the relative distance R between any pixel point (x, y) and a direct path of the ith excitation-sensing channeli(x, y) is related, which is assumed to be a linearly decaying elliptical distribution, expressed as follows:
in the formulas (3) and (4), beta is a size parameter for controlling the influence area of the elliptical distribution, the value of the size parameter beta is 0.1, and (x)a,i,ya,i) To excite the coordinates of sensor A, (x)s,i,ys,i) To receive the coordinates of the sensor S, Da,i(x, y) is the distance of the excitation sensor to any pixel (x, y), Ds,i(x, y) is the distance from the receiving sensor to any pixel (x, y), DiIs the distance from the excitation sensor to the receiving sensor, i.e. the focal length of the ellipse.
The Step4 is specifically as follows:
the damage probability P of each pixel point in each excitation-sensing channel obtained by superposing Step3i(x, y), mapping the damage existence probability P (x, y) of each pixel point into a pixel value and imaging, wherein the position with high damage probability in an imaging graph is the position where the damage exists, and the damage existence probability P (x, y) of each pixel point is as follows:
the meaning of the large damage probability in the invention is that the damage probability is larger than the value of the size parameter beta.
The invention has the beneficial effects that:
1. the accuracy of damage positioning can be effectively improved;
2. the visual effect of damage imaging can be effectively improved, and the definition of the damage imaging is improved;
3. the rough shape and size of the lesion can be preliminarily obtained;
4. the method is simple to operate, needs a small amount of data, does not need the propagation speed of guided waves, and is suitable for complex structures;
5. the damage probability of the position without damage in the imaging result is extremely low, even most of the damage probability is zero, and the condition that the damage probability of the position without damage is higher in the existing damage probability imaging result is effectively remedied.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a schematic representation of the relative distance of the present invention;
FIG. 3 is a schematic view and a physical diagram of a test piece according to the present invention;
FIG. 4 is a time domain, frequency domain diagram of an excitation signal of the present invention;
FIG. 5 is a diagram of the healthy reference signal of paths 1-8 of the present invention and its Hilbert envelope;
FIG. 6 is a graph of the current monitor signal and its Hilbert envelope for paths 1-8 of the present invention;
FIG. 7 is a graph of the lesion scatter signals of paths 1-8 of the present invention and their Hilbert envelopes;
FIG. 8 is a Hilbert transform modulus graph of the health reference signal corresponding to paths 1-8 of the present invention;
FIG. 9 is a numerical graph of the TOF damage factor of the present invention;
FIG. 10 is an X-ray diagram of a test piece of the present invention and a ToF damage factor-based imaging result of fatigue damage probability of carbon fiber composite material.
Detailed Description
The invention is further described with reference to the following drawings and detailed description.
As shown in fig. 3, the embodiment of the present invention takes the damage detection and localization of fatigue damage of the carbon fiber composite material plate as an example, the size of the carbon fiber composite material plate is a dog bone geometry of 25.4cm by 15.24cm, and the left middle edge has a small notch of 5.08mm by 19.3mm designed to induce load concentration.
As shown in fig. 1, a ToF damage factor-based carbon fiber composite material fatigue damage probability imaging method, wherein the ToF damage factor-based damage probability imaging step on a region to be detected comprises:
step 1: uniformly dividing a monitored area in a piezoelectric sensing network into 600 × 360 small grids, and regarding each small grid as a pixel point;
step1.1: respectively arranging a group of one-dimensional linear piezoelectric sensor arrays consisting of 6 piezoelectric sensors on the upper edge and the lower edge of the carbon fiber composite material plate;
step1.2: the sensor array positioned at the upper edge part of the carbon fiber composite material plate is used as an exciter, the number of the sensor array is 1-6 from right to left, the sensor array positioned at the lower part is used as a receiver, and the number of the sensor array is 7-12 from left to right;
step1.3: the actuators 1 to 6 respectively and alternately excite Lamb wave signals, and the receivers respectively receive signals excited by the actuators, for example, the actuator 1 excites Lamb wave signals, the signals are respectively received by the receivers 7 to 12 after being propagated in a monitored structure, the actuator 2 excites Lamb wave signals after the signals are received, the actuators 3 to 6 respectively excite Lamb wave signals after being propagated in the structure, the receivers 7 to 12 respectively receive the signals, and so on, and 6 × 6 to 36 excitation-sensing channels are formed in a conformal manner;
step1.4: the monitored area is evenly divided into 600 × 360 small grids with the same size, and each small grid is regarded as a pixel point.
Step 2: calculating a ToF damage factor of each excitation-sensing channel;
as shown in fig. 4 to fig. 9, the relationship between Lamb wave flight time in the presence of damage in each sensing channel and flight time in the structural health condition is used to improve the damage probability imaging method;
step2.1: under the structural health condition, performing Hilbert transform on the excitation signal and the response signal of each excitation-sensing channel and taking an absolute value to obtain a Hilbert transform module value graph, wherein the time corresponding to the maximum value of the coefficient module values of the excitation signal and the response signal in the graph is the emission time T of the excitation signalbe(i)And the arrival time T of the response signalbr(i)The time of flight of the health signal is
Step2.2: under the condition that the structure has damage, subtracting the health reference signal of the corresponding channel from the response signal of each excitation-sensing channel to obtain a damage scattering signal of each excitation-sensing channel;
step2.3: under the condition that the structure has damage, performing Hilbert transform on the excitation signal and the damage scattering signal of each excitation-sensing channel and taking the absolute value to obtain a Hilbert transform module value graph, wherein the moments corresponding to the maximum values of the coefficient module values of the excitation signal and the damage scattering signal in the graph are respectively the sending moments T of the excitation signalce(i)And the arrival time T of the damage scattered signaldr(i)The time of flight of the impairment scatter signal is
Step2.4: obtaining the ToF damage factor according to the flight time obtained in the step Step2.1-Step2.3, wherein the expression is as follows:
in the formula (1), the reaction mixture is,
the flight time of the health reference signal and the flight time of the damage scattering signal of the ith excitation-sensing path are respectively.
Step 3: calculating the damage existence probability of each pixel point in each excitation-sensing channel according to the ToF damage factor;
calculating the damage existence probability of each pixel point in each excitation-sensing channel according to the ToF damage factor of Step2, wherein 36 excitation-sensing channels are totally arranged in the sensing network, and the damage existence probability P of any pixel point (x, y) in the ith excitation-sensing channeli(x, y) is:
Pi(x,y)＝DIi·Wi[Ri(x,y)] (2)
in the formula (2), DIiRepresenting the difference degree between the monitoring signal and the health reference signal for the damage factor of the ith excitation-sensing channel; wi[Ri(x, y) is a weight distribution function of the ith excitation-sensing channel and the relative distance R between any pixel point (x, y) and a direct path of the ith excitation-sensing channeli(x, y) is related, which is assumed to be a linearly decaying elliptical distribution, expressed as follows:
as shown in fig. 2, in the formulas (3) and (4), β is a size parameter for controlling the ellipse distribution influence area, and has a value of 0.1, (x)a,i,ya,i) To excite the coordinates of sensor A, (x)s,i,ys,i) To receive the coordinates of the sensor S, Da,i(x, y) is the distance of the excitation sensor to any pixel (x, y), Ds,i(x, y) is the distance from the receiving sensor to any pixel (x, y), DiIs the distance from the excitation sensor to the receiving sensor, i.e. the focal length of the ellipse.
Step 4: overlapping the damage probability of each pixel point in each excitation-sensing channel, mapping the damage existence probability of each pixel point into a pixel value and imaging, wherein the position with higher damage probability in an imaging picture is the position where damage possibly exists;
as shown in fig. 10, the damage probability P of each pixel point in each excitation-sensing channel obtained by superimposing Step4i(x, y), mapping the damage existence probability P (x, y) of each pixel point to a pixel value and imaging, wherein the position with higher damage probability in an imaging image is the position where damage possibly exists. The damage existence probability P (x, y) of each pixel point is as follows:
the different color regions in the imaging graph of fig. 10 represent different damage existence probabilities, and the position and approximate size and shape information of the damage can be clearly and clearly seen from the imaging graph.
While the present invention has been described in detail with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, and various changes can be made without departing from the spirit and scope of the present invention.
Claims (5)
1. A carbon fiber composite material fatigue damage probability imaging method based on a ToF damage factor is characterized in that:
step 1: uniformly dividing a monitored area in a piezoelectric sensing network into N small grids, and regarding each small grid as a pixel point;
step 2: calculating a ToF damage factor of each excitation-sensing channel;
step 3: calculating the damage existence probability of each pixel point in each excitation-sensing channel according to the ToF damage factor;
step 4: and overlapping the damage existence probability of each pixel point in each excitation-sensing channel, mapping the damage existence probability of each pixel point into a pixel value and imaging, wherein the position with high damage probability in an imaging picture is the position where damage exists.
2. The ToF damage factor-based carbon fiber composite material fatigue damage probability imaging method according to claim 1, wherein Step1 is specifically as follows:
step1.1: respectively arranging a group of one-dimensional linear piezoelectric sensor arrays consisting of K piezoelectric sensors on the upper edge and the lower edge of the carbon fiber composite material plate;
step1.2: the sensor array positioned at the upper edge part of the carbon fiber composite plate is used as an exciter, the number of the sensor array is 1-K from right to left, the sensor array positioned at the lower part is used as a receiver, and the number of the sensor array is K-2K from left to right;
step1.3: the exciters 1-K respectively excite Lamb wave signals in turn, and the receiver respectively receives the signals excited by the exciters to form K-K2M excitation-sensing channels;
step1.4: the monitored area is evenly divided into N small grids with the same size, and each small grid is regarded as a pixel point.
3. The ToF damage factor-based carbon fiber composite material fatigue damage probability imaging method according to claim 1, wherein Step2 is specifically as follows:
step2.1: under the structural health condition, performing Hilbert transform on the excitation signal and the response signal of each excitation-sensing channel and taking an absolute value to obtain a Hilbert transform module value graph, wherein the time corresponding to the maximum value of the coefficient module values of the excitation signal and the response signal in the graph is the emission time T of the excitation signalbe(i)And the arrival time T of the response signalbr(i)The time of flight of the health signal is TOFbi(t)＝Tbr(i)-Tbe(i)；
Step2.2: under the condition that the structure has damage, subtracting the health reference signal of the corresponding channel from the response signal of each excitation-sensing channel to obtain a damage scattering signal of each excitation-sensing channel;
step2.3: under the condition that the structure has damage, performing Hilbert transform on the excitation signal and the damage scattering signal of each excitation-sensing channel and taking the absolute value to obtain a Hilbert transform module value graph, wherein the moments corresponding to the maximum values of the coefficient module values of the excitation signal and the damage scattering signal in the graph are respectively the sending moments T of the excitation signalce(i)And the arrival time T of the damage scattered signaldr(i)Time of flight of the lesion scatter signal is TOFdi(t)＝Tdr(i)-Tce(i)；
Step2.4: the TOF damage factor is obtained according to the flight time calculated by Step2.1-Step2.3, and the expression is as follows:
in the formula (1), the reaction mixture is,
the flight time of the health reference signal and the flight time of the damage scattering signal of the ith excitation-sensing path are respectively.
4. The ToF damage factor-based carbon fiber composite material fatigue damage probability imaging method according to claim 1, wherein Step3 is specifically as follows:
calculating the damage existence probability of each pixel point in each excitation-sensing channel according to the ToF damage factor in Step2, and assuming that M excitation-sensing channels are shared in the sensing network, the damage existence probability P of any pixel point (x, y) in the ith excitation-sensing channeli(x, y) is:
Pi(x,y)＝DITi·Wi[Ri(x,y)] (2)
in the formula (2), DITiA ToF damage factor for the ith excitation-sensing channel, representing the degree of difference between the characteristic monitoring signal and the health reference signal; wi[Ri(x, y) is a weight distribution function of the ith excitation-sensing channel and the relative distance R between any pixel point (x, y) and a direct path of the ith excitation-sensing channeli(x, y) is related, which is assumed to be a linearly decaying elliptical distribution, expressed as follows:
in the formulas (3) and (4), beta is a size parameter for controlling the influence area of the elliptical distribution, (x)a,i,ya,i) To excite the coordinates of sensor A, (x)s,i,ys,i) To receive the coordinates of the sensor S, Da,i(x, y) is the distance of the excitation sensor to any pixel (x, y), Ds,i(x, y) is the distance from the receiving sensor to any pixel (x, y), DiIs the distance from the excitation sensor to the receiving sensor, i.e. the focal length of the ellipse.
5. The ToF damage factor-based carbon fiber composite material fatigue damage probability imaging method according to claim 1, wherein Step4 is specifically as follows:
the damage probability P of each pixel point in each excitation-sensing channel obtained by superposing Step3i(x, y), mapping the damage existence probability P (x, y) of each pixel point into a pixel value and imaging, wherein the position with high damage probability in an imaging graph is the position where the damage exists, and the damage existence probability P (x, y) of each pixel point is as follows:
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