CN111551634B - Positioning method and system for identifying impact area based on time sequence - Google Patents

Positioning method and system for identifying impact area based on time sequence Download PDF

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CN111551634B
CN111551634B CN202010523854.3A CN202010523854A CN111551634B CN 111551634 B CN111551634 B CN 111551634B CN 202010523854 A CN202010523854 A CN 202010523854A CN 111551634 B CN111551634 B CN 111551634B
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CN111551634A (en
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王奕首
王庚
王悦
吴迪
卿新林
孙虎
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Xiamen University
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Abstract

The invention provides a positioning method and a positioning system for identifying an impact area based on a time sequence, wherein the positioning method for identifying the impact area based on the time sequence is used for realizing the positioning of the impact area by dividing the positioning area, then arranging a sensor, then establishing a pre-sampling library and carrying out time sequence identification according to the pre-sampling library for comparison. Compared with the traditional positioning method, the positioning method for identifying the impact area based on the time sequence provided by the invention has the advantages that the influence of the wave velocity difference of the impact wave caused by the anisotropy of the material on the positioning result is reduced, the detection precision is improved, the impact area can be accurately positioned, and when the positioning method is applied to the overhaul and maintenance of the composite material structure, the overhaul and maintenance of the composite material structure can be limited in the impact area, so that the overhaul and maintenance time and the economic cost are shortened; meanwhile, the safety and the reliability of the structure of the area to be identified are improved.

Description

Positioning method and system for identifying impact area based on time sequence
Technical Field
The invention relates to the field of impact positioning of aircraft composite material structures, in particular to a positioning method and a positioning system for identifying an impact area based on a time sequence.
Background
The composite material has the advantages of high specific strength, high specific modulus, high temperature resistance, corrosion resistance and the like. In recent years, the method is widely applied to the engineering fields of aviation, aerospace and the like. In the fields of aviation, aerospace and the like, the structure adopting the composite material inevitably suffers various types of impacts (bird strike, flying stones, falling of maintenance tools and the like) in the service process, various damages such as matrix fracture, delamination and the like are easily generated after the structure suffers the impacts, the surface of the damages is almost invisible, but the damaged parts can be expanded along with the lapse of time, the bearing capacity of the structure is greatly reduced, and the potential threat is formed on the whole damage and the failure of the structure.
The traditional detection and flaw detection methods comprise a nondestructive detection method, a triangulation method and the like, the nondestructive detection method needs to carry out integral detection on the structure to determine the existence and the specific position of the damage, and a large amount of time and economic cost are consumed. The triangulation method and other methods perform geometric positioning by calculating the arrival time of the shock wave (related to the wave velocity), and the premise is that the wave velocity is constant.
However, the composite material is a new material formed by optimally combining material components with different properties, and has anisotropy, namely the traveling speeds of impact waves in all directions are different, so that a large error is generated by using a triangulation-based positioning method, and the problem of inaccurate positioning is easy to occur.
Disclosure of Invention
In order to solve the problem mentioned in the background art that the composite material is a new material formed by optimally combining material components with different properties, has anisotropy, and is easy to have inaccurate positioning when a traditional impact positioning method is used, the invention provides a positioning method and a positioning system for identifying an impact region based on a time sequence, wherein the positioning method for identifying the impact region based on the time sequence comprises the following steps:
s10, dividing the area to be identified into a plurality of positioning areas;
s20, arranging sensors in the positioning areas respectively;
s30, performing impact training in advance in the positioning area and collecting sensor sample signals to form a pre-sampling library corresponding to the positioning area and the sample signals;
s40, acquiring impact signals through a sensor when identifying impact areas;
and S50, performing time series identification comparison on the collected impact signal and the sample signal in the pre-sampling library to realize impact area positioning.
Further, the method for constructing the pre-sampling library in step S30 includes:
s31, arranging M sensors according to the geometric shape of the region to be identified, dividing X training regions, and numbering the training regions;
s32, selecting training points with Y being more than or equal to 1 to carry out impact training in each training area in the step S31; the impact signals collected in each training area are marked as the same class, and the class labels are the numbers of the training areas;
and S33, collecting signals of each sensor under each training point, and establishing a pre-sampling library containing X types of training points X X Y.
Further, the training area uniformly covers the area to be identified.
Further, the method of time-series identification in step S50 includes:
s51, calculating a time sequence distance set D between the acquired impact signal and a pre-sampling library according to a DTW algorithm;
and S52, arranging the time sequence distance elements in the time sequence distance set D in ascending order, taking the first n time sequence distance elements, counting the number of the time sequence distance elements in each category, and identifying the category containing the most time sequence distance elements as an impact area.
Further, the DTW algorithm formula in step S51 is as follows:
Figure GDA0002875707390000031
wherein:
a is an impact signal;
b is a sample signal in the pre-sampling library;
w is the shortest path between the timing of the impulse signal and the timing of the sample signals in the pre-sampling bank;
Wnis the nth time series distance element in W, Wn＝(i,j)n(ii) a The (i, j) is A in the matrixiAnd BjThe distance between two points.
Further, in step S51, the impact signal includes a plurality of sensor signals, and for a certain impact signal, the time-series distance d between each acquired sensor signal and the sampled sensor signal at a point corresponding to the pre-sampling library is calculateds1、ds2...dsm(ii) a Adding the time sequence distances to obtain a time sequence distance d between the impact signal and a certain point corresponding to the pre-sampling libraryn；
And then, a time sequence distance set D of each acquired sensor signal and all training points in the pre-sampling library is obtained.
Further, in step S52, the time-series distances D in the time-series distance set D are determined by the K-nearest neighbor methodkPerforming ascending arrangement; and according to the number Y of the training points of each training area, taking the first K time sequence distance elements, and counting the positioning area types to which the time sequence distance elements belong, wherein the positioning area containing the most time sequence distance elements is identified as an impact area.
Further, K is an odd number not greater than Y.
The invention additionally provides a system for identifying the location of impact regions based on a time series, comprising a sensor and an analysis device for interactively performing the method as defined in any of the above.
Compared with the traditional positioning method, the positioning method for identifying the impact area based on the time sequence provided by the invention has the advantages that the influence of the wave velocity difference of the impact wave caused by the anisotropy of the material on the positioning result is reduced, the detection precision is improved, the impact area can be accurately positioned, and when the positioning method is applied to the overhaul and maintenance of the composite material structure, the overhaul and maintenance of the composite material structure can be limited in the impact area, so that the overhaul and maintenance time and the economic cost are shortened; meanwhile, the safety and the reliability of the structure of the area to be identified are improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a block diagram of a positioning method for identifying impact regions based on time series according to the present invention;
FIG. 2 is a schematic diagram of a process of establishing a pre-sampling library to locate an impact region via a sensor, a training point, and an impact point;
FIG. 3 is a flow chart of impact region location based on time series identification;
FIG. 4 is a schematic diagram of identification region division of geometric shapes of two regions to be identified;
FIG. 5 is a schematic representation of the response signal of one of the sensors at a given impact;
FIG. 6 is a schematic diagram of a DTW calculation process;
FIG. 7 is a schematic diagram of a method for calculating the distance between an impact signal and the nth point of a pre-sampling library;
FIG. 8 is a schematic diagram of K neighbor classification;
FIG. 9 is a schematic diagram of composite plate sensor arrangement and identification area division;
fig. 10 is a confusion matrix diagram of the impact region identification result.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. The terms "couple" or "couples" and the like are not restricted to physical or mechanical connections, but may include electrical connections, optical connections, and the like, whether direct or indirect.
As shown in fig. 1, fig. 2 and fig. 3, an embodiment of the present invention provides a positioning method and a positioning system for identifying an impact region based on a time sequence, where the positioning method for identifying the impact region based on the time sequence includes:
s10, dividing the area to be identified into a plurality of positioning areas;
s20, arranging sensors in the positioning areas respectively; the sensors in this step include but are not limited to piezoelectric sensors, fiber bragg grating sensors;
s30, performing impact training in advance in the positioning area and collecting sensor sample signals to form a pre-sampling library corresponding to the positioning area and the sample signals;
s40, triggering or acquiring impact signals in real time through a sensor when identifying impact areas; for example, when the actual working state is that the generation of the impact signal is detected, the sensor signal is started to be acquired;
and S50, performing time series identification comparison on the collected impact signal and the sample signal in the pre-sampling library to realize impact area positioning.
Specifically, the method for constructing the pre-sampling library in step S30 includes:
s31, arranging M sensors according to the geometric shape of the region to be identified, dividing X training regions (as shown in FIG. 4), and numbering the training regions as region 1 and region 2 … region X; the training area uniformly covers the area to be identified;
s32, selecting Y (Y is more than or equal to 1) training points to perform impact training in each training area in the step S31; marking the sample impact signals collected in each training area as the same class and marking the class labels as the area numbers, namely 'area 1, area 2 … area X';
and S33, collecting sample signals of each sensor under each training point, and establishing a pre-sampling library containing X types of training points X X Y, wherein the data structure of the pre-sampling library is shown in a table 1.
TABLE 1
Training Point numbering Categories/regions Individual sensor signal
1 Region 1 Teach point 1 sensor signal data
2 Region 2 Teach point 2 sensor signal data
… … …
X*Y Region X Number of training points X X Y sensor signalsAccording to
Specifically, the method of time-series identification in step S50 includes:
s51, calculating a time sequence distance set D between the acquired impact signal and a pre-sampling library according to a DTW algorithm; in the step, the impact response signal of the piezoelectric sensor is a time sequence in nature, and the impact method realizes impact area positioning by using an algorithm based on time sequence identification;
the similarity between time sequences is calculated (i.e., the distance between sequences is smaller, the similarity is higher).
A DTW (dynamic Time warping) dynamic Time warping algorithm, which is a dynamic planning algorithm for calculating the similarity of 2 Time sequences, especially sequences with different lengths. Mainly applied to time series data, consider two sequences A, B. Their lengths are a, b, respectively, and time warping is performed to find the shortest distance between the 2 sequences.
As shown in fig. 6, in order to perform the alignment operation, we need to construct a matrix of a × b; the matrix element (i, j) represents AiAnd BjDistance d (A) between two pointsi,Bj) The distance calculation uses the Euclidean distance, i.e., d (A)i,Bj)＝(Ai,Bj)2. The DTW algorithm is to find a starting point from the origin to (A)a,Bb) The shortest path of (2). We define this shortest path as W, and the nth time-series distance element of W is defined as Wn＝(i,j)nAnd then:
W＝W1+W2+…+Wn；
the shortest path to the target is:
Figure GDA0002875707390000071
wherein:
a is an impact signal;
b is a sample signal in the pre-sampling library;
w is the shortest path between the timing of the impulse signal and the timing of the sample signals in the pre-sampling bank;
Wnis the nth time series distance element in W, Wn＝(i,j)n(ii) a The (i, j) is A in the matrixiAnd BjThe distance between two points.
The impulse signal comprises a plurality of sensor signals, and for a certain impulse signal, as shown in fig. 7, the time sequence distance d between each acquired sensor signal and the corresponding sampled sensor signal at the nth point of the pre-sampling library is calculateds1、ds2...dsm(the timing distance is formulated by the DTW algorithm); adding the time sequence distances to obtain a time sequence distance d between the impact signal and a certain point corresponding to the pre-sampling libraryn(ii) a And, dnThe class label of (1) is the class label corresponding to the nth point (if the 1 st point is in the area 1, the corresponding class label is "area 1"; if the 5 th point is in the area 5, the corresponding label is "area 5"), that is: dn＝ds1+ds2+…+dsm
Then, a time sequence distance set D of each acquired sensor signal and all training points in the pre-sampling library is obtained:
D＝[d1,d2,…,dn,dx*Y]；
s52, arranging the time sequence distance elements in the time sequence distance set D in an ascending order through a K nearest neighbor classification method, taking the first K time sequence distance elements in the time sequence distance set D, counting the number of the time sequence distance elements in each positioning area, and identifying the positioning area containing the most time sequence distance elements as an impact area.
Specifically, in step S52, as shown in fig. 8, the time-series distances D in the time-series distance set D are set by the K-nearest neighbor methodnPerforming ascending arrangement; according to the number Y of training points in each training area, taking the first K (n is an odd number not greater than Y) time sequence distance elements, and counting the positioning area types to which the time sequence distance elements belong, wherein the positioning area containing the most time sequence distance elements is identified as an impact area. In the step, the first K time sequence distance elements are taken as one pass of a K neighbor algorithmProgram, in conjunction with FIG. 8:
the distance set D has been previously sorted in an ascending order (D is from small to large), at this time, the first K elements are the K elements that are found to be closest (the distance is the smallest) to the target to be detected, the closest distance is the most similar, and we judge the type of the target to be detected by looking at which type of the K points is the most (voting process), that is, which type of sample is the most similar to it, we consider it as that type (for example, K is 6, the first 6 sample types are ABACCA, we consider the target type as a).
Regarding the value of K:
if the value of K is too large, too many samples with too far distance from the target or not large similarity are taken in, and the classification precision is reduced; considering that K is larger than the limit, the whole D set is taken, that is, all samples are taken regardless of similarity or distance, the number of various samples is meaningless, the target class cannot be predicted, or the target may belong to any class, so the size of K should be appropriate.
In this embodiment, K is set to be not greater than the number of training points in a single region, because ideally, the most similar points to the point to be measured are Y training points in the same region as the point to be measured, and if the most similar points are included in the maximum Y taken by K, more points are not beneficial to a result with higher precision (K generally only takes 1, 3); in addition, K is set to be an odd number in this embodiment, so as to avoid the occurrence of two or more consistent class numbers (AABB, AB, AABBCC), and the most one cannot be found, that is, the class cannot be determined, so K should be an odd number not greater than the number of training points (if ABC still occurs (this is very rare), K is adjusted to be 1, and K is an adjustable term).
According to the positioning method for identifying the impact area based on the time sequence, provided by the embodiment of the invention, the positioning process is completed by a time sequence identification method, namely DTW (dynamic time warping) calculation signal distance/similarity and a K neighbor algorithm in a classification mode, so that the influence of the wave velocity difference of impact waves caused by the anisotropy of materials on the positioning result is reduced, and the detection precision is improved.
Example 1
The following describes the technical solution of the present invention in detail with reference to fig. 9.
In this example, a composite material sheet (480mm by 480mm) comprising a T-shaped reinforcing structure was used for the validation.
The method comprises the following specific steps:
step 1: and arranging sensors by combining structural features and dividing 4 × 4 training areas, wherein structural parameters and area division are shown in fig. 4, and 5 training points are arranged in each training area for training and collecting sensor signals to construct a pre-sampling library.
Step 2: and (4) acquiring an impact signal, and positioning an impact area by adopting a time sequence identification method based on time sequence identification DTW and K neighbor classification. The method comprises the following specific steps:
step 2.1: and calculating to obtain a time sequence distance set D of the signal and the pre-sampling library by combining a DTW algorithm.
Step 2.2: arranging time sequence distance elements in a time sequence distance set D in an ascending order, taking the first K elements, counting the number of the elements in each category, wherein the category (namely a positioning Area) containing the most time sequence distance elements is an identification result, wherein K is 1, each Area is subjected to impact verification for 3 times, an identification result confusion matrix is shown as the following 10, the vertical coordinate corresponding to the number in the graph represents an actual Area, the horizontal coordinate represents a prediction Area, and if the horizontal and vertical coordinates corresponding to the coordinates are the same, namely the True impact position (True Area) is the same as the prediction impact position (Predicted Area), the identification result of the impact is accurate. Example (c): if the number corresponding to the coordinates (B, C) is 1, and the number corresponding to the coordinates (B, B) is 2, the result shows that in 3 times of impact recognition, the B area is recognized as the B area for 2 times, the B area is recognized as the C area for 1 time, and the accuracy rate reaches 66.7 percent.
It can be seen that, on the experimental board with the T-shaped stiffened plate laid by the programmed prepreg, the positioning identification method provided by the embodiment can accurately identify the impacts of 16 areas.
According to the positioning method for identifying the impact area based on the time sequence, provided by the embodiment of the invention, the impact area is positioned by dividing the positioning area, then arranging the sensors, then establishing the pre-sampling library and identifying and comparing the time sequence according to the pre-sampling library. The positioning method for identifying the impact area based on the time sequence can accurately position the impact area, and can limit the overhaul and maintenance of the composite material structure in the impact area when being applied to the overhaul and maintenance of the composite material structure, thereby shortening the overhaul and maintenance time and the economic cost; meanwhile, the safety and the reliability of the structure of the area to be identified are improved.
The positioning method for identifying the impact area based on the time sequence provided by the embodiment of the invention enables the visual maintenance of the aircraft structure to be possible, and has important significance for reducing the maintenance and repair cost and improving the safety and reliability of the structure.
Embodiments of the present invention additionally provide a system for identifying a location of an impact region based on a time series, comprising a sensor and an analysis device for interactively performing a method as any above.
Specifically, the sensor is arranged in a positioning area divided by an area to be identified and used for acquiring a training sample signal and an impact signal;
the analysis device is used for collecting the sample signals and the impact signals transmitted by the sensor, storing and analyzing the signal data, establishing a pre-sampling library and performing time sequence identification comparison.
The invention provides an impact area positioning method for a composite material structure, and the method is also suitable for non-composite material structures such as metal and the like.
According to the positioning method for identifying the impact region based on the time sequence, provided by the embodiment of the invention, the positioning principle is based on the similarity measurement of the impact signal, the wave velocity is not related, and the influence of anisotropic characteristics is small, so that the accurate positioning effect can be still realized on a complex plane containing a T-shaped reinforcing structure in the embodiment. Specifically, the signal distance/similarity is calculated through DTW, and the positioning process is completed through a classification mode by combining a K neighbor algorithm, so that the influence of the shock wave velocity difference caused by the anisotropy of the material on the positioning result is reduced.
Although terms such as system, sensor, area to be identified, location area, etc. are used more often herein, the possibility of using other terms is not excluded. These terms are used merely to more conveniently describe and explain the nature of the present invention; they are to be construed as being without limitation to any additional limitations that may be imposed by the spirit of the present invention.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (5)

1. A positioning method for identifying an impact area based on a time series is characterized by comprising the following steps:
s10, dividing the area to be identified into a plurality of positioning areas;
s20, arranging sensors in the positioning areas respectively;
s30, performing impact training in advance in the positioning area and collecting sensor sample signals to form a pre-sampling library corresponding to the positioning area and the sample signals;
s40, acquiring impact signals through a sensor when identifying impact areas;
s50, carrying out time sequence identification comparison on the collected impact signal and the sample signal in the pre-sampling library to realize impact area positioning;
the method of time-series identification in step S50 includes:
s51, calculating a time sequence distance set D between the acquired impact signal and a pre-sampling library according to a DTW algorithm;
s52, arranging the time sequence distance elements in the time sequence distance set D in an ascending order, taking the first K time sequence distance elements, counting the number of the time sequence distance elements in each category, and identifying the category containing the most time sequence distance elements as an impact area;
the DTW algorithm formula in step S51 is as follows:
Figure FDA0002899027720000011
wherein:
a is an impact signal;
b is a sample signal in the pre-sampling library;
w is the shortest path between the timing of the impulse signal and the timing of the sample signals in the pre-sampling bank;
Wnis the nth time series distance element in W, Wn＝(i,j)n(ii) a The (i, j) is A in the matrixiAnd BjThe distance between two points;
in step S51, the impact signal includes a plurality of sensor signals, and for a certain impact signal, the time-series distance d between each acquired sensor signal and the sampled sensor signal at a point corresponding to the pre-sampling library is calculateds1、ds2...dsm(ii) a Adding the time sequence distances to obtain a time sequence distance d between the impact signal and a certain point corresponding to the pre-sampling libraryn(ii) a Obtaining a time sequence distance set D between each acquired sensor signal and all training points of a pre-sampling library;
in step S52, the time-series distances D in the time-series distance set D are determined by the K-nearest neighbor methodnPerforming ascending arrangement; and according to the number Y of the training points of each training area, taking the first K time sequence distance elements, and counting the positioning area types to which the time sequence distance elements belong, wherein the positioning area containing the most time sequence distance elements is identified as an impact area.
2. The positioning method for identifying impact regions based on time series according to claim 1, wherein: the construction method of the pre-sampling library in the step S30 comprises the following steps:
s31, arranging M sensors according to the geometric shape of the region to be identified, dividing X training regions, and numbering the training regions;
s32, selecting training points with Y being more than or equal to 1 to carry out impact training in each training area in the step S31; the sample impact signals collected in each training area are marked as the same class, and the class labels are the numbers of the training areas;
and S33, collecting signals of each sensor under each training point, and establishing a pre-sampling library containing X types of training points X X Y.
3. The positioning method for identifying impact regions based on time series according to claim 2, wherein: the training area uniformly covers the area to be identified.
4. The positioning method for identifying impact regions based on time series according to claim 1, wherein: k is an odd number not greater than Y.
5. A system for identifying the location of impact zones on the basis of a time sequence, characterized in that it comprises sensors and analysis means for performing the method of any one of claims 1 to 4 interactively.
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