CN116738173A - Data acquisition and analysis method for aircraft assembly process - Google Patents
Data acquisition and analysis method for aircraft assembly process Download PDFInfo
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- CN116738173A CN116738173A CN202310707359.1A CN202310707359A CN116738173A CN 116738173 A CN116738173 A CN 116738173A CN 202310707359 A CN202310707359 A CN 202310707359A CN 116738173 A CN116738173 A CN 116738173A
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Abstract
The application relates to the technical field of aircraft assembly, in particular to a data acquisition and analysis method in an aircraft assembly process. The method comprises the following steps: receiving transmission data of a measuring instrument; when the transmission data is not abnormal, adding a trusted data tag; when the transmission data is an abnormal value, fitting the abnormal value by adopting a fourth-order Dragon lattice tower algorithm; and carrying out correlation analysis on the first transmission data without abnormality or the second transmission data after fitting. The method for acquiring and analyzing the data in the aircraft assembly process aims to solve the problem that physical environment data in the existing aircraft assembly process cannot be acquired dynamically and accurately and depends on post detection.
Description
Technical Field
The application relates to the technical field of aircraft assembly, in particular to a data acquisition and analysis method in an aircraft assembly process.
Background
The aircraft assembly process is quite complex, and has the characteristics of more parts, large size, small rigidity, complex appearance and structure, high value, high precision requirement and the like. The structure assembly quality determines the performance and service life of the aircraft, and the heterogeneous laminated structure is easy to generate assembly out-of-tolerance and assembly defects due to the forming precision, complex assembly acting force, geometric errors of multiple sources and variable assembly environment influence, so that the assembly quality is influenced. Therefore, the unified collection and analysis of the assembly process data is urgent.
At present, technologies such as measurement auxiliary assembly and digital detection are widely applied to assembly detection of complex mechanical products, but the assembly quality is mostly detected after assembly is finished, and the assembly quality lacks data acquisition and analysis of assembly acting force and assembly environment in the assembly process and does not have the function of guiding on-site assembly in real time; the aircraft assembly has the characteristics of long assembly period, complex and huge assembly scene and the like, and the data is easy to be subjected to external interference such as temperature, vibration and the like in the assembly process, so that a great amount of noise, distortion and other data abnormal conditions appear in the data, partial distortion data are required to be corrected, and the data before correction are recorded, so that the disc is convenient to multiplex in the future; and the aircraft structural member has long production and manufacturing period and high value, once the assembly failure loss is large and the whole assembly period is influenced, the data acquisition and analysis of the assembly process are further required to be further enhanced, problems are found and solved during assembly, and the on-site assembly process guiding capability is improved, so that the assembly can be adjusted in time during the assembly process, and the loss is avoided.
Accordingly, the inventors provide a method of aircraft assembly process data acquisition and analysis.
Disclosure of Invention
(1) Technical problem to be solved
The embodiment of the application provides a data acquisition and analysis method in an aircraft assembly process, which solves the technical problem that physical environment data cannot be acquired dynamically and accurately and is dependent on post detection in the existing aircraft assembly process.
(2) Technical proposal
The application provides a data acquisition and analysis method in an aircraft assembly process, which comprises the following steps:
receiving transmission data of a measuring instrument;
when the transmission data is abnormal, adding a trusted data tag; when the transmission data is an abnormal value, fitting the abnormal value by adopting a fourth-order Dragon-Grating-tower algorithm;
and carrying out correlation analysis on the first transmission data without abnormality or the second transmission data after fitting.
Further, before receiving the transmission data of the measuring instrument, the method further comprises:
acquiring the equipment number of the measuring instrument and reading a system reference number, and judging that the measuring instrument is normal if the equipment number corresponds to the system reference number; if the equipment number does not correspond to the system reference number, marking the measuring instrument as an in-doubt measuring instrument, and simultaneously newly building the in-doubt measuring instrument number.
Further, when the transmission data is an outlier, fitting the outlier by adopting a fourth-order Dragon-Gregory tower algorithm, and specifically comprising the following steps:
determining abnormal values of the transmission data according to a threshold value;
and when the transmission data carries out abnormal values, fitting the abnormal values by using the fourth-order Dragon-grid-tower algorithm.
Further, the threshold includes a maximum tolerable force of the mounting material, an empirical value.
Further, the correlation analysis is performed on the transmission data without abnormality or the abnormal value after fitting, specifically including the following steps:
determining a reference sequence reflecting the assembly quality characteristics and a comparison sequence affecting the final quality of the aircraft assembly in the aircraft assembly process according to the first transmission data or the second transmission data;
normalizing the reference sequence and the comparison sequence, and calculating a gray correlation coefficient;
determining the association degree according to the gray correlation coefficient;
and calculating the assembly deviation degree by using the association degree.
Further, the determining the association degree according to the gray correlation coefficient specifically includes: and carrying out normalization processing and sequencing on the association degrees, and determining the target association degrees meeting the set conditions.
Further, the calculating the assembly deviation degree by using the association degree comprises the following steps:
establishing a standard database by utilizing the collected assembly process data;
carrying out mean value calculation on the data of the standard database to obtain mean value data;
and calculating the assembly deviation according to the target association degree, the mean value data and the comparison sequence.
Further, the association degree is normalized by adopting a mean value method.
Further, the normalizing process for the reference sequence and the comparison sequence specifically includes: and normalizing the reference sequence and the comparison sequence by adopting a mean value method.
Further, after calculating the fitting deviation degree by using the association degree, the method further comprises:
and comparing the assembly deviation with a deviation critical value, and determining the assembly quality according to a comparison result.
(3) Advantageous effects
In conclusion, the abnormal judgment is carried out on the collected transmission data, the simulation value is calculated on the abnormal value through fitting, and the correlation analysis method is adopted to carry out correlation analysis on the assembly process data, so that the method is beneficial to guiding on-site assembly.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort to a person of ordinary skill in the art.
FIG. 1 is a schematic flow chart of a method for data acquisition and analysis in an aircraft assembly process according to an embodiment of the present application;
FIG. 2 is a schematic diagram of an aircraft assembly process data acquisition and analysis system according to an embodiment of the present application;
fig. 3 is a schematic flow chart of a method for collecting and analyzing data in a machine assembling process according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in further detail below with reference to the accompanying drawings and examples. The following detailed description of the embodiments and the accompanying drawings are provided to illustrate the principles of the application and are not intended to limit the scope of the application, i.e., the application is not limited to the embodiments described.
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The application will be described in detail below with reference to the drawings in connection with embodiments.
Fig. 1 is a schematic flow chart of a method for collecting and analyzing data in an aircraft assembly process according to an embodiment of the present application, as shown in fig. 1, the method may include the following steps:
s100, receiving transmission data of a measuring instrument;
s200, when the transmission data is abnormal, adding a trusted data tag; when the transmission data is an abnormal value, fitting the abnormal value by adopting a fourth-order Dragon lattice tower algorithm;
s300, carrying out correlation analysis on the first transmission data without abnormality or the second transmission data after fitting.
In the above embodiment, the data acquisition and analysis device includes a measuring instrument (specifically, a sensor), an upper computer, and a PLC, where the measuring instrument, the PLC, and the upper computer are connected by a bus, the data is transmitted by the bus, and the data is integrated into a unified array or structure after reaching the upper computer by a type, and the suspicious data is marked by a data acquisition module,
and finally, predicting the difference between the actual assembly and the expected index through the deviation index, and displaying the prediction result in an information prompt mode, so that the method can be used for guiding the on-site assembly process.
As an optional embodiment, in step S100, before receiving the transmission data of the measuring instrument, the method further includes:
acquiring the equipment number of the measuring instrument and reading the system reference number, and judging that the measuring instrument is normal if the equipment number corresponds to the system reference number; if the equipment number does not correspond to the system reference number, marking the measuring instrument as an in-doubt measuring instrument, and simultaneously newly building the in-doubt measuring instrument number.
As an optional implementation manner, in step S200, when the transmission data is an outlier, a fourth-order longgrid-base tower algorithm is used to fit the outlier, which specifically includes the following steps:
s201, judging abnormal values of transmission data according to a threshold value;
and S202, when the abnormal value is carried out on the transmission data, fitting the abnormal value by using a fourth-order Dragon lattice tower algorithm.
Wherein, for satisfyingAnd a sampling data set of the functional relation, wherein a fourth-order Dragon-Grave-Tower algorithm is used for fitting the abnormal value according to the following formula:
wherein K is 1 Is the slope at the beginning of the time period;
K 2 is the slope of the midpoint of the time period, where Euler slope K is used 1 To determine y at point t n A value of +h/2;
K 3 is also the slope of the midpoint, but here the slope K is used 2 Determining a y value;
K 4 is the slope of the end of the time period, its y value is K 3 And (5) determining.
y in Sampling data of the nth period representing the ith factor, t n The sampling time of the nth period, h is the step length, so thatConstant y i(n+1) Can be represented by the trusted value y in Fitting is performed by adding a step size (h).
As an alternative embodiment, the threshold value comprises a maximum tolerable force, empirical value, of the fitting material.
As an optional implementation manner, in step S300, correlation analysis is performed on transmission data without anomalies or the fitted anomalies, and specifically includes the following steps:
s301, determining a reference sequence reflecting the assembly quality characteristics and a comparison sequence influencing the final quality of the aircraft assembly in the aircraft assembly process according to the first transmission data or the second transmission data;
s302, carrying out normalization processing on the reference sequence and the comparison sequence, and calculating a gray correlation coefficient;
s303, determining the association degree according to the gray correlation coefficient;
s304, calculating the assembly deviation degree by using the association degree.
In the above embodiment, the slave data y in Selecting a reference sequence X reflecting the quality of the assembly during the assembly of an aircraft 0 n And a comparison series X affecting the final quality of the aircraft assembly i n N represents the number of values in the array;
to reference the number X 0 n And comparing the number series X i n Normalization is carried out, and a mean value method is generally adopted for processing:
in the method, in the process of the application,respectively represent reference number series X 0 n And comparing the number series X i n The kth value of> Respectively represent reference number series X 0 n And comparing the number series X i n Average of all values of (c), max (X 0 N )、max(X i N ) Respectively represent reference number series X 0 n And comparing the number series X i n Maximum value of all values of (2), min (X 0 N )、min(X i N ) Respectively represent reference number series X 0 n And comparing the number series X i n The maximum value and the minimum value among all the values of the comparison sequence and the reference sequence are calculated to obtain gray correlation coefficients:
wherein x is 0 (j)、x 0 (k) Represents the j, k values in the reference number column, x i (j)、x i (k) Represents the j-th and k-th values in the i-th comparison number column,representing the minimum absolute value of the difference between the comparison series and the reference series,representing the minimum value taking the minimum absolute value of the differences between all the comparison series and the reference series,and the same is true. i represents the i element, j and k represent the j and k values, ρ is a resolution coefficient, and 0.5 is generally taken.
According to the calculated gray correlation coefficient, the calculation formula of the correlation degree is as follows:
as an optional implementation manner, in step S301, the degree of association is determined according to the gray correlation coefficient, specifically: and carrying out normalization processing and sequencing on the association degrees, and determining the target association degrees meeting the set conditions.
As an optional embodiment, in step S304, the fitting deviation degree is calculated by using the association degree, and specifically includes the following steps:
s3041, establishing a standard database by using the collected assembly process data;
s3042, carrying out mean value calculation on data of a standard database to obtain mean value data;
s3043, calculating the assembly deviation degree according to the target association degree, the mean value data and the comparison sequence.
In step S3041, the collection system is used to collect the assembly process data, and multiple groups of data with the assembly quality data meeting the quality requirement are selected to form a standard database.
In step S3042, the mean value data is calculated as follows:
where i denotes an i-th element, a denotes a-th group data, and j denotes a j-th value.
In step S3043, the degree of deviation is calculated as follows:
where m represents the number of degrees of association between two of the n values in the array, where,
as an optional embodiment, in step S300, after calculating the fitting deviation degree by using the association degree, the method further includes: and comparing the assembly deviation with a deviation critical value, and determining the assembly quality according to the comparison result.
Data acquisition and x in the assembly process i (j) And comparing, calculating the deviation degree by using the association degree, comparing with a deviation degree critical value, and sending a deviation excessive prompt to an upper computer if the deviation excessive prompt exceeds the deviation degree critical value, wherein the assembly may be out of tolerance.
Fig. 2 is an implementation diagram of an overall scheme, wherein sensor readings such as temperature, displacement, force and strain are obtained through a PLC, state information such as switching value is collected through a PLC input/output module, an executing mechanism such as a motor is connected through a field real-time bus, the PLC is communicated with an upper computer through an ADS interface, a fitting algorithm and a correlation algorithm run in the upper computer, and when the system judges that assembly is possibly unqualified through collected data, the system directly sends a signal to stop assembly and waits for manual adjustment confirmation.
FIG. 3 is a flowchart of an assembly process acquisition and analysis method, wherein the specific process is basically consistent with the technical scheme, the system is connected and then self-inspected, if the sensors are normal, the sensor data is received, otherwise, a new sensor number is established for acquisition; judging the data during acquisition, adding a label if no abnormal value exists, and firstly fitting by using an algorithm and then adding if the abnormal value exists; and then, gray correlation analysis is carried out, correlation is calculated, normalization processing is carried out, data dimension reduction is carried out, and finally, assembly deviation is calculated according to the correlation and a standard database, and a prediction result is output.
It should be understood that, in the present specification, each embodiment is described in an incremental manner, and the same or similar parts between the embodiments are all referred to each other, and each embodiment is mainly described in a different point from other embodiments. The application is not limited to the specific steps and structures described above and shown in the drawings. Also, a detailed description of known method techniques is omitted here for the sake of brevity.
The above is only an example of the present application and is not limited to the present application. Various modifications and alterations of this application will become apparent to those skilled in the art without departing from the scope of this application. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (10)
1. A method for data acquisition and analysis during aircraft assembly, the method comprising the steps of:
receiving transmission data of a measuring instrument;
when the transmission data is abnormal, adding a trusted data tag; when the transmission data is an abnormal value, fitting the abnormal value by adopting a fourth-order Dragon-Grating-tower algorithm;
and carrying out correlation analysis on the first transmission data without abnormality or the second transmission data after fitting.
2. The aircraft assembly process data collection and analysis method according to claim 1, further comprising, prior to receiving the transmission data of the measuring instrument:
acquiring the equipment number of the measuring instrument and reading a system reference number, and judging that the measuring instrument is normal if the equipment number corresponds to the system reference number; if the equipment number does not correspond to the system reference number, marking the measuring instrument as an in-doubt measuring instrument, and simultaneously newly building the in-doubt measuring instrument number.
3. The method for collecting and analyzing data in an aircraft assembly process according to claim 1, wherein when the transmission data is an outlier, a fourth-order longgrid tower algorithm is adopted to fit the outlier, and the method specifically comprises the following steps:
determining abnormal values of the transmission data according to a threshold value;
and when the transmission data carries out abnormal values, fitting the abnormal values by using the fourth-order Dragon-grid-tower algorithm.
4. A method of aircraft assembly process data acquisition and analysis according to claim 3, wherein the threshold value comprises a maximum tolerable force, empirical value, of the assembly material.
5. The method for collecting and analyzing data of aircraft assembly process according to claim 1, wherein the correlation analysis is performed on the transmission data without anomalies or the fitted anomalies, specifically comprising the steps of:
determining a reference sequence reflecting the assembly quality characteristics and a comparison sequence affecting the final quality of the aircraft assembly in the aircraft assembly process according to the first transmission data or the second transmission data;
normalizing the reference sequence and the comparison sequence, and calculating a gray correlation coefficient;
determining the association degree according to the gray correlation coefficient;
and calculating the assembly deviation degree by using the association degree.
6. The method for collecting and analyzing aircraft assembly process data according to claim 5, wherein the determining the degree of association according to the gray correlation coefficient is specifically:
and carrying out normalization processing and sequencing on the association degrees, and determining the target association degrees meeting the set conditions.
7. The method for collecting and analyzing data of aircraft assembly process according to claim 6, wherein said calculating assembly deviation using said correlation comprises the steps of:
establishing a standard database by utilizing the collected assembly process data;
carrying out mean value calculation on the data of the standard database to obtain mean value data;
and calculating the assembly deviation according to the target association degree, the mean value data and the comparison sequence.
8. The method of claim 6, wherein the correlation is normalized by means of an averaging method.
9. The method for collecting and analyzing aircraft assembly process data according to claim 5, wherein the normalizing the reference sequence and the comparison sequence comprises:
and normalizing the reference sequence and the comparison sequence by adopting a mean value method.
10. The method of claim 5, wherein after calculating the assembly deviation using the correlation, further comprising:
and comparing the assembly deviation with a deviation critical value, and determining the assembly quality according to a comparison result.
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