CN112798143A - Molding frame state monitoring method based on integrated optical fiber sensor - Google Patents
Molding frame state monitoring method based on integrated optical fiber sensor Download PDFInfo
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- CN112798143A CN112798143A CN202110389091.2A CN202110389091A CN112798143A CN 112798143 A CN112798143 A CN 112798143A CN 202110389091 A CN202110389091 A CN 202110389091A CN 112798143 A CN112798143 A CN 112798143A
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- 239000013307 optical fiber Substances 0.000 title claims abstract description 79
- 238000000034 method Methods 0.000 title claims abstract description 50
- 238000012544 monitoring process Methods 0.000 title claims abstract description 20
- 238000000465 moulding Methods 0.000 title description 3
- 238000013528 artificial neural network Methods 0.000 claims abstract description 22
- 238000005259 measurement Methods 0.000 claims abstract description 15
- 238000013178 mathematical model Methods 0.000 claims abstract description 12
- 238000012549 training Methods 0.000 claims description 10
- 238000009434 installation Methods 0.000 claims description 7
- 238000013507 mapping Methods 0.000 claims description 6
- 239000000463 material Substances 0.000 claims description 6
- 230000001419 dependent effect Effects 0.000 claims description 3
- 238000009415 formwork Methods 0.000 claims 1
- 229910000831 Steel Inorganic materials 0.000 description 4
- 239000010959 steel Substances 0.000 description 4
- 239000000835 fiber Substances 0.000 description 3
- 230000010354 integration Effects 0.000 description 3
- 238000003062 neural network model Methods 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 238000001514 detection method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 210000002569 neuron Anatomy 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000013598 vector Substances 0.000 description 1
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Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K11/00—Measuring temperature based upon physical or chemical changes not covered by groups G01K3/00, G01K5/00, G01K7/00 or G01K9/00
- G01K11/32—Measuring temperature based upon physical or chemical changes not covered by groups G01K3/00, G01K5/00, G01K7/00 or G01K9/00 using changes in transmittance, scattering or luminescence in optical fibres
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
- G01L1/00—Measuring force or stress, in general
- G01L1/24—Measuring force or stress, in general by measuring variations of optical properties of material when it is stressed, e.g. by photoelastic stress analysis using infrared, visible light, ultraviolet
- G01L1/242—Measuring force or stress, in general by measuring variations of optical properties of material when it is stressed, e.g. by photoelastic stress analysis using infrared, visible light, ultraviolet the material being an optical fibre
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
Abstract
A method for monitoring the state of a fixture based on an integrated optical fiber sensor relates to the field of automatic monitoring of fixture assembly, is based on a fixture provided with a positioner, and comprises the following steps: acquiring real-time data of the temperature and the strain of different areas of the fixture through an optical fiber sensor to obtain the acquisition amount of the temperature and the strain; measuring the spatial position of the fixture positioner by using digital measuring equipment while acquiring data by using the optical fiber sensor, and acquiring the spatial position information of the positioner as a measured value under the current temperature and strain; repeating the processes of sensor acquisition and locator measurement, establishing a data set in which the acquisition quantity and the measured value are in one-to-one correspondence, and establishing a mathematical model between the temperature and the strain of the acquisition quantity and the spatial position of the locator by adopting a BP neural network algorithm. The position change of the positioner in the assembling process is monitored, the state of the positioner can be better monitored, and the quality of an assembled product is prevented from being too poor.
Description
Technical Field
The invention relates to the field of automatic monitoring, in particular to a method for monitoring the state of a fixture based on an integrated optical fiber sensor.
Background
The stability of the state of the aircraft assembly fixture influences the assembly precision of aircraft components, and is an important index for ensuring the accuracy and coordination accuracy of aircraft assembly. At present, the detection of the state of the fixture is mainly to detect the position of a locator on the fixture through a laser tracker and judge whether the locator meets the requirement of positioning precision. The detection mode can only detect the state change of the model frame when the model frame is statically placed, and cannot monitor the state of the positioner in the assembling process, so that the product out-of-tolerance caused by overlarge deviation of the positioner is possibly caused, and the assembling quality of the airplane is influenced.
Disclosure of Invention
The invention aims to: the method comprises the steps of utilizing the high integration of the optical fiber sensor to collect real-time data of temperature and strain of different areas of a fixture in the assembling process, acquiring position change parameters of a fixture positioner, and establishing a BP neural network model between the data collection amount of the integrated optical fiber sensor and the position change of the positioner by analyzing big data, thereby realizing the monitoring of the position change of the positioner in the assembling process and solving the problems.
The technical scheme adopted by the invention is as follows:
a method for monitoring the state of a fixture based on an integrated optical fiber sensor is based on a fixture provided with a positioner and comprises the following steps:
step S1: acquiring real-time data of the temperature and the strain of different areas of the fixture through an optical fiber sensor to obtain the acquisition amount of the temperature and the strain;
step S2: measuring the spatial position of the fixture positioner by using digital measuring equipment while acquiring data by using the optical fiber sensor, and acquiring the spatial position information of the positioner as a measured value under the current temperature and strain;
step S3: and repeating the sensor acquisition in the step S1 and the locator measurement process in the step S2, establishing a data set in which the acquired quantity and the measured value are in one-to-one correspondence, and establishing a mathematical model between the temperature and the strain of the acquired quantity and the spatial position of the locator by adopting a BP neural network algorithm.
In order to better realize the scheme, the optical fiber sensor is an integrated optical fiber sensor and comprises an optical fiber strain sensor and an optical fiber temperature sensor, a strain sensing element of the optical fiber strain sensor and a temperature sensing element of the optical fiber temperature sensor are connected together in a serial or parallel mode, and real-time data acquisition is carried out on the temperature and the strain quantity of different areas of the moulding frame.
In order to better implement the scheme, further, at least one optical fiber temperature sensor is arranged on each component made of different materials of the fixture.
In order to better implement the scheme, further, the mounting method for mounting the optical fiber strain sensor comprises the following steps: and introducing the fixture and the positioner into a virtual simulation environment, applying a load to simulate the deformation of the fixture body and the positioner at the tail end of the positioner according to the deformation of the actual fixture on site, and estimating the load by dividing the total mass of the product on the fixture by the number of the positioners on the fixture.
In order to better implement the present solution, further, the method for establishing the data set in which the acquisition amount and the measurement value are in one-to-one correspondence specifically includes: the positioner is loaded through the loading device, real-time data acquisition is carried out on the temperature and the strain of different areas of the fixture in the assembling process through the optical fiber sensor, the strain and the temperature value are obtained, meanwhile, the space position of the fixture positioner is measured through the digital measuring equipment, and the one-to-one corresponding relation between the sensor data acquisition value and the positioner position measurement value is formed.
In order to better realize the scheme, random loads with different directions and sizes are applied to the positioner within the actual loaded space range through the loading device, the state of the fixture is changed by changing the size and the direction of the loads, and a large amount of training sample data is obtained by repeating the data acquisition and spatial position measurement processes.
In order to better implement the present solution, further, the mathematical model established by the BP neural network algorithm in step S3 has the input of the temperature and the dependent variable acquired by the optical fiber sensor, and the output of the mathematical model is the position coordinate of the positioner, and then the input and the output are used to train the neural network, so as to establish the nonlinear mapping relationship between the input and the output.
In the scheme, real-time data acquisition is mainly carried out on the temperature and the strain of different areas of the fixture through the integrated optical fiber sensor. And when the sensor collects data, the digital measuring equipment is used for measuring the spatial position of the fixture positioner, and the spatial position information of the positioner under the current temperature and strain is obtained. Repeating the processes of sensor acquisition and locator measurement, establishing a data set in which the acquisition quantity and the measured value are in one-to-one correspondence, and establishing a mathematical model between the acquisition quantity, namely the temperature, the strain and the spatial position of the locator by adopting a BP neural network algorithm.
In addition, in order to monitor the state of the fixture in the assembling process, a mathematical model between the data acquisition quantity of the integrated optical fiber sensor and the spatial position coordinates of the positioner in the assembling process is established, and a mapping relation between the data acquisition quantity and the spatial position coordinates of the positioner is established by adopting a BP neural network algorithm through a large amount of training sample data. Wherein the input of the BP neural network is the temperature and strain information collected by the integrated optical fiber sensor, so as to
In a manner shown in which
In order to be the amount of strain,
the output is the locator position coordinate information, expressed in (x, y, z) form, as a temperature value.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
1. according to the method for monitoring the fixture state based on the integrated optical fiber sensor, in the assembly process, the optical fiber sensor is utilized to acquire real-time data of temperature and strain of different areas of the fixture in real time, position change parameters of a fixture positioner are acquired at the same time, and a BP neural network model between the data acquisition quantity of the integrated optical fiber sensor and the position change of the positioner is established by analyzing big data, so that the position change of the positioner in the assembly process is monitored;
2. according to the method for monitoring the state of the fixture based on the integrated optical fiber sensor, in the assembly process, the high integration of the optical fiber sensor is utilized, the real-time data acquisition is carried out on the temperature and the strain of different areas of the fixture, meanwhile, the position change parameters of the fixture positioner are obtained, and the BP neural network model between the data acquisition quantity of the integrated optical fiber sensor and the position change of the positioner is established by analyzing the big data, so that the position change of the positioner in the assembly process is monitored, the state of the positioner can be better monitored, and the poor quality of the assembled product is avoided.
Drawings
In order to more clearly illustrate the technical solution, the drawings needed to be used in the embodiments are briefly described below, and it should be understood that, for those skilled in the art, other related drawings can be obtained according to the drawings without creative efforts, wherein:
FIG. 1 is a schematic flow chart of the steps of the present invention;
FIG. 2 is a schematic diagram of the integration principle of the integrated fiber sensor of the present invention;
FIG. 3 is a finite element simulated strain cloud of the inventive fixture deformation;
FIG. 4 is a schematic view of the integrated fiber optic sensor of the present invention loaded after installation;
in the figure, 1-computer, 2-network cable, 3-demodulator, 4-optical fiber, 5-optical fiber temperature sensor, 6-serial connection, 7-optical fiber strain sensor, 8-parallel connection, 9-type frame body, 10-positioner component, 11-positioning point, 12-steel wire rope, 13-loading weight and 14-loading device.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, 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 should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and therefore should not be considered as a limitation to the scope of protection. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
In the description of the present invention, it is to be noted that, unless otherwise explicitly specified or limited, the terms "disposed," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
The present invention will be described in detail with reference to fig. 1 to 4.
Example 1:
a method for monitoring the state of a fixture based on an integrated optical fiber sensor is based on a fixture provided with a positioner, as shown in figure 1, and comprises the following steps:
step S1: acquiring real-time data of the temperature and the strain of different areas of the fixture through an optical fiber sensor to obtain the acquisition amount of the temperature and the strain;
step S2: measuring the spatial position of the fixture positioner by using digital measuring equipment while acquiring data by using the optical fiber sensor, and acquiring the spatial position information of the positioner as a measured value under the current temperature and strain;
step S3: and repeating the sensor acquisition in the step S1 and the locator measurement process in the step S2, establishing a data set in which the acquired quantity and the measured value are in one-to-one correspondence, and establishing a mathematical model between the temperature and the strain of the acquired quantity and the spatial position of the locator by adopting a BP neural network algorithm.
The working principle is as follows: in the scheme, real-time data acquisition is mainly carried out on the temperature and the strain of different areas of the fixture through the integrated optical fiber sensor. And when the sensor collects data, the digital measuring equipment is used for measuring the spatial position of the fixture positioner, and the spatial position information of the positioner under the current temperature and strain is obtained. Repeating the processes of sensor acquisition and locator measurement, establishing a data set in which the acquisition quantity and the measured value are in one-to-one correspondence, and establishing a mathematical model between the acquisition quantity, namely the temperature, the strain and the spatial position of the locator by adopting a BP neural network algorithm.
Example 2:
in this embodiment, on the basis of embodiment 1, the optical fiber sensor is an integrated optical fiber sensor, and includes an optical fiber strain sensor and an optical fiber temperature sensor, and the strain sensing element of the optical fiber strain sensor and the temperature sensing element of the optical fiber temperature sensor are connected together in a serial or parallel manner, so as to perform real-time data acquisition on the temperature and the strain amount of different areas of the fixture.
At least one optical fiber temperature sensor is mounted on each of the components of the fixture made of different materials.
The installation method for installing the optical fiber strain sensor comprises the following steps: and introducing the fixture and the positioner into a virtual simulation environment, applying a load to simulate the deformation of the fixture body and the positioner at the tail end of the positioner according to the deformation of the actual fixture on site, and estimating the load by dividing the total mass of the product on the fixture by the number of the positioners on the fixture.
The method for establishing the data set with the one-to-one correspondence between the acquisition quantity and the measurement value specifically comprises the following steps: the positioner is loaded through the loading device, real-time data acquisition is carried out on the temperature and the strain of different areas of the fixture in the assembling process through the optical fiber sensor, the strain and the temperature value are obtained, meanwhile, the space position of the fixture positioner is measured through the digital measuring equipment, and the one-to-one corresponding relation between the sensor data acquisition value and the positioner position measurement value is formed.
Random loads with different directions and sizes are applied to the positioner within the actual loaded space range through the loading device, the state of the fixture is changed by changing the size and the direction of the loads, and a large amount of training sample data is obtained by repeating the data acquisition and space position measurement processes.
The mathematical model established by the BP neural network algorithm in step S3 has the input of the temperature and the dependent variable acquired by the optical fiber sensor and the output of the position coordinate of the positioner, and then the input and the output are used to train the neural network to establish the nonlinear mapping relationship between the input and the output.
In addition, in order to monitor the state of the fixture in the assembling process, a mathematical model between the data acquisition quantity of the integrated optical fiber sensor and the spatial position coordinates of the positioner in the assembling process is established, and a mapping relation between the data acquisition quantity and the spatial position coordinates of the positioner is established by adopting a BP neural network algorithm through a large amount of training sample data. Wherein the input of the BP neural network is the temperature and strain information collected by the integrated optical fiber sensor, so as to
In a manner shown in which
In order to be the amount of strain,
the output is the locator position coordinate information, expressed in (x, y, z) form, as a temperature value.
Other parts of this embodiment are the same as those of embodiment 1, and thus are not described again.
Example 3:
in this embodiment, on the basis of embodiment 1 or embodiment 2, a conventional fixture as shown in fig. 4 is inspected, a fixture body and positioner components are simplified to a certain extent, and then the fixture body and the positioner components are introduced into a virtual simulation environment, actual loads of the positioners are estimated according to the total weight of the product, that is, the total weight of the product is evenly distributed on the positioners, the same virtual loads are applied to the positioners in the simulation environment, and deformation conditions of the fixture body and the positioner components are simulated, so as to form a finite element simulation strain cloud diagram of fixture deformation as shown in fig. 3.
Then, respectively carrying out the installation of the optical fiber strain sensor and the installation of the optical fiber temperature sensor, as shown in fig. 2, the installation of the optical fiber strain sensor: according to the strain cloud chart generated by the simulation, a region with large strain on the fixture is marked, and the optical fiber strain sensor is installed in the region.
Installation of the optical fiber temperature sensor: because different materials have different expansion coefficients to temperature, the model frame is divided according to the material properties, and an optical fiber temperature sensor is arranged in each different material area.
In order to simplify the wiring of the sensors on the fixture, the temperature sensing elements and the strain sensing elements are connected together in series.
The real-time monitoring model of the fixture locator is established by adopting a BP neural network, and a training sample data set of the model locator is acquired by adopting the following method:
and a steel wire rope is fixed at one end of the tail end of the positioner and penetrates through a steel wire hole of the loading device, and the other end of the steel wire rope is connected with a weight. And (3) selecting a loading plane at will, applying loads in different directions to the tail end of the positioner by using a loading device, and changing the size of the load by increasing or decreasing the number of the weights. The loading process is repeated to form a range of loads applied to the end of the positioner.
Temperature of different areas of the fixture during load application by integrated fiber optic sensors
And strain
And acquiring real-time data, and detecting the spatial positions x, y and z of the fixture positioner by using digital measuring equipment while acquiring the strain and temperature values to form a one-to-one corresponding relationship.
Repeating the loading and data acquisition process to acquire a large amount of acquired data of the integrated optical fiber sensor and the spatial position information of the locator as input and output training sample data of a subsequent neural network algorithm, wherein the formats of an input sample E and an output sample P of the BP neural network are as follows:
wherein n is the number of the optical fiber strain sensors on the fixture, generally not more than 10, m is the number of the optical fiber temperature sensors, s is the number of the training samples, and s is not less than 500.
The network structure of the BP neural network adopts a three-layer neural network structure comprising an input layer, a hidden layer and an output layer, the number of neurons of the input layer and the output layer is mainly determined according to the dimension of an input/output sample, wherein the number of the input layer is the number (m + n) of integrated optical fiber sensors, and the number of the output layer is the number 3 of three-dimensional vectors of the spatial positions of the localizer. The number a of hidden layer neurons is determined according to a trial algorithm, and the formula is as follows:
wherein b is a constant between 1 and 10.
And finally, training the neural network through input and output training samples, establishing a mapping relation between the data acquisition quantity of the integrated optical fiber sensor and the position of the positioner, and monitoring the state change of the framework in real time in the assembling process.
Other parts of this embodiment are the same as any of embodiments 1-2 described above, and thus are not described again.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and all simple modifications and equivalent variations of the above embodiments according to the technical spirit of the present invention are included in the scope of the present invention.
Claims (7)
1. A method for monitoring the state of a fixture based on an integrated optical fiber sensor is based on the fixture provided with a positioner and is characterized by comprising the following steps:
step S1: acquiring real-time data of the temperature and the strain of different areas of the fixture through an optical fiber sensor to obtain the acquisition amount of the temperature and the strain;
step S2: measuring the spatial position of the fixture positioner by using digital measuring equipment while acquiring data by using the optical fiber sensor, and acquiring the spatial position information of the positioner as a measured value under the current temperature and strain;
step S3: and repeating the sensor acquisition in the step S1 and the locator measurement process in the step S2, establishing a data set in which the acquired quantity and the measured value are in one-to-one correspondence, and establishing a mathematical model between the temperature and the strain of the acquired quantity and the spatial position of the locator by adopting a BP neural network algorithm.
2. The method for monitoring the state of the fixture based on the integrated optical fiber sensor as claimed in claim 1, wherein: the optical fiber sensor is an integrated optical fiber sensor and comprises an optical fiber strain sensor and an optical fiber temperature sensor, wherein a strain sensing element of the optical fiber strain sensor and a temperature sensing element of the optical fiber temperature sensor are connected together in a serial or parallel mode, and real-time data acquisition is carried out on the temperature and the strain of different areas of the formwork.
3. The method for monitoring the state of the fixture based on the integrated optical fiber sensor as claimed in claim 2, wherein: at least one optical fiber temperature sensor is mounted on each of the components of the fixture made of different materials.
4. The method for monitoring the state of the fixture based on the integrated optical fiber sensor as claimed in claim 2, wherein: the installation method for installing the optical fiber strain sensor comprises the following steps: and introducing the fixture and the positioner into a virtual simulation environment, applying a load to simulate the deformation of the fixture body and the positioner at the tail end of the positioner according to the deformation of the actual fixture on site, and estimating the load by dividing the total mass of the product on the fixture by the number of the positioners on the fixture.
5. The method for monitoring the state of the fixture based on the integrated optical fiber sensor as claimed in claim 1, wherein: the method for establishing the data set with the one-to-one correspondence between the acquisition quantity and the measurement value specifically comprises the following steps: the positioner is loaded through the loading device, real-time data acquisition is carried out on the temperature and the strain of different areas of the fixture in the assembling process through the optical fiber sensor, the strain and the temperature value are obtained, meanwhile, the space position of the fixture positioner is measured through the digital measuring equipment, and the one-to-one corresponding relation between the sensor data acquisition value and the positioner position measurement value is formed.
6. The method for monitoring the state of the fixture based on the integrated optical fiber sensor as claimed in claim 5, wherein: random loads with different directions and sizes are applied to the positioner within the actual loaded space range through the loading device, the state of the fixture is changed by changing the size and the direction of the loads, and a large amount of training sample data is obtained by repeating the data acquisition and space position measurement processes.
7. The method for monitoring the state of the fixture based on the integrated optical fiber sensor as claimed in claim 1, wherein: the mathematical model established by the BP neural network algorithm in step S3 has the input of the temperature and the dependent variable acquired by the optical fiber sensor and the output of the position coordinate of the positioner, and then the input and the output are used to train the neural network to establish the nonlinear mapping relationship between the input and the output.
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| CN113932725A (en) * | 2021-09-24 | 2022-01-14 | 成都飞机工业（集团）有限责任公司 | Calibration method of optical fiber strain sensor for load monitoring of cantilever type positioner |
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Application publication date: 20210514 |