CN117664198A - Multi-parameter monitoring and decoupling method for rotary mechanical blade based on FBG sensor - Google Patents

Multi-parameter monitoring and decoupling method for rotary mechanical blade based on FBG sensor Download PDF

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Publication number
CN117664198A
CN117664198A CN202311733542.5A CN202311733542A CN117664198A CN 117664198 A CN117664198 A CN 117664198A CN 202311733542 A CN202311733542 A CN 202311733542A CN 117664198 A CN117664198 A CN 117664198A
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strain
fbg sensor
temperature
blade
data
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卿新林
张义亮
梁智洪
王奕首
廖运来
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Xiamen University
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Xiamen University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D5/00—Mechanical means for transferring the output of a sensing member; Means for converting the output of a sensing member to another variable where the form or nature of the sensing member does not constrain the means for converting; Transducers not specially adapted for a specific variable
    • G01D5/26—Mechanical means for transferring the output of a sensing member; Means for converting the output of a sensing member to another variable where the form or nature of the sensing member does not constrain the means for converting; Transducers not specially adapted for a specific variable characterised by optical transfer means, i.e. using infrared, visible, or ultraviolet light
    • G01D5/32—Mechanical means for transferring the output of a sensing member; Means for converting the output of a sensing member to another variable where the form or nature of the sensing member does not constrain the means for converting; Transducers not specially adapted for a specific variable characterised by optical transfer means, i.e. using infrared, visible, or ultraviolet light with attenuation or whole or partial obturation of beams of light
    • G01D5/34—Mechanical means for transferring the output of a sensing member; Means for converting the output of a sensing member to another variable where the form or nature of the sensing member does not constrain the means for converting; Transducers not specially adapted for a specific variable characterised by optical transfer means, i.e. using infrared, visible, or ultraviolet light with attenuation or whole or partial obturation of beams of light the beams of light being detected by photocells
    • G01D5/353—Mechanical means for transferring the output of a sensing member; Means for converting the output of a sensing member to another variable where the form or nature of the sensing member does not constrain the means for converting; Transducers not specially adapted for a specific variable characterised by optical transfer means, i.e. using infrared, visible, or ultraviolet light with attenuation or whole or partial obturation of beams of light the beams of light being detected by photocells influencing the transmission properties of an optical fibre
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/16—Measuring arrangements characterised by the use of optical techniques for measuring the deformation in a solid, e.g. optical strain gauge
    • G01B11/165—Measuring arrangements characterised by the use of optical techniques for measuring the deformation in a solid, e.g. optical strain gauge by means of a grating deformed by the object
    • 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
    • G01K11/3206—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 at discrete locations in the fibre, e.g. using Bragg scattering
    • 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
    • G01L1/246—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 using integrated gratings, e.g. Bragg gratings

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Length Measuring Devices By Optical Means (AREA)

Abstract

The invention relates to the technical field of rotary machine monitoring and optical fiber sensing, and provides a multi-parameter monitoring and decoupling method for rotary machine blades based on an FBG sensor, which comprises the steps of measuring the temperature coefficient of the FBG sensor and collecting reflection spectrum center wavelength data of the FBG sensor along with temperature change in real time; calculating the real-time temperature of the blade; according to the relative transformation quantity of the first wavelength and the real-time temperature, decoupling wavelength change data caused by structural thermal strain of the strain FBG sensor at different temperatures, and obtaining structural thermal strain data and mechanical strain data of the strain FBG sensor; and calculating the first load data and the load variation of the blade according to the blade state information, and decoupling the second load data. According to the invention, through the cooperative work of the temperature FBG sensor and the strain FBG sensor, multi-parameter monitoring and decoupling of temperature, thermal strain, mechanical strain, loads in different directions and the like can be realized only by using a sparse FBG sensor network.

Description

Multi-parameter monitoring and decoupling method for rotary mechanical blade based on FBG sensor
Technical Field
The invention relates to the technical field of rotary machine monitoring and optical fiber sensing, in particular to a rotary machine blade multi-parameter monitoring and decoupling method based on an FBG sensor.
Background
The service environment of the major equipment mainly comprising aeroengines, wind turbines and the like is severe and changeable, and the major equipment is subjected to continuously changing loads, so that the subsystem and the components generate strong mechanical stress, and the structural health condition of the major equipment is seriously threatened. The blade root is used as one of the most loaded parts of the rotating mechanical blade of the heavy equipment, the structural strength and the stability of the blade root are critical to the safe operation of the equipment, and real-time monitoring is needed.
The fiber bragg grating sensor can be affected by temperature change during load monitoring, and meanwhile, the external temperature change can cause thermal expansion of the blade structure to bring monitoring errors, and the thermal expansion of the structure is generally far greater than that of the fiber materials. The environmental temperature change range is often larger in the long-time monitoring process, and the monitoring is greatly influenced. For example, the temperature of the aero-engine blade in the service process can reach hundreds to thousands of degrees celsius, and the wind turbine generator in cold areas can be heated sometimes in order to avoid icing of the blade, so that larger temperature change is brought.
At present, load monitoring based on an optical fiber grating sensor usually only considers the influence of temperature on an optical fiber, but does not consider the structural thermal strain caused by the temperature, or only compensates the structural thermal strain by using a simple material structure, so that a complex blade composite material structure is difficult to replace, the glass transition temperature of part of the composite material is in the range of the blade temperature, the thermal strain curve is complex, and the thermal strain is difficult to compensate by using a simple linear method.
Due to the influences of factors such as environmental temperature change, complexity of a blade structure, various interferences during calibration, continuously-changing structural states during service and the like, the load born by each blade is difficult to accurately decouple in the prior art. Therefore, a new method for identifying and decoupling the load of the rotating mechanical blade of the heavy equipment is needed to solve the above technical drawbacks.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a multi-parameter monitoring and decoupling method for a rotary mechanical blade based on an FBG sensor, which is characterized by comprising the following steps:
s10, measuring the temperature coefficient of an FBG sensor, and collecting reflection spectrum center wavelength data of the FBG sensor mounted on the blade along with temperature change in real time, wherein the FBG sensor comprises the temperature FBG sensor and a strain FBG sensor, and the reflection spectrum center wavelength data comprises a first wavelength relative change amount of the temperature FBG sensor and a second wavelength relative change amount of the strain FBG sensor;
s20, calculating the real-time temperature of the blade through a first wavelength relative change of the temperature FBG sensor along with the temperature change;
s30, decoupling wavelength change data caused by structural thermal strain of the strain FBG sensor at different temperatures according to the first wavelength relative transformation amount and the real-time temperature to obtain structural thermal strain data of the strain FBG sensor;
s40, constructing a structural thermal strain database through structural thermal strain data of the strain FBG sensor at different temperatures, and fitting a structural thermal strain formula of the strain FBG sensor;
s50, calibrating the strain FBG sensor, acquiring the second wavelength relative variation of the strain FBG sensor in different states of the blade under the calibration condition in real time, recording the state information of the blade at the current moment, calculating real-time temperature variation and a structural thermal strain formula according to the temperature FBG sensor, and performing temperature compensation on calibration data of the strain FBG sensor to obtain mechanical strain data of the strain FBG sensor;
s60, calculating first load data and load variation of the blade according to the blade state information;
s70, constructing a strain-load reverse push algorithm by combining the first load data, the load variation and the mechanical strain data, and decoupling the second load data.
In an embodiment, each blade at least comprises four strain FBG sensors and four temperature FBG sensors, the central grid areas of the strain FBG sensors and the temperature FBG sensors are required to be perpendicular to the same horizontal plane of the center of the blade, and the strain FBG sensors are arranged at preset angle intervals.
In one embodiment, the first wavelength relative change of the FBG sensor can be expressed as:
wherein K is Temp As the temperature coefficient of the FBG sensor, which is related to the thermal expansion coefficient and the thermo-optic coefficient of the fiber grating, Δt is the ambient temperature variation, the real-time temperature can be expressed as:
wherein T0 is the initial temperature of the environment lambda Bt Is the first center wavelength data of the temperature FBG sensor, delta lambda Bt For the first central wavelength variation of the temperature FBG sensor, K represents different moments, K Temp_t The temperature coefficient of the temperature FBG sensor at time k.
In one embodiment, the second wavelength relative variation of the strain FBG sensor while the blade is in service can be expressed as:
wherein lambda is Bs Second center wavelength data of strain FBG sensor for blade service, delta lambda Bs Second wavelength variation, alpha, of strain FBG sensor for blade service fiber Is the thermal expansion coefficient of the optical fiber, alpha structure For the thermal expansion coefficient of the blade structure, K Temp_s For the temperature coefficient of the strain FBG sensor, delta T is the ambient temperature variation, K ε Is the strain coefficient of the optical fiber, delta epsilon m Is the mechanical strain data to which the optical fiber is subjected.
In one embodiment, the second wavelength relative change of the strain FBG sensor caused by the structural thermal strain at different temperatures when the blade is stationary can be expressed as:
the structural thermal strain data calculation mode of the strain FBG sensor is as follows:
wherein f (T) is the relative change of the second wavelength in the static state of the calibration bladeStructural thermal strain function, Δε, fitted to real-time temperature t K is the thermal strain data to which the optical fiber is subjected ε Is the strain coefficient of the optical fiber.
In an embodiment, the actual mechanical strain data calculation formula of the strain FBG sensor when the blade is in service is as follows:
in one embodiment, the second load data decoupling equation for different directions is:
wherein G is a system transfer function,for the first load data, +.>For n load variations in different directions, where n is smaller than i,/-, and->A second amount of wavelength relative change at the time of calibration for the ith strain FBG sensor,for the current second wavelength relative variation of the ith strain FBG sensor, [. Cndot.] H For matrix [. Cndot.]Is a complex matrix of the matrix.
In an embodiment, S80, the second load data is corrected in real time based on the adaptive kalman filter, so as to obtain corrected third load data in different directions.
In one embodiment, the system equation for the adaptive Kalman filter is:
wherein, among them,the prior estimated value of the third load data of the state at the moment k is obtained according to the system equation and the state of the FBG sensor at the moment k-1; u (u) k-1 For the system input value at the time of k-1, specifically the difference between the relative change amounts of the second wavelength of the strain FBG sensor at the time of k-1 and the time of k-2, A is the system matrix of the system equation, B is the input matrix of the system equation, q k-1 System state noise at time k-1;
the prior error covariance matrix is:
wherein Q is k-1 For the state noise covariance matrix at time k-1, the Kalman gain is:
wherein C is an output matrix, R k-1 Measuring a noise covariance matrix for the k-1 moment;
the state variable posterior estimate is:
wherein Y is k For output variables, i.e. second load data; r is (r) k-1 The noise posterior error covariance matrix is measured for time k-1 as:
wherein I is an identity matrix.
In one embodiment, the k-time adaptive Kalman filter system state noise and observation noise and their covariance are represented by the principle of maximum a posteriori estimation:
where m is the set window length.
Based on the above, compared with the prior art, the rotary mechanical blade multi-parameter monitoring and decoupling method based on the FBG sensor provided by the invention has the following beneficial effects:
1. according to the invention, through the cooperative work of the temperature FBG sensor and the strain FBG sensor, multi-parameter monitoring and decoupling of temperature, thermal strain, mechanical strain, loads in different directions and the like can be realized only by using a sparse FBG sensor network.
2. The invention introduces the evaluation index of the relative variation of the center wavelength, reduces the relative error between different optical fibers, and has higher sensitivity and universality.
3. The invention can self-correct under the conditions of inaccurate structural parameters and change of calibration states by matching the least square method with the self-adaptive Kalman filter, and has the advantages of high sensitivity, strong adaptability and less required parameters.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
For a clearer description of embodiments of the invention or of the solutions of the prior art, the drawings that are needed in the description of the embodiments or of the prior art will be briefly described, it being obvious that the drawings in the description below are some embodiments of the invention, and that other drawings can be obtained from them without inventive effort for a person skilled in the art; the positional relationships described in the drawings in the following description are based on the orientation of the elements shown in the drawings unless otherwise specified.
FIG. 1 is a flow chart of a method for monitoring and decoupling multiple parameters of a rotary mechanical blade based on an FBG sensor according to an embodiment of the present invention;
FIG. 2 is a side view of a wind turbine blade mounting FBG sensor according to an embodiment of the invention;
FIG. 3 is a top view of a wind turbine blade mounting FBG sensor according to a first embodiment of the present invention;
fig. 4 is a flowchart of a multi-parameter monitoring and decoupling method for a rotary mechanical blade based on an FBG sensor according to a second embodiment of the present invention.
Reference numerals:
1-blade 11-blade root 2-temperature FBG sensor
3 strain FBG sensor
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention; the technical features designed in the different embodiments of the invention described below can be combined with each other as long as they do not conflict with each other; all other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be noted that all terms used in the present invention (including technical terms and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs and are not to be construed as limiting the present invention; it will be further understood that terms used herein should be interpreted as having a meaning that is consistent with their meaning in the context of this specification and the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
A rotary mechanical blade multi-parameter monitoring and decoupling method based on FBG sensors comprises the following steps:
s10, measuring the temperature coefficient of an FBG sensor, and collecting reflection spectrum center wavelength data of the FBG sensor mounted on the blade 1 along with temperature change in real time, wherein the FBG sensor comprises a temperature FBG sensor 2 and a strain FBG sensor 3, and the reflection spectrum center wavelength data comprises a first wavelength relative change amount of the temperature FBG sensor 2 and a second wavelength relative change amount of the strain FBG sensor 3;
s20, calculating the real-time temperature of the blade 1 through a first wavelength relative change amount of the temperature FBG sensor 2 along with the temperature change;
s30, decoupling wavelength change data caused by structural thermal strain of the strain FBG sensor 3 at different temperatures according to the first wavelength relative transformation amount and the real-time temperature to obtain structural thermal strain data of the strain FBG sensor 3;
s40, constructing a structural thermal strain database through structural thermal strain data of the strain FBG sensor 3 at different temperatures, and fitting a structural thermal strain formula of the strain FBG sensor 3;
s50, calibrating the strain FBG sensor 3, acquiring the second wavelength relative variation of the strain FBG sensor 3 in different states of the blade 1 under the calibration condition in real time, recording the blade state information at the current moment, calculating real-time temperature variation and a structural thermal strain formula according to the temperature FBG sensor 2, and performing temperature compensation on calibration data of the strain FBG sensor 3 to obtain mechanical strain data of the strain FBG sensor 3;
s60, calculating first load data and load variation of the blade 1 according to the blade state information;
s70, constructing a strain-load reverse push algorithm by combining the first load data, the load variation and the mechanical strain data, and decoupling the second load data.
S10, measuring the temperature coefficient of an FBG sensor, and collecting reflection spectrum center wavelength data of the FBG sensor mounted on the blade 1 along with temperature change in real time, wherein the FBG sensor comprises a temperature FBG sensor 2 and a strain FBG sensor 3, and the reflection spectrum center wavelength data comprises a first wavelength relative change amount of the temperature FBG sensor 2 and a second wavelength relative change amount of the strain FBG sensor 3.
In specific implementation, before the FBG sensors are installed, the temperature coefficients of the strain FBG sensor 3 and the temperature FBG sensor 2 are measured, the FBG sensors are installed on the blade 1, the original center wavelengths of the strain FBG sensor 3 and the temperature FBG sensor 2 are recorded, and the first wavelength relative change amount of the temperature FBG sensor 2 and the second wavelength relative change amount of the strain FBG sensor 3 are obtained.
Preferably, as shown in fig. 2, each blade 1 at least includes four strain FBG sensors 3 and four temperature FBG sensors 2, the central grid areas of the strain FBG sensors 3 and the temperature FBG sensors 2 are required to be perpendicular to the same horizontal plane in the center of the blade 1, and the strain FBG sensors 3 are arranged at preset angle intervals.
In specific implementation, when the wind turbine generator blade load is monitored and decoupled, the preset angle between the strain FBG sensors is 90 degrees, and each strain FBG sensor 3 is matched with 1 temperature FBG sensor 2. Because in the wind turbine blade, because the number of the strain FBG sensors 3 installed on each blade 1 is less than 4, the accuracy is greatly reduced when the load is decoupled, and the accurate load cannot be obtained, the number of the strain FBG sensors 3 on each blade 1 is not less than 4, and the strain FBG sensors are positioned on the same section of the blade 1.
It should be noted that, the above installation method can be better suitable for wind turbine generator blades, and is not necessarily suitable for rotating blades 1 of other machines, the arrangement positions of the FBG sensors need to be specifically set according to the actual conditions of the blades 1 of different machines, the number and the arrangement method are not limited, and the FBG sensors do not need to be located at specific positions and at specific angles.
Before the FBG sensor is pasted, the bonding surface needs to be polished, and absolute ethyl alcohol or acetone is used for cleaning the pasting area. Meanwhile, for the composite material blade 1 or other material blades 1, the FBG sensor can be integrated into the structure of the blade 1 during manufacturing by adopting an embedded sensor method.
S20, calculating the real-time temperature of the blade 1 through a first wavelength relative change amount of the temperature FBG sensor 2 along with the temperature change;
the first wavelength relative change of the FBG sensor can be expressed as:
wherein lambda is B Is the center wavelength of the FBG sensor, deltalambda B Is the central wavelength variation of the FBG sensor (here FBG sensorComprising a temperature FBG sensor 2 and a strain FBG sensor 3), K Temp As the temperature coefficient of the FBG sensor, which is related to the thermal expansion coefficient and the thermo-optic coefficient of the fiber grating, Δt is the ambient temperature variation, the real-time temperature can be expressed as:
wherein T0 is the initial temperature of the environment lambda Bt Is the first center wavelength data of the temperature FBG sensor 2, Δλλ Bt For the first central wavelength variation of the temperature FBG sensor 2, t represents different moments, K Temp_t The temperature coefficient of the temperature FBG sensor 2 at time t.
S30, decoupling wavelength change data caused by structural thermal strain of the strain FBG sensor 3 at different temperatures according to the first wavelength relative transformation amount and the real-time temperature to obtain structural thermal strain data of the strain FBG sensor 3;
s40, constructing a structural thermal strain database through structural thermal strain data of the strain FBG sensor 3 at different temperatures, and fitting a structural thermal strain formula of the strain FBG sensor 3;
s50, calibrating the strain FBG sensor 3, acquiring the second wavelength relative variation of the strain FBG sensor 3 in different states of the blade 1 under the calibration condition in real time, recording the blade state information at the current moment, calculating real-time temperature variation and a structural thermal strain formula according to the temperature FBG sensor 2, and performing temperature compensation on calibration data of the strain FBG sensor 3 to obtain mechanical strain data of the strain FBG sensor 3.
In an embodiment, the second wavelength relative variation of the strain FBG sensor 3 when the blade 1 is in service can be expressed as:
wherein lambda is Bs Second center wavelength data of strain FBG sensor 3 when blade 1 is in service, Δλλ Bs Strain FBG transmission for blade 1 in serviceSecond wavelength variation of sensor 3, alpha fiber Is the thermal expansion coefficient of the optical fiber, alpha Structure For the thermal expansion coefficient of the blade structure, K Temp_s For the strain FBG sensor 3 temperature coefficient, deltaT is the ambient temperature variation, K ε Is the strain coefficient of the optical fiber, delta epsilon m Is the mechanical strain data to which the optical fiber is subjected.
The second wavelength relative change amount of the strain FBG sensor 3 in service is composed of three parts: firstly, real-time temperature; secondly, structural thermal strain data; thirdly, mechanical strain data; the real-time temperature is obtained from the above steps, and then the structural thermal strain data and mechanical strain data also need to be obtained.
Preferably, structural thermal strain data are calculated when the wind turbine generator is stationary, at the moment, the external wind speed is small, the wind speed and the wind direction are relatively stable, and reflection spectrum center wavelength data of each FBG sensor in each blade 1 along with temperature change are respectively collected by using a self heating mode or a local heating mode of a bonding area of the blade 1. In order to ensure that the structure of the blade 1 is sufficiently heated and stable, each measured temperature is heated and maintained for at least 5 minutes, and the range of the calibration temperature can include the ambient temperature and the self-heating temperature of the interior of the blade 1 during normal operation.
The second wavelength relative change of the strain FBG sensor 3 caused by the structural thermal strain at different temperatures when the blade 1 is stationary can be expressed as:
the structural thermal strain data calculation mode of the strain FBG sensor 3 is as follows:
wherein f (T) is the second relative change in wavelength of the calibration blade 1 in the stationary stateStructural heat stress fitted to real-time temperatureFunction change, Δε t K is the thermal strain data to which the optical fiber is subjected ε Is the strain coefficient of the optical fiber.
The real-time temperature of the blade 1 and the structural thermal strain data of the strain FBG sensor 3 under the calibration condition are calculated through the steps, and the actual mechanical strain data calculation formula of the strain FBG sensor 3 when the blade 1 is in service is as follows under the condition that the relative variation of the second wavelength is known:
after mechanical strain data of the strain FBG sensor 3 are obtained through calculation, blade state information is collected, and load data of the blade 1 in different directions are calculated, specifically:
s60, calculating first load data and load variation of the blade 1 according to the state information of the blade 1;
the recorded blade state information comprises the gravity center and the weight of the rotary mechanical blade 1, the section position of the FBG sensor, the pitch angle of the current blade 1, the elevation angle of the hub, the azimuth angle of the wind wheel, the inclination angle of the blade 1 and the like. And solving corresponding theoretical loads in different directions by combining the blade state information with the coordinate change.
S70, constructing a strain-load reverse push algorithm by combining the first load data, the load variation and the mechanical strain data, and decoupling the second load data.
The second load data decoupling formula in different directions is:
wherein G is a system transfer function,second payload data for n different directions, < >>First carriers of n different directionsLotus data->For n load variations in different directions, where n is smaller than i,/-, and->Second wavelength relative variation, for the calibration of the ith strain FBG sensor 3,/>For the current second wavelength relative variation of the ith strain FBG sensor 3, [. Cndot.] H For matrix [. Cndot.]Is a complex matrix of the matrix.
In specific implementations, the strain-load inverse estimation is as follows: if in a linear system, the theoretical shimmy bending moment M of the rotating blade 1 of the wind turbine generator is obtained through the state information of the blade x And theoretical waving bending moment M y At a given input variable M x And M y And output variableAfter that, the corresponding system transfer function G can be determined, including:
wherein,the i-th strain FBG sensor 3 measures a relative wavelength change due to a mechanical strain, i being the number of strain FBGs on the same blade 1. According to the load change delta M obtained under the least square method and the calibration condition x_c 、ΔM y_c And the relative wavelength variation due to the mechanical strain in the nominal state +.>The system transfer function G is calculated as:
in the method, in the process of the invention,is->For the measured data, which is generally the full rank, the calculation formula is:
in the method, in the process of the invention,is->Is a complex matrix of the matrix.
The real-time shimmy bending moment and the flap bending moment can be expressed as:
is the first load data.
It should be noted that the type of load applied to the rotary blade 1 is specifically determined according to the specific mechanical blade 1, and is not limited to the theoretical shimmy bending moment M in the present embodiment x And theoretical waving bending moment M y Other different types of loads may be included, all of which may be decoupled by the methods provided by the present invention.
The specific working process is as follows: under the static state of the blade 1, calculating real-time temperature and structural thermal strain data of the blade 1, constructing a structural thermal strain database through the structural thermal strain data, measuring real-time wavelength changes of a temperature FBG sensor 2 and a strain FBG sensor 3 mounted on the blade 1 during service after the structural thermal strain data is fitted, calculating the real-time temperature changes through the temperature FBG sensor 2, and performing temperature compensation on calibration data to obtain mechanical strain data of the strain FBG sensor 3. And then combining the mechanical strain data, the initial load data and the load variation to construct a strain-load reverse push algorithm, and decoupling loads in different directions.
Example two
In order to solve the problem of errors in the above-mentioned situations, the invention also provides a second embodiment, and in particular, a multi-parameter monitoring and decoupling method for a rotary mechanical blade 1 based on an FBG sensor, which further comprises the following steps:
and S80, correcting the second load data in real time based on the self-adaptive Kalman filter to obtain corrected third load data in different directions.
The system equation of the adaptive kalman filter is:
wherein,the prior estimated value of the third load data of the state at the moment k is obtained according to the system equation and the state of the FBG sensor at the moment k-1; u (u) k-1 For the system input value at time k-1, specifically the difference between the second wavelength relative change amounts of the strain FBG sensor at time k-1 and time k-2, A is the system matrix of the system equation, B is the input matrix of the system equation, q k-1 The system state noise at time k-1.
In specific implementation, A and B are a system matrix and an input matrix of a system equation constructed according to an actual system respectively, and are realThe inter-system is determined according to different details of physical models of FBG sensors mounted on different blades 1, and A and B are equation parameters of the physical models; in the wind turbine blade model, A can be an identity matrix I, and B can be a system transfer function G; however, it should be noted that the specific a and B may be selected by those skilled in the art according to the actual model, and are not limited to the above-defined values.U is the corrected third load data k-1 Is the difference between the second wavelength relative change of the strain FBG sensor 3 at times k-1 and k-2. />
The prior error covariance matrix is:
wherein A is T Transposed matrix of A, Q k-1 For the state noise covariance matrix at time k-1, the Kalman gain is:
wherein C is an output matrix, C T Is the conjugate transpose matrix of C, R k-1 Measuring a noise covariance matrix for the k-1 moment;
the state variable posterior estimate is:
wherein Y is k For output variables, i.e. second load data; r is (r) k-1 Measuring noise for time k-1;
the posterior error covariance matrix is:
wherein I is an identity matrix, namely a matrix with positive angle lines of 1 and the rest values of 0.
In order to solve the problem of system non-convergence caused by unknown or inaccurate state noise and observation noise, the noise parameters are represented by using maximum posterior estimation, and the influence of data states which are too long is reduced by limiting the window length of the maximum posterior estimation, so as to enhance the dynamic characteristics of the system.
The state noise and observation noise of the k-moment self-adaptive Kalman filter system and the covariance thereof are expressed as follows by a principle of maximum posterior estimation:
wherein q k For the system state noise at time k, Q k For the covariance matrix of the system state noise at the moment k, rk is the measurement noise at the moment k, R k The noise covariance matrix is measured for time k, m is the set window length, j is used for intra-cycle counting,is->And (3) conjugate transpose matrix.
And when k is smaller than the window length m, taking all data before k as average solving noise parameters. When k is greater than m, only the data calculation within the window length is taken in order to prevent too much data from having too much effect on the current state. Unlike the prior art, which uses empirical value to calculate and evaluate noise parameters, the noise parameters related to the maximum posterior estimation calculation are calculated by using a statistical method, and the existing results are replaced by the previous data, so that the noise can be analyzed in real time according to the system variation, and the obtained noise parameters are more accurate compared with the empirical value.
Example III
The multi-parameter monitoring and decoupling method for the rotary mechanical blade based on the FBG sensor provided by the first embodiment and the second embodiment is applicable to multi-parameter measurement and decoupling of the load of the rotary mechanical blade of various heavy equipment, including but not limited to wind turbines, aero-engine fan blades and the like. It should be noted that, when the multi-parameter monitoring and decoupling method for rotary mechanical blades based on FBG sensors provided in the first embodiment and the second embodiment of the present invention is applied to rotary mechanical blades of aero-engine fan blades and other major equipment, the arrangement positions and the arrangement number of the FBG sensors need to be set according to the actual situation, and are not limited to the positions and the number defined in the first embodiment, and the blade load type also needs to be adjusted according to the actual situation.
In addition, it should be understood by those skilled in the art that although many problems exist in the prior art, each embodiment or technical solution of the present invention may be modified in only one or several respects, without having to solve all technical problems listed in the prior art or the background art at the same time. Those skilled in the art will understand that nothing in one claim should be taken as a limitation on that claim.
Although terms such as FBG sensor, temperature coefficient, reflected spectrum center wavelength data, temperature FBG sensor, strain FBG sensor, first wavelength relative variation, second wavelength relative variation, blade, real-time temperature, structural thermal strain data, blade status information, mechanical strain data, first load data, load variation, and second load data are more used herein, the possibility of using other terms is not precluded. These terms are used merely for convenience in describing and explaining the nature of the invention; they are to be interpreted as any additional limitation that is not inconsistent with the spirit of the present invention; the terms first, second, and the like in the description and in the claims of embodiments of the invention and in the above-described figures, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.

Claims (10)

1. The multi-parameter monitoring and decoupling method for the rotary mechanical blade based on the FBG sensor is characterized by comprising the following steps of:
s10, measuring the temperature coefficient of an FBG sensor, and collecting reflection spectrum center wavelength data of the FBG sensor mounted on a blade along with temperature change in real time, wherein the FBG sensor comprises the temperature FBG sensor and a strain FBG sensor, and the reflection spectrum center wavelength data comprises a first wavelength relative change amount of the temperature FBG sensor and a second wavelength relative change amount of the strain FBG sensor;
s20, calculating the real-time temperature of the blade through a first wavelength relative change amount of the temperature FBG sensor along with the temperature change;
s30, decoupling wavelength change data caused by structural thermal strain of the strain FBG sensor at different temperatures according to the first wavelength relative transformation amount and the real-time temperature to obtain structural thermal strain data of the strain FBG sensor;
s40, constructing a structural thermal strain database through the structural thermal strain data of the strain FBG sensor at different temperatures, and fitting a structural thermal strain formula of the strain FBG sensor;
s50, calibrating the strain FBG sensor, acquiring the second wavelength relative variation of the strain FBG sensor in different states of the blade under the calibration condition in real time, recording the state information of the blade at the current moment, calculating real-time temperature variation and the structural thermal strain formula according to the temperature FBG sensor, and performing temperature compensation on calibration data of the strain FBG sensor to obtain mechanical strain data of the strain FBG sensor;
s60, calculating first load data and load variation of the blade according to the blade state information;
s70, constructing a strain-load reverse push algorithm by combining the first load data, the load variation and the mechanical strain data, and decoupling the second load data.
2. The method for multi-parameter monitoring and decoupling of rotary mechanical blades based on FBG sensors according to claim 1, wherein each blade at least comprises four strain FBG sensors and four temperature FBG sensors, the central grid areas of the strain FBG sensors and the temperature FBG sensors are required to be perpendicular to the same horizontal plane of the center of the blade, and the strain FBG sensors are arranged at preset angle intervals.
3. The method of multi-parameter monitoring and decoupling of rotary machine blades based on FBG sensors according to claim 1, wherein the first wavelength relative variation of the FBG sensors is expressed as:
wherein lambda is B Is the center wavelength of the FBG sensor, deltalambda B K is the central wavelength variation of the FBG sensor Temp As the temperature coefficient of the FBG sensor, which is related to the thermal expansion coefficient and the thermo-optic coefficient of the fiber grating, Δt is the ambient temperature variation, the real-time temperature can be expressed as:
wherein T0 is the initial temperature of the environment lambda Bt Is the first center wavelength data of the temperature FBG sensor, delta lambda Bt For the first central wavelength variation of the temperature FBG sensor, K represents different moments, K Temp_t The temperature coefficient of the temperature FBG sensor at time k.
4. The method for multi-parameter monitoring and decoupling of rotary mechanical blades based on FBG sensors according to claim 1, wherein the second wavelength relative change of the strain FBG sensor when the blade is in service can be expressed as:
wherein lambda is Bs Second center wavelength data of the strain FBG sensor when the blade is in service, delta lambda Bs Second wavelength variation, alpha, of the strain FBG sensor when serving the blade fiber Is the thermal expansion coefficient of the optical fiber, alpha Structure For the thermal expansion coefficient of the blade structure, K Temp_s For the temperature coefficient of the strain FBG sensor, delta T is the ambient temperature variation, K ε Is the strain coefficient of the optical fiber, delta epsilon m Is the mechanical strain data to which the optical fiber is subjected.
5. The method of multi-parameter monitoring and decoupling of rotary mechanical blades based on FBG sensors according to claim 4, wherein the second wavelength relative change of the strained FBG sensor caused by structural thermal strain at different temperatures in the stationary state of the blade can be expressed as:
the structural thermal strain data calculation mode of the strain FBG sensor is as follows:
wherein f (T) is the relative change of the second wavelength in the static state of the calibration bladeStructural thermal strain function, Δε, fitted to real-time temperature t K is the thermal strain data to which the optical fiber is subjected ε Is the strain coefficient of the optical fiber.
6. The method for multi-parameter monitoring and decoupling of rotary mechanical blades based on FBG sensors according to claim 5, wherein the actual mechanical strain data calculation formula of the strain FBG sensors during service of the blades is:
7. the FBG sensor-based multi-parameter monitoring and decoupling method for rotary mechanical blades according to claim 1, wherein the second load data decoupling formula for different directions is:
wherein G is a system transfer function,for the first load data, +.>The load variation for n different directions; n is less than i #>A second relative amount of wavelength change for the ith said strain FBG sensor calibration,for the current second wavelength relative variation of the ith strain FBG sensor, [. Cndot.] H For matrix [. Cndot.]Is a complex matrix of the matrix.
8. The FBG sensor based rotary mechanical vane multi-parameter monitoring and decoupling method according to claim 1, further comprising:
and S80, correcting the second load data in real time based on the self-adaptive Kalman filter to obtain corrected third load data in different directions.
9. The FBG sensor based rotary mechanical vane multi-parameter monitoring and decoupling method according to claim 8, wherein the system equation of the adaptive kalman filter is:
wherein,the third load data priori estimated value of the moment k is obtained according to a system equation and the state of the FBG sensor at moment k-1; u (u) k-1 Input values for the system at time k-1, in particularThe difference between the second wavelength relative change amounts of the strain FBG sensors at the time points k-1 and k-2 is that A is a system matrix of a system equation, B is an input matrix of the system equation, and q k-1 System state noise at time k-1;
the prior error covariance matrix is:
wherein A is T Transposed matrix of A, Q k-1 For the state noise covariance matrix at time k-1, the Kalman gain is:
wherein C is an output matrix, C T Is the transposed matrix of C, R k-1 Measuring a noise covariance matrix for the k-1 moment;
the state variable posterior estimate is:
wherein Y is k The output variable is specifically second load data; r is (r) k-1 Measuring noise for time k-1;
the posterior error covariance matrix is:
wherein I is an identity matrix.
10. The FBG sensor based rotary mechanical vane multi-parameter monitoring and decoupling method according to claim 9, characterized in that the k moment adaptive kalman filter system state noise and observation noise and their covariance are represented by the principle of maximum a posteriori estimation:
wherein q k For the system state noise at time k, Q k For the covariance matrix of the system state noise at k moment, r k For measuring noise at time k, R k The noise covariance matrix is measured for time k, m is the window length set, j is used for counting in cycles.
CN202311733542.5A 2023-12-15 2023-12-15 Multi-parameter monitoring and decoupling method for rotary mechanical blade based on FBG sensor Pending CN117664198A (en)

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