CN117824875A - Temperature monitoring method of heating equipment - Google Patents
Temperature monitoring method of heating equipment Download PDFInfo
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Abstract
The invention relates to the technical field of temperature control, in particular to a temperature monitoring method of heating equipment, which comprises the following steps: obtaining the temperature distribution of the heating equipment based on CAE simulation; acquiring the surface temperature and the internal temperature of the heating equipment according to the temperature distribution; constructing an internal temperature judgment model of the heat generating device based on the surface temperature and the internal temperature; and based on the model, monitoring the temperature of the heating equipment. The invention can rapidly and accurately judge the temperature inside the heating equipment.
Description
Technical Field
The invention relates to the technical field of temperature control, and particularly provides a temperature monitoring method of heating equipment.
Background
When the generator is in operation, if heat cannot be timely emitted, the temperature inside the generator can be increased continuously. If this temperature is too high, damage to the generator components may occur, possibly even leading to equipment failure or fire safety issues. Therefore, monitoring and analysis of the internal temperature of the generator is particularly important. However, the existing monitoring technology cannot monitor the temperature of the key parts inside the generator directly, so that it is difficult to accurately judge the hot spot of the internal temperature of the generator. The traditional temperature monitoring mode mainly relies on temperature sensors to measure, but the sensors can only be installed outside the generator, so that the temperature change inside the generator can not be effectively monitored, and the temperature of hot spots inside the generator can not be accurately estimated.
Disclosure of Invention
In order to overcome the defects, the invention provides a temperature monitoring method of heating equipment, which can rapidly and accurately judge the temperature inside the heating equipment.
In a first aspect, the present invention provides a temperature monitoring method of a heat generating device, comprising:
obtaining the temperature distribution of the heating equipment based on CAE simulation;
acquiring the surface temperature and the internal temperature of the heating equipment according to the temperature distribution;
constructing an internal temperature judgment model of the heat generating device based on the surface temperature and the internal temperature;
and based on the model, monitoring the temperature of the heating equipment.
Further, the input of the internal temperature judgment model of the heating equipment is the surface temperature of the heating equipment, and the output of the internal temperature judgment model of the heating equipment is the internal temperature of the heating equipment.
Further, after the step of monitoring the temperature of the heat generating device based on the model, the method further includes:
test points are respectively arranged on the surface and the inside of the heating equipment;
obtaining the temperature of the test point based on the temperature distribution;
and correcting the model according to the temperature of the test point.
Further, the setting test points on the surface and the inside of the heat generating device respectively includes:
presetting at least one surface test point on the surface of the heating equipment, and presetting at least one internal test point in the heating equipment;
the obtaining the temperature of the test point based on the temperature distribution includes:
under a specific working condition, respectively obtaining a first temperature of the surface test point and a second temperature of the internal test point according to the temperature distribution;
said calibrating said model according to the temperature of said test point comprises:
under the specific working condition, obtaining a third temperature of the internal test point according to the first temperature of the surface test point based on the model;
judging whether the difference value between the second temperature and the third temperature is larger than a preset threshold value or not;
and if the parameters are larger than the preset parameters, adjusting the parameters of the model.
Further, the adjusting parameters of the model includes:
taking the second temperature as a true value, and calculating the loss of the third temperature through a preset loss function;
and carrying out back propagation based on the loss to update parameters of the poster generation model, and completing the current round of iterative training.
Further, the performing temperature monitoring of the heat generating device based on the model includes:
acquiring the measurement temperature of a preset measurement point on the surface of the heating equipment;
obtaining the temperature distribution inside the heating equipment according to the model;
and determining the overheating position inside the heating equipment according to the temperature distribution.
Further, the heat generating device is a generator.
Further, the obtaining the temperature distribution of the heating equipment based on the CAE simulation includes:
and obtaining the temperature distribution of the type of generator based on CAE simulation according to the type of the generator.
Further, according to the type of the generator, obtaining the temperature distribution of the type of the generator based on the CAE simulation includes:
according to the type of the generator, establishing an electromagnetic equation, a mechanical equation and a circuit equation of the generator of the type;
acquiring boundary conditions of the generator under different working conditions;
based on CAE simulation, the temperature distribution of the type of generator is obtained according to the equation and the boundary condition.
Further, the different operating conditions include a short circuit fault.
The technical scheme provided by the invention has at least one or more of the following beneficial effects:
according to the invention, the temperature distribution of the heating equipment is obtained based on CAE simulation, the internal temperature judgment model of the heating equipment is constructed according to the temperature distribution, the prediction of the internal temperature of the equipment can be realized through the surface temperature of the equipment, the damage condition of the equipment body is predicted in advance, the occurrence of faults or overheat conditions is avoided, and the defects of infrared detection can be effectively overcome.
Drawings
The present disclosure will become more readily understood with reference to the accompanying drawings. As will be readily appreciated by those skilled in the art: the drawings are for illustrative purposes only and are not intended to limit the scope of the present invention. Moreover, like numerals in the figures are used to designate like parts, wherein:
FIG. 1 is a schematic flow diagram of the main steps of a temperature monitoring method according to one embodiment of the invention;
FIG. 2 is a flow chart diagram of a temperature monitoring method including the steps of correcting a model according to a temperature of a test point according to one embodiment of the present invention;
FIG. 3 is a schematic flow chart of the main steps for obtaining the temperature distribution of a generator of this type based on CAE simulation, according to the type of generator, according to one embodiment of the invention.
Detailed Description
Some embodiments of the invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
In the description of the present invention, a "processor" may include hardware, software, or a combination of both. The processor may be a central processor, a microprocessor, an image processor, a digital signal processor, or any other suitable processor. The processor has data and/or signal processing functions. The processor may be implemented in software, hardware, or a combination of both. Non-transitory computer readable storage media include any suitable medium that can store program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, and the like.
The invention provides a temperature monitoring method of heating equipment, referring to FIG. 1, comprising the following steps:
s10, obtaining the temperature distribution of the heating equipment based on CAE simulation;
s20, acquiring the surface temperature and the internal temperature of the heating equipment according to the temperature distribution;
s30, constructing an internal temperature judgment model of the heating equipment based on the surface temperature and the internal temperature;
and S40, based on the model, monitoring the temperature of the heating equipment.
In the present invention, a heat generating device is a device capable of generating and outputting heat, and is generally used for heating a liquid, gas or solid. These devices may be of various shapes and sizes, such as generators, electric boilers, kettles, heat sinks and the like. They generally use electricity or fuel as an energy source to heat substances such as water or air by converting energy into heat energy. Heating devices are widely used in the fields of home, industry, medical treatment, etc. to ensure safety and comfort of people and objects.
In the invention, the surface temperature of the heating equipment is the temperature of the outer surface of the heating equipment, and the surface temperature of the heating equipment can be directly measured by a temperature sensor arranged on the outer surface of the heating equipment.
The internal temperature of the heat generating device differs from the surface temperature of the heat generating device in that the internal temperature of the heat generating device is used to characterize the internal temperature of the heat generating device, which temperature cannot be measured directly with a temperature sensor.
The surface and the inside of the heating device are mutually independent and mutually connected, the mutually independent means that the surface and the inside are two areas without mutual intersection, and the mutually connected means that the surface and the inside jointly form the whole heating device.
When the temperature sensor is attached to the heat generating device, the value that can be directly measured reflects the surface temperature.
For example, the heating device is a generator, the surface temperature is the temperature of the generator shell, the temperature can be directly monitored by a temperature sensor, and the internal temperature is the temperature of the stator and rotor windings.
According to the invention, the temperature distribution of the heating equipment is constructed through the S10, the distribution can be regarded as a forward heat transfer model, heat is sourced from the inside of the heating equipment and then is diffused until the surface of the heating equipment, the measured temperature of the surface can be obtained through measuring the temperature of the surface, and the heat dissipation conditions are different under different working conditions, so that the temperature distribution conditions of any point inside the heating equipment and any point on the surface under different working conditions and fault conditions, namely the temperature field of the heating equipment, are obtained through the S10.
S10, the temperature field obtained by the forward heat transfer model can obtain the corresponding relation between the surface temperature of any position on the surface of the heating equipment and the temperature of any position inside the heating equipment, the first characteristic position on the surface and the second characteristic position inside the heating equipment are selected, the corresponding first characteristic position temperature and second characteristic position temperature are obtained, and the number of the first characteristic position and the second characteristic position is set according to actual needs.
And training the model by utilizing a plurality of corresponding groups of surface temperatures and internal temperatures to learn the mapping relation between the surface temperatures and the internal temperatures, so as to obtain an internal temperature judgment model.
When training the model, the data is divided into a training set and a testing set. The invention establishes the relationship between the surface temperature and the internal temperature through a machine learning algorithm.
The trained model reflects the relation between the external temperature and the internal temperature, so that the temperature of the heating equipment can be monitored.
In one embodiment, the input of the internal temperature judgment model of the heat generating device is the surface temperature of the heat generating device, and the output of the internal temperature judgment model of the heat generating device is the internal temperature of the heat generating device.
And (3) under different working conditions and fault conditions, the surface temperature and the corresponding internal temperature are obtained from the forward heat transfer model constructed in the step (S10), the surface temperature and the internal temperature are utilized to train an internal temperature judgment model, the internal temperature judgment model obtains the corresponding relation between the surface temperature and the internal temperature under different working conditions and fault conditions through machine learning, so that a model is built, when the surface temperature of heating equipment is input under a specific working condition, the corresponding internal temperature can be output, and the internal temperature judgment model can be regarded as an inversion model and is reversely deduced according to the surface temperature.
The internal temperature of the heating equipment which is difficult to directly monitor is converted into the external temperature of the heating equipment through measurement, and the corresponding internal temperature is accurately and quickly obtained after the internal temperature judgment model is adopted.
In one embodiment, the step S40 of performing temperature monitoring of the heat generating device based on the model includes:
s401, acquiring the measurement temperature of a preset measurement point on the surface of the heating equipment;
s402, obtaining the temperature distribution inside the heating equipment according to the model;
s403, determining the overheating position inside the heating equipment according to the temperature distribution.
The surface of the device may select 1 or more characteristic points as preset measuring points, and when the surface of the heat generating device where heat is concentrated is preferably densely distributed, for example, when the heat generating device is a generator, the number of the preset measuring points may be increased at the motor winding, for example, a first preset measuring point, a second preset measuring point and a third preset measuring point are respectively set at the motor winding.
And obtaining the temperature of the first site and the temperature of the second site in the heating equipment according to the input data (a first preset measuring point, a second preset measuring point and a third preset measuring point) by using an internal temperature judging model, and if the temperature of the second site is greater than an allowable upper limit value, indicating that the second site is overheated, and timely processing, such as fault detection, cooling system adjustment and the like, is needed.
The invention may also extract features from the surface temperature data, such as time series features, spectral analysis features, spatial distribution features, etc. Features including temperature gradients, temperature rates of change, etc. are considered to better reflect internal temperature conditions.
For example, a cloud chart can be set for surface temperature data from 80-200 ℃, different colors represent different temperature intervals, and temperature differentiation is carried out by using a cloud chart gradient.
The overheat location may be set as a location of great concern inside the generator. Assuming that the cloud image is set to be red at 180 ℃, attention can be paid to whether certain positions turn red or not, and whether overheat exists or not is judged.
In one embodiment, S40, after the step of monitoring the temperature of the heat generating device based on the model, referring to fig. 2, the method further includes:
s50, respectively setting test points on the surface and the inside of the heating equipment;
s60, obtaining the temperature of the test point based on the temperature distribution;
s70, correcting the model according to the temperature of the test point.
Because the measurement of the temperature is a corresponding point, the test point in the embodiment is a feature point for acquiring the temperature, similar to the measurement point in the embodiment, the test point is a selected feature point, and only different names in different scenes play a role in distinguishing from the previous embodiment.
The measurement point of the previous embodiment is used to obtain the overheat position inside the heat generating device, and the test point of the present embodiment is used for model calibration, so as to determine whether the model needs to be corrected, and both the test point of the present embodiment and the measurement point of the previous embodiment belong to the selected feature points.
In one embodiment, the step S50 of disposing test points on the surface and the inside of the heat generating device includes:
presetting at least one surface test point on the surface of the heating equipment, and presetting at least one internal test point in the heating equipment;
the step S60 of obtaining the temperature of the test point based on the temperature distribution includes:
under a specific working condition, respectively obtaining a first temperature of the surface test point and a second temperature of the internal test point according to the temperature distribution;
the step S70, calibrating the model according to the temperature of the test point includes:
under the specific working condition, obtaining a third temperature of the internal test point according to the first temperature of the surface test point based on the model;
judging whether the difference value between the second temperature and the third temperature is larger than a preset threshold value or not;
and if the parameters are larger than the preset parameters, adjusting the parameters of the model.
In an application scenario, preset surface test points to surface test points of the heating equipment are A, B and C respectively, and preset internal test points inside the heating equipment are M and N respectively.
Under a specific working condition, the first temperatures of the surface test points A, B and C are respectively A1, B1 and C1 according to the temperature distribution, and the second temperatures of the internal test points M and N are respectively M1 and N1.
Under the specific working condition, A1, B1 and C1 are used as the input of a model, and the third temperatures of the internal test points M and N are respectively M2 and N2.
Through calculation, the difference between M1 and M2 is not greater than a preset threshold (the preset threshold can be set to be 5% -10% of M1), and if the difference between M1 and M2 is smaller, the error is considered to be satisfied, and the value of M2 is indicated to be available.
Through calculation, if the difference between N1 and N2 is larger than a preset threshold, the value error of N2 is not satisfied, and if the difference between N2 and N1 is too large, the model is calibrated, so that the errors of the test points are smaller than the preset threshold.
In calibration, the internal temperature judgment model, namely the inversion model, is calibrated by adopting the temperature distribution, namely the forward heat transfer model, of the heating equipment constructed in the step S10.
Evaluating an error value based on the forward heat transfer model and the inversion model results; and further carrying out correction iterative calculation until the error value meets the requirement of less than or equal to 1% (5% -10% can also be set), and stopping calculation after convergence.
The model establishes a forward heat transfer model through machine learning, can perform modeling and prediction under the condition of not completely knowing the system, and can better handle nonlinearity and complexity. Verification of the built model is required to ensure its accuracy and reliability in different situations, by comparison with the actual data. Specifically, the machine-learned model and CAE simulation data are compared and verified, so that accuracy and reliability of results are ensured.
In one embodiment, the adjusting parameters of the model includes:
taking the second temperature as a true value, and calculating the loss of the third temperature through a preset loss function;
back-propagating based on the loss to update parameters of the poster generation model,
and finishing the current round of iterative training.
After at least one iterative training, until the difference between the second temperature and the third temperature is not greater than a preset threshold.
Therefore, according to the measured data, parameters in the model can be further optimized, so that the accuracy of the model is improved.
In one embodiment, the heat generating device is a generator.
In one embodiment, the obtaining the temperature distribution of the heat generating device based on the CAE simulation includes:
and obtaining the temperature distribution of the type of generator based on CAE simulation according to the type of the generator.
There are various types of generators, such as synchronous generators, induction generators, dc generators, and the like. Each type of generator has a different mathematical model.
In one embodiment, referring to fig. 3, S10, deriving a temperature profile of a generator of the type based on CAE simulation, depending on the type of the generator, includes:
s100, establishing an electromagnetic equation, a mechanical equation and a circuit equation of the generator according to the type of the generator;
s101, acquiring boundary conditions of the generator under different working conditions;
s102, based on CAE simulation, obtaining the temperature distribution of the type of generator according to the equation and the boundary condition.
In this embodiment, S100 is a process of establishing a mathematical model of the generator.
S100 is to determine the type of modeling. First, the type of generator to be modeled, such as a synchronous generator, an induction generator, a direct current generator, etc., needs to be determined. Each type of generator has a different mathematical model. And mathematical models built for each type of generator are well known. Only the generator type needs to be specified.
The mathematical model includes electromagnetic equations, mechanical equations, and circuit equations.
Establishing an electromagnetic equation: an electromagnetic equation is established to describe the electromagnetic equation based on the selected generator type. Faraday's law of electromagnetic induction and ampere's loop law are commonly included. These equations will describe the relationship between magnetic field, current and potential. From the aspect of heating, the electromagnetic equation is mainly applied to calculate electromagnetic loss as a heat source of later thermal simulation.
Establishing a mechanical equation: in order to take into account the mechanical movement of the generator, a mechanical equation needs to be established describing the movement and mechanical load of the rotor, and from the aspect of heating, the main characteristic is that magneto-thermal, such as eddy current loss generated by the rotating magnetic field of the motor, can only cause heating.
Establishing a circuit equation: the electromagnetic equation and the mechanical equation are combined with the circuit equation in a mode of algorithm based on the inside of CAE thermal simulation software, and the relation among current, voltage and power is described after the combination. This includes electrical connection between the generator winding and the load.
Electromagnetic, mechanical and circuit equations act on the generator:
the circuit equation is used as a control circuit of the generator to control the start of the generator, the mechanical equation is the mechanical motion of the generator after the generator is started, the electromagnetic equation is the basis of the calculation of the magnetic loss after the generator operates, and the magnetic loss is the heat source. For example, when the generator is started under the control circuit, the generator generates magnetic field to generate magnetic loss after stable operation, and the winding heats.
As a preference of the present embodiment, in the above three equations, magnetic field saturation is considered, and in particular, for some high-power or high-magnetic-field-density generators, magnetic field saturation effects need to be considered, which can be simulated by a magnetization curve (B-H curve).
The magnetization curve is an important parameter of the material and is a key factor for calculating loss, so that the loss heat can be calculated more accurately.
The process of establishing the mathematical model is a theoretical process, and boundary conditions under different working conditions and faults need to be introduced in order to be related to the actual working conditions of the generator, so that the operation and heating rules of the generator can be accurately and comprehensively simulated.
S101, acquiring boundary conditions including the structure, material parameters, cooling mode, cooling system and the like of the motor.
In addition, it is also necessary to obtain actual measurement data of the surface temperature of the generator, which can be obtained by a temperature sensor or a thermal infrared imager.
The structure of the motor is the body structure of the generator and can be characterized by a physical model or a three-dimensional model.
The heat transfer equation and the energy balance equation need to be considered in establishing a physical model of the internal temperature distribution of the generator. If a physical model is used, a data driven approach can be combined with the physical model for hybrid modeling.
1. Heat conduction equation: first, consider the heat conduction inside the generator. Assuming uniform stator and rotor materials, the temperature profile can be described using a thermal conduction equation. In one-dimensional case, this equation can be written as:
wherein:
k is the thermal conductivity of the material.
T is the temperature profile.
X is a position coordinate.
Is a heat source item, and includes joule heat caused by electric current, mechanical friction heat and the like.
ρ is the material density.
C is the specific heat capacity of the material.
t is time.
2. Energy balance equation: considering the heat source inside the motor, we can write the energy balance equation:
wherein:
u is the internal energy density.
Representing an internal heat source such as joule heat caused by an electric current.
Indicating the amount of heat dissipated by heat transfer and radiation.
3. Boundary conditions: you need to define appropriate boundary conditions such as initial conditions of temperature and heat transfer conditions between stator/rotor.
4. Solving the equation: by solving the heat conduction equation and the energy balance equation, the evolution of the temperature distribution with time can be obtained.
In practical application, more refinement and complexity are needed, and factors such as material properties, boundary conditions, unsteadiness and the like of different parts are considered. The model may be solved using numerical methods, such as finite element analysis (Finite Element Analysis, FEA) or computational fluid dynamics (Computational Fluid Dynamics, CFD) engineering tools.
And establishing a physical model of internal temperature distribution of the generator, and taking the factors such as the geometric shape, the thermal conductivity, the heat dissipation system and the like of the generator into consideration to construct a mathematical model describing the internal temperature distribution.
The model algorithms are all based on, for example, automatic solving operation of algorithms in Fluent thermal simulation software to obtain temperature distribution, and the temperature distribution in the generator can be obtained through calculation of Fluent software.
The construction process of the three equations is that the physical models correspond, and the mathematical model is based on the factors such as the geometric shape, the heat conductivity, the heat dissipation system and the like of the motor when the physical models are constructed.
The structure of the motor also includes necessary structural parameters such as winding resistance, inductance, magnetic field strength, permeability, load characteristics, etc.
The material parameters of the motor comprise parameters of conductor materials, silicon steel sheets, insulating parts, shell materials and winding materials.
The cooling mode and the cooling system comprise a water cooling system and an air cooling system.
These are boundary conditions for forward model simulation calculations that will be used for equations in the mathematical model.
The boundary conditions are determined according to the type of the generator, for example, nonlinear ferromagnetic material parameters are main parameters of loss, and a cooling system influences temperature distribution of thermal simulation analysis in the later period, and serve as environmental boundary conditions. The whole temperature field distribution of the generator is obtained through simulation calculation.
After the equation and the boundary condition are combined, the temperature field distribution of the generator can be obtained through CAE simulation calculation.
The embodiment adopts a discretization modeling mode, wherein the discretization modeling is as follows: the continuous-time model is converted into a discrete-time model to perform numerical simulation. This typically involves a differential equation or discretization of a differential equation. For example, under the working condition, the continuous working operation of the motor is converted into average loss of 5-10 electrical cycles, if the loss of 1 electrical cycle is 50Hz, 20ms is used, 5 electrical cycles are 100ms, the total loss is obtained by integrating the accumulated loss of 100ms, and the average loss is obtained by dividing the total loss by 100ms, so that the continuous time model is converted into discrete time.
The simulation of the present embodiment may use numerical simulation software or circuit simulation tools to verify the accuracy of the model. The effectiveness of the model is verified by simulating the response of the generator under different working conditions.
In one embodiment, the different operating conditions include a short circuit fault.
The invention builds the temperature distribution of the heating equipment, namely the forward heat transfer model, introduces the short circuit fault into the forward heat transfer simulation model of the internal temperature field of the generator on the basis of the conventional generator thermal simulation, and considers more comprehensively.
According to the invention, based on the inversion model of the internal hot spot temperature of the surface temperature of the generator, the internal temperature calculation of the generator can be realized through the integral structure of the equipment, the damage condition of the generator equipment body can be predicted in advance, the short circuit is prevented from influencing the power supply of the system and the personal safety, and the internal temperature distribution rule is deduced through the simulation model and the big data algorithm by utilizing the direction of the surface temperature distribution rule, so that the defect of infrared detection can be effectively overcome.
It should be noted that, although the foregoing embodiments describe the steps in a specific order, it will be understood by those skilled in the art that, in order to achieve the effects of the present invention, the steps are not necessarily performed in such an order, and may be performed simultaneously (in parallel) or in other orders, and these variations are within the scope of the present invention.
It will be appreciated by those skilled in the art that the present invention may implement all or part of the above-described methods according to the above-described embodiments, or may be implemented by means of a computer program for instructing relevant hardware, where the computer program may be stored in a computer readable storage medium, and where the computer program may implement the steps of the above-described embodiments of the method when executed by a processor. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable storage medium may include: any entity or device, medium, usb disk, removable hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier wave signals, telecommunications signals, software distribution media, and the like capable of carrying the computer program code. It should be noted that the computer readable storage medium may include content that is subject to appropriate increases and decreases as required by jurisdictions and by jurisdictions in which such computer readable storage medium does not include electrical carrier signals and telecommunications signals.
Further, the invention also provides a control device. In one control device embodiment according to the present invention, the control device includes a processor and a storage device, the storage device may be configured to store a program for executing the above-described method embodiment to construct an internal temperature determination model of the heat generating apparatus based on the surface temperature and the internal temperature, and the processor may be configured to execute the program in the storage device, including, but not limited to, the program for executing the above-described method embodiment to construct the internal temperature determination model of the heat generating apparatus based on the surface temperature and the internal temperature. For convenience of explanation, only those portions of the embodiments of the present invention that are relevant to the embodiments of the present invention are shown, and specific technical details are not disclosed, please refer to the method portions of the embodiments of the present invention. The control device may be a control device formed of various electronic devices.
Further, the invention also provides a computer readable storage medium. In one embodiment of the computer readable storage medium according to the present invention, the computer readable storage medium may be configured to store a program for executing the above embodiment of the method for constructing an internal temperature judgment model of the heat generating device based on the surface temperature and the internal temperature, the program being loadable and executable by a processor to realize the above construction of the internal temperature judgment model of the heat generating device based on the surface temperature and the internal temperature. For convenience of explanation, only those portions of the embodiments of the present invention that are relevant to the embodiments of the present invention are shown, and specific technical details are not disclosed, please refer to the method portions of the embodiments of the present invention. The computer readable storage medium may be a storage device including various electronic devices, and optionally, the computer readable storage medium in the embodiments of the present invention is a non-transitory computer readable storage medium.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will fall within the scope of the present invention.
Claims (10)
1. A method of monitoring the temperature of a heat generating device, comprising:
obtaining the temperature distribution of the heating equipment based on CAE simulation;
acquiring the surface temperature and the internal temperature of the heating equipment according to the temperature distribution;
constructing an internal temperature judgment model of the heat generating device based on the surface temperature and the internal temperature;
and based on the model, monitoring the temperature of the heating equipment.
2. The method of claim 1, wherein the input of the internal temperature determination model of the heat generating device is a surface temperature of the heat generating device and the output of the internal temperature determination model of the heat generating device is an internal temperature of the heat generating device.
3. The method of claim 1, wherein after the step of monitoring the temperature of the heat generating device based on the model, the method further comprises:
test points are respectively arranged on the surface and the inside of the heating equipment;
obtaining the temperature of the test point based on the temperature distribution;
and correcting the model according to the temperature of the test point.
4. A method according to claim 3, wherein said placing test points on and within the surface and interior of the heat generating device, respectively, comprises:
presetting at least one surface test point on the surface of the heating equipment, and presetting at least one internal test point in the heating equipment;
the obtaining the temperature of the test point based on the temperature distribution includes:
under a specific working condition, respectively obtaining a first temperature of the surface test point and a second temperature of the internal test point according to the temperature distribution;
said calibrating said model according to the temperature of said test point comprises:
under the specific working condition, obtaining a third temperature of the internal test point according to the first temperature of the surface test point based on the model;
judging whether the difference value between the second temperature and the third temperature is larger than a preset threshold value or not;
and if the parameters are larger than the preset parameters, adjusting the parameters of the model.
5. The method of claim 4, wherein said adjusting parameters of said model comprises:
taking the second temperature as a true value, and calculating the loss of the third temperature through a preset loss function;
and carrying out back propagation based on the loss to update parameters of the poster generation model, and completing the current round of iterative training.
6. The method of claim 1, wherein said monitoring the temperature of the heat generating device based on said model comprises:
acquiring the measurement temperature of a preset measurement point on the surface of the heating equipment;
obtaining the temperature distribution inside the heating equipment according to the model;
and determining the overheating position inside the heating equipment according to the temperature distribution.
7. The method of claim 1, wherein the heat generating device is a generator.
8. The method of claim 7, wherein deriving a temperature profile of the heat generating device based on CAE simulation comprises:
and obtaining the temperature distribution of the type of generator based on CAE simulation according to the type of the generator.
9. The method of claim 8, wherein deriving a temperature profile for the type of generator based on CAE simulation based on the type of generator comprises:
according to the type of the generator, establishing an electromagnetic equation, a mechanical equation and a circuit equation of the generator of the type;
acquiring boundary conditions of the generator under different working conditions;
based on CAE simulation, the temperature distribution of the type of generator is obtained according to the equation and the boundary condition.
10. The method of claim 9, wherein the different operating condition comprises a short circuit fault.
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