CN113465660B - Non-contact temperature measurement and material component detection device and method based on conductivity - Google Patents
Non-contact temperature measurement and material component detection device and method based on conductivity Download PDFInfo
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Abstract
The invention discloses a non-contact temperature measurement and material component detection device and method based on conductivity, which are used for realizing non-contact temperature measurement and material component detection. The non-contact temperature measuring and material component detecting device based on the conductivity comprises an alternating current power supply, an electric energy quality analyzer, a computer, a detecting coil and a coil magnetic core. The detection method comprises the following steps: firstly, installing equipment and setting power supply parameters, then carrying out experimental calibration on the relationship between the material conductivity and the temperature and the material components, establishing a relevant curve and deducing a relevant fitting function, and finally outputting detection data by adopting a computer to realize the measurement of the material temperature and the material components. Another method is to construct a database and a deep learning model by using a deep learning method after the experimental calibration, and then output data. The detection device and the detection method provided by the invention can realize non-contact temperature measurement and material component detection without being influenced by the complex environment in the container and interfering the characteristics of the material.
Description
Technical Field
The invention belongs to the field of temperature measurement and the technical field of material component detection, and particularly relates to a non-contact temperature measurement and material component detection device and method based on conductivity.
Background
The traditional temperature measurement method mainly adopts contact temperature measurement, such as thermocouple and thermal resistance temperature measurement, and through years of research and development, the contact temperature measurement method has developed relatively mature and is widely applied to the industry. However, the contact type temperature measurement is directly contacted with the measured object in a short distance, and the measuring device is easy to age, damage and the like under a high-temperature working environment, so that the temperature measurement result is inaccurate.
The non-contact temperature measuring method does not need to be in contact with a measured object, does not interfere with a temperature field, has the characteristics of simple principle, good dynamic response characteristic and convenient and fast installation, and is widely applied to industrial fields such as power station boilers, rotary kilns, fuel cells and the like. Therefore, the research on the application of the non-contact temperature measurement technology in the industry is of great significance.
Magnetic temperature measurement is a non-contact temperature measurement method, which utilizes the relationship between the electromagnetic characteristic and the temperature characteristic of a measured object to measure temperature. In the case of a rotary kiln for direct reduction, the electrical conductivity of iron ore, which usually contains a large amount of Fe, changes with the time of smelting2O3，Fe3O4Etc. are reduced to iron to varying degrees at different temperatures, in which process the change in the composition of the iron ore causes a consequent change in conductivity. When the temperature in the kiln exceeds the Curie temperature of the material, the ferromagnetic material is converted into a paramagnetic material, namely the relative magnetic conductivity is about 1, and the component change of the material only changes the self electric conductivity at the moment, so that the temperature and the iron content of the iron ore at the corresponding point of the rotary kiln can be judged according to the change of the electric conductivity of the material in the kiln.
The existing methods commonly used for detecting the components of the materials mainly comprise a chemical analysis method, an instrumental analysis method and a ferromagnetic measurement method, wherein the chemical analysis method is used for analyzing the content of the components of the materials by using the chemical method, and the instrumental analysis method is used for analyzing the change of the measured rays by using an optical principle, but cannot carry out real-time rapid measurement and cannot measure the materials which are in working conditions. The ferromagnetic measurement method is a technology for detecting by using the relation between the electromagnetic property of a detected object and the content of the material components in the material detection method, mainly aims at ferromagnetic materials, has the characteristics of non-contact and real-time performance, can carry out rapid and accurate measurement without changing the material, and has wider application prospect. However, when the temperature exceeds the curie point of the material, the material becomes paramagnetic material, and the component of the material cannot be detected by a ferromagnetic measurement method, so the invention provides a non-contact material component detection method based on conductivity, which can detect the conductivity change of the material under a high-temperature environment and further detect the component change of the material.
At present, no relevant research is available on a method and an instrument for detecting the conductivity of the material in the rotary kiln for direct reduction, the conductivity is mostly detected by measuring metal at a short distance, the device is small, and although the precision is high, the research on non-contact temperature measurement by using the conductivity is few.
Disclosure of Invention
In order to achieve the above object, embodiments of the present invention provide a non-contact temperature measurement and material component detection device based on conductivity, which can detect component changes such as iron content of a material at different times and at different positions; the change of the conductivity of the material can be detected; the device has the advantages of high sensitivity of detection results, simple test process, convenient and fast installation, no influence from the complex environment inside the container, no interference on the characteristics of materials, and realization of non-contact temperature measurement and material component detection.
The embodiment of the invention also provides a non-contact temperature measurement and material component detection method based on the conductivity.
In order to achieve at least one of the technical purposes, the invention adopts the technical scheme that the non-contact temperature measurement and material component detection device based on the conductivity is provided, and comprises an alternating current power supply, an electric energy quality analyzer, a computer, a detection coil and a coil magnetic core, wherein the alternating current power supply is a power-frequency high-power alternating current power supply; the input end of the electric energy quality analyzer is connected with the outgoing line of the detection coil, and the output end of the electric energy quality analyzer is connected with the computer; the computer is a common computer and is connected with the output end of the power quality analyzer through a data port; the detection coil is composed of an air-core coil and a coil magnetic core, and the air-core coil is tightly wound on the coil magnetic core; two outgoing lines of the detection coil are simultaneously connected with an alternating current power supply and an electric energy quality analyzer and are respectively used for driving and impedance detection; the coil magnetic core is a low-frequency high-temperature-resistant magnetic core.
Furthermore, the non-contact temperature measurement and material component detection device based on the conductivity further comprises an embedded magnetic core, wherein the embedded magnetic core is a low-frequency high-temperature-resistant magnetic core, is arranged in the detected equipment and is positioned between the detection device and the material; the embedded magnetic core is made of silicon steel and has the diameter of 50-60 mm.
Further, the current of the alternating current power supply is 1-2A, and the frequency is 45-55 Hz; the winding of the detection coil is an enameled wire, and the wire diameter is 0.8-1.2 mm; the number of turns of the coil of the detection coil is 1000-2000 turns; the height of the detection coil is 50-60 m; the coil magnetic core is formed by stacking sheet-shaped silicon steel, the width of the coil magnetic core is 50-60 mm, and the length of the coil magnetic core is 90-100 mm.
Preferably, the current magnitude of the alternating current power supply is 1.5A, and the frequency is 50 Hz; the wire diameter of the coil of the detection coil is 1.0mm, the number of turns of the coil is 1500 turns, and the height of the coil is 55 mm; the width of the coil magnetic core is 55mm, and the length of the coil magnetic core is 95 m.
The invention adopts another technical scheme that a non-contact temperature measurement and material component detection method of a non-contact temperature measurement and material component detection device based on conductivity is provided, and the method comprises the following steps:
step 1: the device is arranged on the detected equipment, so that the material to be detected is positioned in the range of the magnetic field generated by the detection coil, and the basic parameters of the alternating current power supply are set;
step 2: laboratory calibration of the data was performed in advance before the test was performed: firstly, the change relation between the temperature and the material conductivity is calibrated, equipment is started, the material is put into the equipment to be in a normal working condition, the actual temperature of a detection point can be measured by using a contact temperature measurement method, and meanwhile, the coil impedance information is recorded. Stopping the equipment, sampling after the material is cooled, measuring the conductivity of the material by using a conductivity detector, calibrating parameters of each detection point after multiple measurements to obtain a relation curve of the conductivity, the impedance and the temperature of the material under an ideal working condition, and deducing a fitting function; similarly, calibrating the change relationship between the material components and the conductivity, sampling materials with different components, measuring the conductivity of the materials, calibrating to obtain a relationship curve among the conductivity, the impedance and the components of the materials, and deducing a fitting function;
and step 3: firstly, constructing a detection platform on a computer: utilizing QT software to design a graphical interface, wherein the graphical interface comprises a conductivity change curve window, an impedance change curve window, a temperature monitoring curve window and a material composition curve window; and then, putting the materials into the device for measurement, receiving impedance information from a port for inputting data, calling the previously obtained fitting functions of the conductivity and the impedance of the materials and the temperature and the fitting functions of the conductivity and the impedance of the materials and the components of the materials, calculating the temperature and the components of the materials to be measured, and detecting the materials and the temperature of the device.
The invention adopts a further technical scheme that a non-contact temperature measurement and material component detection method of a non-contact temperature measurement and material component detection device based on conductivity is provided, and comprises the following steps:
the method comprises the following steps: the device is arranged on equipment, so that the material to be detected is in a magnetic field range generated by the detection coil, and basic parameters of an alternating current power supply are set;
step two: processing the detection signal by adopting a deep learning network: firstly, acquiring data to construct a data set, wherein only one characteristic of the data is an impedance signal, the type of the data is a 2D tensor, the 2D tensor composed of the material conductivity, the material components and the temperature is used as a target, one part of the data set is used as training data, and the other part of the data set is used as verification data;
then, an algorithm framework is built, three full-connection layers are simply stacked during network construction, each layer is initially provided with 64 units in the first two full-connection layers, a relu function is selected as an activation function, and the last full-connection layer is only provided with one unit and is not activated; when a network is compiled, the optimizer selects an rmsprop optimizer, selects a mse loss function, namely the square of the difference between a predicted value and a target value, is used for evaluating the difference degree of the predicted value and the true value of the model, measures whether the current task is successfully completed or not, and then takes the average absolute error as a monitoring index in the training process;
training a model on a training set and verifying and evaluating the model on a verification set after compiling is completed, and verifying the reliability of the training model by adopting K-fold cross: dividing data into K partitions, instantiating K identical models, training each model on K-1 partitions, and evaluating the rest of partitions, wherein the verification score of the trained model is equal to the average value of the verification scores of the K instantiated models; adjusting parameters of the model according to a verification result, wherein the parameters comprise the number of rounds and the size of a hidden layer, and training the final model on all training data by using the optimal parameters; different data sets are required to be constructed for different devices, and training is respectively carried out according to the method so as to expand the applicability of the device;
step three: after the algorithm design is completed, a detection platform is set up on a computer by utilizing QT software and the algorithm is called, materials are put into the computer for measurement, impedance signals are input into the computer, a trained model is called, and finally the conductivity, temperature and material component information of the materials are output.
And furthermore, the calibration method in the second step comprises calibrating the change relation between the temperature and the material conductivity, starting the equipment, putting the material into the equipment to enable the material to be in a normal working condition, measuring the actual temperature of the detection point by using a contact temperature measurement method, and simultaneously recording the impedance information of the coil. Stopping the equipment, sampling after the material is cooled, measuring the conductivity of the material by using a conductivity detector, calibrating parameters of each detection point after multiple measurements to obtain a relation curve of the conductivity, the impedance and the temperature of the material under an ideal working condition, and deducing a fitting function; similarly, the variation relationship between the material components and the conductivity is calibrated, the materials with different components are sampled, the conductivity of the materials is measured, the relationship curves among the conductivity, the impedance and the components of the materials are obtained after calibration, and a fitting function is deduced.
Preferably, the K-fold cross-validation is a 4-fold cross-validation: the data is divided into 4 partitions, 4 identical models are instantiated, each model is trained on 3 partitions and evaluated on the remaining one, the verification score of the model is equal to the average of the 4 verification scores.
The invention has the beneficial effects that:
1. the device adopts a high-precision power quality analyzer, the precision of the measurement result is high, and the impedance change of the coil is linearly related to the logarithm of the conductivity; meanwhile, various parameters of the coil of the device are optimized, and the detection sensitivity of the device is improved; and the diameter of the embedded magnetic core is the same as the width of the coil magnetic core, so that the magnetic field of the tested equipment can be enhanced, and the magnetic field aggregation is better.
2. The detection mode of the invention is non-contact measurement, which is not influenced by the internal environment of the equipment to be detected and does not interfere the characteristics of the material, thus effectively avoiding the problems of easy damage, aging and the like of the device in the contact temperature measurement method, prolonging the service life of the device and reducing the maintenance cost; meanwhile, the material component detection method also effectively solves the limitation of a ferromagnetic measurement method, can be applied to material component detection in a high-temperature environment, and can detect component changes such as iron content and the like of the material at different moments and different positions.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a graphical representation of the resistivity of siderite at atmospheric pressure versus temperature according to an example of the present invention; (ii) a
FIG. 2 is a schematic structural diagram of a non-contact temperature measurement and material composition detection apparatus according to an embodiment of the present invention;
FIG. 3 is a flowchart of a calibration-based process according to an embodiment of the present invention;
FIG. 4 is a flowchart of a deep learning based procedure according to an embodiment of the present invention;
FIG. 5 is a schematic cross-sectional view of an embodiment of the apparatus of the present invention in use on a rotary kiln;
FIG. 6 is a schematic cross-sectional view of the material distribution during operation of a rotary kiln according to an embodiment of the present invention;
FIG. 7 is a schematic cross-sectional view of a rotary kiln with multiple test points according to an embodiment of the present invention;
in the figure: 1. an alternating current power supply; 2. a power quality analyzer; 3. a computer; 4. a detection coil; 5. a coil core; 6. large granules; 7. powdery or small granules; 8. embedding a magnetic core; 9. a refractory lining; 10. an outer steel wall of the rotary kiln; 11. lining holes; 12. a steel wall hole; 13. the inner wall of the rotary kiln; 14. a first detection point; 15. a second detection point; 16. a third detection point; 17. a fourth detection point; 18. and (3) feeding.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
FIG. 1 is a schematic diagram of the resistivity versus temperature curve of siderite at atmospheric pressure, from which it can be seen that siderite resistivity is non-linear with temperature and shows a sudden change after reaching a certain temperature range. The reason why the resistivity of siderite decreases with increasing temperature is that Fe3+、Fe2+The content is increased, the composition of the substance is changed, and according to the electron conduction mechanism, the more electrons, the better the conductivity of the mineral, and the lower the resistivity. Therefore, the sudden change of the resistivity is caused by the decomposition of the siderite and a series of reactions thereof, so that Fe, FeO and Fe occur simultaneously at a certain time at the temperature of 510-630 DEG C3O4、FeCO3The resistivity rho of the siderite is obviously reduced, and the conductivity sigma is obviously increased as can be seen from sigma 1/rho, so that a theoretical basis is provided for the feasibility of the invention.
As shown in fig. 2, the invention provides a non-contact temperature measurement and material composition detection device based on conductivity, which comprises an alternating current power supply 1, an electric energy quality analyzer 2, a computer 3, a detection coil 4 and a coil magnetic core 5. The device utilizes the relation of the conductivity of the material to be detected and material temperature, composition, realizes real-time non-contact measurement to the temperature and the composition of material, and testing result sensitivity is high, and the test process is simple, and the device installation is convenient, and long service life can not receive the influence of the inside complex environment of container, does not disturb the characteristic of material itself, is applicable to most of equipment that use ferromagnetic material, especially is applicable to inclosed medium and large-scale equipment such as rotary kiln that detect the requirement to temperature, material rigorously.
The alternating current power supply 1 is a power frequency high-power alternating current power supply and is used for driving the detection coil to generate an alternating electromagnetic field and forming an eddy current field on the surface of the object to be detected; the invention has strict requirements on the type selection of the power supply, the method is non-contact measurement, a large gap exists between the power supply and the material, and the magnetic field reaching the material must be strong enough in the application of large-scale equipment. Due to the skin effect of an electromagnetic field on the surface of a ferromagnetic material, the frequency of a power supply is selected to be low-frequency 45-55 Hz, an eddy current field generated on the surface of a material to be detected under low frequency is small, the blocking effect of the eddy current field can be reduced, the skin depth is increased, the electromagnetic property of the material is fully reflected, in addition, the magnetic field penetration depth is large in the range, the conductivity change of the material can be fully reflected, in addition, if the frequency of the power supply is too high, the generated eddy current field is gathered on the surface of the material, the material components are complex, and the whole conductivity change of the material is difficult to accurately reflect; the power of the power supply is selected according to practical application, and meanwhile, the characteristics of the detection coil need to be considered, so that the influence of overheating of the coil on a measurement result is avoided.
The electric energy quality analyzer 2 adopts a JHDZ-3 handheld three-phase electric energy quality analyzer, the basic measurement precision of true effective values of voltage and current reaches 0.1 percent and 0.5 percent, the basic measurement precision of phase angles reaches +/-0.2 degrees, the test frequency is within the range of 45Hz to 55Hz, the current and voltage information of the detection coil 4 at a specific moment can be accurately measured, and the current and voltage information of the detection coil 4 at the specific moment can be accurately measured through a formula
And the impedance of the coil is calculated, and all test results can be directly copied to a computer through a USB interface, so that great convenience is provided for constructing a detection platform. The input end of the power quality analyzer 2 is connected with the outgoing line of the detection coil 4 for outputtingThe output end is connected with the computer 3, the current voltage and the phase information of the detection coil 4 are measured and transmitted through the data port, and after the computer 3 preprocesses the received data, the conductivity, the temperature and the material composition of the object to be measured are calculated according to a set algorithm. The material temperature rises to cause the material components to change, the conductivity of the material changes along with the material, the size of the generated eddy current field is different from that of the generated alternating current impedance, the impedance information of the detection coil 4 can be detected by the electric energy quality analyzer 2, and the temperature and the component changes of the material at the moment can be reflected after data processing.
The computer 3 is a common computer, is connected with the electric energy quality analyzer 2 through a data port, processes transmission data, calculates the conductivity of a measured object, deduces a related fitting function by adopting a least square method according to a characteristic relation curve of temperature, material components and conductivity, constructs an experimental interface by adopting QT software, inverts detected data, and records the variation trend of the temperature, the material components and the conductivity in real time so as to carry out functional design such as control, early warning and working condition analysis. In the application device of the rotary kiln, the difference between the position signals of the steel wall of the rotary kiln and the actual detection point is also considered, and the induced voltage signal at the detection point is accurately acquired.
The detection coil 4 is composed of an air-core coil and a coil magnetic core 5, and the air-core coil is tightly wound on the coil magnetic core 5; two outgoing lines of the detection coil 4 are simultaneously connected with an alternating current power supply 1 and a power quality analyzer 2 and are respectively used for driving and impedance detection. The detection coil 4 is large in height, small in number of turns and long in tubular shape, alternating current with specified frequency is introduced to generate a stable alternating magnetic field, magnetic induction lines reach the surface of the material directly or after being conducted by a magnetic core, and an eddy current field for blocking the change of the original magnetic field is formed in the material, so that the eddy current field counteracts the detection coil 4 and changes the alternating current impedance of the coil. The detection coil 4 has various parameters including the coil wire diameter, the number of turns, the coil height, the magnetic core diameter and the magnetic core length diameter, which influence the measurement result and the sensitivity of the device, and is optimized according to the application scene.
The change relation between the conductivity sigma of the measured object and the alternating-current impedance Z of the detection coil satisfies the following equation:
in the formula: ω -coil excitation angular frequency;
μ0-magnetic permeability of the vacuum;
σ — conductivity of the material under test;
w is the total number of turns of the coil;
beta-with R2An intrinsic decay constant normalized to a reference;
R1，R2-coil outer diameter, inner diameter;
u, H-coil height, lift-off height;
J1(a) -a first order bessel function of the first kind, α being a bessel function argument;
j-an imaginary symbol;
as can be known from the above equation, the change of the coil alternating current impedance of the detection coil 4 is related to the parameters of the above equation, when the power current intensity, the frequency, the detection distance and the size of the measured object are determined, the impedance of the coil is related to only the magnetic permeability and the electric conductivity of the measured object, and in a high-temperature environment, the temperature exceeds the curie temperature of the measured object, the magnetic permeability of the material is sharply reduced to become a paramagnetic material, the relative magnetic permeability is about 1, and at this time, the impedance change rate Δ Z of the coil is related to only the electric conductivity σ of the measured object, and the two are in a single-valued functional relationship: Δ Z ═ F (σ).
Coil magnetic core 5 is low frequency high temperature resistant magnetic core, and low frequency magnetic core has good magnetic effect and lower hysteresis loss of leading under low frequency work, can strengthen the gathering nature in former magnetic field, makes the magnetic field intensity on material surface big enough, again because test site temperature is higher, therefore coil magnetic core 5 selects low frequency high temperature resistant magnetic core.
Preferably, since the outer wall of the device to be detected is spaced between the measuring device and the material, the embedded magnetic core 8 needs to be installed between the detecting device and the material for magnetic conduction, so as to reduce air leakage flux. The embedded magnetic core 8 is a low-frequency high-temperature-resistant magnetic core, is arranged in the tested equipment and is positioned between the detection device and the material. Because the temperature of a measurement site is high, a power supply signal is low frequency, the magnetic core has strong temperature resistance and low frequency working capacity, a silicon steel sheet is selected as the embedded magnetic core 8, the silicon steel sheet has good magnetic conductivity, the eddy current effect can be reduced due to high internal resistance, the temperature of the magnetic core is reduced, and the self characteristic can be still maintained at 700 ℃.
Preferably, the winding of the detection coil 4 is an enameled wire, the wire diameter is 0.8-1.2 mm, and the enameled wire can bear 5-10A alternating current.
Preferably, the number of turns of the coil of the detection coil 4 is 1000-2000 turns, the generated magnetic field is increased, the number of turns of the coil is not too high, and the phenomenon that the magnetic field is weakened due to too large integral impedance is avoided.
Preferably, the height of the detection coil 4 is 50-60 m, and the magnetic field aggregation is good.
Preferably, coil magnetic core 5 adopts the lamination of slice silicon steel sheet, and the width is 50 ~ 60mm, and length is 90 ~ 100mm, increase and material, air area of contact, reduce the magnetic resistance.
Preferably, the power supply current is 1-2A, so that the coil can generate a sufficient strong magnetic field, and the influence of overheating of the coil on impedance change is avoided.
Preferably, the power frequency is 45 Hz-55 Hz, the penetration depth is large, the change of the whole conductivity of the material can be fully reflected, and a strong enough eddy current field is generated to cause the impedance change.
Preferably, the coil wire diameter of the detection coil 4 is 1.0mm, the number of turns is 1500, the height of the coil is 55mm, the magnetic core is formed by laminating sheet-shaped silicon steel sheets, the width of the magnetic core is 55mm, the length of the magnetic core is 95m, the magnitude of the power supply current is 1.5A, and the power supply frequency is 50 Hz.
As shown in fig. 3, the present invention also provides a calibration method based non-contact temperature measurement and material composition detection method using the non-contact temperature measurement and material composition detection device based on conductivity, firstly, the device of the present invention is installed on the equipment, the material to be detected is in the range of the magnetic field generated by the detection coil 4, the basic parameters of the ac power supply 1 are set, and the data is calibrated in the laboratory before the detection: firstly, calibrating the change relation between the temperature and the material conductivity, starting the equipment, putting the material into the equipment to enable the material to be in a normal working condition, measuring the actual temperature of a detection point by using a contact type temperature measurement method, simultaneously recording coil impedance information, then stopping the equipment, sampling after the material is cooled, measuring the material conductivity by using a conductivity detector, calibrating parameters of each detection point after multiple measurements to obtain a relation curve of the material conductivity, the impedance and the temperature under an ideal working condition, and deducing a fitting function; similarly, calibrating the change relationship between the material components and the conductivity, sampling materials with different components, measuring the conductivity of the materials, calibrating to obtain a relationship curve among the conductivity, the impedance and the components of the materials, and deducing a fitting function; after the laboratory calibration is completed, a detection platform is constructed on the computer 3: utilizing QT software to design a graphical interface, which comprises a conductivity change curve window, an impedance change curve window, a temperature monitoring curve window and a material composition curve window; and then, putting the materials into the device for measurement, receiving impedance information from a port for inputting data, calling the fitting function of the conductivity and the impedance of the materials and the temperature and the fitting function of the conductivity and the impedance of the materials and the components of the materials, and calculating the temperature and the components of the materials to be measured, namely, detecting the components and the temperature of the materials of the device. For the application in different devices, the calibration data used is different, the data acquisition, parameter calibration and database construction can be carried out on the common devices, and the parameters of different devices are called through the setting of the detection platform to realize the measurement, so that the universality of the device is improved.
As shown in fig. 4, the present invention further provides a non-contact temperature measurement and material component detection method based on deep learning and using a non-contact temperature measurement and material component detection device based on conductivity, wherein the device of the present invention is firstly installed on a device, so that a material to be detected is in a magnetic field range generated by a detection coil 4, then basic parameters of an ac power supply 1 are set, and a deep learning network is used to process detection signals: because there is no universal data set, firstly, data is collected to construct a data set, the data is characterized by only one, namely impedance signal, and the type of the data is 2D tensor, so that data standardization is not needed before training, the 2D tensor composed of material conductivity, material components and temperature obtained in the calibration method is used as a target, one part of the data set is used as training data, and the other part of the data set is used as verification data; then, an algorithm framework is built, due to the fact that training data are few, overfitting is prone to being serious, and the data processing type is simple, when a network is built, only three full-connection layers are simply stacked, each layer is initially provided with 64 units in the first two full-connection layers, a relu function is selected as an activation function, the last full-connection layer is only provided with one unit, and activation is not needed; when a network is compiled, the optimizer selects an rmsprop optimizer, selects a mse loss function, namely the square of the difference between a predicted value and a target value, is used for evaluating the difference degree between the predicted value and a true value of a model, measures whether the current task is successfully completed or not, and then takes an average absolute error (MAE) as a monitoring index in a training process; training the model on a training set after compiling is completed, and verifying the evaluation model on a verification set: because the verification set is less, the reliability of a K-fold cross-validation training model can be adopted, 4-fold cross-validation is used, data is divided into 4 partitions, 4 identical models are instantiated, each model is trained on (4-1) partitions, and evaluation is carried out on the rest partition, and the verification score of the trained model is equal to the average value of the verification scores of the 4 instantiated models; adjusting parameters of the model according to a verification result, wherein the parameters comprise the number of rounds and the size of a hidden layer, and training the final model on all training data by using the optimal parameters; different data sets are required to be constructed for different devices, and training is respectively carried out according to the method so as to expand the applicability of the device; after the algorithm design is completed, a detection platform is built by QT software and the algorithm is called, materials are put into the detection platform for measurement, impedance signals are input into the platform, a trained model is called, and finally the conductivity, temperature and material composition information of the materials are output. The method has the advantages of good flexibility, strong real-time performance, higher measurement precision and stronger practicability, and better meets the requirements of modern industrial production.
In a specific embodiment, as shown in fig. 5, when the non-contact temperature measurement and material composition detection device based on conductivity is applied to temperature measurement and material composition detection of a rotary kiln, the device is firstly installed and fixed outside the bottom of the rotary kiln, and a steel wall hole 12 is formed in an outer steel wall 10 of the rotary kiln, so that interference of a magnetic induction line forming a magnetic loop through the steel wall and an eddy effect of the steel wall can be reduced, and detection sensitivity of the device is improved; then, lining holes 11 are formed in the refractory lining 9, and the thickness of 4cm is reserved without penetrating the refractory lining 9; then, the embedded magnetic core 8 made of silicon steel sheets is arranged in the inner lining hole 11 for magnetic conduction, so that the magnetic field reaching the materials can be enhanced, and the magnetic field aggregation is better; the embedded magnetic core 8 is not in contact with the coil magnetic core 5 of the detection coil 4; setting the power and frequency of an alternating current power supply 1, driving a detection coil 4 to generate an alternating electromagnetic field by alternating current generated by the alternating current power supply 1, when an embedded magnetic core 8 and a coil magnetic core 5 are opposite to each other in the rotation process of the rotary kiln, enabling the generated electromagnetic field to reach the surface of a material 18 through the embedded magnetic core 8, forming an eddy current field on the surface of the material 18, generating a magnetic field opposite to the original magnetic field to block the original magnetic field to change so that the alternating impedance of the detection coil 4 changes, analyzing the current voltage and the phase information of the detection coil 4 by an electric energy quality analyzer 2, transmitting the current voltage and the phase information to a computer 3 through a data interface, carrying out sampling pretreatment on data on the computer 3 to obtain alternating impedance, calculating corresponding conductivity according to a predetermined conductivity-impedance characteristic curve and a fitting function, and calculating corresponding conductivity according to temperature-conductivity, And inverting the material component-conductivity characteristic curve to obtain the temperature and the material component at the moment, or outputting the material temperature and component information by using a deep learning network. The power frequency is 50Hz, the penetration depth is large, the change of the material conductivity can be fully reflected, the coil wire diameter of the detection coil 4 is 1.0mm, the number of turns is 1500 turns, the coil height is 55mm, the detection coil magnetic core is formed by stacking sheet-shaped silicon steel sheets, the width is 55mm, the length is 95mm, and the power current is 1.5A.
Because the inner wall 13 of the rotary kiln is a material stacking area, the rotary kiln runs slowly, the materials 18 continuously roll and move forward for a certain distance, in the rolling process, large-particle materials 6 with large particle size and regular shape form an outer layer, and powder or small-particle materials 7 form an inner core (see fig. 6 and 7), so that the device is arranged at the bottom of the rotary kiln, and the materials 18 are always positioned right above the device.
Because the rotary kiln rotates slowly, the time required by one rotation is 1-2 min, and the detection point is only measured once per rotation, in order to ensure the accuracy of measurement, a plurality of detection points (see fig. 7) need to be additionally arranged on the outer steel wall 10 of the rotary kiln, such as a first detection point 14, a second detection point 15, a third detection point 16 and a fourth detection point 17, so that a plurality of groups of data are collected, and the influence of accidental factors is reduced. In addition, the detection platform must distinguish the signals at the detection point of the rotary kiln from the signals passing through the outer steel wall 10 of the rotary kiln, and accurately analyze the data of the detection point.
If the temperature is controlled improperly, the ring formation problem of the rotary kiln is easily caused, once the ring formation occurs, the material movement, the air flow movement, the thermal regulation, the reduction process and various reactions in the kiln are all damaged, and the kiln is forced to be stopped when the ring formation is serious. The method and the device can effectively detect the ring formation phenomenon in the kiln and prevent the ring formation in time. The caking on the furnace lining is mainly caused by low-melting-point compounds generated by coal ash and iron oxide powder in the reduction process, when the ring formation occurs at the detection point, the change of a detection signal is influenced, and when the conductivity at the detection point is measured and calculated to be at a certain abnormal value and is lasting and unchanged, the ring formation coil can be judged to occur at the position, and the temperature needs to be properly adjusted.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (4)
1. A non-contact temperature measurement and material component detection method of a non-contact temperature measurement and material component detection device based on conductivity is characterized by comprising the following steps:
step 1: installing a detection device on the detected equipment to enable the iron mineral material to be detected to be in the range of the magnetic field generated by the detection coil (4), and setting basic parameters of the alternating current power supply (1);
step 2: laboratory calibration of the data was performed in advance before the test was performed: firstly, calibrating the change relation between the temperature and the material conductivity, starting the equipment, putting the material into the equipment to enable the material to be in a normal working condition, measuring the actual temperature of a detection point by using a contact temperature measuring method, simultaneously recording impedance information, then stopping the equipment, sampling after the material is cooled, measuring the material conductivity by using a conductivity detector, calibrating parameters of each detection point after multiple measurements to obtain a relation curve of the material conductivity, the impedance and the temperature under an ideal working condition, and deducing a fitting function; then calibrating the change relation between the material components and the conductivity, sampling materials with different components, measuring the conductivity of the materials, calibrating to obtain a relation curve among the conductivity, the impedance and the components of the materials, and deducing a fitting function;
and 3, step 3: firstly, a detection platform is constructed on a computer (3): utilizing QT software to design a graphical interface, which comprises a conductivity change curve window, an impedance change curve window, a temperature monitoring curve window and a material composition curve window; then, the materials are put into the device for measurement, impedance information is received from a port for inputting data, a fitting function of the conductivity and the impedance of the materials and the temperature and a fitting function of the conductivity and the impedance of the materials and the components of the materials which are obtained before are called, the temperature and the components of the materials to be measured are calculated, and the materials and the temperature of the device can be detected;
the detection device comprises an alternating current power supply (1), a power quality analyzer (2), a computer (3), a detection coil (4) and a coil magnetic core (5), wherein the alternating current power supply (1) is a power frequency alternating current power supply; the input end of the power quality analyzer (2) is connected with the outgoing line of the detection coil (4), and the output end of the power quality analyzer is connected with the computer (3); the computer (3) is connected with the output end of the power quality analyzer (2) through a data port; the detection coil (4) is composed of an air coil and a coil magnetic core (5), and the air coil is tightly wound on the coil magnetic core (5); two outgoing lines of the detection coil (4) are simultaneously connected with an alternating current power supply (1) and an electric energy quality analyzer (2) and are respectively used for driving and impedance detection; the coil magnetic core (5) is a low-frequency high-temperature resistant magnetic core;
the detection device further comprises an embedded magnetic core (8), wherein the embedded magnetic core (8) is a low-frequency high-temperature-resistant magnetic core, is arranged in the detected equipment and is positioned between the detection device and the material; the embedded magnetic core (8) is made of silicon steel, and the diameter of the embedded magnetic core is 50-60 mm;
the current of the alternating current power supply (1) is 1-2A, and the frequency is 45-55 Hz; the winding of the detection coil (4) is an enameled wire, and the wire diameter is 0.8-1.2 mm; the number of turns of the coil of the detection coil (4) is 1000-2000 turns; the height of the detection coil (4) is 50-60 m; the coil magnetic core (5) is formed by laminating sheet silicon steel, the width is 50-60 mm, and the length is 90-100 mm;
the change relation between the conductivity sigma of the measured object and the impedance change rate Delta Z of the detection coil satisfies the following equation:
wherein the content of the first and second substances,
k＝R1/R2，h＝H/R2，u＝U/R2and omega is the coil excitation angular frequency; mu.s0Magnetic permeability in vacuum; sigma is the conductivity of the material to be detected; w is the total number of turns of the coil; beta is represented by R2An intrinsic decay constant normalized to a reference; r1，R2The outer diameter and the inner diameter of the coil are set; u and H are coil height and lift-off height; j. the design is a square1(a) Is as followsA first order Bessel function, wherein alpha is a Bessel function independent variable; j is an imaginary symbol.
2. The non-contact temperature measurement and material component detection method of the non-contact temperature measurement and material component detection device based on the conductivity as claimed in claim 1, further comprising the steps of:
the method comprises the following steps: processing the detection signal by adopting a deep learning network: firstly, acquiring data to construct a data set, wherein only one characteristic of the data is an impedance signal, the type of the data is a 2D tensor, the 2D tensor composed of the material conductivity, the material components and the temperature is used as a target, one part of the data set is used as training data, and the other part of the data set is used as verification data;
then, an algorithm framework is built, three full connection layers are simply stacked during network construction, each layer of the first two full connection layers is initially provided with 64 units, a relu function is selected as an activation function, and the last full connection layer is only provided with one unit and is not activated; when a network is compiled, the optimizer selects an rmsprop optimizer, selects a mse loss function, namely the square of the difference between a predicted value and a target value, evaluates the difference degree between the predicted value and a true value of a model, measures whether the current task is successfully completed or not, and takes the average absolute error as a monitoring index in the training process;
training a model on a training set and verifying and evaluating the model on a verification set after compiling is completed, and verifying the reliability of the training model by adopting K-fold cross: dividing data into K partitions, instantiating K identical models, training each model on K-1 partitions, and evaluating the rest of partitions, wherein the verification score of the trained model is equal to the average value of the verification scores of the K instantiated models; adjusting parameters of the model according to a verification result, wherein the parameters comprise the number of rounds and the size of a hidden layer, and training the final model on all training data by using the optimal parameters; different data sets are constructed for different detected equipment, and training is respectively carried out to expand the applicability of the device;
step two: after the algorithm design is completed, a detection platform is built on the computer (3) by utilizing QT software and the algorithm is called, the materials are put into the computer for measurement, impedance signals are input into the computer (3), the trained model is called, and finally the conductivity, the temperature and the material composition information of the materials are output.
3. The non-contact temperature measurement and material component detection method of the non-contact temperature measurement and material component detection device based on conductivity according to claim 1, wherein the calibration method in the second step comprises calibrating the change relationship between temperature and material conductivity, starting equipment, putting materials into normal working conditions, measuring actual temperature at detection points by using a contact temperature measurement method, recording coil impedance information, stopping the equipment, sampling after the materials are cooled, measuring the material conductivity by using a conductivity detector, performing parameter calibration on each detection point after multiple measurements to obtain the relationship curve between the material conductivity, the impedance and the temperature under ideal working conditions, and deriving a fitting function; similarly, the variation relationship between the material components and the conductivity is calibrated, the materials with different components are sampled, the conductivity of the materials is measured, the relationship curves among the conductivity, the impedance and the components of the materials are obtained after calibration, and a fitting function is deduced.
4. The non-contact temperature measurement and material component detection method using the non-contact temperature measurement and material component detection device based on conductivity as claimed in claim 2, wherein the K-fold cross validation is 4-fold cross validation: the data was divided into 4 partitions, 4 identical models were instantiated, each model was trained on 3 partitions and evaluated on the remaining one, the model's verification score was equal to the average of the 4 verification scores.
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