CN113092322A - Device and method for online monitoring of lubricating oil abrasive particles based on electromagnetic tomography technology - Google Patents
Device and method for online monitoring of lubricating oil abrasive particles based on electromagnetic tomography technology Download PDFInfo
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- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
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- G01N27/90—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables for investigating the presence of flaws using eddy currents
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
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- G01N27/82—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables for investigating the presence of flaws
- G01N27/90—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables for investigating the presence of flaws using eddy currents
- G01N27/9046—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables for investigating the presence of flaws using eddy currents by analysing electrical signals
Abstract
The invention provides an online monitoring device and method for lubricating oil abrasive particles based on an electromagnetic tomography technology, which are used for solving the problems that the existing online monitoring device for the lubricating oil abrasive particles is easily influenced by the environment and cannot detect the size and the position of the abrasive particles. The monitoring device comprises a channel switching module and an image reconstruction computer, wherein the channel switching module is connected with a small-scale biplane electromagnetic tomography sensor, the small-scale biplane electromagnetic tomography sensor comprises an excitation coil and a detection coil, and the excitation coil and the detection coil are both arranged on a lubricating oil pipeline; the channel switching module is connected with the signal processing circuit, the signal processing circuit is connected with the image reconstruction computer, the image reconstruction computer is connected with the single chip microcomputer, and the single chip microcomputer is connected with the channel switching module. The invention is not influenced by non-metallic impurities such as water, bubbles and the like and the color of lubricating oil, can realize the quick, non-contact, non-invasive, low-cost and visual monitoring of the lubricating oil abrasive particles, and can acquire the size and position information of the abrasive particles.
Description
Technical Field
The invention relates to the technical field of online monitoring of lubricating oil abrasive particles, in particular to an online monitoring device and method of lubricating oil abrasive particles based on an electromagnetic tomography technology.
Background
The engine works under severe conditions of high speed, high temperature, heavy load and the like, the bearing is easy to wear, even violently worn, a large amount of metal abrasive particles can be generated in lubricating oil due to sudden violent wear, the size and the number of the abrasive particles can represent the wear degree of the engine, the engine fault can be timely monitored and early warned by effectively monitoring and analyzing the engine on line, catastrophic accidents are avoided, and important reference information is provided for fault diagnosis. The online monitoring of the lubricating oil abrasive particles enables abrasive particle information parameters to correspond to the engine abrasion degree, and the abrasion property, degree, category and reason are determined by analyzing abrasive particle information including abrasive particle quantity, size, material and morphology, so that the abrasion mechanism is analyzed and the fault source is positioned. The dynamic change trend of the lubricating oil abrasive particles is monitored in time, and the method has great significance for effectively monitoring the mechanical health condition of the engine.
At present, engine lubricating oil abrasive particle monitoring is divided into offline monitoring and online monitoring, and the traditional lubricating oil abrasive particle offline monitoring technology comprises spectral analysis, ferrographic analysis, abrasive particle counting and the like. The off-line monitoring mainly depends on subjective observation and judgment of people, the technical efficiency is low, the effectiveness is poor, the practicability is poor, errors are easy to generate, lubricating oil abrasive particles are reported in a wrong mode and in a missing mode, and the requirement on the experience of people is high. In addition, the offline monitoring is influenced by factors such as sampling period, sampling part and the like, so that the sample is not representative and the problem cannot be found; and because the lubricating oil cannot be continuously monitored, sudden faults cannot be found, and the best maintenance opportunity is missed.
The online monitoring technology of the lubricating oil abrasive particles comprises an ultrasonic detection technology, an optical detection technology, a capacitance method, a charge method, an inductance method, an X-ray method and the like. The ultrasonic detection technology utilizes the scattering/reflecting effect of the lubricating oil abrasive particles on ultrasonic waves to detect the abrasive particles above 250um, but bubbles, sensor mounting positions, ultrasonic frequency, vibration and noise can seriously influence the monitoring performance. The optical detection technology mainly uses a laser light source or a light emitting diode light source, and the lubricating oil abrasive particles shield a part of light to generate reflection or scattering, so that the light reaching the charge coupled device or the photoelectric tube is attenuated. The capacitance method realizes the monitoring of the metal abrasive particles by measuring the change of the dielectric constant, has high sensitivity, is insensitive to the interference of material characteristics, is not influenced by the color of lubricating oil, but is influenced by the generation speed of the abrasive particles, and has the conditions of missing detection and false detection. When the lubricating oil abrasive particles flow, the charge method generates induced charges on the surface of the sensor under the action of electrostatic induction, and the induced charges are converted into voltage signals to realize counting. The inductance method realizes the abrasive particle detection by utilizing the modulation effect of an induction magnetic field generated by metal abrasive particles on a main magnetic field, has high speed, is not influenced by other nonmetals except the metal abrasive particles, does not completely meet the requirement of abrasive particle detection resolution, and has the condition of false detection when a plurality of abrasive particles pass through the method simultaneously. The X-ray method utilizes X-rays to excite the energy of the abrasive particles to generate a dispersive fluorescence spectrum to detect the content of the metal components, is not influenced by environment, is sensitive to the content of metal elements, cannot be used for detecting the number and the size of the abrasive particles, and is only suitable for detecting the concentration or the content of the elements.
An Electromagnetic Tomography (EMT) technology is a process Tomography complex parameter distribution detection technology based on the Electromagnetic induction principle, can simultaneously image conductivity distribution and permeability distribution substances in a pipeline or a container, and has a dual-mode characteristic. The EMT has the advantages of high speed, non-contact, non-invasion, low cost and the like, is particularly suitable for long-term monitoring of complex industrial fields, is applied to the fields of molten steel visual detection, multiphase flow measurement, nondestructive detection and the like, and has great potential and application prospect.
Therefore, in order to timely and accurately monitor the dynamic change trend of the lubricating oil abrasive particles, the electromagnetic tomography technology is applied to online monitoring of the lubricating oil abrasive particles, sudden severe abrasion which may occur to an engine is monitored in real time, a three-dimensional image of distribution of the lubricating oil abrasive particles is reconstructed, the abrasive particle shapes are analyzed, the size and position information of the abrasive particles is obtained, when the size and position information exceeds a threshold value, an alarm is sent out, major accidents are avoided, and the method has great significance and application value.
Disclosure of Invention
Aiming at the technical problems that the existing online monitoring technology for the lubricating oil abrasive particles is easily influenced by the environment, has missing detection and false detection and can not detect the size and the position of the abrasive particles, the invention provides the online monitoring device and the method for the lubricating oil abrasive particles based on the electromagnetic tomography technology, wherein a small-scale double-plane electromagnetic tomography sensor is arranged around a tested lubricating oil pipeline, by injecting sinusoidal AC signal into the exciting coil, an alternating exciting magnetic field is generated in the measured object field, when metal abrasive particles exist in the tested lubricating oil pipeline, the distribution of the alternating excitation magnetic field is changed, so that the change of the induced voltage value of the detection coil is caused, and a three-dimensional image of the distribution of the metal abrasive particles is reconstructed according to the change of the voltage value, the size and position information of the lubricating oil abrasive particles is visually displayed, the dynamic change trend of the lubricating oil abrasive particles is timely and accurately monitored, and the rapid, non-contact, non-invasive, low-cost and visual monitoring of the lubricating oil abrasive particles is realized.
In order to achieve the purpose, the technical scheme of the invention is realized as follows: an online monitoring device for lubricating oil abrasive particles based on an electromagnetic tomography technology comprises a channel switching module and an image reconstruction computer, wherein the channel switching module is connected with a small-scale biplane electromagnetic tomography sensor, the small-scale biplane electromagnetic tomography sensor comprises an excitation coil and a detection coil, the excitation coil and the detection coil are both arranged on a lubricating oil pipeline, and the detection coil detects an induced voltage signal of the lubricating oil pipeline under the action of an alternating excitation magnetic field generated by the excitation coil in real time; the channel switching module is connected with the signal processing circuit, the signal processing circuit is connected with the image reconstruction computer, the image reconstruction computer is connected with the single chip microcomputer, the single chip microcomputer is connected with the channel switching module, the signal processing circuit is used for processing an induced voltage signal detected by the detection coil to obtain induced voltage amplitude information and transmitting the induced voltage amplitude information to the image reconstruction computer, the image reconstruction computer receives the induced voltage amplitude information and reconstructs a three-dimensional image of lubricating oil abrasive particle distribution and displays the three-dimensional image, meanwhile, a control instruction of the image reconstruction computer is transmitted to the channel switching module through the single chip microcomputer, and the single chip microcomputer receives the control instruction of the image reconstruction computer and controls the channel switching module to complete switching of an excitation coil and the detection coil of the small-scale biplane electromagnetic tomography.
The signal processing circuit comprises a phase-locked amplifier, a signal conditioning module and a power amplification module, wherein the phase-locked amplifier is respectively connected with the signal conditioning module and the power amplification module, the signal conditioning module and the power amplification module are both connected with the channel switching module, and the phase-locked amplifier is connected with the image reconstruction computer; the image reconstruction computer controls the phase-locked amplifier to generate an excitation signal, and the power amplification module is used for performing power amplification on the excitation signal and increasing the magnetic field intensity generated by the excitation coil; the signal conditioning module amplifies and filters weak signals measured by the detection coil, and the lock-in amplifier acquires the measurement signals of the detection coil, demodulates data and acquires amplitude information of the measurement induced voltage signals.
The small-scale biplane electromagnetic tomography sensor comprises a cylinder body, wherein the cylinder body is sleeved on an oil sliding pipeline, and an excitation coil and a detection coil are uniformly fixed on the outer wall of the cylinder body.
The excitation coil and the detection coil are both composed of electromagnetic coils, and the electromagnetic coils are of a biplane O-shaped structure.
The electromagnetic coil is formed by uniformly winding enamelled copper wires with the wire diameter of 0.4mm, and has the same inductance value under the same excitation frequency; the electromagnetic coil comprises two layers of coils, each layer of coil is of a closed O-shaped structure, and the object field radius and the interlayer spacing of each layer of coil are adjustable; each layer of the electromagnetic coil has 6, 8, 12, 16 or 20 coils, and the number of the coil layers and the number of the coil turns of each layer are adjustable.
The single chip microcomputer is a Stm32 single chip microcomputer; the channel switching module is realized by a switching circuit consisting of a network relay or a multiplexer with the model of ADG 406; the power amplification module adopts a current feedback amplifier with the model number of LT 1210; the phase-locked amplifier is an MFLI phase-locked amplifier.
The monitoring method of the online lubricating oil abrasive particle monitoring device based on the electromagnetic tomography technology comprises the following steps:
the method comprises the following steps: the image reconstruction computer sends a signal to control the phase-locked amplifier to generate an alternating current sinusoidal signal of dozens to hundreds of kHz, the alternating current sinusoidal signal is output to an excitation coil selected by the channel switching module through the power amplification module and is injected into the excitation coil, the excitation coil generates an alternating excitation magnetic field in the space of a measured lubricating oil pipeline, under the action of the alternating excitation magnetic field, the surface of metal abrasive particles in the lubricating oil pipeline generates eddy current, the eddy current induces a secondary magnetic field, the secondary magnetic field influences the distribution of the alternating excitation magnetic field to form a composite magnetic field, and the induced voltage and the impedance of the detection coil are changed;
step two: the singlechip controls the channel switching module to select the detection coils in sequence, and outputs the induced voltage signal to the lock-in amplifier through the signal conditioning module, so that the collection and data demodulation of weak measurement signals are completed, the amplitude of the induced voltage signal is obtained, and the amplitude is transmitted to the image reconstruction computer;
step three: the single chip microcomputer receives signals of the image reconstruction computer, controls the channel switching module to sequentially select the excitation coils, and the rest of the excitation coils are used as the detection coils, so that the measurement of the induced voltage signals in all projection directions is completed, and the amplitude values of the induced voltage signals are transmitted to the image reconstruction computer;
step four: and the image reconstruction computer reconstructs a three-dimensional image of the distribution of the lubricating oil abrasive particles by using an image reconstruction algorithm according to the amplitude and the sensitivity matrix of the induced voltage signal, so that the size and the position information of the lubricating oil abrasive particles are obtained.
The image reconstruction algorithm is realized by the following steps:
(1) solving the sensitivity matrix by using a model perturbation method, solving the positive problem of electromagnetic tomography by using an experimental method, and acquiring a boundary measurement voltage value and the sensitivity matrix;
(2) describing the nonlinear relation between the measured voltage value and the conductivity distribution by adopting a linear approximation method, simplifying an electromagnetic tomography nonlinear model, and obtaining a normalized linear equation between the measured value and the conductivity distribution: u is Sg; wherein, U is an M multiplied by 1 dimensional normalized measurement voltage value vector; s is a normalized sensitivity matrix of MxNnV; g is an Nx 1-dimensional normalized conductivity distribution vector representing an image gray value; m is the number of measured voltage values; n is the number of cells subdivided by the measured object field;
(3) the sparse signal is sampled in a low-dimensional mode by using a compressed sensing theory, and the reconstruction of a conductivity distribution vector is realized by using an optimization method;
(4) and reconstructing a three-dimensional image of the distribution of the lubricating oil abrasive particles according to the conductivity distribution obtained by solving, obtaining the size and position information of the lubricating oil abrasive particles, and realizing the monitoring of the dynamic change trend of the lubricating oil abrasive particles.
The positive problem of electromagnetic tomography is the typical initial boundary value problem:
where μ is the magnetic permeability, A is the vector magnetic potential, ω is the angular frequency, σ is the electrical conductivity, JsIs the excitation current density;
the mathematical model based on the compressed sensing theory, which is constructed by using the compressed sensing theory to carry out low-dimensional sampling on the sparse signal in the step (3), is as follows: U-Sg-S psiFFTs; in which Ψ has a size of N × NFFTIs an FFT sparse basis; s, of size nx 1, is a sparse coefficient vector;
carrying out Gaussian random arrangement on the conductivity distribution vector S, namely, disordering the sequence of each row of the vector S to generate a new random observation matrix SnewAt the same time, the measured value vector U is disordered according to the same rule to generate a new measured value vector UnewThen the electromagnetic tomography positive problem model is:
wherein s isoptAn objective function representing a sparse coefficient vector s, AEMTIs a sensing matrix, goptAn objective function representing the gray value g of the image;
converting the constrained convex optimization problem into an unconstrained convex optimization problem, and using l1Regularization model, introducing regularization parameter lambda to obtain a solving model:
fast iterative threshold shrinkage algorithm is adopted to solve the l in the model1And solving the norm regularization problem to obtain a conductivity distribution vector.
The fast iterative threshold shrinkage algorithm solves for1The norm regularization problem, and the method for obtaining the optimal sparse coefficient vector s comprises the following steps:
inputting: l2 lambdamax((AEMT)TAEMT)；
Step 0: taking initial value
t1＝1
Step k, wherein k is more than or equal to 1: and (3) calculating:
sk＝PL(yk)
wherein
PL(sk)＝Tλt(sk-2t(AEMT)T(AEMTsk-Unew))
Tα(s)i＝(|si|-α)+sgn(si)
Solving to obtain an optimal sparse coefficient vector s;
wherein L represents the Liphoz constant, λmaxRepresenting the maximum eigenvalue of the matrix, T representing the transpose of the matrix, skRepresenting the value of the sparse coefficient vector s at the kth iteration, PL() Representing the iterative contraction operator, ykDenotes the value of y, t, at the k-th iterationk+1Denotes the (k + 1) th iteration step, tkDenotes the kth iteration step size, yk+1Denotes the value of y, s, at the k +1 th iterationk-1Represents the value of the sparse coefficient vector s at the k-1 iteration, TλtRepresenting the contraction operator, Tα(s)iRepresenting a contraction operator definition, siAnd the value of a sparse coefficient vector s at the ith iteration is represented, alpha represents a noise reduction parameter, and sgn represents a sign function.
Compared with the prior art, the invention has the beneficial effects that:
(1) the invention utilizes an excitation magnetic field generated by injecting an alternating current signal into an excitation coil as a main magnetic field, and the magnetic field is only sensitive to metal abrasive particles in a lubricating oil pipeline, is not influenced by non-metal impurities such as water, bubbles and the like, and is not influenced by the color of the lubricating oil.
(2) The method can realize rapid, non-contact, non-invasive, low-cost and visual monitoring of the lubricating oil abrasive particles, reconstruct a three-dimensional image of the distribution of the lubricating oil abrasive particles, analyze the morphology of the abrasive particles, acquire the size and position information of the abrasive particles, and send out an alarm when the size and position information exceed a threshold value.
(3) The invention can simultaneously image the conductive and magnetic conductive substances in the lubricating oil pipeline and has the characteristic of dual modes.
(4) The invention can be applied to the industrial field of complex lubricating oil pipelines with high temperature, high speed, heavy load and the like to carry out long-term online monitoring.
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 schematic block diagram of a monitoring device of the present invention.
FIG. 2 is a schematic structural diagram of a small-scale biplane electromagnetic tomography sensor of the present invention.
Fig. 3 is a schematic structural diagram of the electromagnetic coil in fig. 2.
FIG. 4 is a flow chart of the monitoring method of the present invention.
In the figure, 1 is a small-scale biplane electromagnetic tomography sensor, 11 is a cylinder, 12 is an electromagnetic coil, 2 is a channel switching module, 3 is a signal conditioning module, 4 is a power amplification module, 5 is a phase-locked amplifier, 6 is an image reconstruction computer, 7 is a single chip microcomputer, and 8 is a lubricating oil pipeline.
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 obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
Embodiment 1, as shown in fig. 1, an online monitoring device for lubricant abrasive particles based on electromagnetic tomography technology includes a channel switching module 2 and an image reconstruction computer 6, where the channel switching module 2 is connected to a small-scale biplane electromagnetic tomography sensor 1, the small-scale biplane electromagnetic tomography sensor 1 includes an excitation coil and a detection coil, both the excitation coil and the detection coil are disposed on a lubricant pipeline 8, and the detection coil detects an induced voltage signal of the lubricant pipeline 8 in real time under the action of an alternating excitation magnetic field generated by the excitation coil; the channel switching module 2 is connected with a signal processing circuit, the signal processing circuit is connected with an image reconstruction computer 6, the image reconstruction computer 6 is connected with a single chip microcomputer 7, the single chip microcomputer 7 is connected with the channel switching module 2, the signal processing circuit is used for processing an induced voltage signal detected by a detection coil to obtain induced voltage amplitude information and transmitting the induced voltage amplitude information to the image reconstruction computer 6, the image reconstruction computer 6 receives the measured induced voltage amplitude information and reconstructs a three-dimensional image of lubricating oil abrasive particle distribution for display, meanwhile, a control instruction of the image reconstruction computer 6 is transmitted to the channel switching module 2 through the single chip microcomputer 7, and the single chip microcomputer 7 receives the control instruction of the image reconstruction computer 6 and controls the channel switching module 2 to complete switching of an excitation coil and the detection coil of the small-scale biplane electromagnetic tomography sensor 1. The singlechip 7 is a Stm32 singlechip and is a microcontroller with high performance, low cost, low power consumption and strong configurability. The channel switching module 2 is composed of a network relay or a multiplexer with the model of ADG406, realizes the excitation of the measured object field at each angle, and acquires the induced voltage signals in each projection direction; the channel switching module 2 completes switching of the excitation coil and the detection coil under the instruction control of the single chip microcomputer 7, the detection coil is connected with the signal processing circuit, and an induced voltage signal detected by the detection coil is transmitted to the signal processing circuit.
The sine signal is introduced into the excitation coil, an alternating excitation magnetic field is generated in a measured object field, when metal abrasive particles exist in the measured object field, namely the lubricating oil pipeline 8, the distribution of the alternating excitation magnetic field is changed, the induced voltage value of the detection coil is changed, the image reconstruction computer 6 reconstructs a three-dimensional image of the distribution of the metal abrasive particles according to the change, the size and the position information of the abrasive particles are reflected, and therefore the rapid, non-contact, non-invasive, low-cost and visual monitoring of the lubricating oil abrasive particles is achieved.
The signal processing circuit comprises a phase-locked amplifier 5, a signal conditioning module 3 and a power amplification module 4, wherein the phase-locked amplifier 5 is respectively connected with the signal conditioning module 3 and the power amplification module 4, the signal conditioning module 3 and the power amplification module 4 are both connected with the channel switching module 2, and the phase-locked amplifier 5 is connected with an image reconstruction computer 6; the image reconstruction computer 6 is connected with the lock-in amplifier 5, and controls the lock-in amplifier 5 to generate an excitation signal by utilizing the upper computer programming, and the power amplification module 4 is used for performing power amplification on the excitation signal and increasing the magnetic field intensity generated by the excitation coil; the signal conditioning module 3 performs differential amplification and filtering processing on the weak induction voltage signal measured by the detection coil so as to improve the signal-to-noise ratio and the anti-interference performance of the signal, the lock-in amplifier 5 acquires the weak measurement signal detected by the detection coil in real time, and multiplies and accumulates the data signal and the reference signal by using a digital quadrature demodulation algorithm so as to obtain the amplitude and phase information of the measured induction voltage signal. The power amplification module 4 adopts a current feedback amplifier with the model number being LT1210 to realize power amplification of the excitation current; the phase-locked amplifier 5 is an MFLI phase-locked amplifier using Zurich Instruments (Zurich Instruments), and realizes generation of an excitation signal and collection of a weak induction voltage signal.
As shown in fig. 2, the small-scale biplane electromagnetic tomography sensor 1 is an O-type, small-scale, and biplane structure, and can realize closed three-dimensional imaging of a small-scale lubricating oil pipeline, and includes a cylinder 11, the cylinder 11 is sleeved on the lubricating oil pipeline 8, so as to facilitate generation of an excitation magnetic field and measurement of data, and the excitation coil and the detection coil are uniformly fixed on the outer wall of the cylinder 11. And comprehensively weighing factors of three aspects of the size of the electromagnetic coils, the number of the electromagnetic coils and the calculation complexity, and finally determining the number of the electromagnetic coils.
As shown in fig. 3, the excitation coil and the detection coil are each composed of an electromagnetic coil 12, and the electromagnetic coil 12 is of a biplane, O-shaped structure. The electromagnetic coil 12 is formed by uniformly winding enamelled copper wires with the wire diameter of 0.4mm, and has the same inductance value under the same excitation frequency, so that the interference caused by the inconsistency of the electromagnetic coil is reduced; the electromagnetic coil 12 comprises two layers of coils, each layer of coil is of a closed O-shaped structure, closed detection of the pipeline is realized, the object field radius and the interlayer spacing of each layer of coil are adjustable, and the electromagnetic coil is suitable for detecting pipelines with different calibers; each layer of the electromagnetic coil 12 has 6, 8, 12, 16 or 20 coils, and the number of the coil layers and the number of the coil turns of each layer are adjustable.
Embodiment 2, as shown in fig. 4, a method for monitoring an online monitoring device for abrasive grains in lubricating oil based on an electromagnetic tomography technology includes the steps of:
the method comprises the following steps: the image reconstruction computer 6 sends a signal to control the phase-locked amplifier 5 to generate an alternating current sinusoidal signal of dozens to hundreds of kHz, the alternating current sinusoidal signal is output to an excitation coil selected by an ADG406 multiplexer of the channel switching module 2 through the power amplification module 4, a sinusoidal signal is injected into the excitation coil, the excitation coil generates an alternating excitation magnetic field in the space of the measured lubricating oil pipeline 8, under the action of the alternating excitation magnetic field, eddy currents are generated on the surface of metal abrasive particles in the lubricating oil pipeline 8, a secondary magnetic field is induced by the eddy currents, the secondary magnetic field influences the distribution of the alternating excitation magnetic field to form a composite magnetic field, and the induced voltage and the impedance of the detection coil are changed.
Step two: the Stm32 single chip microcomputer 7 receives the control signal sent by the image reconstruction computer 6, controls the high and low levels of the ADG406 multiplexer pin of the channel switching module 2, sequentially selects the detection coil, the detection coil outputs the induction voltage signal to the lock-in amplifier 5 through the signal conditioning module 3, completes the collection and data demodulation of the weak measurement signal, obtains the amplitude of the induction voltage signal, and transmits the amplitude to the image reconstruction computer 6.
Step three: the Stm32 singlechip 7 receives a control signal sent by the image reconstruction computer 6, controls the high and low levels of the ADG406 multiplexer pin of the channel switching module 2, selects an excitation coil, and uses the rest as detection coils; after the induction voltage signals of all the detection coils are acquired, selecting the next coil as an excitation coil in the clockwise or anticlockwise direction again, using the rest coils as the detection coils, and acquiring the induction voltage signals of all the detection coils; and so on, completing the measurement of the induced voltage signals in each projection direction; the measured induced voltage signal is multiplied and accumulated by a digital orthogonal demodulation algorithm with a reference signal to obtain the amplitude and phase information of the signal, and the amplitude of the induced voltage signal is transmitted to an image reconstruction computer 6.
Step four: and the image reconstruction computer 6 reconstructs a three-dimensional image of the distribution of the lubricating oil abrasive particles by using an image reconstruction algorithm according to the amplitude and the sensitivity matrix of the induced voltage signal, so that the size and the position information of the lubricating oil abrasive particles are obtained.
The image reconstruction algorithm is realized by the following steps:
(1) solving the sensitivity matrix by using a model perturbation method, solving the positive problem of electromagnetic tomography by using an experimental method, and acquiring a boundary measurement voltage value and the sensitivity matrix;
the electromagnetic tomography technology conforms to Maxwell equation set, and the positive electromagnetic tomography problem is a typical initial boundary value problem:
where μ is the magnetic permeability, A is the vector magnetic potential, ω is the angular frequency, σ is the electrical conductivity, JsIs the excitation current density. Mu and sigma are the properties of the material to be measured itself, omega and JsDetermined by the alternating excitation signal. By solving the formula (1), the vector magnetic potential A is obtained, the magnetic induction intensity B is calculated by the vector magnetic potential A, and further the normalized measurement voltage value vector U can be obtained.
When the feasibility is verified in the early stage, a finite element method in a numerical solution method is often adopted to solve the positive problem of electromagnetic tomography, and a model perturbation method is utilized to solve the sensitivity matrix of the system. The method solves the positive EMT problem by using an experimental method, namely monitors a lubricating oil pipeline by using a built lubricating oil abrasive particle online monitoring experimental device, obtains a boundary measurement voltage value by using a phase-locked amplifier, further obtains a system sensitivity matrix, and provides important prior information for solving the inverse EMT problem.
(2) Describing the nonlinear relation between the measured voltage value and the conductivity distribution by adopting a linear approximation method, simplifying an electromagnetic tomography nonlinear model, and obtaining a normalized linear equation between the measured value and the conductivity distribution: u is Sg; wherein, U is an M multiplied by 1 dimensional normalized measurement voltage value vector; s is a normalized sensitivity matrix of MxNnV; g is an Nx 1-dimensional normalized conductivity distribution vector representing an image gray value; m is the number of measured voltage values; and N is the number of cells divided by the measured object field.
Electromagnetic tomography can be qualitatively described as:
Vij＝∫∫Dσ(x,y)F(x,y,μ(x,y),σ(x,y))dxdy (2)
wherein, VijIs the measured voltage between the detection coil i and the detection coil j; d is the sectional area of the area to be measured, and the sectional area is the sectional area of the lubricating oil pipeline; σ (x, y) is the conductivity; f is a sensitive field distribution function; μ (x, y) represents permeability. Considering only the conductivity σ (x, y) distribution, equation (2) is simplified as follows:
V＝F(σ) (3)
taylor expansion is performed on equation (3) to obtain:
wherein, F (σ)0) Denotes that F (σ) is at σ0The function value of (c); sigma0Denotes that F (σ) is at σ0Performing Taylor expansion; o (| | σ - σ)0||2) Representing an infinitesimal quantity.
To simplify the EMT nonlinear model, a linear approximation method can be used to describe the nonlinear relationship between the measured values and the conductivity distribution. By ignoring higher order terms, equation (4) can be simplified as:
ΔV＝S(σ)Δσ (5)
wherein the content of the first and second substances,
by normalizing equation (5), a normalized linear equation between the measured value and the conductivity distribution can be obtained:
U＝Sg (7)
wherein, U is an M multiplied by 1 dimensional normalized measurement voltage value vector; s is a normalized sensitivity matrix of MxNnV; g is an N × 1 dimensional normalized conductivity distribution vector representing the image gray scale value.
The essence of EMT image reconstruction is to solve the conductivity distribution vector g with the known measurement voltage value vector U and the sensitivity matrix S. Aiming at the problem of the undercharacterization of the EMT inverse problem, the compressive sensing is applied to the solution of the EMT inverse problem, the sparse signal is subjected to low-dimensional sampling by using a compressive sensing theory, and the signal is reconstructed by using an optimization method.
(3) The sparse signal is sampled in a low-dimensional mode by using a compressed sensing theory, and the reconstruction of a conductivity distribution vector is realized by using an optimization method;
the mathematical model based on the compressed sensing theory, which is constructed by using the compressed sensing theory to carry out low-dimensional sampling on the sparse signal in the step (3), is as follows: U-Sg-S psiFFTs＝As； (8)
In which Ψ has a size of N × NFFTIs an FFT sparse basis; s, of size N × 1, is a sparse coefficient vector.
In order to make the sensing matrix A in the formula (8) satisfy the RIP (Restricted isometric Property), the conductivity distribution vector S is subjected to Gaussian random arrangement, i.e. the sequence of the rows of the vector S is disordered, and a new random observation matrix S is generatednewAt the same time, the measured value vector U is disordered according to the same rule to generate a new measured value vector UnewThen the electromagnetic tomography positive problem model is:
wherein s isoptAn objective function representing s, s being a sparse coefficient vector, AEMTIs a sensing matrix, goptThe objective function of g is expressed.
For convenient solution, the constrained convex optimization problem is converted into an unconstrained convex optimization problem, and l is used1Regularization model, introducing regularization parameter lambda to obtain a solving model:
fast Iterative threshold Shrinkage (FISTA) Algorithm is adopted to solve for l in the model1Solving the norm regularization problem to obtain a conductivity distribution vector, and reconstructing the lubricating oil millA three-dimensional image of the particle distribution.
The fast iterative threshold shrinkage algorithm solves for1The norm regularization problem is solved, an optimal sparse coefficient vector s is obtained, the optimal sparse coefficient vector s is FISTA with a fixed step length, and the method comprises the following steps:
inputting: l2 lambdamax((AEMT)TAEMT)；
Step 0: taking initial value
Number of iterations t1＝1；
Step k, wherein k is more than or equal to 1: and (3) calculating:
sk＝PL(yk)
wherein
PL(sk)＝Tλt(sk-2t(AEMT)T(AEMTsk-Unew))；
Tα(s)i＝(|si|-α)+sgn(si)；
Solving to obtain an optimal sparse coefficient vector s; the conductivity distribution vector s is obtained by the equation (10)opt。
Wherein L represents the Liphoz constant, λmaxRepresenting the maximum eigenvalue of the matrix, T representing the transpose of the matrix, skRepresenting the value of s at the kth iteration, PL() Representing the iterative contraction operator, ykDenotes the value of y, t, at the k-th iterationk+1Denotes the (k + 1) th iteration step, tkDenotes the kth iteration step size, yk+1Denotes the value of y, s, at the k +1 th iterationk-1Denotes the value of s, T, at the k-1 iterationλtRepresenting the contraction operator, Tα(s)iRepresenting a contraction operator definition, siDenotes the value of s at the i-th iteration, alpha denotes the noise reduction parameter, sgn denotes the sign function.
(4) And normalizing the conductivity distribution vector to be between 0 and 1, and corresponding to the RGB color. According to the obtained conductivity distribution vector soptAnd filling corresponding colors in the subdivision cells according to the normalized value of the conductivity distribution vector, and reconstructing a three-dimensional image of the distribution of the lubricating oil abrasive particles. Different substances in the lubricating oil pipeline have different conductivity distributions, the different conductivity distributions correspond to different colors in the three-dimensional reconstruction image of the lubricating oil abrasive particle distribution, and the size and the position information of the lubricating oil abrasive particles are obtained according to the distribution positions and the ranges of the different colors. The electromagnetic tomography technology has the characteristics of rapidness, non-contact, non-invasion, low cost, visual monitoring and the like, and can realize the online real-time monitoring of the dynamic change trend of the lubricating oil abrasive particles.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. An online monitoring device for lubricating oil abrasive particles based on an electromagnetic tomography technology comprises a channel switching module (2) and an image reconstruction computer (6), and is characterized in that the channel switching module (2) is connected with a small-scale biplane electromagnetic tomography sensor (1), the small-scale biplane electromagnetic tomography sensor (1) comprises an excitation coil and a detection coil, the excitation coil and the detection coil are both arranged on a lubricating oil pipeline (8), and the detection coil detects an induced voltage signal of the lubricating oil pipeline (8) under the action of an alternating excitation magnetic field generated by the excitation coil in real time; the channel switching module (2) is connected with a signal processing circuit, the signal processing circuit is connected with an image reconstruction computer (6), the image reconstruction computer (6) is connected with the singlechip (7), the singlechip (7) is connected with the channel switching module (2), the signal processing circuit is used for processing the induction voltage signal detected by the detection coil to obtain induction voltage amplitude information and transmitting the induction voltage amplitude information to an image reconstruction computer (6), the image reconstruction computer (6) receives the induction voltage amplitude information and reconstructs a three-dimensional image of the distribution of the lubricating oil abrasive particles for display, meanwhile, a control instruction of the image reconstruction computer (6) is transmitted to the channel switching module (2) through the single chip microcomputer (7), and the single chip microcomputer (7) receives the control instruction of the image reconstruction computer (6) and controls the channel switching module (2) to complete switching of an excitation coil and a detection coil of the small-scale biplane electromagnetic tomography sensor (1).
2. The online monitoring device for the lubricating oil abrasive particles based on the electromagnetic tomography technology is characterized in that the signal processing circuit comprises a phase-locked amplifier (5), a signal conditioning module (3) and a power amplification module (4), the phase-locked amplifier (5) is respectively connected with the signal conditioning module (3) and the power amplification module (4), the signal conditioning module (3) and the power amplification module (4) are both connected with the channel switching module (2), and the phase-locked amplifier (5) is connected with an image reconstruction computer (6); the image reconstruction computer (6) controls the phase-locked amplifier (5) to generate an excitation signal, and the power amplification module (4) is used for performing power amplification on the excitation signal and increasing the magnetic field intensity generated by the excitation coil; the signal conditioning module (3) amplifies and filters weak signals measured by the detection coil, and the lock-in amplifier (5) collects the measurement signals of the detection coil, demodulates data and acquires amplitude information of the measured induction voltage signals.
3. The online monitoring device for the lubricating oil abrasive particles based on the electromagnetic tomography technology as claimed in claim 1 or 2, characterized in that the small-scale bi-planar electromagnetic tomography sensor (1) comprises a cylinder body (11), the cylinder body (11) is sleeved on the lubricating oil pipeline (8), and the excitation coil and the detection coil are uniformly fixed on the outer wall of the cylinder body (11).
4. The online monitoring device for oil wear particles based on electromagnetic tomography technology according to claim 3, characterized in that the excitation coil and the detection coil are both composed of electromagnetic coils (12), and the electromagnetic coils (12) are of a bi-planar O-shaped structure.
5. The online monitoring device for the lubricating oil abrasive particles based on the electromagnetic tomography technology as claimed in claim 4, characterized in that the electromagnetic coil (12) is formed by uniformly winding enamelled copper wires with the wire diameter of 0.4mm, and has the same inductance value under the same excitation frequency; the electromagnetic coil (12) comprises two layers of coils, each layer of coil is of a closed O-shaped structure, and the object field radius and the interlayer spacing of each layer of coil are adjustable; each layer of the electromagnetic coil (12) has 6, 8, 12, 16 or 20 coils, and the number of the coil layers and the number of the coil turns of each layer are adjustable.
6. The online monitoring device for the lubricating oil abrasive particles based on the electromagnetic tomography technology as claimed in claim 5, wherein the single chip microcomputer (7) is an Stm32 single chip microcomputer; the channel switching module (2) is realized by a switching circuit consisting of a network relay or a multiplexer with the model of ADG 406; the power amplification module (4) adopts a current feedback amplifier with the model number being LT 1210; the phase-locked amplifier (5) is an MFLI phase-locked amplifier.
7. The online monitoring device for the lubricating oil abrasive particles based on the electromagnetic tomography technology as claimed in claim 2 or 5, characterized in that the monitoring method comprises the following steps:
the method comprises the following steps: the image reconstruction computer (6) sends a signal to control the phase-locked amplifier (5) to generate an alternating current sinusoidal signal of dozens to hundreds of kHz, the alternating current sinusoidal signal is output to an excitation coil selected by the channel switching module (2) through the power amplification module (4), the sinusoidal signal is injected into the excitation coil, the excitation coil generates an alternating excitation magnetic field in the space of the measured lubricating oil pipeline (8), under the action of the alternating excitation magnetic field, eddy currents are generated on the surfaces of metal abrasive particles in the lubricating oil pipeline (8), a secondary magnetic field is induced by the eddy currents, the secondary magnetic field influences the distribution of the alternating excitation magnetic field to form a composite magnetic field, and the induced voltage and the impedance of the detection coil are changed;
step two: the single chip microcomputer (7) controls the channel switching module (2) to sequentially select the detection coils, the induced voltage signals are output to the phase-locked amplifier (5) through the signal conditioning module (3), the collection and data demodulation of weak measurement signals are completed, the amplitude values of the induced voltage signals are obtained, and the amplitude values are transmitted to the image reconstruction computer;
step three: the singlechip (7) receives signals of the image reconstruction computer to control the channel switching module to sequentially select the excitation coils and the rest as the detection coils, completes the measurement of the induced voltage signals in each projection direction and transmits the amplitude values of the induced voltage signals to the image reconstruction computer (6);
step four: and the image reconstruction computer (6) reconstructs a three-dimensional image of the distribution of the lubricating oil abrasive particles by using an image reconstruction algorithm according to the amplitude and the sensitivity matrix of the induced voltage signal, so that the size and the position information of the lubricating oil abrasive particles are obtained.
8. The online monitoring method for the lubricating oil abrasive particles based on the electromagnetic tomography technology as claimed in claim 7, wherein the image reconstruction algorithm is realized by the following steps:
(1) solving the sensitivity matrix by using a model perturbation method, solving the positive problem of electromagnetic tomography by using an experimental method, and acquiring a boundary measurement voltage value and the sensitivity matrix;
(2) describing the nonlinear relation between the measured voltage value and the conductivity distribution by adopting a linear approximation method, simplifying an electromagnetic tomography nonlinear model, and obtaining a normalized linear equation between the measured value and the conductivity distribution: u is Sg; wherein, U is an M multiplied by 1 dimensional normalized measurement voltage value vector; s is a normalized sensitivity matrix of MxNnV; g is an Nx 1-dimensional normalized conductivity distribution vector representing an image gray value; m is the number of measured voltage values; n is the number of cells subdivided by the measured object field;
(3) the sparse signal is sampled in a low-dimensional mode by using a compressed sensing theory, and the reconstruction of a conductivity distribution vector is realized by using an optimization method;
(4) and reconstructing a three-dimensional image of the distribution of the lubricating oil abrasive particles according to the conductivity distribution obtained by solving, obtaining the size and position information of the lubricating oil abrasive particles, and realizing the monitoring of the dynamic change trend of the lubricating oil abrasive particles.
9. The online monitoring method for the lubricating oil abrasive particles based on the electromagnetic tomography technology as claimed in claim 8, wherein the electromagnetic tomography positive problem is a typical initial boundary value problem:
where μ is the magnetic permeability, A is the vector magnetic potential, ω is the angular frequency, σ is the electrical conductivity, JsIs the excitation current density;
the mathematical model based on the compressed sensing theory, which is constructed by using the compressed sensing theory to carry out low-dimensional sampling on the sparse signal in the step (3), is as follows: U-Sg-S psiFFTs; in which Ψ has a size of N × NFFTIs an FFT sparse basis; s, of size nx 1, is a sparse coefficient vector;
carrying out Gaussian random arrangement on the conductivity distribution vector S, namely, disordering the sequence of each row of the vector S to generate a new random observation matrix SnewAt the same time, the measured value vector U is disordered according to the same rule to generate a new measured value vector UnewThen the electromagnetic tomography positive problem model is:
wherein s isoptAn objective function representing a sparse coefficient vector s, AEMTIs a sensing matrix, goptAn objective function representing the gray value g of the image;
converting the constrained convex optimization problem into an unconstrained convex optimization problem, and using l1Regularization model, introducing regularization parameter lambda to obtain a solving model:
fast iterative threshold shrinkage algorithm is adopted to solve the l in the model1Solving the norm regularization problem to obtain the conductanceA rate distribution vector.
10. The online monitoring method for the oil abrasive particles based on the electromagnetic tomography technology as claimed in claim 9, wherein the fast iterative threshold shrinkage algorithm is used for solving the solution l1The norm regularization problem, and the method for obtaining the optimal sparse coefficient vector s comprises the following steps:
inputting: l2 lambdamax((AEMT)TAEMT)；
Step 0: taking initial value
t1＝1
Step k, wherein k is more than or equal to 1: and (3) calculating:
sk＝PL(yk)
wherein
PL(sk)＝Tλt(sk-2t(AEMT)T(AEMTsk-Unew))
Tα(s)i＝(|si|-α)+sgn(si)
Solving to obtain an optimal sparse coefficient vector s;
wherein L represents the Liphoz constant, λmaxRepresenting the maximum eigenvalue of the matrix, T representing the transpose of the matrix, skRepresenting the value of the sparse coefficient vector s at the kth iteration, PL() Representing the iterative contraction operator, ykDenotes the value of y, t, at the k-th iterationk+1Denotes the (k + 1) th iteration step, tkDenotes the kth iteration step size, yk+1Represents the k +1 thValue of y, s, at the time of the second iterationk-1Represents the value of the sparse coefficient vector s at the k-1 iteration, TλtRepresenting the contraction operator, Tα(s)iRepresenting a contraction operator definition, siAnd the value of a sparse coefficient vector s at the ith iteration is represented, alpha represents a noise reduction parameter, and sgn represents a sign function.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113625208A (en) * | 2021-08-10 | 2021-11-09 | 中国科学院自动化研究所 | Three-dimensional magnetic particle imaging system and method based on multi-channel excitation and detection |
| WO2023118590A1 (en) * | 2021-12-24 | 2023-06-29 | Commissariat A L'energie Atomique Et Aux Energies Alternatives | Optical method and device for detecting particles sensitive to a magnetic field in a sample |
| CN117214284A (en) * | 2023-11-09 | 2023-12-12 | 深圳市亚泰光电技术有限公司 | Oil product detection method based on electromagnetic induction technology |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101566639A (en) * | 2009-06-05 | 2009-10-28 | 天津大学 | Molten steel flow-speed measurement method and device based on electromagnetic tomography |
| CN103217365A (en) * | 2013-03-29 | 2013-07-24 | 电子科技大学 | Online oil way abrasive particle monitoring device |
| CN103926276A (en) * | 2014-03-25 | 2014-07-16 | 天津大学 | Online oil liquid abrasive particle monitoring device and measuring method |
| CN107340544A (en) * | 2016-11-29 | 2017-11-10 | 北京理工大学 | A kind of highly sensitive minute metallic particle on-line detecting system and method |
| CN107544040A (en) * | 2017-07-18 | 2018-01-05 | 天津大学 | A kind of magnetic conductivity electromagnetic chromatographic method based on magnetoresistive transducer |
| CN109696469A (en) * | 2019-01-15 | 2019-04-30 | 北京交通大学 | A kind of high-speed rail wheel electromagnetic chromatographic method of detection and device |
| CN110286145A (en) * | 2019-08-14 | 2019-09-27 | 上海海事大学 | A kind of Metal Crack electromagnetic test imager |
| CN111398413A (en) * | 2020-04-26 | 2020-07-10 | 电子科技大学 | Double-layer symmetrical differential plane eddy current detection sensor |
| CN111650110A (en) * | 2020-06-02 | 2020-09-11 | 郑州轻工业大学 | Storage tank bottom plate corrosion detection device and method based on electromagnetic tomography |
-
2021
- 2021-04-13 CN CN202110394471.5A patent/CN113092322A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101566639A (en) * | 2009-06-05 | 2009-10-28 | 天津大学 | Molten steel flow-speed measurement method and device based on electromagnetic tomography |
| CN103217365A (en) * | 2013-03-29 | 2013-07-24 | 电子科技大学 | Online oil way abrasive particle monitoring device |
| CN103926276A (en) * | 2014-03-25 | 2014-07-16 | 天津大学 | Online oil liquid abrasive particle monitoring device and measuring method |
| CN107340544A (en) * | 2016-11-29 | 2017-11-10 | 北京理工大学 | A kind of highly sensitive minute metallic particle on-line detecting system and method |
| CN107544040A (en) * | 2017-07-18 | 2018-01-05 | 天津大学 | A kind of magnetic conductivity electromagnetic chromatographic method based on magnetoresistive transducer |
| CN109696469A (en) * | 2019-01-15 | 2019-04-30 | 北京交通大学 | A kind of high-speed rail wheel electromagnetic chromatographic method of detection and device |
| CN110286145A (en) * | 2019-08-14 | 2019-09-27 | 上海海事大学 | A kind of Metal Crack electromagnetic test imager |
| CN111398413A (en) * | 2020-04-26 | 2020-07-10 | 电子科技大学 | Double-layer symmetrical differential plane eddy current detection sensor |
| CN111650110A (en) * | 2020-06-02 | 2020-09-11 | 郑州轻工业大学 | Storage tank bottom plate corrosion detection device and method based on electromagnetic tomography |
Non-Patent Citations (6)
| Title |
|---|
| 叶坤涛;郭振龙;贺文熙;: "基于Curvelet变换的压缩传感超分辨率重建", 计算机应用与软件, no. 10, 15 October 2016 (2016-10-15) * |
| 司家屯, 曲民兴, 徐锋: "利用有限元法分析涡流远场效应的能流通道", 南京航空航天大学学报, no. 03, 30 June 1991 (1991-06-30) * |
| 张勇: "一种求解弹性l2-lq正则化问题的算法", 运筹学学报, vol. 20, no. 4, 31 December 2016 (2016-12-31), pages 11 - 20 * |
| 张立峰;刘昭麟;田沛;: "基于压缩感知的电容层析成像图像重建算法", 电子学报, no. 02, 15 February 2017 (2017-02-15), pages 353 - 358 * |
| 李小利;杨晓梅;: "基于RPCA视频去噪算法的自适应优化方法", 计算机应用与软件, no. 09, 15 September 2016 (2016-09-15) * |
| 李旭超;边素轩;李玉叶;: "图像恢复中的凸能量泛函正则化模型综述", 中国图象图形学报, no. 04, 16 April 2016 (2016-04-16) * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113625208A (en) * | 2021-08-10 | 2021-11-09 | 中国科学院自动化研究所 | Three-dimensional magnetic particle imaging system and method based on multi-channel excitation and detection |
| WO2023118590A1 (en) * | 2021-12-24 | 2023-06-29 | Commissariat A L'energie Atomique Et Aux Energies Alternatives | Optical method and device for detecting particles sensitive to a magnetic field in a sample |
| FR3131374A1 (en) * | 2021-12-24 | 2023-06-30 | Commissariat à l'Energie Atomique et aux Energies Alternatives | Device and optical method for detecting particles sensitive to a magnetic field in a sample |
| CN117214284A (en) * | 2023-11-09 | 2023-12-12 | 深圳市亚泰光电技术有限公司 | Oil product detection method based on electromagnetic induction technology |
| CN117214284B (en) * | 2023-11-09 | 2024-01-19 | 深圳市亚泰光电技术有限公司 | Oil product detection method based on electromagnetic induction technology |
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