Analysis method of the influence of thermal expansion coefficient on the performance of lubricating oil metal particle signal sensor

Abstract

The present invention provides a method for analyzing the effect of thermal expansion coefficient on the performance of a lubricating oil metal particle signal sensor. The method comprises calculating the deformation of a coil bobbin material caused by temperature changes, establishing an inductance calculation model, determining boundary conditions, performing meshing, calculating the coil inductance of the lubricating oil metal particle signal sensor, obtaining a magnetic induction intensity distribution cloud diagram of the lubricating oil metal particle signal sensor and the inductance values ​​at different temperatures, deducing the effect of the inductance value on the output characteristics of the lubricating oil metal particle signal sensor, selecting total admittance as an evaluation indicator, calculating the total admittance and phase angle at different temperatures, and obtaining the gap change and inductance change under different coil bobbin materials. The present invention establishes a semi-analytical solution for the effect of thermal expansion coefficient on sensor output characteristics, effectively analyzing the influence of different thermal expansion coefficients and material parameters on sensor performance, significantly shortening the research cycle, reducing environmental interference, and effectively carrying out large-scale analytical research.

Description

Technical Field

[0001] The invention belongs to the technical field of special oil state monitoring, and in particular to a method for analyzing the influence of thermal expansion coefficient on the performance of a lubricating oil metal particle signal sensor. Background Art

[0002] Aircraft engines determine whether the main shaft bearings are operating stably by detecting the type, quantity, and form of metal particles in the lubricating oil in real time. To ensure the high sensitivity and accuracy of lubricating oil metal particle signal sensors in applications, research on lubricating oil metal particle signal sensor design optimization was conducted in conjunction with real-world engine operating environments. This research primarily involves interference source analysis, electromagnetic field balance control of sensitive structures, and multi-dimensional anti-interference technology research and verification. This improves the adaptability of lubricating oil metal particle signal sensors in real-world environments, increases detection rates, reduces false alarms, and supports real-time engine status monitoring and fault diagnosis.

[0003] The online lubricating oil dust monitoring system is an integrated whole consisting of a lubricating oil metal particle signal sensor, a signal transmission cable, and a data processing unit. Because the engine is a complex system, subject to a multi-factor coupling environment involving electrical, thermal, magnetic, and mechanical factors, each component of the online lubricating oil dust monitoring system may encounter interference from the external environment or equipment, or even superimposed interference, in complex environments such as temperature, vibration, and electromagnetics. This can lead to a decrease in the dust monitoring system's detection capabilities. Therefore, it is imperative to develop a method to analyze the impact of the thermal expansion coefficient on the performance of lubricating oil metal particle signal sensors, taking into account environmental factors such as vibration and temperature fluctuations, in order to optimize sensor performance, improve system detection rates, and reduce false alarm rates. Summary of the Invention

[0004] In response to the above-mentioned deficiencies in the prior art, the present invention proposes a method for analyzing the effect of thermal expansion coefficient on the performance of lubricating oil metal particle signal sensors. The method includes calculating the deformation of the coil bobbin material caused by temperature changes, establishing an inductance calculation model, determining boundary conditions, performing grid division, solving the coil inductance value of the lubricating oil metal particle signal sensor, obtaining a cloud diagram of the magnetic induction intensity distribution of the lubricating oil metal particle signal sensor and the inductance values ​​at different temperatures, deducing the effect of the inductance value on the output characteristics of the lubricating oil metal particle signal sensor, selecting total admittance as an evaluation index, calculating the total admittance and phase angle at different temperatures, and obtaining the gap change and inductance change under different coil bobbin materials. The present invention establishes a semi-analytical solution for the effect of thermal expansion coefficient on sensor output characteristics, effectively analyzing the influence of different thermal expansion coefficients and material parameters on sensor performance, greatly shortening the research cycle, reducing environmental interference, and effectively carrying out large-scale analytical research.

[0005] The present invention provides a method for analyzing the influence of thermal expansion coefficient on the performance of a lubricating oil metal particle signal sensor, which comprises the following steps:

[0006] S1. Calculate the deformation of the coil frame material caused by temperature change: Calculate the deformation of the coil frame material caused by temperature change δ based on the thermal expansion coefficient k, temperature difference ΔT and outer diameter D of the coil frame of the lubricating oil metal particle signal sensor:

[0007] δ=kΔTD (1);

[0008] S2. Establish an inductance calculation model: Select the electromagnetic module of the lubricating oil metal particle signal sensor, set the number of coil turns, wire diameter, and material properties of the lubricating oil metal particle signal sensor, and draw a geometric model;

[0009] S3. Determine boundary conditions;

[0010] S4. Meshing: Use a mapped mesh generator to mesh the windings, and use a free tetrahedron mesh to mesh the air domain.

[0011] S5. Calculate the coil inductance of the lubricating oil metal particle signal sensor: the input value is the coil structural parameter of the lubricating oil metal particle signal sensor, and the output value is the coil inductance of the lubricating oil metal particle signal sensor;

[0012] S6. Obtaining a magnetic induction intensity distribution cloud map of the lubricating oil metal particle signal sensor and inductance values ​​at different temperatures;

[0013] S61, obtaining information when the lubricating oil metal particle signal sensor operates in a stable state, and obtaining a magnetic induction intensity distribution cloud map of the lubricating oil metal particle signal sensor through processing and analysis;

[0014] S62. Calculate the inductance of the oil metal particle signal sensor at different temperatures based on finite element analysis.

[0015] S7. Derive the influence of inductance value on the output characteristics of the lubricating oil metal particle signal sensor, and select total admittance as the evaluation index;

[0016] S8. Calculate the total admittance and phase angle at different temperatures based on the inductance values ​​at different temperatures obtained in step S6;

[0017] S81. Calculate the total admittance Y1 and Y2 at room temperature and low temperature respectively;

[0018]

[0019]

[0020] Among them, R e Represents resistance; jωLe1 Indicates the inductance at room temperature; jωL e2 Indicates the inductance at low temperature; 1 / jωC e It represents the impedance of a capacitor in an AC circuit;

[0021] S82, calculate the phase angles θ1 and θ2 at room temperature and low temperature respectively;

[0022]

[0023]

[0024] S9. Obtaining the gap change and inductance change under different coil bobbin materials: The coil bobbin material is converted from the first material to the second material, and steps S1 to S8 are repeated to calculate the gap change and inductance change between the housing and the coil bobbin under the change from room temperature to low temperature.

[0025] Furthermore, step S7 specifically includes the following steps:

[0026] S71. Calculate impedance Z e :

[0027]

[0028] Among them, jωL e represents inductance;

[0029] S72. Describe the characteristics of the lubricating oil metal particle signal sensor circuit:

[0030]

[0031] Where, I represents the total circuit current; E represents the voltage;

[0032] S73, using admittance to describe, due to the existence of:

[0033]

[0034] Wherein, I1, I2, I3 represent the first current, the second current, and the third current respectively; E represents the voltage; t represents the time;

[0035] Then we have:

[0036]

[0037]

[0038]

[0039] S74. Calculate the total loop current I:

[0040]

[0041] Among them, I a represents the amplitude; wt represents the argument;

[0042] That is:

[0043]

[0044] S75. Calculate the inductive admittance and the capacitive admittance. The inductive admittance is:

[0045]

[0046] The capacitive admittance is:

[0047]

[0048] S76. Calculate the total admittance Y, which is the sum of the admittance of the inductor and the admittance of the capacitor:

[0049]

[0050] Preferably, the step S3 specifically includes the following steps:

[0051] S31, using a uniform multi-turn model to establish an electromagnetic field model, the control equation of the electromagnetic field model is:

[0052]

[0053] Where J is the current density; B is the relative magnetic permeability of the material used in the model; V is the electric potential; σ is the material conductivity; ω is the angular frequency; A is the magnetic vector potential; D is the relative dielectric constant; H is the magnetic field; J e represents the applied current density; represents the gradient;

[0054] S32. The first end of the conductor in the electromagnetic module of the lubricating oil metal particle signal sensor adopts a grounded boundary condition. Then, the first control equation of the first end of the conductor is:

[0055] V=0 (3);

[0056] S33. The second end of the conductor adopts a terminal boundary condition, and the second control equation of the second end of the conductor is:

[0057]

[0058] Where V is the potential at the first end of the conductor; σ is the surface domain of the model being solved; S is the cross section; n is the normal vector of the boundary; I0 is the flowing current; and Ω is the closed surface.

[0059] Preferably, the step S5 specifically includes the following steps:

[0060] S51. Discrete the solution domain into a series of interrelated small units through discrete means to form a discrete set of equations, and obtain the solution at the unit node, that is, the coil inductance value; for any position outside the unit node, obtain the coil inductance value through interpolation method; the discrete set of equations is:

[0061]

[0062] Ku=F (6)

[0063] Where c represents the coefficient term of the partial differential equation; f represents the source term; K represents the stiffness coefficient matrix; F represents the load vector; u represents the solution vector;

[0064] S52. Use the frequency domain calculation method, set the calculation frequency, add the direct solver PARDISO, and use LU decomposition to decompose the stiffness matrix into an upper triangular U matrix and a lower triangular L matrix, that is:

[0065] K = LU (7);

[0066] S53. Invert the upper triangular U matrix and the lower triangular L matrix respectively to obtain the solution vector u:

[0067] u=U -1 L -1 F (8).

[0068] Preferably, in step S3, it is assumed that when alternating current is passed through the coil of the lubricating oil metal particle signal sensor, the eddy current loss on the surface of the wire is negligible; it is assumed that a whole rectangular conductor is used instead of a densely wound coil, and the influence on the inductance change is ignored.

[0069] Preferably, the material of the coil of the lubricating oil metal particle signal sensor in step S2 is copper, which has a relative magnetic permeability of 1, a relative dielectric constant of 1, and a conductivity of 5.998×10 7 S / m.

[0070] Preferably, the mapping mesh generator in step S4 divides the skeleton coil into four boundary segments, with no other models or small holes in the middle, specifies the corresponding edge grouping relationship of the solution domain in the edge group, and limits the mesh distribution of each edge group; the free tetrahedral mesh sets the maximum cell size, minimum cell size and cell growth rate.

[0071] Compared with the prior art, the technical effects of the present invention are:

[0072] 1. The present invention designs a method for analyzing the influence of thermal expansion coefficient on the performance of lubricating oil metal particle signal sensor. Aiming at the problem of calculating and analyzing the influence of different thermal expansion coefficients on sensor output characteristics, a semi-analytical solution of the influence of thermal expansion coefficient on sensor output characteristics is established based on electromagnetic analysis. The method effectively analyzes the changing law of the influence of different thermal expansion coefficients and material parameters on sensor performance, thereby providing a reference basis for the design optimization and process optimization of lubricating oil metal particle signal sensor.

[0073] 2. The present invention designs a method for analyzing the influence of thermal expansion coefficient on the performance of lubricating oil metal particle signal sensors. Compared with the current method that mainly relies on experimental research, the proposed method has a greatly shortened research cycle, reduces environmental interference, can effectively carry out large-scale analytical research, and establishes a theoretical analysis method for lubricating oil metal particle signal sensors. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Other features, objects and advantages of the present application will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings.

[0075] Figure 1 This is a flow chart of the method for analyzing the influence of thermal expansion coefficient on the performance of the lubricating oil metal particle signal sensor of the present invention;

[0076] Figure 2 It is a schematic diagram of local grid division in a specific embodiment of the present invention;

[0077] Figure 3 This is a schematic diagram of a three-dimensional cloud diagram of magnetic induction intensity distribution in a specific embodiment of the present invention;

[0078] Figure 4 This is a schematic diagram of a two-dimensional cloud diagram of magnetic induction intensity distribution in a specific embodiment of the present invention;

[0079] Figure 5 FIG. 1 is a schematic diagram of inductance calculation in a specific embodiment of the present invention. DETAILED DESCRIPTION

[0080] The present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the relevant invention are shown in the accompanying drawings.

[0081] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0082] Figure 1The present invention shows a method for analyzing the influence of thermal expansion coefficient on the performance of a lubricating oil metal particle signal sensor, which includes the following steps:

[0083] S1. Calculate the deformation of the coil frame material caused by temperature change: Calculate the deformation of the coil frame material caused by temperature change δ based on the thermal expansion coefficient k, temperature difference ΔT and outer diameter D of the coil frame of the lubricating oil metal particle signal sensor:

[0084] δ=kΔTD (1);

[0085] S2. Establish an inductance calculation model: Select the electromagnetic module of the lubricating oil metal particle signal sensor, set the number of coil turns, wire diameter, and material properties of the lubricating oil metal particle signal sensor, and draw a geometric model; the material of the lubricating oil metal particle signal sensor coil is copper, which has a relative magnetic permeability of 1, a relative dielectric constant of 1, and a conductivity of 5.998×107S / m.

[0086] S3. Determine the boundary conditions. Assume that when alternating current flows through the coil of the lubricating oil metal particle signal sensor, the eddy current loss on the surface of the wire is negligible. Assume that a whole rectangular conductor is used instead of a densely wound coil, and ignore the effect on the change in inductance.

[0087] S31. Use a uniform multi-turn model to establish an electromagnetic field model. The control equation of the electromagnetic field model is:

[0088]

[0089] Where J is the current density; B is the relative magnetic permeability of the material used in the model; V is the electric potential; σ is the material conductivity; ω is the angular frequency; A is the magnetic vector potential; D is the relative dielectric constant; H is the magnetic field; J e represents the applied current density; Represents the gradient.

[0090] S32. The first end of the conductor in the electromagnetic module of the lubricating oil metal particle signal sensor adopts a grounded boundary condition. Then the first control equation of the first end of the conductor is:

[0091] V=0 (3).

[0092] S33. The second end of the conductor adopts the terminal boundary condition, so the second control equation of the second end of the conductor is:

[0093]

[0094] Where V is the potential at the first end of the conductor; σ is the surface domain of the model being solved; S is the cross section; n is the normal vector of the boundary; I0 is the flowing current; and Ω is the closed surface.

[0095] S4. Perform meshing: In order to ensure good mesh quality and good solution convergence, a mapping mesh generator is used to mesh the winding. The mapping mesh generator divides the skeleton coil into four boundary segments, with no other models or small holes in the middle. The corresponding edge grouping relationship of the solution domain is specified in the edge group, and the mesh distribution of each edge group is limited. In a specific embodiment, the maximum unit size is 0.1 mm. The air domain is meshed using a free tetrahedral mesh; the free tetrahedral mesh sets the maximum unit size, minimum unit size, and unit growth rate. In a specific embodiment, the local schematic diagram of the meshing is as follows Figure 2 shown.

[0096] S5. Calculate the coil inductance value of the lubricating oil metal particle signal sensor: the input value is the coil structure parameter of the lubricating oil metal particle signal sensor, and the output value is the coil inductance value of the lubricating oil metal particle signal sensor.

[0097] S51. Discrete the solution domain into a series of interrelated small units through discrete means to form a discrete set of equations. The solution at the unit node, that is, the coil inductance value, is obtained. For any position outside the unit node, the coil inductance value is obtained by interpolation. The discrete set of equations is:

[0098]

[0099] Ku=F (6)

[0100] Where c represents the coefficient term of the partial differential equation; f represents the source term; K represents the stiffness coefficient matrix; F represents the load vector; and u represents the solution vector.

[0101] S52. Use the frequency domain calculation method, set the calculation frequency, add the direct solver PARDISO, and use LU decomposition to decompose the stiffness matrix into an upper triangular U matrix and a lower triangular L matrix, that is:

[0102] K=LU (7).

[0103] S53. Invert the upper triangular U matrix and the lower triangular L matrix respectively to obtain the solution vector u:

[0104] u=U -1 L -1 F (8).

[0105] S6. Obtain a magnetic induction intensity distribution cloud map of the lubricating oil metal particle signal sensor and inductance values ​​at different temperatures.

[0106] S61, obtain the information when the lubricating oil metal particle signal sensor runs to a stable state, and obtain the magnetic induction intensity distribution cloud map of the lubricating oil metal particle signal sensor through processing and analysis. In a specific embodiment, the three-dimensional magnetic induction intensity distribution cloud map is as follows: Figure 3 As shown, the two-dimensional cloud diagram of magnetic induction intensity distribution is as follows Figure 4 As shown, the calculated inductance value is as follows Figure 5 shown.

[0107] S62. Based on the finite element method, calculate the inductance value of the oil metal particle signal sensor at different temperatures.

[0108] S7. Derive the influence of inductance value on the output characteristics of the lubricating oil metal particle signal sensor, and select total admittance as the evaluation index;

[0109] S71. Calculate impedance Z e :

[0110]

[0111] Among them, jωL e Indicates inductance; R e Represents resistance; 1 / jωC e It represents the impedance of a capacitor in an AC circuit.

[0112] S72. Describe the characteristics of the lubricating oil metal particle signal sensor circuit:

[0113]

[0114] Where I is the total loop current and E is the voltage.

[0115] S73, using admittance to describe, due to the existence of:

[0116]

[0117] Where I1, I2, I3 represent the first current, the second current, and the third current respectively; E represents the voltage; and t represents the time. Then:

[0118]

[0119]

[0120]

[0121] S74. Calculate the total loop current I:

[0122] I=I1+I2+I3=I a e jwt (15)

[0123] Among them, I a represents the amplitude; wt represents the argument.

[0124] That is:

[0125]

[0126] S75. Calculate the inductive admittance and capacitive admittance. The inductive admittance is:

[0127]

[0128] The capacitive admittance is:

[0129]

[0130] S76. Calculate the total admittance Y, which is the sum of the admittance of the inductor and the admittance of the capacitor:

[0131]

[0132] S8. Calculate the total admittance and phase angle at different temperatures based on the inductance values ​​at different temperatures obtained in step S6;

[0133] S81. Calculate the total admittance Y1 and Y2 at room temperature and low temperature respectively;

[0134]

[0135]

[0136] Among them, jωL e1 Indicates the inductance at room temperature; jωL e2 Indicates the inductance at low temperature.

[0137] S82, calculate the phase angles θ1 and θ2 at room temperature and low temperature respectively;

[0138]

[0139]

[0140] S9. Obtaining the gap change and inductance change under different coil bobbin materials: The coil bobbin material is converted from the first material to the second material, and steps S1 to S8 are repeated to calculate the gap change and inductance change between the housing and the coil bobbin under the change from room temperature to low temperature.

[0141] In a specific embodiment, the first material is PI and the second material is PEEK, wherein the thermal expansion coefficient of PI is approximately 55ppm / °C, the thermal expansion coefficient of the coil skeleton PEEK is approximately 22ppm / °C, the outer diameter D of the coil skeleton is 38mm, the normal temperature is 25°C, the low temperature is -55°C, and the temperature difference ΔT is 80°C.

[0142] Step S1 calculates that when PI is selected as the first material, the deformation amount δ of the coil skeleton material caused by temperature change is 92 μm, and when PEEK is selected as the second material, the deformation amount δ of the coil skeleton material caused by temperature change is 66 μm.

[0143] Step S6 calculates that when PI is selected as the first material, the inductance value of the oil-metal particle signal sensor at room temperature 25°C is 131.01μH according to finite element calculation, and the inductance value of the oil-metal particle signal sensor at low temperature -55°C is 130.25μH according to finite element calculation. Therefore, when the temperature changes from room temperature 25°C to low temperature -55°C, the gap between the shell and the coil skeleton causes the coil inductance to change by 0.76μH. When PEEK is selected as the second material, the inductance value of the oil-metal particle signal sensor at room temperature 25°C is 131.01μH according to finite element calculation, and the inductance value of the oil-metal particle signal sensor at low temperature -55°C is 130.54μH according to finite element calculation. Therefore, when the temperature changes from room temperature 25°C to low temperature -55°C, the gap between the shell and the coil skeleton causes the coil inductance to change by 0.47μH.

[0144] In summary, the coil skeleton material is changed from PI to PEEK. Although the thermal expansion coefficient is reduced, the change from room temperature to low temperature of -55℃ and the gap from 92μm to 66μm will cause the inductance value to change from 0.76μH to 0.47μH.

[0145] The present invention designs a method for analyzing the influence of thermal expansion coefficient on the performance of lubricating oil metal particle signal sensor. Aiming at the problem of calculating and analyzing the influence of different thermal expansion coefficients on sensor output characteristics, a semi-analytical solution of the influence of thermal expansion coefficient on sensor output characteristics is established based on electromagnetic analysis. The method effectively analyzes the changing law of the influence of different thermal expansion coefficients and material parameters on sensor performance, thereby providing a reference basis for the design optimization and process optimization of lubricating oil metal particle signal sensor. Compared with the current method that mainly relies on experimental research, the research cycle of the proposed method is greatly shortened, the environmental interference is reduced, and large-scale analysis and research can be effectively carried out, thereby establishing a theoretical analysis means for lubricating oil metal particle signal sensor.

[0146] Finally, it should be noted that the above embodiments are only intended to illustrate rather than limit the technical solutions of the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the present invention can still be modified or replaced by equivalents. Any modification or partial replacement that does not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for analyzing the effect of thermal expansion coefficient on the performance of lubricating oil metal particle signal sensor, characterized in that: It includes the following steps: S1. Calculate the deformation of the coil frame material caused by temperature change: Calculate the deformation of the coil frame material caused by temperature change δ based on the thermal expansion coefficient k, temperature difference ΔT and outer diameter D of the coil frame of the lubricating oil metal particle signal sensor: δ=kΔTD (1); S2. Establish an inductance calculation model: Select the electromagnetic module of the lubricating oil metal particle signal sensor, set the number of coil turns, wire diameter, and material properties of the lubricating oil metal particle signal sensor, and draw a geometric model; S3. Determine boundary conditions; S4. Meshing: Use a mapped mesh generator to mesh the windings, and use a free tetrahedron mesh to mesh the air domain. S5. Calculate the coil inductance of the lubricating oil metal particle signal sensor: the input value is the coil structural parameter of the lubricating oil metal particle signal sensor, and the output value is the coil inductance of the lubricating oil metal particle signal sensor; S6. Obtaining a magnetic induction intensity distribution cloud map of the lubricating oil metal particle signal sensor and inductance values ​​at different temperatures; S61, obtaining information when the lubricating oil metal particle signal sensor operates in a stable state, and obtaining a magnetic induction intensity distribution cloud map of the lubricating oil metal particle signal sensor through processing and analysis; S62. Calculate the inductance of the oil metal particle signal sensor at different temperatures based on finite element analysis. S7. Derive the influence of inductance value on the output characteristics of the lubricating oil metal particle signal sensor, and select total admittance as the evaluation index; S8. Calculate the total admittance and phase angle at different temperatures based on the inductance values ​​at different temperatures obtained in step S6; S81. Calculate the total admittance Y1 and Y2 at room temperature and low temperature respectively; Among them, R e Represents resistance; jωL e1 Indicates the inductance at room temperature; jωL e2 Indicates the inductance at low temperature; 1 / jωC e It represents the impedance of a capacitor in an AC circuit; S82, calculate the phase angles θ1 and θ2 at room temperature and low temperature respectively; S9. Obtaining the gap change and inductance change under different coil bobbin materials: The coil bobbin material is converted from the first material to the second material, and steps S1 to S8 are repeated to calculate the gap change and inductance change between the housing and the coil bobbin under the change from room temperature to low temperature.

2. The method for analyzing the influence of thermal expansion coefficient on the performance of lubricating oil metal particle signal sensor according to claim 1, characterized in that: The step S7 specifically includes the following steps: S71. Calculate impedance Z e : Among them, jωL e represents inductance; S72. Describe the characteristics of the lubricating oil metal particle signal sensor circuit: Where, I represents the total circuit current; E represents the voltage; S73, using admittance to describe, due to the existence of: Wherein, I1, I2, I3 represent the first current, the second current, and the third current respectively; E represents the voltage; t represents the time; Then we have: S74. Calculate the total loop current I: I=I1+I2+I3=I a yes jwt (15) Among them, I a represents the amplitude; wt represents the argument; That is: S75. Calculate the inductive admittance and the capacitive admittance. The inductive admittance is: The capacitive admittance is: S76. Calculate the total admittance Y, which is the sum of the admittance of the inductor and the admittance of the capacitor:

3. The method for analyzing the influence of thermal expansion coefficient on the performance of lubricating oil metal particle signal sensor according to claim 1, characterized in that: The step S3 specifically includes the following steps: S31, using a uniform multi-turn model to establish an electromagnetic field model, the control equation of the electromagnetic field model is: Where J is the current density; B is the relative magnetic permeability of the material used in the model; V is the electric potential; σ is the material conductivity; ω is the angular frequency; A is the magnetic vector potential; D is the relative dielectric constant; H is the magnetic field; J e represents the applied current density; ▽ represents the gradient; S32. The first end of the conductor in the electromagnetic module of the lubricating oil metal particle signal sensor adopts a grounded boundary condition. Then, the first control equation of the first end of the conductor is: V＝0 (3)； S33. The second end of the conductor adopts a terminal boundary condition, and the second control equation of the second end of the conductor is: Where V is the potential at the first end of the conductor; σ is the surface domain of the model being solved; S is the cross section; n is the normal vector of the boundary; I0 is the flowing current; and Ω is the closed surface.

4. The method for analyzing the influence of thermal expansion coefficient on the performance of lubricating oil metal particle signal sensor according to claim 1, characterized in that: The step S5 specifically The following steps are involved: S51. Discrete the solution domain into a series of interrelated small units through discrete means to form a discrete set of equations, and obtain the solution at the unit node, that is, the coil inductance value; for any position outside the unit node, obtain the coil inductance value through interpolation method; the discrete set of equations is: -▽·(c▽u)=f (5) Ku=F (6) Where c represents the coefficient term of the partial differential equation; f represents the source term; K represents the stiffness coefficient matrix; F represents the load vector; u represents the solution vector; S52. Use the frequency domain calculation method, set the calculation frequency, add the direct solver PARDISO, and use LU decomposition to decompose the stiffness matrix into an upper triangular U matrix and a lower triangular L matrix, that is: K = LU (7); S53. Invert the upper triangular U matrix and the lower triangular L matrix respectively to obtain the solution vector u: u=U -1 L -1 F(8).

5. The method for analyzing the influence of thermal expansion coefficient on the performance of lubricating oil metal particle signal sensor according to claim 1, characterized in that: In step S3, it is assumed that when the coil of the lubricating oil metal particle signal sensor is energized by alternating current, the eddy current loss on the surface of the conductor is negligible; and it is assumed that a whole rectangular conductor is used instead of a densely wound coil, and the influence on the inductance change is ignored.

6. The method for analyzing the effect of thermal expansion coefficient on the performance of lubricating oil metal particle signal sensor according to claim 1, characterized in that: The material of the coil of the lubricating oil metal particle signal sensor in step S2 is copper, which has a relative magnetic permeability of 1, a relative dielectric constant of 1, and a conductivity of 5.998×10 7 S / m.

7. The method for analyzing the effect of thermal expansion coefficient on the performance of lubricating oil metal particle signal sensor according to claim 1, characterized in that: In step S4, the mapping mesh generator divides the skeleton coil into four boundary segments, with no other models or small holes in the middle, specifies the corresponding edge grouping relationship of the solution domain in the edge group, and limits the mesh distribution of each edge group; the free tetrahedral mesh sets the maximum element size, minimum element size and element growth rate.

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