Magnetic suspension platform deformation monitoring system and monitoring method

Abstract

The invention provides a magnetic suspension platform deformation monitoring system and method, and the system comprises Hall sensor arrays which are uniformly disposed at the edge and supporting points of a magnetic suspension motion platform, and are used for detecting the magnetic field intensity of the position where the magnetic suspension motion platform is located, and generating an original analog signal; the signal processing module is connected with the Hall sensor array, performs filtering, amplification and analog-to-digital conversion on the original analog signal, and outputs digital magnetic field data; the data analysis unit is connected with the signal processing module to receive the digital magnetic field data, and magnetic field intensity change is converted into deformation quantity of the magnetic suspension platform through a preset algorithm model; and the feedback control unit is connected with the data analysis unit and a control system of the magnetic suspension motion platform, generates a control instruction according to the deformation quantity, and adjusts the electromagnetic force of the magnetic suspension motion platform to correct the pose. Deformation of the magnetic suspension platform can be accurately monitored in real time, the pose can be corrected in time, and stable operation of the magnetic suspension platform is guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of precision measurement and control, in particular to a magnetic suspension platform deformation monitoring system and a monitoring method. BACKGROUND

[0002] In the field of precision manufacturing and semiconductor industry, magnetic suspension motion platforms are widely used due to their advantages of no friction and high precision. However, as the industry's demand for the precision of motion platforms continues to increase (such as the requirement for positioning precision to reach nanometer level in semiconductor lithography), the problem of micro-deformation of the platform caused by high-speed motion or load change is increasingly prominent. The current mainstream deformation monitoring technologies mainly include laser interferometry, strain gauge measurement and visual measurement, but these methods have obvious limitations: laser interferometer has high precision (up to nanometer level), but it is sensitive to environmental vibration and expensive; strain gauges need to be directly pasted on the surface of the platform, which will introduce additional mass to affect the motion performance, and only local strain can be measured; visual measurement is easily disturbed by environmental light and is difficult to meet the real-time requirement.

[0003] Application No. CN202010983619.4 discloses a magnetic suspension motion platform, which comprises a stator and a mover. The mover comprises a magnet array, which comprises a plurality of magnet rows and a plurality of magnet columns. The stator comprises a first coil layer, a second coil layer and a Hall sensor array stacked in layers. The Hall sensor array comprises a plurality of Hall sensor groups, and each Hall sensor group comprises at least four Hall sensors arranged at the four vertices of a square, with the sides of the square being parallel to the magnet rows or magnet columns. The number and arrangement of the Hall sensor groups satisfy that the magnet array can overlap at least three non-collinear Hall sensor groups during motion, so as to calculate the position of the mover in six degrees of freedom. The above technical solution cannot simultaneously consider the problems of measurement accuracy, response speed and environmental adaptability under high-speed and large acceleration motion conditions. SUMMARY

[0004] In view of the defects in the prior art, the purpose of the present application is to provide a magnetic suspension platform deformation monitoring system and a monitoring method.

[0005] The first aspect of the present application provides a magnetic suspension platform deformation monitoring system, comprising: a Hall sensor array, which is uniformly arranged at the edges and support points of the magnetic suspension motion platform, for real-time detection of the magnetic field strength of the position where the magnetic suspension motion platform is located and generation of original analog signals; a signal processing module connected with the Hall sensor array, for filtering, amplifying and analog-to-digital conversion of the original analog signals, and output of digitalized magnetic field data; a data analysis unit connected with the signal processing module, configured to receive the digitized magnetic field data, and convert the magnetic field strength variation into a deformation variable of the magnetic levitation platform through a preset algorithm model; a feedback control unit connected with the data analysis unit and a control system of the magnetic levitation motion platform, configured to generate a control instruction according to the deformation variable, and adjust the electromagnetic force of the magnetic levitation platform to correct the pose.

[0006] Optionally, the Hall sensor array comprises a plurality of Hall sensors, which are uniformly distributed on the platform edge and support points of the magnetic levitation motion platform and fixed through a support; The signal processing module comprises an analog filter circuit, an amplification circuit and an analog-to-digital conversion circuit; the filter circuit is configured to remove noise interference in the original analog signal, the amplification circuit is configured to increase the signal amplitude, and the analog-to-digital conversion circuit is configured to convert the original analog signal into a digital signal.

[0007] Optionally, the data analysis unit comprises: a calibration module configured to record a reference magnetic field strength B0 of each Hall sensor array in a no-load state, and record a corresponding magnetic field strength variation ΔB after a known deformation is applied, and establish a mapping model of ΔB and a deformation variable δ; a deformation calculation module configured to calculate a variation ΔBi = Bi- B0 according to a real-time collected magnetic field strength Bi, and substitute the variation into the mapping model to solve the deformation variable δi; a three-dimensional synthesis module configured to combine deformation variable data of a plurality of Hall sensors, and calculate an overall deformation of the magnetic levitation motion platform in a three-dimensional space by using an optimization algorithm; wherein the overall deformation of the three-dimensional space comprises displacement and angular deflection.

[0008] Optionally, the mapping model adopts a polynomial fitting model; wherein the deformation variable δ is calculated from the magnetic field strength variation ΔB and fitting coefficients a0 to an; The fitting coefficients a0 to an are generated in the process of establishing the mapping model in the calibration module, and are used to quantify the relationship between the magnetic field variation and the deformation variable δ.

[0009] Optionally, the three-dimensional synthesis module solves the three-dimensional deformation of the magnetic levitation motion platform by using a least squares method, and solves the overall deformation of the magnetic levitation platform by minimizing the error sum of squares of the measurement values of each Hall sensor and the three-dimensional synthesis module.

[0010] Optionally, the data analysis unit is provided with a temperature drift compensation mechanism for eliminating the influence of environmental temperature change on the Hall sensor.

[0011] In a second aspect, the application provides a monitoring method of a magnetic levitation platform deformation monitoring system, comprising: Real-time acquisition of magnetic field intensity data of key positions of the magnetic levitation motion platform through the set Hall sensor array to generate original analog signals; Filtering, amplification and analog-digital conversion processing of the original analog signals through the signal processing module to obtain digital magnetic field data, and pre-processing and temperature drift compensation of the digital magnetic field data; Conversion of the processed magnetic field data into deformation variables of the magnetic levitation motion platform based on a pre-calibrated magnetic field intensity-deformation mapping relationship through the data analysis unit; Input of the deformation variables into a control system of the magnetic levitation motion platform to adjust and correct the pose of the magnetic levitation motion platform; Establishment of a machine learning model according to the deformation variables combined with historical data to predict the deformation trend of the magnetic levitation motion platform and generate a health status report.

[0012] Optionally, the conversion of the processed magnetic field data into deformation variables of the magnetic levitation motion platform based on a pre-calibrated magnetic field intensity-deformation mapping relationship through the data analysis unit comprises: Recording of a reference magnetic field intensity B0 measured by the Hall sensor array under a no-load state of the magnetic levitation motion platform; Recording of a corresponding magnetic field intensity change ΔB after applying a known deformation to establish a mapping model of ΔB and deformation variables δ; According to the current magnetic field intensity Bi, calculating the change amount ΔBi=Bi-B0, and substituting it into the mapping model to solve the deformation variable δi.

[0013] Optionally, after solving the deformation variable δi, it further comprises: through the data of the Hall sensor array, using the least square method, minimizing the error sum of squares of the measurement values of the Hall sensor array and the three-dimensional synthesis module to solve the overall deformation of the magnetic levitation motion platform. Wherein, the overall deformation includes displacement and angular deflection of the magnetic levitation motion platform.

[0014] Optionally, the mapping model adopts polynomial fitting, and the deformation variable δ is calculated from the magnetic field intensity change amount ΔB and fitting coefficients a0 to an.

[0015] The magnetic suspension platform deformation monitoring system and the monitoring method provided by the application adopt a non-contact measurement method, directly detect magnetic field changes through a Hall sensor array, avoid the problem that a traditional contact measurement method is easily disturbed by vibration when moving at high speed, accurately detect magnetic field strength in real time and generate original analog signals, process and output digitized magnetic field data through a signal processing module, convert deformation variables through a data analysis unit, and finally adjust electromagnetic force correction poses through a feedback control unit, so that the magnetic suspension platform deformation can be monitored in real time and accurately and the poses can be corrected in time, thereby ensuring stable operation of the magnetic suspension platform.

[0016] Other technical effects brought by additional features will be further described in corresponding embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0017] Other features, objects, and advantages of the application will become more apparent from the following detailed description of non-limiting embodiments with reference to the drawings: Figure 1 FIG. 1 is a schematic diagram of a magnetic suspension platform deformation monitoring system according to an exemplary embodiment; Figure 2 FIG. 2 is a flowchart of a monitoring method of a magnetic suspension platform deformation monitoring system according to an exemplary embodiment; Figure 3 FIG. 3 is a closed-loop control circuit block diagram according to an exemplary embodiment. DETAILED DESCRIPTION

[0018] The application will be described in detail below with reference to specific embodiments. The following embodiments will help those skilled in the art to further understand the application, but do not limit the application in any form. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the application, and these all belong to the protection scope of the application. The parts not described in detail in the following embodiments can be implemented by using existing technologies.

[0019] In the description of the embodiments of the application, it should be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "bottom", "inner", "outer", "clockwise", "counterclockwise" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the purpose of facilitating the description of the application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the application.

[0020] In addition, the terms "first", "second", etc. are used only for descriptive purposes and are not to be construed as indicating or implying relative importance or an ordered ranking of the indicated technical features. Thus, features defined with "first", "second" can explicitly or implicitly include one or more of the features.

[0021] In the description of the embodiments of the present application, the meaning of "multiple" is two or more than two, unless otherwise explicitly specified. In the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connecting", "fixing" and the like should be understood in a broad sense, for example, can be fixedly connected, can be detachably connected, or integrally connected; can be mechanically connected, or electrically connected; can be directly connected, or indirectly connected through an intermediate medium, or can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0022] In the present application, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units not listed, or optionally includes other steps or units inherent to the process, method, product or device.

[0023] In the prior art, the current mainstream deformation monitoring technology mainly includes laser interference method, strain gauge measurement method and visual measurement method, but these methods all have obvious limitations: the laser interferometer has high precision (up to nanometer level), but is sensitive to environmental vibration and expensive; the strain gauge needs to be directly pasted on the platform surface, which will introduce additional mass to affect the motion performance, and can only measure local strain; the visual measurement method is easily disturbed by environmental light and is difficult to meet the real-time requirement. Especially in high-speed and large-acceleration motion conditions, the existing technology often cannot balance the measurement accuracy, response speed and environmental adaptability. Based on the above problems, the present application provides a magnetic suspension platform deformation monitoring system to solve the above problems.

[0024] Reference Figure 1 As shown in the figure, in an embodiment of the present application, a magnetic suspension platform deformation monitoring system includes a Hall sensor array, a signal processing module, a data analysis unit and a feedback control unit.

[0025] The Hall sensor array is uniformly arranged at the edge and support point of the magnetic levitation motion platform, and is used for detecting the magnetic field strength of the position where the magnetic levitation motion platform is located in real time and generating an original analog signal; the signal processing module is connected with the Hall sensor array, and is used for filtering, amplifying and analog-digital converting the original analog signal, and outputting digitalized magnetic field data; the data analysis unit is connected with the signal processing module, and is used for receiving the digitalized magnetic field data, and converting the magnetic field strength change into a deformation variable of the magnetic levitation platform through a preset algorithm model; the feedback control unit is connected with the data analysis unit and the control system of the magnetic levitation motion platform, and is used for generating a control instruction according to the deformation variable, and adjusting the electromagnetic force of the magnetic levitation platform to correct the pose.

[0026] Specifically, in the running process, first, the Hall sensor array uniformly arranged at the edge and support point of the magnetic levitation motion platform starts to work, detects the magnetic field strength of the position where the platform is located in real time, and converts the detection result into an original analog signal; the signal processing module connected with the Hall sensor array sequentially performs filtering, amplifying and analog-digital converting and other processing on the original analog signal, and outputs digitalized magnetic field data; the data analysis unit receives these digitalized magnetic field data, and accurately converts the change of the magnetic field strength into a deformation variable of the magnetic levitation platform by using a preset algorithm model; the feedback control unit generates a corresponding control instruction according to the obtained deformation variable, and transmits the control instruction to the control system of the magnetic levitation motion platform, so as to adjust the electromagnetic force of the magnetic levitation platform and correct the pose of the platform.

[0027] The magnetic levitation motion platform adopts electromagnetic suspension technology to realize non-contact support, includes a suspension electromagnet, a guide electromagnet and a motion control unit, provides a high-precision motion carrier for a measured object, realizes six-degree-of-freedom suspension through electromagnetic force, and the rigid frame will cause the internal magnetic field distribution to change when the load or thermal deformation occurs.

[0028] It should be noted that the Hall sensor is fixed through a non-magnetic aluminum alloy support, and a gap of 1mm is kept with the magnetic levitation motion platform to avoid contact friction; the Hall sensor output is connected to the signal processing module through a twisted pair; the processed digital signal is transmitted to the data analysis unit through optical coupling isolation, and the process is as follows: Hall signal→filtering and amplifying→ADC→deformation calculation→control instruction→electromagnet driving.

[0029] The micron-level deformation monitoring is realized through non-contact magnetic field detection, which reduces the cost compared with a laser interferometer, and is not limited by the motion speed, and is suitable for high-speed precision motion scenes.

[0030] The above embodiments of the present application can comprehensively and in real time acquire magnetic field strength information through reasonable layout of the Hall sensor array, thereby providing a basis for accurate monitoring. The signal processing module optimizes the original signal, thereby ensuring the accuracy and reliability of the digital magnetic field data. The data analysis unit efficiently and accurately converts the magnetic field change into a deformation variable by means of a preset algorithm model, thereby realizing quantitative monitoring of the platform deformation. The feedback control unit generates a control instruction in a timely manner according to the deformation variable and adjusts the electromagnetic force to correct the pose, thereby effectively preventing various problems caused by platform deformation, greatly improving the stability, safety and precision of the magnetic levitation platform operation, prolonging the service life of the equipment and reducing the maintenance cost.

[0031] In order to detect the magnetic field strength change of each position of the platform in real time, in some specific embodiments of the present application, the Hall sensor array includes a plurality of Hall sensors, and the plurality of Hall sensors are uniformly distributed at the platform edges and support points of the magnetic levitation motion platform and are fixed by a support.

[0032] The signal processing module includes an analog filter circuit, an amplification circuit and an analog-to-digital conversion circuit; the filter circuit is used to remove noise interference in the original analog signal, the amplification circuit is used to increase the signal amplitude, and the analog-to-digital conversion circuit is used to convert the original analog signal into a digital signal.

[0033] The Hall sensor is a linear Hall sensor (such as SS49E), and the signal processing module includes an analog filter circuit (RC low-pass filter), an instrument amplifier (such as AD620) and an analog-to-digital conversion chip (such as ADS1115), which are connected with the Hall sensor through a shielded cable to condition and digitize the original signal.

[0034] It should be noted that the edge sensor group of the Hall sensor array, i.e., the plurality of Hall sensors, are arranged at intervals along the X / Y direction of the platform, and the distance between adjacent Hall sensors can be changed as needed, which is not limited herein, and the planar deformation is monitored; the support point sensor group is located directly above the electromagnet to detect the Z-direction displacement.

[0035] The signal processing module: the filter circuit eliminates high-frequency electromagnetic interference (frequency band 0-1 kHz); the instrument amplifier amplifies the Hall voltage signal (mV level) to the ADC range (0-3.3V).

[0036] The above embodiments of the present application can comprehensively and stably capture the magnetic field information of each key position of the platform by uniformly distributing the Hall sensor array with a plurality of Hall sensors at the edges and support points of the magnetic levitation motion platform and fixing the Hall sensor array with a support; the signal processing module is provided with an analog filter circuit, an amplification circuit and an analog-to-digital conversion circuit, which sequentially denoises, amplifies and converts the original analog signal into a digital signal, thereby improving the signal quality and improving the accuracy and reliability of the monitoring results of the entire magnetic levitation platform deformation monitoring system.

[0037] In some embodiments of the present application, the data analysis unit comprises a calibration module, a deformation calculation module and a three-dimensional synthesis module.

[0038] The calibration module is used to record the reference magnetic field strength B0 of each Hall sensor array in the no-load state, and record the corresponding magnetic field strength change ΔB after applying a known deformation, to establish a mapping model of ΔB and deformation δ; the deformation calculation module is used to calculate the change ΔBi = Bi - B0 of the real-time collected magnetic field strength Bi, and substitute it into the mapping model to solve the deformation δi; the three-dimensional synthesis module is used to combine the deformation data of multiple Hall sensors, and calculate the overall deformation of the magnetic levitation motion platform in the three-dimensional space by using an optimization algorithm.

[0039] The overall deformation of the three-dimensional space includes displacement and angular deflection.

[0040] Specifically, when the data analysis unit is working, the calibration module first records the reference magnetic field strength B0 of each Hall sensor array in the no-load state, then applies a known deformation to the magnetic levitation motion platform, records the corresponding magnetic field strength change ΔB, establishes a mapping model of ΔB and deformation δ, the deformation calculation module calculates the change ΔBi = Bi - B0 of the real-time collected magnetic field strength Bi from the reference magnetic field strength B0, and substitutes ΔBi into the mapping model to solve the deformation δi; finally, the three-dimensional synthesis module combines the deformation data obtained by multiple Hall sensors, and uses an optimization algorithm to calculate the overall deformation of the magnetic levitation motion platform in the three-dimensional space, which includes displacement and angular deflection.

[0041] It should be noted that in the data analysis unit, the calibration data (magnetic field-deformation table) is stored; the three-dimensional deformation is solved in real time, and the displacement accuracy of the magnetic levitation motion platform on the X / Y / Z axis is improved.

[0042] In the above embodiments of the present application, the calibration module provides a calculation basis by establishing a mapping model of magnetic field strength change and deformation, ensuring the accuracy of deformation monitoring, the deformation calculation module quickly solves the deformation by using real-time data and the mapping model, real-time reflects the deformation condition of the magnetic levitation motion platform, and improves the timeliness of monitoring; the three-dimensional synthesis module comprehensively considers the data of multiple Hall sensors, and uses an optimization algorithm to calculate the overall deformation of the three-dimensional space, which can fully grasp the displacement and angular deflection of the platform in the three-dimensional space, effectively avoid the limitations of single sensor monitoring, and improve the comprehensiveness and reliability of deformation monitoring, providing a strong guarantee for the stable operation of the magnetic levitation motion platform.

[0043] In some embodiments of the present application, a polynomial fitting model is used for the mapping model.

[0044] Wherein, the deformation variable δ is calculated by the magnetic field intensity change variable ΔB and the fitting coefficients a0-an; the fitting coefficients a0-an are generated in the process of establishing the mapping model in the calibration module, and are used to quantify the relationship between the magnetic field change variable and the deformation variable δ.

[0045] It should be noted that the deformation variable δ is a known deformation applied; the deformation variable δi is the deformation variable corresponding to the single sensor obtained by solving the real-time measurement of the i-th Hall sensor according to the current magnetic field intensity change ΔBi through the calibrated mapping model in the calculation stage.

[0046] The fitting coefficients a0-an are obtained by fitting a plurality of sets of sample data of the magnetic field intensity change ΔB and the deformation variable δ in the establishment of the mapping model through the polynomial fitting algorithm; and are used to define the magnetic field intensity change ΔB and the deformation variable δ. For example, using n-order polynomial fitting, the mapping relationship can be expressed as: .

[0047] In the formula, , , , are constant term, first-order term coefficient, second-order term coefficient, and n-order term coefficient, respectively.

[0048] In some specific embodiments of the present application, the least square method is used to solve the three-dimensional deformation of the magnetic suspension motion platform for the three-dimensional synthesis module, and the overall deformation of the magnetic suspension platform is solved by minimizing the error sum of squares of the measurement values of each Hall sensor and the three-dimensional synthesis module.

[0049] In the above embodiment, since the relationship between the deformation variable and the magnetic field intensity is affected by many factors, the use of formula fitting may not achieve good results, and the use of formula to establish the mapping relationship is not considered here. Consider selecting a plurality of sampling points within the measurement range interval, and using external measurement tools (such as laser interferometer, grating ruler, etc.) to measure the actual deformation variable and the magnetic field intensity, and establishing the mapping curve of the two by fitting method or interpolation method (such as least square method, Newton interpolation method, Lagrange interpolation method, etc.). The fitting method and the interpolation method are prior art; taking the Newton interpolation method as an example, the interpolation polynomial is established by calculating the difference quotient, and for high-order difference quotient, the difference quotient table can be consulted. It should be emphasized that the purpose of the present application is to establish a continuous deformation variable and magnetic field density mapping relationship through discrete sampling points, and to ensure accuracy, the more the number of sampling points, the higher the accuracy.

[0050] Wherein, the above embodiment, the plurality of Hall sensor deformation variable by least squares method into the overall deformation. The measurement of the Hall sensor array and three-dimensional synthetic module error refers to the error between the actual position change measured by the Hall sensor and the position change calculated by the overall deformation to be solved, and the least squares method is used to find the deformation matrix that makes the sum of squares of the error minimum. The specific formula calculation process is as follows: The overall motion state of the platform is set as a six-dimensional vector: In the formula, , , Respectively, the three-dimensional translation component of the magnetic suspension motion platform; Respectively, the rotation angle (small angle approximation) of the magnetic suspension motion platform around the three axes; Is a six-dimensional real vector.

[0051] The prediction value formula is established, and the square error is constructed: The displacement of the i th sensor on the magnetic suspension motion platform is: In the formula, Is the overall translation vector (capital T represents vector transpose); Is the rotation vector (capital T represents vector transpose); Is the initial position coordinate of the i th sensor in the platform coordinate system.

[0052] Projecting to the measurement direction of the sensor, the prediction value is obtained: In the formula, Is the measurement direction unit vector of the i th sensor, and the three-dimensional unit vector is .

[0053] Expand to form a matrix form, and the prediction value Is: In the formula, Is the projection component of ; Is the rotation vector.

[0054] The above expansion matrix is written in matrix form: In the formula, Is the corresponding coefficient row vector of the i th sensor; ​​​​​ i.e. including translation and rotation; (T denotes vector transpose); are the components of the initial position coordinates of the i-th sensor on the x, y, z axes, respectively; The error is: ; where, is the actual value, i.e. the value measured by the Hall sensor; is the predicted value, i.e. the sensor value corresponding to the solution x obtained using the least squares method; The sum of squares is: ; Using the least squares method, the state vector x is solved for which the error sum of squares is minimum; All form the coefficient matrix: ; All measured values form the vector: ; .

[0055] The error sum of squares can then be written as: ; here the square of the two-norm of the vector is the sum of squares from above, and X is a six-dimensional vector; Using the least squares method, the error is minimized, i.e. the minimum value is sought for , i.e. the optimal estimate of the state vector , the solution is: .

[0056] where, is the transpose matrix of the coefficient matrix A; is the inverse matrix of the product of the matrix and A.

[0057] In some specific embodiments of the application, a temperature drift compensation mechanism is provided for the data analysis unit, for eliminating the influence of environmental temperature changes on the Hall sensor.

[0058] The above-mentioned embodiments of the present application first adopt a non-contact measurement method, directly detect the change of the magnetic field through a Hall sensor array, avoid the problem that the traditional contact measurement method is easily disturbed by vibration when moving at high speed, and simultaneously ensure the stability of the data by using a high-speed ADC and a real-time filtering algorithm in the signal processing module. Secondly, the system realizes high-precision measurement at a sub-micron level, can realize full-degree deformation analysis through multi-sensor spatial distribution and three-dimensional deformation synthesis technology based on the least square method, further improves the long-term stability by cooperating with temperature drift compensation technology, reduces the hardware cost by adopting the Hall sensor scheme, has strong anti-interference ability to industrial environment such as dust and oil stains, and further reduces the system complexity by using a low-power chip in the embedded data processing.

[0059] Referring to Figure 2 In another embodiment of the present application, a monitoring method of a magnetic suspension platform deformation monitoring system is provided, which is realized by a magnetic suspension platform deformation detection system, and includes the following steps: S1, collecting the magnetic field intensity data of the key positions of the magnetic suspension motion platform in real time through the set Hall sensor array, and generating an original analog signal; S2, filtering, amplifying and analog-digital converting the original analog signal through the signal processing module to obtain digitalized magnetic field data, and pre-processing and temperature drift compensating the digitalized magnetic field data; S3, converting the processed magnetic field data into the deformation variable of the magnetic suspension motion platform based on the pre-calibrated magnetic field intensity-deformation mapping relationship through the data analysis unit; S4, inputting the deformation variable into the control system of the magnetic suspension motion platform to adjust and correct the pose of the magnetic suspension motion platform; S5, establishing a machine learning model according to the deformation variable and historical data, predicting the deformation trend of the magnetic suspension motion platform, and generating a health status report.

[0060] Specifically, in the magnetic suspension platform deformation detection system, a Hall sensor array is arranged in advance, which works in real time and continuously collects the magnetic field strength information at these key positions, and converts the collected information into raw analog signals. The signal processing module is connected to the Hall sensor array and receives the raw analog signals generated by the Hall sensor array. Subsequently, the signal processing module filters the raw analog signals to remove noise interference; then, the signal processing module amplifies the signals to improve the amplitude of the signals, making them easier to analyze subsequently; finally, the signal processing module performs analog-to-digital conversion to convert the analog signals into digital magnetic field data. After obtaining the digital magnetic field data, the data analysis unit pre-processes and compensates for temperature drift of the digital magnetic field data to eliminate the influence of temperature changes on the magnetic field data; the data analysis unit receives the processed and compensated digital magnetic field data, and according to the pre-calibrated magnetic field strength-deformation mapping relationship, uses a specific algorithm to convert the magnetic field data into the deformation of the magnetic suspension motion platform; and inputs the calculated deformation into the control system of the magnetic suspension motion platform, and the control system generates corresponding control instructions according to the received deformation information to adjust the electromagnetic force of the magnetic suspension motion platform. At the same time, the system stores the deformation data obtained each time, combines historical data, and uses a machine learning algorithm to establish a machine learning model. Through the model, the system can predict the future deformation trend of the magnetic suspension motion platform, and generate a health status report according to the deformation, to intuitively present the running state of the platform.

[0061] The Hall sensor array is composed of a plurality of Hall sensors.

[0062] The above embodiments of the present application comprehensively and timely collect data through the Hall sensor array, providing a basis for accurate monitoring; the signal processing and compensation improve the data quality, ensuring the accuracy of subsequent analysis; the deformation variable conversion based on the mapping relationship realizes the quantitative monitoring of deformation; the pose adjustment forms a closed-loop control, improving the stability of the platform operation; the trend prediction and report generation can detect problems in advance, facilitate reasonable maintenance, prolong the service life of the platform, and reduce operation and maintenance costs.

[0063] In some specific embodiments of the present application, the conversion of the processed magnetic field data into the deformation of the magnetic suspension motion platform by the data analysis unit based on the pre-calibrated magnetic field strength-deformation mapping relationship includes: In the no-load state of the magnetic suspension motion platform, the reference magnetic field strength B0 obtained by the Hall sensor array is recorded; after a known deformation is applied, the corresponding magnetic field strength change ΔB is recorded, and a mapping model of ΔB and the deformation δ is established; according to the current magnetic field strength Bi, the change ΔBi = Bi-B0 is calculated, and the deformation δi is solved by substituting the mapping model.

[0064] Specifically, the deformation calculation model is established and solved by the data analysis unit. The data analysis unit is based on the mapping relationship between the pre-calibrated magnetic field strength and deformation, and uses a polynomial fitting algorithm to calculate the deformation of the platform. The specific steps are as follows: Calibration stage: In the no-load state, record the reference magnetic field strength B0 of each Hall sensor; after applying a known deformation, record the corresponding magnetic field strength change ΔB, and establish a mapping model of ΔB and deformation δ. Real-time calculation stage: calculate the change ΔBi = Bi-B0 according to the current magnetic field strength Bi, and substitute it into the mapping model to solve the deformation δi.

[0065] In some embodiments of the present application, after solving the deformation δi, it further includes: using the least squares method, through minimizing the error sum of squares of the measurement values of the Hall sensor array and the three-dimensional synthesis module, solving the overall deformation of the magnetic levitation motion platform.

[0066] Wherein, the overall deformation includes displacement and angular deflection of the magnetic levitation motion platform.

[0067] The above embodiments of the present application can realize full freedom deformation analysis through multi-sensor spatial distribution and least squares three-dimensional deformation synthesis technology, and further improve long-term stability by combining temperature drift compensation technology.

[0068] In terms of real-time control, the feedback control unit transmits the deformation data to the platform motion controller through the CAN bus, dynamically adjusts the electromagnet current to compensate for the deformation, and feeds back the deformation data to the magnetic levitation controller in real time through the closed-loop control loop. The response time is less than 1ms, which can dynamically adjust the electromagnetic force to suppress deformation, and at the same time, combined with machine learning prediction model to realize active compensation, which is significantly superior to the traditional monitoring system which can only provide offline data.

[0069] Wherein, the closed-loop control loop includes: deformation detection stage (measured by each Hall sensor), data transmission and processing stage (using the least squares method to calculate the overall deformation displacement of the platform), control decision stage (using control algorithms such as PID, MPC, etc. Output control signal), and execution compensation stage (outputting the control signal to the actuator, i.e. magnetic levitation motion platform).

[0070] Reference Figure 3As shown, first, the given amount (the desired magnetic suspension platform state, such as the reference state without significant deformation) is compared with the actual deformation data fed back by the measuring device (link 3), to obtain a deviation signal, i.e. the deformation detection stage; then, the controller (link 1, platform motion controller) generates a control instruction (a signal for dynamically adjusting the electromagnet current) according to the deviation signal, i.e. the data transmission and processing stage; then, the controlled object (link 2, magnetic suspension motion platform) generates an output (the actual platform deformation) under the action of the control instruction and the disturbance (such as deformation interference caused by external factors); then, the measuring device collects the output (deformation data) in real time, i.e. the control decision stage, and transmits the deformation data back to the comparison link through the CAN bus, forming a closed-loop control loop.

[0071] In some embodiments of the present application, a polynomial fitting is used for the mapping model, and the deformation δ is calculated from the magnetic field strength change ΔB and the fitting coefficients a0 to an.

[0072] Further, a machine learning model is established according to the deformation and historical data to predict the deformation trend of the magnetic suspension motion platform and generate a health status report. The calculated deformation is input into the control system of the magnetic suspension motion platform to adjust the electromagnetic force to correct the platform pose and ensure the motion accuracy; at the same time, a machine learning model is established based on historical data by the data analysis unit to predict the platform deformation trend and generate a health status report for preventive maintenance.

[0073] The machine learning model (such as LSTM) is used to predict the deformation trend.

[0074] The present application includes a multi-sensor data fusion algorithm, a temperature drift compensation mechanism, and a deformation prediction model based on machine learning, which can realize sub-micron level deformation detection and automatically generate a platform health status report, integrates intelligent health monitoring functions, and can automatically generate a health status report to support predictive maintenance by analyzing historical data to count fault characteristics such as bearing wear and electromagnet aging. Through four key technical points: non-contact sensing Hall array design, high-precision signal processing algorithm, real-time closed-loop control system, and intelligent machine learning algorithm, it is particularly suitable for semiconductor lithography machines, precision machine tools, and other industrial application scenarios with extremely high requirements for measurement accuracy and reliability.

[0075] The preferred features in the above embodiments can be used alone in any embodiment, and can also be used in any combination without conflict. In addition, parts not described in detail in the embodiments can be implemented using existing technology.

[0076] The above describes some specific embodiments of the present application. It needs to be understood that the present application is not limited to the above specific embodiments, and various modifications or changes can be made by those skilled in the art within the scope of the claims, which does not affect the essential content of the present application. The above preferred features can be combined in any manner without conflict.

Claims

1. A magnetic levitation platform deformation monitoring system, characterized in that, The method comprises the following steps: A Hall sensor array is uniformly arranged at the edge and support point of a magnetic levitation motion platform to detect the magnetic field strength of the position where the magnetic levitation motion platform is located in real time and generate an original analog signal; A signal processing module is connected to the Hall sensor array to filter, amplify, and analog-digital convert the original analog signal, and output digitized magnetic field data; A data analysis unit is connected to the signal processing module to receive the digitized magnetic field data, and convert the magnetic field strength change into a deformation variable of the magnetic levitation platform through a preset algorithm model; A feedback control unit is connected to the data analysis unit and the control system of the magnetic levitation motion platform to generate a control instruction according to the deformation variable and adjust the electromagnetic force correction pose of the magnetic levitation platform.

2. The magnetic levitation platform deformation monitoring system according to claim 1, wherein, The Hall sensor array comprises a plurality of Hall sensors which are uniformly distributed at the edge and support point of the magnetic levitation motion platform and are fixed by a support; The signal processing module comprises an analog filter circuit, an amplification circuit, and an analog-digital conversion circuit; the filter circuit is used to remove noise interference in the original analog signal, the amplification circuit is used to increase the signal amplitude, and the analog-digital conversion circuit is used to convert the original analog signal into a digital signal.

3. The magnetic levitation platform deformation monitoring system according to claim 1, wherein, The data analysis unit comprises: A calibration module is used to record the reference magnetic field strength B0 of each Hall sensor array in a no-load state, record the corresponding magnetic field strength change ΔB after a known deformation is applied, and establish a mapping model of ΔB and the deformation variable δ; The deformation calculation module is configured to calculate a change amount ΔBi = Bi - B0 according to the real-time collected magnetic field intensity Bi, and substitute the change amount ΔBi into the mapping model to solve a deformation variable ; A three-dimensional synthesis module is used to combine the deformation variable data of a plurality of Hall sensors, and calculate the overall deformation of the magnetic levitation motion platform in a three-dimensional space by using an optimization algorithm; The overall deformation of the three-dimensional space comprises displacement and angular deflection.

4. The magnetic levitation platform deformation monitoring system according to claim 3, characterized in that, The mapping model adopts a polynomial fitting model; The deformation variable δ is calculated from the magnetic field strength change ΔB and fitting coefficients a0 to an; The fitting coefficients a0 to an are generated in the process of establishing the mapping model in the calibration module, and are used to quantify the relationship between the magnetic field change and the deformation variable δ.

5. The magnetic levitation platform deformation monitoring system according to claim 3, wherein, The three-dimensional synthesis module solves the three-dimensional deformation of the magnetic levitation motion platform by using the least square method, minimizes the error sum of squares of the measurement values of each Hall sensor and the three-dimensional synthesis module, and solves the overall deformation of the magnetic levitation platform.

6. The magnetic levitation platform deformation monitoring system according to claim 3, wherein, The data analysis unit is provided with a temperature drift compensation mechanism to eliminate the influence of environmental temperature change on the Hall sensor.

7. A monitoring method of a magnetic levitation platform deformation monitoring system, characterized by, The method comprises the following steps: The magnetic field strength data of the key positions of the magnetic levitation motion platform are collected in real time by the Hall sensor array arranged, and original analog signals are generated; The original analog signals are filtered, amplified, and analog-digital converted by the signal processing module to obtain digitized magnetic field data, and the digitized magnetic field data are preprocessed and temperature drift compensated; The magnetic field data after processing are converted into the deformation variable of the magnetic levitation motion platform based on the pre-calibrated magnetic field strength-deformation mapping relationship by the data analysis unit. Input the deformation variable into a control system of the magnetic levitation motion platform, and adjust and correct a pose of the magnetic levitation motion platform; According to the deformation variable and historical data, a machine learning model is established to predict a deformation trend of the magnetic levitation motion platform and generate a health status report.

8. The monitoring method of claim 7, wherein, The processed magnetic field data is converted into the deformation variable of the magnetic levitation motion platform by the data analysis unit based on a pre-calibrated magnetic field strength-deformation mapping relationship, including: In a no-load state of the magnetic levitation motion platform, a reference magnetic field strength B0 measured by the Hall sensor array is recorded; After a known deformation is applied, a corresponding magnetic field strength change ΔB is recorded, and a mapping model of ΔB and the deformation variable δ is established; According to the current magnetic field strength Bi, the change amount ΔBi=Bi-B0 is calculated, and the mapping model is substituted to solve the deformation variable δi.

9. The monitoring method of claim 8, wherein, After the deformation variable δi is solved, the data of the Hall sensor array is further used to solve the overall deformation of the magnetic levitation motion platform by using the least square method and minimizing the error sum of squares of the measurement values of the Hall sensor array and the three-dimensional synthesis module; The overall deformation includes displacement and angular deflection of the magnetic levitation motion platform.

10. The monitoring method of claim 8, wherein, The mapping model uses polynomial fitting, and the deformation variable δ is calculated from the magnetic field strength change amount ΔB and fitting coefficients a0 to an.

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