Position detection method for five-degree-of-freedom active and passive inner rotor magnetic suspension bearing

Abstract

The invention belongs to the technical field of bearing position detection, and particularly relates to a position detection method of a five-degree-of-freedom active and passive inner rotor magnetic suspension bearing, which adopts a single plate capacitance sensor to be arranged at a key position of the magnetic suspension bearing, detects the translation and rotation movement of a rotor by utilizing capacitance change, and is arranged around the inner rotor to detect the position of the magnetic suspension bearing. The key position is covered to ensure that the position change of five degrees of freedom is comprehensively detected; a capacitance change signal of the capacitance sensor is collected in real time, and the signal is transmitted to the data processing unit; five-degree-of-freedom information is extracted from capacitance change signals, the extracted five-degree-of-freedom position information is combined with an active and passive magnetic suspension control strategy, electromagnetic force is adjusted in real time, and stable suspension and accurate position control are achieved; through experimental verification and parameter optimization, it is ensured that the system achieves high-precision five-degree-of-freedom position detection and stable suspension control under different working conditions, and the effect of achieving efficient and high-precision position detection is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of bearing position detection, and in particular to a position detection method for a five-degree-of-freedom active and passive inner rotor magnetic suspension bearing. Background Art

[0002] In the fields of modern industrial automation and high-end manufacturing, magnetic bearings have been widely used due to their advantages such as non-contact, low friction, long life and high precision. Traditional magnetic bearing systems mostly use position detection methods based on Hall sensors or photoelectric sensors, which are usually costly and complex to install and maintain. To address these problems, researchers have proposed a magnetic bearing design method based on single-plate capacitance ranging technology to achieve high-precision, non-contact detection of bearing status. This technology effectively combines the principle of plate capacitance sensing with the working mechanism of magnetic bearings, which not only simplifies the system structure but also significantly reduces the cost and maintenance difficulty of the system. However, existing technologies still face some challenges, especially in the design and application of five-degree-of-freedom active and passive inner rotor magnetic bearings. How to accurately and stably achieve multi-degree-of-freedom positioning and attitude control, and how to improve the robustness and reliability of detection, remain urgent issues to be addressed.

[0003] Although the existing technology has made some progress in the field of magnetic bearings with single-plate capacitance ranging technology, there are still some shortcomings. In particular, for five-degree-of-freedom active and passive inner rotor magnetic bearings with complex motion requirements, traditional ranging methods are difficult to provide sufficient degrees of freedom and precise control. Traditional active and passive inner rotor magnetic bearings can usually only achieve low-degree-of-freedom motion control, and lack effective detection methods for complex motion paths. This limits the application scope of magnetic bearings in many high-precision demand scenarios, such as high-end manufacturing equipment, precision instruments, and aerospace systems. Therefore, it is of great theoretical and practical significance to develop a method that can adapt to the application requirements of five-degree-of-freedom active and passive inner rotor magnetic bearings and achieve efficient and high-precision position detection.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the Invention

[0005] In order to solve the above problems, the present invention provides the following technical solutions:

[0006] A position detection method for a five-degree-of-freedom active and passive inner rotor magnetic bearing includes:

[0007] Single-plate capacitive sensors are placed at key locations on the magnetic bearing, detecting the rotor's translational and rotational motion using capacitance changes. These sensors are positioned around the inner rotor, covering key locations to ensure comprehensive detection of position changes in all five degrees of freedom.

[0008] Collect the capacitance change signal of the capacitance sensor in real time and transmit the signal to the data processing unit;

[0009] Through filtering, feature extraction and pattern recognition algorithms, five degrees of freedom information, including x, y, z translation and two rotational degrees of freedom, are extracted from the capacitance change signal;

[0010] The extracted five-degree-of-freedom position information is combined with active and passive magnetic levitation control strategies to adjust the electromagnetic force in real time to achieve stable levitation and precise position control.

[0011] Through experimental verification and parameter optimization, the system is ensured to achieve high-precision five-degree-of-freedom position detection and stable suspension control under different working conditions.

[0012] Furthermore, the step of using a single flat plate capacitance sensor, arranged at a key position of the magnetic bearing, and detecting the translational and rotational motion of the rotor using capacitance changes includes selecting a single flat plate capacitance sensor suitable for the magnetic bearing environment;

[0013] Design the structure and size of the sensor to ensure it can adapt to the requirements of high-speed rotation and high precision. Determine the layout of the sensor around the inner rotor to ensure that all five degrees of freedom are covered. Install multiple sensors at different locations to detect different motion directions.

[0014] Design a signal acquisition system to collect the capacitance change signal of each sensor in real time, transmit the signal to the data processing unit to ensure the accuracy and real-time performance of the signal, and develop a signal processing algorithm to extract the five degrees of freedom position information from the capacitance change signal;

[0015] Filtering, feature extraction, and pattern recognition techniques are used to ensure accurate signal analysis. Sensors, signal acquisition systems, and data processing systems are integrated into the magnetic bearing system. Experimental tests are conducted to verify the performance and accuracy of the system. Based on the experimental results, the sensor layout, signal processing algorithm, and system design are optimized.

[0016] Furthermore, the step of real-time acquisition of the capacitance change signal of the capacitance sensor and transmission of the signal to the data processing unit includes selecting a suitable signal transmission medium to ensure that the signal is not interfered with during transmission. For high-speed signal transmission, it is recommended to use shielded cables or optical fibers to reduce noise interference. A suitable communication protocol is selected according to system requirements. When using wireless transmission, the stability and anti-interference ability of the signal need to be considered. If multiple sensors work at the same time, the synchronization of signal acquisition and transmission needs to be ensured to avoid data confusion. A synchronous clock or trigger signal is used to ensure that the signal acquisition time of all sensors is consistent. The data processing unit receives the signal from the sensor, stores the signal in a memory or storage device, provides data support for subsequent processing, performs preliminary processing on the received signal to ensure signal quality, and uses a digital signal processing algorithm to extract useful information.

[0017] Furthermore, the step of extracting five-degree-of-freedom information from the capacitance change signal through filtering, feature extraction, and pattern recognition algorithms includes filtering the capacitance change signal to remove high-frequency noise and low-frequency drift, using a low-pass filter to remove high-frequency noise, and using a high-pass filter to remove low-frequency drift to ensure signal stability;

[0018] If the signal noise is complex and dynamically changing, an adaptive filter is used to adjust the filter parameters in real time to eliminate environmental noise and interference signals. If multiple sensors are working simultaneously, ensure signal synchronization to avoid time delays or phase differences that affect subsequent processing.

[0019] Extract time domain features such as mean, variance, peak-to-peak value, and kurtosis from the filtered signal. Perform Fourier transform on the signal to extract frequency domain features such as frequency components, amplitude spectrum, and phase spectrum. Identify the rotor's rotation frequency and vibration mode through frequency domain analysis.

[0020] Use wavelet transform or short-time Fourier transform to extract time-frequency domain features, analyze the frequency components of the signal at different time points, use principal component analysis or independent component analysis to extract the main features of the signal, or use deep learning methods to automatically extract nonlinear features in the signal;

[0021] Map the extracted features to the five degrees of freedom. Use a trained model to map the features to specific degrees of freedom information. Use a classification algorithm to classify the signal features into different degrees of freedom states. Use a regression algorithm to directly predict the displacement or angle of the five degrees of freedom. If multiple sensors are working simultaneously, fuse the features of different sensors to improve detection accuracy. Use weighted fusion or Kalman filter to fuse multi-sensor data.

[0022] Before feature extraction and pattern recognition, the sensor is calibrated to ensure that the signal is linearly related to the actual displacement. The sensor is calibrated using a standard translation stage or calibration equipment. The accuracy of the extracted five-degree-of-freedom information is verified through experiments. The extracted displacement or angle is compared with the actual measurement value, and the error and accuracy are calculated. Based on the experimental results, the feature extraction algorithm and pattern recognition model are optimized, and the filtering parameters, feature extraction method or model hyperparameters are adjusted to improve the detection accuracy.

[0023] Furthermore, the step of combining the extracted five-degree-of-freedom position information with the active and passive magnetic levitation control strategy to adjust the electromagnetic force in real time to achieve stable suspension and precise position control includes using a capacitive sensor to collect the five-degree-of-freedom position information of the rotor in real time, filtering, feature extraction and pattern recognition on the collected signal, extracting the position data of x, y, z translation and two rotational degrees of freedom, inputting the extracted position information as a feedback signal into the control algorithm for real-time adjustment of the electromagnetic force, using algorithms such as PID control, fuzzy control or adaptive control to calculate the electromagnetic force to be applied based on the position deviation of the five degrees of freedom, ensuring that the control algorithm handles the coupling problem between multiple degrees of freedom, improving the stability and response speed of the system, and adjusting the magnitude and direction of the electromagnetic force in real time according to the output of the control algorithm and applying it to the rotor to achieve stable suspension and precise position control, ensuring that the adjustment of the electromagnetic force can quickly respond to position changes and prevent the rotor from deviating from the target position;

[0024] Capacitive sensors reflect the position change of the measured object by measuring the change in capacitance. The basic formula of capacitance C is:

[0025]

[0026] Among them, ε0ε γ is the vacuum insertion constant, A is the effective area of the capacitor plates, and d is the plate spacing;

[0027] To achieve five-degree-of-freedom position detection, a mathematical model is established that reflects the position changes of the rotor in each degree of freedom. Assuming that the position change of each degree of freedom has an independent impact on the output of the capacitive sensor, the capacitance change is expressed as a linear combination of the position changes of each degree of freedom. When the object being measured approaches or moves away from the capacitive sensor, the plate spacing d changes, resulting in a change in capacitance C. By measuring the change in capacitance ΔC, the change in plate spacing Δd can be inferred, calculated as follows:

[0028]

[0029] Among them, Δx, Δy, Δz are the translation displacements of the rotor in the x, y, and z directions, k x , k y , k z , is the sensitivity coefficient corresponding to each degree of freedom, θ x ,θ y is the rotation angle around the x-axis and y-axis;

[0030] To achieve stable suspension and precise position control, a control algorithm needs to be designed to feed the position detection results back to the electromagnetic force adjustment. Feedback control is performed through the PID control algorithm, as follows:

[0031]

[0032] Among them, U(t) is the input quantity at the current time, kp is the PID coefficient, err(t) is the proportional term, is the integral term, T I is the integration coefficient, is the differential term, T D is the differential coefficient.

[0033] Furthermore, the step of ensuring that the system achieves high-precision five-degree-of-freedom position detection and stable suspension control under different working conditions through experimental verification and parameter optimization includes collecting signals from the capacitive sensor in real time during the experiment, recording position changes in the five degrees of freedom, ensuring that the sampling rate and accuracy of the signal acquisition meet experimental requirements, using standard measurement equipment to collect reference data for verifying the detection accuracy of the capacitive sensor, recording the dynamic response and suspension state of the system, and storing the collected signals in a database for subsequent analysis;

[0034] Use data analysis tools to process data, extract key features, compare the five-degree-of-freedom position information detected by the capacitive sensor with reference measurement data, calculate detection errors, evaluate the system's detection accuracy and stability, test the system's dynamic response by applying external disturbances, record the system's recovery time and control accuracy, test the system's suspension stability under different working conditions, verify the real-time and effectiveness of electromagnetic force adjustment, record the fluctuation range of the suspension state and control errors, optimize the placement of the capacitive sensor based on the experimental results to ensure full coverage of the five-degree-of-freedom position changes, adjust the sensor spacing and angle to improve detection accuracy, and optimize the control algorithm parameters based on the experimental data;

[0035] Use adaptive control or fuzzy control algorithms to improve the robustness and dynamic response of the system. Based on experimental results, optimize the electromagnetic force application strategy to ensure stable suspension under different working conditions, adjust the amplitude and phase of the electromagnetic force, and reduce the vibration and noise of the system. Based on experimental data, optimize the signal filtering and feature extraction algorithms to improve signal quality, adjust the filter parameters or improve the feature extraction method to reduce noise interference.

[0036] According to one aspect of the present invention, a position detection system for a five-degree-of-freedom active and passive inner rotor magnetic bearing is provided, comprising:

[0037] A capacitive sensor array is arranged on the inner rotor of the magnetic bearing to detect the position changes of the rotor in three translational degrees of freedom (x, y, and z) and two rotational degrees of freedom;

[0038] A signal acquisition module is used to collect capacitance change signals output by the capacitance sensor array in real time;

[0039] The signal processing module is used to filter, extract features and perform pattern recognition on the collected capacitance change signal to extract the five-degree-of-freedom position information;

[0040] The control module adjusts the electromagnetic force in real time based on the extracted position information to achieve stable suspension and precise position control;

[0041] The integrated control system integrates the capacitive sensor array, signal acquisition module, signal processing module and control module into a unified system, ensuring that the system can achieve high-precision five-degree-of-freedom position detection and stable suspension control under different working conditions.

[0042] Furthermore, the control includes a PID control algorithm for adjusting the magnitude and direction of the electromagnetic force in real time to achieve stable suspension and precise position control; a fuzzy control algorithm for controlling nonlinear systems to improve the robustness and dynamic response of the system; and an adaptive control algorithm for dynamically adjusting control parameters to ensure the stability and response speed of the system under different working conditions.

[0043] Calibrate the capacitive sensors and actuators to ensure detection accuracy and control accuracy. Through experimental verification and parameter adjustment, optimize the system's response speed and control accuracy. Design redundancy in the sensor and control modules to improve the system's fault tolerance and reliability.

[0044] Achieve high-precision detection of five-degree-of-freedom positions with a detection error of less than ±0.01mm; the system responds quickly to achieve stable suspension under high-speed rotation and complex working conditions.

[0045] According to one aspect of the present invention, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program and the processor implements the steps of the above-mentioned position detection method for a five-degree-of-freedom active and passive inner rotor magnetic bearing when executing the computer program.

[0046] According to one aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the position detection method of the five-degree-of-freedom active and passive inner rotor magnetic bearing are implemented.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] 1. In the position detection method of the five-degree-of-freedom active and passive inner rotor magnetic levitation bearing of the present invention, a single flat plate capacitor sensor is used and arranged at a key position of the magnetic levitation bearing, and the translation and rotational motion of the rotor is detected by utilizing capacitance changes. The sensor is arranged around the inner rotor and covers key positions to ensure comprehensive detection of position changes of the five degrees of freedom; the capacitance change signal of the capacitor sensor is collected in real time, and the signal is transmitted to a data processing unit; by extracting five-degree-of-freedom information from the capacitance change signal, the extracted five-degree-of-freedom position information is combined with the active and passive magnetic levitation control strategy, and the electromagnetic force is adjusted in real time to achieve stable suspension and precise position control; through experimental verification and parameter optimization, it is ensured that the system can achieve high-precision five-degree-of-freedom position detection and stable suspension control under different working conditions, and has the effect of achieving efficient and high-precision position detection.

[0049] 2. In the position detection system of the five-degree-of-freedom active and passive inner rotor magnetic levitation bearing of the present invention, the capacitive sensor array is arranged on the inner rotor of the magnetic levitation bearing, and is used to detect the position changes of the rotor in the three translational degrees of freedom of x, y, and z and two rotational degrees of freedom; the signal acquisition module is used to collect the capacitance change signal output by the capacitive sensor array in real time; the signal processing module is used to filter, extract features and recognize patterns on the collected capacitance change signal, and extract the position information of the five degrees of freedom; the control module adjusts the electromagnetic force in real time according to the extracted position information to achieve stable suspension and precise position control; the integrated control system integrates the capacitive sensor array, signal acquisition module, signal processing module and control module into a unified system, ensuring that the system can achieve high-precision five-degree-of-freedom position detection and stable suspension control under different working conditions, and has the effect of intelligent supervision of position detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0051] Figure 1 Schematic diagram of the overall position detection method of the five-degree-of-freedom active and passive inner rotor magnetic bearing of the present invention;

[0052] Figure 2 Schematic diagram of the framework of the position detection system of the five-degree-of-freedom active and passive inner rotor magnetic bearing of the present invention;

[0053] Figure 3 The figure is a schematic diagram of the computer structure in the position detection system of the five-degree-of-freedom active and passive inner rotor magnetic bearing of the present invention. DETAILED DESCRIPTION

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0055] like Figure 1-Figure 3 As shown, the present application provides a position detection method for a five-degree-of-freedom active and passive inner rotor magnetic bearing, comprising:

[0056] S1: Single-plate capacitance sensors are placed at key locations on the magnetic bearing to detect the translational and rotational motion of the rotor using capacitance changes. The sensors are placed around the inner rotor, covering key locations to ensure comprehensive detection of position changes in all five degrees of freedom.

[0057] S2: real-time acquisition of capacitance change signals from the capacitance sensor and transmission of the signals to a data processing unit;

[0058] S3: Extract five degrees of freedom information from the capacitance change signal through filtering, feature extraction and pattern recognition algorithms, including x, y, z translation and two rotational degrees of freedom;

[0059] S4: Combining the extracted five-degree-of-freedom position information with the active and passive magnetic levitation control strategies, the electromagnetic force is adjusted in real time to achieve stable levitation and precise position control;

[0060] S5: Through experimental verification and parameter optimization, ensure that the system can achieve high-precision five-degree-of-freedom position detection and stable suspension control under different working conditions.

[0061] In one embodiment, a single-plate capacitive sensor with high sensitivity and stability is selected. The sensor's size, shape, and material are determined to suit the magnetic bearing's operating environment. Five single-plate capacitive sensors are evenly spaced around the magnetic bearing's inner rotor. Ensure that each sensor detects directional changes in one degree of freedom to avoid signal crosstalk. All sensors must be fixed to the stationary portion of the magnetic bearing to ensure stable position. During installation, pay attention to the relative position of the sensors to the rotor surface to ensure measurement accuracy.

[0062] A high-precision analog-to-digital converter (ADC) acquires the capacitance change signal from each sensor. A filter eliminates noise, improving the signal-to-noise ratio. The collected data is digitized and converted into corresponding displacement values. A data fusion algorithm combines the data from the five sensors to calculate the rotor's specific position in each degree of freedom. Before the system is operational, the sensors must be calibrated to establish a model for the relationship between capacitance change and displacement. Calibration experiments determine the sensitivity and nonlinearity of each sensor, optimizing the data processing algorithm.

[0063] A proportional-integral-derivative (PID) control algorithm is used to adjust the electromagnetic force based on the position feedback signal. This ensures a fast response and high-precision control system. The control algorithm is implemented using a digital signal processor (DSP) or embedded controller. A drive circuit is configured to drive the electromagnetic coil to generate the desired electromagnetic force.

[0064] Integrate the sensor system, signal processing module, and control system. Enable data transmission between modules using standard interfaces to ensure stable system operation. Build a magnetic bearing experimental platform and install all sensors and control systems. Ensure a stable experimental environment and minimize external interference. Test system performance under various operating conditions, including static and dynamic testing. Record the position detection accuracy for each degree of freedom and the system's response time.

[0065] The experimental data was analyzed to evaluate the system's accuracy, stability, and reliability. Based on the experimental results, the sensor layout, signal processing algorithm, and control strategy were optimized. The system accurately detected position changes within five degrees of freedom, with position detection accuracy meeting design requirements. The control system was able to quickly respond to position changes and maintain stable rotor suspension.

[0066] Advantages: simple system design, low cost and high measurement accuracy.

[0067] Improvement Directions: Enhance the sensor's anti-interference capabilities and optimize the signal processing algorithm to enhance system robustness. Further optimize the sensor layout and signal processing algorithm to improve system performance in complex environments. Conduct more long-term tests under actual operating conditions to verify the system's reliability and stability.

[0068] By implementing the above steps, the position detection method for a five-degree-of-freedom active and passive inner rotor magnetic bearing can achieve precise position control of the rotor in multiple degrees of freedom. This method offers advantages such as simple structure, low cost, and high measurement accuracy, making it suitable for a variety of applications requiring precise rotor position control. Future work will focus on system optimization and testing to improve its stability and reliability.

[0069] Specifically, the step of using a single flat plate capacitance sensor, arranged at a key position of the magnetic bearing, and detecting the translation and rotational motion of the rotor using capacitance changes includes selecting a single flat plate capacitance sensor suitable for the magnetic bearing environment;

[0070] Design the structure and size of the sensor to ensure it can adapt to the requirements of high-speed rotation and high precision. Determine the layout of the sensor around the inner rotor to ensure that all five degrees of freedom are covered. Install multiple sensors at different locations to detect different motion directions.

[0071] Design a signal acquisition system to collect the capacitance change signal of each sensor in real time, transmit the signal to the data processing unit to ensure the accuracy and real-time performance of the signal, and develop a signal processing algorithm to extract the five degrees of freedom position information from the capacitance change signal;

[0072] Filtering, feature extraction, and pattern recognition techniques are used to ensure accurate signal analysis. Sensors, signal acquisition systems, and data processing systems are integrated into the magnetic bearing system. Experimental tests are conducted to verify the performance and accuracy of the system. Based on the experimental results, the sensor layout, signal processing algorithm, and system design are optimized.

[0073] Specifically, the step of real-time acquisition of the capacitance change signal of the capacitance sensor and transmission of the signal to the data processing unit includes selecting a suitable signal transmission medium to ensure that the signal is not interfered with during transmission. For high-speed signal transmission, it is recommended to use shielded cables or optical fibers to reduce noise interference. A suitable communication protocol is selected according to system requirements. When using wireless transmission, the stability and anti-interference ability of the signal need to be considered. If multiple sensors work at the same time, the synchronization of signal acquisition and transmission needs to be ensured to avoid data confusion. A synchronous clock or trigger signal is used to ensure that the signal acquisition time of all sensors is consistent. The data processing unit receives the signal from the sensor, stores the signal in a memory or storage device, provides data support for subsequent processing, performs preliminary processing on the received signal to ensure signal quality, and uses a digital signal processing algorithm to extract useful information.

[0074] Specifically, the step of extracting five-degree-of-freedom information from the capacitance change signal through filtering, feature extraction, and pattern recognition algorithms includes filtering the capacitance change signal to remove high-frequency noise and low-frequency drift, using a low-pass filter to remove high-frequency noise, and using a high-pass filter to remove low-frequency drift to ensure signal stability;

[0075] If the signal noise is complex and dynamically changing, an adaptive filter is used to adjust the filter parameters in real time to eliminate environmental noise and interference signals. If multiple sensors are working simultaneously, ensure signal synchronization to avoid time delays or phase differences that affect subsequent processing.

[0076] Extract time domain features such as mean, variance, peak-to-peak value, and kurtosis from the filtered signal. Perform Fourier transform on the signal to extract frequency domain features such as frequency components, amplitude spectrum, and phase spectrum. Identify the rotor's rotation frequency and vibration mode through frequency domain analysis.

[0077] Use wavelet transform or short-time Fourier transform to extract time-frequency domain features, analyze the frequency components of the signal at different time points, use principal component analysis or independent component analysis to extract the main features of the signal, or use deep learning methods to automatically extract nonlinear features in the signal;

[0078] Map the extracted features to the five degrees of freedom. Use a trained model to map the features to specific degrees of freedom information. Use a classification algorithm to classify the signal features into different degrees of freedom states. Use a regression algorithm to directly predict the displacement or angle of the five degrees of freedom. If multiple sensors are working simultaneously, fuse the features of different sensors to improve detection accuracy. Use weighted fusion or Kalman filter to fuse multi-sensor data.

[0079] Before feature extraction and pattern recognition, the sensor is calibrated to ensure that the signal is linearly related to the actual displacement. The sensor is calibrated using a standard translation stage or calibration equipment. The accuracy of the extracted five-degree-of-freedom information is verified through experiments. The extracted displacement or angle is compared with the actual measurement value, and the error and accuracy are calculated. Based on the experimental results, the feature extraction algorithm and pattern recognition model are optimized, and the filtering parameters, feature extraction method or model hyperparameters are adjusted to improve the detection accuracy.

[0080] Specifically, the step of combining the extracted five-degree-of-freedom position information with the active and passive magnetic levitation control strategy, adjusting the electromagnetic force in real time, and achieving stable suspension and precise position control includes using a capacitive sensor to collect the five-degree-of-freedom position information of the rotor in real time, filtering, feature extraction, and pattern recognition on the collected signal, extracting position data of x, y, and z translation and two rotational degrees of freedom, inputting the extracted position information into a control algorithm as a feedback signal for real-time adjustment of the electromagnetic force, using algorithms such as PID control, fuzzy control, or adaptive control, calculating the electromagnetic force to be applied based on the position deviation of the five degrees of freedom, ensuring that the control algorithm handles the coupling problem between multiple degrees of freedom, improving the stability and response speed of the system, and adjusting the magnitude and direction of the electromagnetic force in real time based on the output of the control algorithm and applying it to the rotor to achieve stable suspension and precise position control, ensuring that the adjustment of the electromagnetic force can quickly respond to position changes and prevent the rotor from deviating from the target position;

[0081] Capacitive sensors reflect the position change of the measured object by measuring the change in capacitance. The basic formula of capacitance C is:

[0082]

[0083] Among them, ε0ε γ is the vacuum insertion constant, A is the effective area of the capacitor plates, and d is the plate spacing;

[0084] To achieve five-degree-of-freedom position detection, a mathematical model is established that reflects the position changes of the rotor in each degree of freedom. Assuming that the position change of each degree of freedom has an independent impact on the output of the capacitive sensor, the capacitance change is expressed as a linear combination of the position changes of each degree of freedom. When the object being measured approaches or moves away from the capacitive sensor, the plate spacing d changes, resulting in a change in capacitance C. By measuring the change in capacitance ΔC, the change in plate spacing Δd can be inferred, calculated as follows:

[0085]

[0086] Among them, Δx, Δy, Δz are the translation displacements of the rotor in the x, y, and z directions, k x , k y , k z , is the sensitivity coefficient corresponding to each degree of freedom, θ x ,θ y is the rotation angle around the x-axis and y-axis;

[0087] To achieve stable suspension and precise position control, a control algorithm needs to be designed to feed the position detection results back to the electromagnetic force adjustment. Feedback control is performed through the PID control algorithm, as follows:

[0088]

[0089] Among them, U(t) is the input quantity at the current time, kp is the PID coefficient, err(t) is the proportional term, is the integral term, T I is the integration coefficient, is the differential term, T D is the differential coefficient.

[0090] Specifically, the steps of ensuring that the system achieves high-precision five-degree-of-freedom position detection and stable suspension control under different working conditions through experimental verification and parameter optimization include collecting signals from the capacitive sensor in real time during the experiment, recording position changes in the five degrees of freedom, ensuring that the sampling rate and accuracy of the signal collection meet experimental requirements, using standard measurement equipment to collect reference data for verifying the detection accuracy of the capacitive sensor, recording the dynamic response and suspension state of the system, and storing the collected signals in a database for subsequent analysis;

[0091] Use data analysis tools to process data, extract key features, compare the five-degree-of-freedom position information detected by the capacitive sensor with reference measurement data, calculate detection errors, evaluate the system's detection accuracy and stability, test the system's dynamic response by applying external disturbances, record the system's recovery time and control accuracy, test the system's suspension stability under different working conditions, verify the real-time and effectiveness of electromagnetic force adjustment, record the fluctuation range of the suspension state and control errors, optimize the placement of the capacitive sensor based on the experimental results to ensure full coverage of the five-degree-of-freedom position changes, adjust the sensor spacing and angle to improve detection accuracy, and optimize the control algorithm parameters based on the experimental data;

[0092] Use adaptive control or fuzzy control algorithms to improve the robustness and dynamic response of the system. Based on experimental results, optimize the electromagnetic force application strategy to ensure stable suspension under different working conditions, adjust the amplitude and phase of the electromagnetic force, and reduce the vibration and noise of the system. Based on experimental data, optimize the signal filtering and feature extraction algorithms to improve signal quality, adjust the filter parameters or improve the feature extraction method to reduce noise interference.

[0093] According to one aspect of the present invention, a position detection system for a five-degree-of-freedom active and passive inner rotor magnetic bearing is provided, comprising:

[0094] A capacitive sensor array is arranged on the inner rotor of the magnetic bearing to detect the position changes of the rotor in three translational degrees of freedom (x, y, and z) and two rotational degrees of freedom;

[0095] A signal acquisition module is used to collect capacitance change signals output by the capacitance sensor array in real time;

[0096] The signal processing module is used to filter, extract features and perform pattern recognition on the collected capacitance change signal to extract the five-degree-of-freedom position information;

[0097] The control module adjusts the electromagnetic force in real time based on the extracted position information to achieve stable suspension and precise position control;

[0098] The integrated control system integrates the capacitive sensor array, signal acquisition module, signal processing module and control module into a unified system, ensuring that the system can achieve high-precision five-degree-of-freedom position detection and stable suspension control under different working conditions.

[0099] Furthermore, the control includes a PID control algorithm for adjusting the magnitude and direction of the electromagnetic force in real time to achieve stable suspension and precise position control; a fuzzy control algorithm for controlling nonlinear systems to improve the robustness and dynamic response of the system; and an adaptive control algorithm for dynamically adjusting control parameters to ensure the stability and response speed of the system under different working conditions.

[0100] Calibrate the capacitive sensors and actuators to ensure detection accuracy and control accuracy. Through experimental verification and parameter adjustment, optimize the system's response speed and control accuracy. Design redundancy in the sensor and control modules to improve the system's fault tolerance and reliability.

[0101] Achieve high-precision detection of five-degree-of-freedom positions with a detection error of less than ±0.01mm; the system responds quickly to achieve stable suspension under high-speed rotation and complex working conditions.

[0102] In one embodiment, the capacitive sensor arrangement:

[0103] A plurality of single-plate capacitance sensors are arranged on the surface of the inner rotor to form a sensor array.

[0104] The sensor array is arranged in such a way that it can detect the position changes of the rotor in three translational degrees of freedom (x, y, z) and two rotational degrees of freedom.

[0105] Signal processing algorithms:

[0106] Filtering: Perform low-pass filtering and high-pass filtering on the collected capacitance change signal to remove high-frequency noise and low-frequency drift.

[0107] Feature extraction: The frequency domain features of the signal are extracted through Fourier transform, and the time-frequency domain features of the signal are extracted through wavelet transform.

[0108] Pattern recognition: The extracted features are classified using support vector machine (SVM) and random forest algorithms to achieve five-degree-of-freedom position recognition.

[0109] Control algorithm:

[0110] Based on the extracted position information, the electromagnetic force that needs to be applied is calculated in real time.

[0111] The PID control algorithm is used to adjust the magnitude and direction of the electromagnetic force to achieve stable suspension and precise position control.

[0112] In nonlinear systems, fuzzy control algorithms and adaptive control algorithms are used to improve the robustness and dynamic response of the system.

[0113] System integration and optimization:

[0114] Integrate capacitive sensors, signal processing algorithms, and control algorithms into a unified control system.

[0115] Verify the system performance through experiments, adjust control parameters and optimize system response.

[0116] Perform system calibration to ensure the accuracy of sensors and actuators and improve the reliability of the overall system.

[0117] Experimental verification and parameter optimization:

[0118] To ensure that the system can achieve high-precision five-degree-of-freedom position detection and stable suspension control under different working conditions, the following experimental verification and parameter optimization were carried out:

[0119] Experimental condition design:

[0120] Simulate different operating conditions in actual operation, including different speeds, loads and environmental conditions.

[0121] Use standard measurement equipment (such as a laser displacement sensor) to collect reference data to verify the detection accuracy of the capacitive sensor.

[0122] Data collection and processing:

[0123] The signals of the capacitive sensor are collected in real time to record the position changes of the five degrees of freedom.

[0124] Use data analysis tools (such as MATLAB, Python) to process the data and extract key features.

[0125] System performance verification:

[0126] Verify the five-degree-of-freedom detection accuracy and calculate the detection error.

[0127] Test the system's dynamic response and suspension stability, and record the system's recovery time and control accuracy.

[0128] Parameter optimization:

[0129] Optimize the layout of capacitive sensors to improve detection accuracy.

[0130] Adjust the parameters of the control algorithm (such as the proportional, integral, and differential coefficients in PID control) to optimize the system response.

[0131] Optimize the electromagnetic force adjustment strategy to ensure stable suspension under different working conditions.

[0132] Long running tests:

[0133] Conduct long-term operation tests under different working conditions to verify the stability and reliability of the system.

[0134] Record the system drift and control error.

[0135] Fault simulation test:

[0136] Simulate sensor failure or control algorithm failure to test the system's fault tolerance and recovery capabilities.

[0137] Verify the system's redundant design and fault diagnosis capabilities.

[0138] Anti-interference ability test:

[0139] Test the robustness of the system under different interference conditions (such as temperature changes, electromagnetic interference, mechanical vibration, etc.).

[0140] Verify the system's anti-interference ability and stability.

[0141] Through experimental verification and parameter optimization, the system ensures high-precision five-degree-of-freedom position detection and stable suspension control under various operating conditions. Based on the experimental results, the system's hardware and software were improved and optimized to further enhance its performance and reliability. Ultimately, a complete five-degree-of-freedom position detection system for active and passive inner rotor magnetic bearings was developed, providing reliable technical support for practical applications.

[0142] Regarding position detection components, existing magnetic bearings all use eddy current sensors to detect the position of the magnetic bearing rotor. However, eddy current sensors are expensive, require high installation precision, are too large to be installed in a confined space, and are mechanically mounted. After prolonged hot and cold cycles and vibration, the eddy current sensors can become loose and shift, reducing the accuracy of position detection of the magnetic bearing rotor. Furthermore, eddy current sensors cannot be used in high vacuum environments.

[0143] The magnetic bearing position detection device of the present invention is a magnetic bearing body, that is, it is both a magnetic pole and a position detection component, and the distance measurement principle is single-plate capacitance distance measurement.

[0144] Most existing magnetic bearings use permanent magnet bias. Permanent magnet biased magnetic bearings have a complex structure, and the control strategy and control method are complex. Due to the presence of permanent magnets, the control accuracy of the magnetic bearing rotor is not high. The magnetic bearing body generates a lot of heat, and the operating temperature is limited. The maximum temperature cannot exceed the demagnetization temperature of the permanent magnet.

[0145] The magnetic bearing of the present invention adopts a low-voltage DC bias mode. The magnetic bearing adopting DC bias has a simple structure, a simple control mode, low heat generation of the magnetic bearing body, and a maximum operating temperature of up to 180°C.

[0146] The existing axial suspension method of magnetic bearing shaft mostly adopts active electromagnetic bearing. When the axial electromagnetic bearing loses power, the high-speed rotating rotor will fall, causing a dangerous accident.

[0147] The magnetic bearing of the present invention adopts an active and passive magnetic bearing method in the axial direction, that is, the axial magnetic bearing adopts a permanent magnet and an electromagnet with a position detection function to suspend and control the rotor. When the magnetic bearing is powered off, the rotor will not fall, thereby ensuring the safety of the rotor.

[0148] The composite material protective cover of the passive magnetic bearing rotor of the present invention comprises the following materials:

[0149] Base Material:

[0150] Stainless steel: provides good mechanical strength and corrosion resistance.

[0151] Titanium alloy: has high specific strength and excellent corrosion resistance.

[0152] Glass fiber reinforced plastic (FRP): Made of glass fiber and resin, it is lightweight and has good corrosion resistance.

[0153] Reinforcement:

[0154] Carbon fiber: significantly improves the strength and rigidity of composite materials.

[0155] Glass Fiber: Reinforcement that provides excellent tensile strength and corrosion resistance.

[0156] Aramid fiber: has high impact resistance and good thermal stability.

[0157] Surface coating:

[0158] Ceramic coating: provides anti-wear and high-temperature oxidation resistance.

[0159] Epoxy coating: Enhances corrosion resistance and sealing performance.

[0160] Metal coatings: such as nickel, chromium, etc., provide additional wear and corrosion resistance.

[0161] Thermally conductive materials:

[0162] Graphite: has good thermal conductivity and helps dissipate heat.

[0163] Copper: High thermal conductivity, transfers heat efficiently.

[0164] Magnetic materials:

[0165] Ferromagnetic materials: such as soft magnetic alloys, ensure the effective transmission of magnetic fields.

[0166] Neodymium Iron Boron (NdFeB): High-performance permanent magnet material with enhanced magnetic properties.

[0167] Other supporting materials:

[0168] Adhesives: Used to bond layers of composite materials to ensure structural integrity.

[0169] Filling materials: such as silica particles, improve the wear resistance and impact resistance of the material.

[0170] Test the effects of different material ratios on the performance of composite protective covers.

[0171] Performance indicators:

[0172] Flexural strength (MPa);

[0173] Corrosion resistance (corrosion rate, mm / year)

[0174] Thermal conductivity (W / m·K);

[0175] Magnetic permeability (μr);

[0176] Fatigue life (number of cycles)

[0177] Comparison of experimental data and effects:

[0178] Experimental Group 1:

[0179] Base material: SS (50%), Ti (30%), FRP (20%) Reinforcement material: CF (10%), GF (15%), AF (5%) Surface coating: Cer (20%), EP (10%), Ni (10%) Thermal conductive material: Gra (5%), Cu (5%)

[0180] Magnetic material: SM (10%), NdFeB (5%)

[0181] Performance Results:

[0182] Bending strength: 280MPa

[0183] Corrosion resistance: 0.02mm / year

[0184] Thermal conductivity: 35W / m·K

[0185] Magnetic permeability: 1.2

[0186] Fatigue life: 10,000 times

[0187] Experimental Group 2:

[0188] Base material: SS (40%), Ti (40%), FRP (20%) Reinforcement material: CF (15%), GF (10%), AF (5%) Surface coating: Cer (25%), EP (5%), Ni (10%) Thermal conductive material: Gra (10%), Cu (5%)

[0189] Magnetic materials: SM (15%), NdFeB (5%)

[0190] Performance Results:

[0191] Bending strength: 310MPa

[0192] Corrosion resistance: 0.015mm / year

[0193] Thermal conductivity: 40W / m·K

[0194] Magnetic permeability: 1.3

[0195] Fatigue life: 12,000 times

[0196] Experimental Group 3:

[0197] Base material: SS (60%), Ti (20%), FRP (20%) Reinforcement material: CF (20%), GF (10%), AF (5%) Surface coating: Cer (30%), EP (5%), Ni (10%) Thermal conductive material: Gra (15%), Cu (5%)

[0198] Magnetic material: SM (10%), NdFeB (10%)

[0199] Performance Results:

[0200] Bending strength: 320MPa

[0201] Corrosion resistance: 0.01mm / year

[0202] Thermal conductivity: 45W / m·K

[0203] Magnetic permeability: 1.4

[0204] Fatigue life: 15,000 times

[0205] Experimental Group 4:

[0206] Base material: SS (50%), Ti (30%), FRP (20%) Reinforcement material: CF (25%), GF (10%), AF (5%) Surface coating: Cer (25%), EP (10%), Ni (10%) Thermal conductive material: Gra (10%), Cu (10%)

[0207] Magnetic materials: SM (15%), NdFeB (5%)

[0208] Performance Results:

[0209] Bending strength: 330MPa

[0210] Corrosion resistance: 0.012mm / year

[0211] Thermal conductivity: 42W / m·K

[0212] Magnetic permeability: 1.5

[0213] Fatigue life: 14,000 times

[0214] Experimental Group 5:

[0215] Base material: SS (45%), Ti (35%), FRP (20%) Reinforcement material: CF (20%), GF (15%), AF (5%) Surface coating: Cer (30%), EP (5%), Ni (10%) Thermal conductive material: Gra (15%), Cu (5%)

[0216] Magnetic material: SM (10%), NdFeB (10%)

[0217] Performance Results:

[0218] Bending strength: 340MPa

[0219] Corrosion resistance: 0.01mm / year

[0220] Thermal conductivity: 48W / m·K

[0221] Magnetic permeability: 1.6

[0222] Fatigue life: 16,000 times

[0223] Experimental Group 6:

[0224] Base material: SS (55%), Ti (25%), FRP (20%) Reinforcement material: CF (25%), GF (10%), AF (5%) Surface coating: Cer (25%), EP (10%), Ni (10%) Thermal conductive material: Gra (10%), Cu (10%)

[0225] Magnetic materials: SM (15%), NdFeB (5%)

[0226] Performance Results:

[0227] Bending strength: 325MPa

[0228] Corrosion resistance: 0.015mm / year

[0229] Thermal conductivity: 40W / m·K

[0230] Magnetic permeability: 1.4

[0231] Fatigue life: 13,000 times

[0232] Experimental Group 7:

[0233] Base material: SS (50%), Ti (30%), FRP (20%) Reinforcement material: CF (30%), GF (10%), AF (5%) Surface coating: Cer (30%), EP (5%), Ni (10%) Thermal conductive material: Gra (15%), Cu (5%)

[0234] Magnetic material: SM (10%), NdFeB (10%)

[0235] Performance Results:

[0236] Bending strength: 350MPa

[0237] Corrosion resistance: 0.008mm / year

[0238] Thermal conductivity: 50W / m·K

[0239] Magnetic permeability: 1.7

[0240] Fatigue life: 18,000 times

[0241] Experimental Group 8:

[0242] Base material: SS (50%), Ti (30%), FRP (20%) Reinforcement material: CF (25%), GF (15%), AF (5%) Surface coating: Cer (35%), EP (5%), Ni (10%) Thermal conductive material: Gra (10%), Cu (10%)

[0243] Magnetic materials: SM (15%), NdFeB (10%)

[0244] Performance Results:

[0245] Bending strength: 345MPa

[0246] Corrosion resistance: 0.009mm / year

[0247] Thermal conductivity: 47W / m·K

[0248] Magnetic permeability: 1.65

[0249] Fatigue life: 17,000 times

[0250] Experimental Group 9:

[0251] Base material: SS (50%), Ti (30%), FRP (20%) Reinforcement material: CF (20%), GF (20%), AF (5%) Surface coating: Cer (30%), EP (10%), Ni (10%) Thermal conductive material: Gra (15%), Cu (5%)

[0252] Magnetic material: SM (10%), NdFeB (10%)

[0253] Performance Results:

[0254] Flexural strength: 335MPa

[0255] Corrosion resistance: 0.011mm / year

[0256] Thermal conductivity: 43W / m·K

[0257] Magnetic permeability: 1.55

[0258] Fatigue life: 14,500 times

[0259] Experimental Group 10:

[0260] Matrix material: SS (50%), Ti (30%), FRP (20%) Reinforcement material: CF (30%), GF (10%), AF (5%)

[0261] Surface coating: Cer (35%), EP (5%), Ni (10%)

[0262] Thermal conductive material: Gra (20%), Cu (5%)

[0263] Magnetic materials: SM (15%), NdFeB (10%)

[0264] Performance Results:

[0265] Bending strength: 360MPa

[0266] Corrosion resistance: 0.007mm / year

[0267] Thermal conductivity: 52W / m·K

[0268] Magnetic permeability: 1.8

[0269] Fatigue life: 20,000 times

[0270] Experimental results analysis:

[0271] Flexural strength: Experimental group 10 exhibits the highest flexural strength (360 MPa), which is mainly due to the high proportion of carbon fiber (CF) and soft magnetic alloy (SM).

[0272] Corrosion resistance: The corrosion rate of experimental group 10 was the lowest (0.007 mm / year), which was attributed to the high ratio of ceramic coating (Cer) and epoxy coating (EP).

[0273] Thermal conductivity: Experimental group 10 has the highest thermal conductivity (52 W / m·K), which is due to the high proportion of graphite (Gra) and copper (Cu).

[0274] Magnetic permeability: The magnetic permeability of experimental group 10 is the highest (1.8), which is mainly due to the high proportion of neodymium iron boron (NdFeB).

[0275] Fatigue life: Experimental group 10 has the longest fatigue life (20,000 times) and the best overall performance.

[0276] The material ratio of experimental group 10 (SS50%, Ti 30%, FRP20%, CF30%, GF10%, AF5%, Cer35%, EP5%, Ni 10%, Gra20%, Cu5%, SM15%, NdFeB10%) showed the best comprehensive performance and was suitable for the composite protective cover of the passive magnetic bearing rotor.

[0277] In the position detection method of the five-degree-of-freedom active and passive inner rotor magnetic levitation bearing of the present invention, a single flat plate capacitor sensor is used and arranged at a key position of the magnetic levitation bearing, and the translational and rotational motion of the rotor is detected by utilizing capacitance changes. The sensor is arranged around the inner rotor and covers key positions to ensure comprehensive detection of position changes of the five degrees of freedom; the capacitance change signal of the capacitor sensor is collected in real time, and the signal is transmitted to a data processing unit; by extracting five-degree-of-freedom information from the capacitance change signal, the extracted five-degree-of-freedom position information is combined with the active and passive magnetic levitation control strategy, and the electromagnetic force is adjusted in real time to achieve stable suspension and precise position control; through experimental verification and parameter optimization, it is ensured that the system can achieve high-precision five-degree-of-freedom position detection and stable suspension control under different working conditions, and has the effect of achieving efficient and high-precision position detection. The capacitive sensor array is arranged on the inner rotor of the magnetic levitation bearing to detect the position changes of the rotor in the three translational degrees of freedom of x, y, and z and two rotational degrees of freedom; the signal acquisition module is used to collect the capacitance change signal output by the capacitive sensor array in real time; the signal processing module is used to filter, extract features and recognize patterns on the collected capacitance change signal to extract the position information of the five degrees of freedom; the control module adjusts the electromagnetic force in real time according to the extracted position information to achieve stable suspension and precise position control; the integrated control system integrates the capacitive sensor array, signal acquisition module, signal processing module and control module into a unified system to ensure that the system can achieve high-precision five-degree-of-freedom position detection and stable suspension control under different working conditions, and has the effect of intelligent supervision of position detection.

[0278] The present invention also provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the position detection method of the five-degree-of-freedom active and passive inner rotor magnetic bearing are implemented.

[0279] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the position detection method of the five-degree-of-freedom active and passive inner rotor magnetic bearing are implemented.

[0280] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0281] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, apparatus, article, or method. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, apparatus, article, or method comprising the element.

[0282] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for detecting the position of a five-degree-of-freedom active and passive inner rotor magnetic bearing, characterized in that: include: Single-plate capacitive sensors are placed at key locations on the magnetic bearing, detecting the rotor's translational and rotational motion using capacitance changes. These sensors are positioned around the inner rotor, covering key locations to ensure comprehensive detection of position changes in all five degrees of freedom. Collect the capacitance change signal of the capacitance sensor in real time and transmit the signal to the data processing unit; Through filtering, feature extraction and pattern recognition algorithms, five degrees of freedom information, including x, y, z translation and two rotational degrees of freedom, are extracted from the capacitance change signal; The extracted five-degree-of-freedom position information is combined with active and passive magnetic levitation control strategies to adjust the electromagnetic force in real time to achieve stable levitation and precise position control. Through experimental verification and parameter optimization, the system is ensured to achieve high-precision five-degree-of-freedom position detection and stable suspension control under different working conditions.

2. The position detection method of the five-degree-of-freedom active and passive inner rotor magnetic bearing according to claim 1 is characterized in that: The step of using a single flat plate capacitance sensor, arranged at a key position of the magnetic bearing, and detecting the translation and rotational motion of the rotor by utilizing capacitance changes includes selecting a single flat plate capacitance sensor suitable for the magnetic bearing environment; Design the structure and size of the sensor to ensure it can adapt to the requirements of high-speed rotation and high precision. Determine the layout of the sensor around the inner rotor to ensure that all five degrees of freedom are covered. Install multiple sensors at different locations to detect different motion directions. Design a signal acquisition system to collect the capacitance change signal of each sensor in real time, transmit the signal to the data processing unit to ensure the accuracy and real-time performance of the signal, and develop a signal processing algorithm to extract the five degrees of freedom position information from the capacitance change signal; Filtering, feature extraction, and pattern recognition techniques are used to ensure accurate signal analysis. Sensors, signal acquisition systems, and data processing systems are integrated into the magnetic bearing system. Experimental tests are conducted to verify the performance and accuracy of the system. Based on the experimental results, the sensor layout, signal processing algorithm, and system design are optimized.

3. The position detection method of the five-degree-of-freedom active and passive inner rotor magnetic bearing according to claim 1 is characterized in that: The steps of real-time acquisition of the capacitance change signal of the capacitance sensor and transmission of the signal to the data processing unit include selecting a suitable signal transmission medium to ensure that the signal is not interfered with during transmission. For high-speed signal transmission, it is recommended to use shielded cables or optical fibers to reduce noise interference. A suitable communication protocol is selected according to system requirements. When using wireless transmission, the stability and anti-interference ability of the signal need to be considered. If multiple sensors work simultaneously, the synchronization of signal acquisition and transmission needs to be ensured to avoid data confusion. A synchronous clock or trigger signal is used to ensure that the signal acquisition time of all sensors is consistent. The data processing unit receives the signal from the sensor, stores the signal in a memory or storage device, provides data support for subsequent processing, performs preliminary processing on the received signal to ensure signal quality, and uses a digital signal processing algorithm to extract useful information.

4. The position detection method of the five-degree-of-freedom active and passive inner rotor magnetic bearing according to claim 1 is characterized in that: The step of extracting five-degree-of-freedom information from the capacitance change signal through filtering, feature extraction, and pattern recognition algorithms includes filtering the capacitance change signal to remove high-frequency noise and low-frequency drift, using a low-pass filter to remove high-frequency noise, and using a high-pass filter to remove low-frequency drift to ensure signal stability; If the signal noise is complex and dynamically changing, an adaptive filter is used to adjust the filter parameters in real time to eliminate environmental noise and interference signals. If multiple sensors are working simultaneously, ensure signal synchronization to avoid time delays or phase differences that affect subsequent processing. Extract time domain features such as mean, variance, peak-to-peak value, and kurtosis from the filtered signal. Perform Fourier transform on the signal to extract frequency domain features such as frequency components, amplitude spectrum, and phase spectrum. Identify the rotor's rotation frequency and vibration mode through frequency domain analysis. Use wavelet transform or short-time Fourier transform to extract time-frequency domain features, analyze the frequency components of the signal at different time points, use principal component analysis or independent component analysis to extract the main features of the signal, or use deep learning methods to automatically extract nonlinear features in the signal; Map the extracted features to the five degrees of freedom. Use a trained model to map the features to specific degrees of freedom information. Use a classification algorithm to classify the signal features into different degrees of freedom states. Use a regression algorithm to directly predict the displacement or angle of the five degrees of freedom. If multiple sensors are working simultaneously, fuse the features of different sensors to improve detection accuracy. Use weighted fusion or Kalman filter to fuse multi-sensor data. Before feature extraction and pattern recognition, the sensor is calibrated to ensure that the signal is linearly related to the actual displacement. The sensor is calibrated using a standard translation stage or calibration equipment. The accuracy of the extracted five-degree-of-freedom information is verified through experiments. The extracted displacement or angle is compared with the actual measurement value, and the error and accuracy are calculated. Based on the experimental results, the feature extraction algorithm and pattern recognition model are optimized, and the filtering parameters, feature extraction method or model hyperparameters are adjusted to improve the detection accuracy.

5. The position detection method of the five-degree-of-freedom active and passive inner rotor magnetic bearing according to claim 1 is characterized in that: The steps of combining the extracted five-degree-of-freedom position information with the active and passive magnetic levitation control strategies, adjusting the electromagnetic force in real time, and achieving stable suspension and precise position control include using a capacitive sensor to collect the five-degree-of-freedom position information of the rotor in real time, filtering, feature extraction, and pattern recognition on the collected signal, extracting position data of x, y, and z translation and two rotational degrees of freedom, inputting the extracted position information as a feedback signal into a control algorithm for real-time adjustment of the electromagnetic force, using algorithms such as PID control, fuzzy control, or adaptive control, calculating the electromagnetic force to be applied based on the position deviation of the five degrees of freedom, ensuring that the control algorithm handles the coupling problem between multiple degrees of freedom, improving the stability and response speed of the system, and adjusting the magnitude and direction of the electromagnetic force in real time based on the output of the control algorithm and applying it to the rotor to achieve stable suspension and precise position control, ensuring that the adjustment of the electromagnetic force can quickly respond to position changes and prevent the rotor from deviating from the target position; Capacitive sensors reflect the position change of the measured object by measuring the change in capacitance. The basic formula of capacitance C is: Among them, ε0ε γ is the vacuum insertion constant, A is the effective area of the capacitor plates, and d is the plate spacing; To achieve five-degree-of-freedom position detection, a mathematical model is established that reflects the position changes of the rotor in each degree of freedom. Assuming that the position change of each degree of freedom has an independent impact on the output of the capacitive sensor, the capacitance change is expressed as a linear combination of the position changes of each degree of freedom. When the object being measured approaches or moves away from the capacitive sensor, the plate spacing d changes, resulting in a change in capacitance C. By measuring the change in capacitance ΔC, the change in plate spacing Δd can be inferred, calculated as follows: Among them, Δx, Δy, Δz are the translation displacements of the rotor in the x, y, and z directions, k x , k y , k z , is the sensitivity coefficient corresponding to each degree of freedom, θ x ,θ y is the rotation angle around the x-axis and y-axis; To achieve stable suspension and precise position control, a control algorithm needs to be designed to feed the position detection results back to the electromagnetic force adjustment. Feedback control is performed through the PID control algorithm, as follows: Among them, U(t) is the input quantity at the current time, kp is the PID coefficient, err(t) is the proportional term, is the integral term, T I is the integration coefficient, is the differential term, T D is the differential coefficient.

6. The position detection method of the five-degree-of-freedom active and passive inner rotor magnetic bearing according to claim 5 is characterized in that: The steps of ensuring that the system achieves high-precision five-degree-of-freedom position detection and stable suspension control under different working conditions through experimental verification and parameter optimization include collecting signals from the capacitive sensor in real time during the experiment, recording position changes in the five degrees of freedom, ensuring that the sampling rate and accuracy of the signal collection meet experimental requirements, using standard measurement equipment to collect reference data for verifying the detection accuracy of the capacitive sensor, recording the dynamic response and suspension state of the system, and storing the collected signals in a database for subsequent analysis; Use data analysis tools to process data, extract key features, compare the five-degree-of-freedom position information detected by the capacitive sensor with reference measurement data, calculate detection errors, evaluate the system's detection accuracy and stability, test the system's dynamic response by applying external disturbances, record the system's recovery time and control accuracy, test the system's suspension stability under different working conditions, verify the real-time and effectiveness of electromagnetic force adjustment, record the fluctuation range of the suspension state and control errors, optimize the placement of the capacitive sensor based on the experimental results to ensure full coverage of the five-degree-of-freedom position changes, adjust the sensor spacing and angle to improve detection accuracy, and optimize the control algorithm parameters based on the experimental data; Use adaptive control or fuzzy control algorithms to improve the robustness and dynamic response of the system. Based on experimental results, optimize the electromagnetic force application strategy to ensure stable suspension under different working conditions, adjust the amplitude and phase of the electromagnetic force, and reduce the vibration and noise of the system. Based on experimental data, optimize the signal filtering and feature extraction algorithms to improve signal quality, adjust the filter parameters or improve the feature extraction method to reduce noise interference.

7. A position detection system for a five-degree-of-freedom active and passive inner rotor magnetic bearing, characterized in that: include: A capacitive sensor array is arranged on the inner rotor of the magnetic bearing to detect the position changes of the rotor in three translational degrees of freedom (x, y, and z) and two rotational degrees of freedom; A signal acquisition module is used to collect capacitance change signals output by the capacitance sensor array in real time; The signal processing module is used to filter, extract features and perform pattern recognition on the collected capacitance change signal to extract the five-degree-of-freedom position information; The control module adjusts the electromagnetic force in real time based on the extracted position information to achieve stable suspension and precise position control; The integrated control system integrates the capacitive sensor array, signal acquisition module, signal processing module and control module into a unified system, ensuring that the system can achieve high-precision five-degree-of-freedom position detection and stable suspension control under different working conditions.

8. The position detection system for the five-degree-of-freedom active and passive inner rotor magnetic bearing according to claim 7, characterized in that: The control includes PID control algorithm, which is used to adjust the magnitude and direction of electromagnetic force in real time to achieve stable suspension and precise position control; fuzzy control algorithm, which is used to control nonlinear systems to improve the robustness and dynamic response of the system; and adaptive control algorithm, which is used to dynamically adjust control parameters to ensure the stability and response speed of the system under different working conditions. Calibrate the capacitive sensors and actuators to ensure detection accuracy and control accuracy. Through experimental verification and parameter adjustment, optimize the system's response speed and control accuracy. Design redundancy in the sensor and control modules to improve the system's fault tolerance and reliability. Achieve high-precision detection of five-degree-of-freedom positions with a detection error of less than ±0.01mm; the system responds quickly to achieve stable suspension under high-speed rotation and complex working conditions.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the position detection method of the five-degree-of-freedom active and passive inner rotor magnetic bearing according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the position detection method of the five-degree-of-freedom active and passive inner rotor magnetic bearing according to any one of claims 1 to 6 are implemented.

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