A method of online estimating rotational speed of an electromagnetic bearing system

Abstract

The application provides a method for online estimation of rotating speed of an electromagnetic bearing system, which comprises the following steps: initially estimating the rotating speed of the electromagnetic bearing system to obtain an initial estimation value of the rotating speed; filtering the initial estimation value of the rotating speed by using a double-loop filtering method; identifying the filtered rotating speed value in real time and eliminating gross errors, and then further regulating the precision of the rotating speed value by using a filter, so as to obtain the rotating speed of the electromagnetic bearing system. The method has the advantages of short response time, high calculation precision, good tracking performance, and self-adjustable noise resistance and precision through parameter adjustment, and can effectively solve the defects of low robustness, low precision and poor real-time performance of the prior art.

Description

Technical Field

[0001] This invention belongs to the field of speed estimation for electromagnetic bearing systems. Background Technology

[0002] Electromagnetic bearing systems offer advantages such as low friction, no need for lubrication or sealing, and low power consumption, making them suitable for certain rotating machinery with special requirements. Their application is becoming increasingly widespread. Rotational speed is crucial information for monitoring the normal operation of an electromagnetic bearing system and is also essential for suppressing synchronous vibration. Currently, speed sensors are commonly used to measure the rotational speed of electromagnetic bearing systems, such as Hall effect speed sensors, photoelectric encoders, magnetoresistive speed sensors, and eddy current speed sensors. However, speed sensors have relatively low reliability; harsh environments such as high temperature and high pressure can cause them to malfunction. Furthermore, in units with complex mechanical structures and limited space, it may be impossible to install speed sensors. Similarly, missing system pins or external damage can also prevent speed sensors from functioning properly. All of these factors affect the stable and reliable operation of the electromagnetic bearing system. Utilizing speed estimation algorithms for online, real-time speed estimation can not only avoid the negative impact of speed sensors on the system but also optimize the system's mechanical structure to a certain extent, improving overall system performance.

[0003] Currently, the main speed estimation algorithms include: nonparametric power spectrum estimation, extended Kalman filter method, subspace tracking spectrum estimation, phase-locked loop method, and notch filter method. However, nonparametric power spectrum estimation is affected by spectral leakage and performs poorly in time-varying applications. Extended Kalman filter and subspace tracking spectrum estimation methods both suffer from computational complexity. Phase-locked loop method has a large estimation error at high frequencies. Notch filter method has a slow convergence speed. Electromagnetic bearing systems require high real-time speed information and high accuracy. However, the existing speed estimation algorithms mentioned above cannot simultaneously meet the requirements of electromagnetic bearing systems in terms of robustness, accuracy, and tracking performance. Summary of the Invention

[0004] The present invention aims to at least partially solve one of the technical problems in the related art.

[0005] Therefore, the purpose of this invention is to propose a method for online estimation of the rotational speed of an electromagnetic bearing system, which simultaneously meets the requirements of the electromagnetic bearing system in terms of robustness, accuracy, and tracking performance.

[0006] To achieve the above objectives, a first aspect of the present invention provides a method for online estimation of the rotational speed of an electromagnetic bearing system, comprising:

[0007] An initial estimate of the rotational speed of the electromagnetic bearing system is obtained;

[0008] The initial speed estimate is filtered using a dual-loop filtering method.

[0009] The filtered rotational speed value is identified in real time and gross errors are eliminated. Then, the accuracy is further adjusted using a filter to obtain the rotational speed of the electromagnetic bearing system.

[0010] In addition, the method for online estimation of the rotational speed of an electromagnetic bearing system according to the above embodiments of the present invention may also have the following additional technical features:

[0011] Furthermore, in one embodiment of the present invention, the initial estimation of the rotational speed of the electromagnetic bearing system includes:

[0012] The displacement signal of the electromagnetic bearing system is filtered using a low-pass filter;

[0013] The position of the filtered displacement signal is calibrated by offset calibration, and the amplitude of the filtered displacement signal is calibrated to make it move around the center of the origin.

[0014] The initial estimated rotational speed is calculated based on the angle difference of the rotor of the electromagnetic bearing system at different times.

[0015] Furthermore, in one embodiment of the present invention, the filtering of the initial speed estimate using a dual-loop filtering method includes:

[0016] An improved sliding filter technique is used to perform initial filtering on the initial speed estimate:

[0017]

[0018]

[0019] Where L1 represents the length of the sliding memory, and the symbol corresponding to its memory element is... χ 1 (k) is a pointer to the sliding memory; This represents the result of the initial filtering; k represents the discrete time.

[0020] An improved jitter filtering technique is used to perform a second filtering on the initial speed estimate after the first filtering:

[0021]

[0022]

[0023] Where L2 represents the length of the jump memory, and the symbol corresponding to its memory element is... χ 2 (k) is a pointer to jumping memory. χ represents the result of the second filtering. 2 (0) = 0,

[0024] Furthermore, in one embodiment of the present invention, the real-time identification and error removal of the filtered rotational speed value includes:

[0025] By setting a threshold, gross errors in the filtered rotational speed values ​​are identified and removed in real time. The judgment condition is as follows:

[0026]

[0027] Where τ1, τ2, τ3, and τ4 are cached variables, and τ1(0) = 0, τ2(0) = 0, τ3(0) = 0, and τ4(0) = 0; T e To determine the threshold, its value is set to 1Hz; k b (k b =1,2,3…) are counting parameters; if the judgment condition is true, then let If the stated judgment condition is not met, then let

[0028] After identifying and eliminating gross errors, the algorithm accuracy is further adjusted using the following filters:

[0029]

[0030] The output memory length is L3, and its memory elements consist of symbolic elements. express;

[0031] Finally, the output speed Ω of the electromagnetic bearing system is determined. final .

[0032] To achieve the above objectives, a second aspect of the present invention provides an apparatus for online estimation of the rotational speed of an electromagnetic bearing system, comprising the following modules:

[0033] The initial estimation module is used to make an initial estimate of the rotational speed of the electromagnetic bearing system and obtain an initial estimated value of the rotational speed.

[0034] A dual-loop filtering module is used to filter the initial speed estimate using a dual-loop filtering method;

[0035] The real-time reference averaging module is used to identify and eliminate errors in the filtered rotational speed value in real time, and then use the filter to further adjust its accuracy, thereby obtaining the rotational speed of the electromagnetic bearing system.

[0036] Furthermore, in one embodiment of the present invention, the initial estimation module is further configured to:

[0037] The displacement signal of the electromagnetic bearing system is filtered using a low-pass filter;

[0038] The position of the filtered displacement signal is calibrated by offset calibration, and the amplitude of the filtered displacement signal is calibrated to make it move around the center of the origin.

[0039] The initial estimated rotational speed is calculated based on the angle difference of the rotor of the electromagnetic bearing system at different times.

[0040] Furthermore, in one embodiment of the present invention, the dual-loop filtering module is further configured to:

[0041] An improved sliding filter technique is used to perform initial filtering on the initial speed estimate:

[0042]

[0043]

[0044] Where L1 represents the length of the sliding memory, and the symbol corresponding to its memory element is... χ 1 (k) is a pointer to the sliding memory; This represents the result of the initial filtering; k represents the discrete time.

[0045] An improved jitter filtering technique is used to perform a second filtering on the initial speed estimate after the first filtering:

[0046]

[0047]

[0048] Where L2 represents the length of the jump memory, and the symbol corresponding to its memory element is... χ 2 (k) is a pointer to jumping memory. χ represents the result of the second filtering. 2 (0) = 0,

[0049] Furthermore, in one embodiment of the present invention, the real-time reference averaging module is further configured to:

[0050] By setting a threshold, gross errors in the filtered rotational speed values ​​are identified and removed in real time. The judgment condition is as follows:

[0051]

[0052] Where τ1, τ2, τ3, and τ4 are cached variables, and τ1(0) = 0, τ2(0) = 0, τ3(0) = 0, and τ4(0) = 0; Te To determine the threshold, its value is set to 1Hz; k b (k b =1,2,3…) are counting parameters; if the judgment condition is true, then let If the stated judgment condition is not met, then let

[0053] After identifying and eliminating gross errors, the algorithm accuracy is further adjusted using the following filters:

[0054]

[0055] The output memory length is L3, and its memory elements are composed of symbolic characters. express;

[0056] Finally, the output speed Ω of the electromagnetic bearing system is determined. final .

[0057] To achieve the above objectives, a third aspect of the present invention provides a computer device, characterized in that it includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements a method for online estimation of the rotational speed of an electromagnetic bearing system as described above.

[0058] To achieve the above objectives, a fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements a method for online estimation of the rotational speed of an electromagnetic bearing system as described above.

[0059] The online method for estimating the rotational speed of an electromagnetic bearing system proposed in this invention has a short response time, high calculation accuracy, and good tracking performance. Furthermore, its noise immunity and accuracy can be adjusted through parameter settings. This method effectively solves the shortcomings of existing technologies, such as low robustness, low accuracy, and poor real-time performance. Attached Figure Description

[0060] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0061] Figure 1 This is a flowchart illustrating a method for online estimation of the rotational speed of an electromagnetic bearing system provided in an embodiment of the present invention.

[0062] Figure 2 This is a schematic diagram of an online device for estimating the rotational speed of an electromagnetic bearing system, provided in an embodiment of the present invention.

[0063] Figure 3This is a schematic diagram of an algorithm for online estimation of the rotational speed of an electromagnetic bearing system provided in an embodiment of the present invention;

[0064] Figure 4 This is a schematic diagram of the rotor motion trajectory under the action of an initial estimation module provided in an embodiment of the present invention. Part a represents the original rotor motion estimation, part b represents the rotor trajectory after processing by a digital low-pass filter, part c represents the rotor trajectory after amplitude and bias correction, and part d represents Ω. e Schematic diagram of the calculation principle;

[0065] Figure 5 This is a schematic diagram of the principle of a dual-ring filter module provided in an embodiment of the present invention;

[0066] Figure 6 This is a schematic diagram of the principle of a real-time reference averaging module provided in an embodiment of the present invention. Detailed Implementation

[0067] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0068] The method for online estimation of the rotational speed of an electromagnetic bearing system according to an embodiment of the present invention is described below with reference to the accompanying drawings.

[0069] Figure 1 This is a flowchart illustrating a method for online estimation of the rotational speed of an electromagnetic bearing system, provided in an embodiment of the present invention.

[0070] like Figure 1 As shown, the method for online estimation of the rotational speed of an electromagnetic bearing system includes the following steps:

[0071] S101: Perform an initial estimate of the rotational speed of the electromagnetic bearing system to obtain an initial estimated value of the rotational speed;

[0072] Furthermore, in one embodiment of the present invention, the initial estimation of the rotational speed of the electromagnetic bearing system includes:

[0073] The displacement signal of the electromagnetic bearing system is filtered using a low-pass filter;

[0074] The position of the filtered displacement signal is calibrated by offset calibration, and the amplitude of the filtered displacement signal is calibrated to make it move around the center of the origin.

[0075] The initial estimated rotational speed is calculated based on the angle difference of the rotor of the electromagnetic bearing system at different times.

[0076] S102: The initial speed estimate is filtered using a dual-loop filtering method;

[0077] Furthermore, in one embodiment of the present invention, the filtering of the initial speed estimate using a dual-loop filtering method includes:

[0078] An improved sliding filter technique is used to perform initial filtering on the initial speed estimate:

[0079]

[0080]

[0081] Where L1 represents the length of the sliding memory, and the symbol corresponding to its memory element is... χ 1 (k) is a pointer to the sliding memory; This represents the result of the initial filtering; k represents the discrete time.

[0082] An improved jitter filtering technique is used to perform a second filtering on the initial speed estimate after the first filtering:

[0083]

[0084]

[0085] Where L2 represents the length of the jump memory, and the symbol corresponding to its memory element is... χ 2 (k) is a pointer to jumping memory. χ represents the result of the second filtering. 2 (0) = 0,

[0086] S103: The filtered rotational speed value is identified in real time and gross errors are eliminated. Then, the accuracy is further adjusted using a filter to obtain the rotational speed of the electromagnetic bearing system.

[0087] Furthermore, in one embodiment of the present invention, the real-time identification and error removal of the filtered rotational speed value includes:

[0088] By setting a threshold, gross errors in the filtered rotational speed values ​​are identified and removed in real time. The judgment condition is as follows:

[0089]

[0090] Where τ1, τ2, τ3, and τ4 are cached variables, and τ1(0) = 0, τ2(0) = 0, τ3(0) = 0, and τ4(0) = 0; T eTo determine the threshold, its value is set to 1Hz; k b (k b =1,2,3…) are counting parameters; if the judgment condition is true, then let If the stated judgment condition is not met, then let

[0091] After identifying and eliminating gross errors, the algorithm accuracy is further adjusted using the following filters:

[0092]

[0093] The output memory length is L3, and its memory elements are composed of symbolic characters. express;

[0094] Finally, the output speed Ω of the electromagnetic bearing system is determined. final .

[0095] The online method for estimating the rotational speed of an electromagnetic bearing proposed in this invention has a short response time, high calculation accuracy, and good tracking performance. Furthermore, its noise immunity and accuracy can be adjusted through parameter settings. This method effectively solves the shortcomings of existing technologies, such as low robustness, low accuracy, and poor real-time performance.

[0096] To achieve the above embodiments, the present invention also proposes a device for online estimation of the rotational speed of an electromagnetic bearing system.

[0097] Figure 2 This is a schematic diagram of a device for online estimation of the rotational speed of an electromagnetic bearing system, provided as an embodiment of the present invention.

[0098] like Figure 2 As shown, the device for online estimation of the rotational speed of the electromagnetic bearing system includes: an initial estimation module 100, a dual-loop filtering module 200, and a real-time reference averaging module 300, wherein...

[0099] The initial estimation module is used to make an initial estimate of the rotational speed of the electromagnetic bearing system and obtain an initial estimated value of the rotational speed.

[0100] A dual-loop filtering module is used to filter the initial speed estimate using a dual-loop filtering method;

[0101] The real-time reference averaging module is used to identify and eliminate gross errors in the filtered rotational speed value in real time, and then use the filter to further adjust its accuracy, thereby obtaining the rotational speed of the electromagnetic bearing system.

[0102] Figure 3The flowchart illustrates the algorithm of this invention. The initial estimation module adaptively adjusts the bias and amplitude of the displacement signal to obtain an initial estimate of the rotational speed. The dual-loop filtering module adjusts the algorithm's accuracy through parameter control, reduces the impact of noise, and controls the algorithm's running state. The real-time reference averaging module removes gross errors, improves the algorithm's robustness, and further enhances its accuracy.

[0103] Furthermore, in one embodiment of the present invention, the initial estimation module is further configured to:

[0104] The displacement signal of the electromagnetic bearing system is filtered using a low-pass filter;

[0105] The position of the filtered displacement signal is calibrated by offset calibration, and the amplitude of the filtered displacement signal is calibrated to make it move around the center of the origin.

[0106] The initial estimated rotational speed is calculated based on the angle difference of the rotor of the electromagnetic bearing system at different times.

[0107] Specifically, such as Figure 4 As shown in section a, the rotor undergoes positive whirl within the clearance of the electromagnetic bearing. Due to the presence of harmonic components in the displacement signal, the rotor's shaft center trajectory is an irregular circle. Furthermore, the DC component of the sensor's fluctuations causes the center of the shaft center trajectory to deviate from the electrical center of the displacement sensor, and the amplitude of the displacement signal continuously changes with rotational speed and time. All these characteristics affect the accuracy of the algorithm. The initial estimation module performs initial speed estimation based on the displacement signal, and before calculating the speed, it performs filtering, shaft center trajectory alignment, and amplitude adjustment on the displacement signal.

[0108] The initial estimation module first uses a digital first-order low-pass filter to filter the displacement sensor signal, which can reduce the influence of noise and harmonic interference to a certain extent. This paper assumes that the displacement signal input to the algorithm is the displacement sensor signal at end A, and the filter structure is shown in the following equation:

[0109]

[0110] in and This is the filtered displacement signal; k (k = 1, 2, 3, ...) is the discrete time; σ is the filter bandwidth coefficient, and the relationship between this coefficient and the filter cutoff frequency is:

[0111]

[0112] Where f c T is the filter cutoff frequency; s This is the sampling period for the control system. In practical applications, f cThe value should be equal to the rated speed of the unit, so as not to have a significant impact on the amplitude of the same frequency component.

[0113] like Figure 4 As shown in part b, after filtering the displacement signal, the center of its shaft trajectory still does not coincide with the electrical center of the sensor, and the amplitude also changes with rotational speed and time. Therefore, the initial estimation module adaptively solves for the bias coefficient and amplitude coefficient of the filtered displacement signal. The peaks and troughs of the filtered displacement signal in the x and y directions at end A can be obtained by the following formula:

[0114]

[0115] Where A xp and A xv They are respectively The peaks and troughs; A yp and A yv They are respectively The peaks and troughs. A xp A xv A yp and A yv Every L a Once the sampling point is updated, k a For the corresponding count. L a It should be as long as possible to simultaneously contain the peaks and troughs of the vibration signal, but L a Too long a length will affect the algorithm's adaptability. Generally, L... a One-tenth of the system sampling rate can be selected. Using A xp A xv A yp and A yv We can obtain:

[0116]

[0117] Where u x (k a ) and u y (k a ) is the displacement offset coefficient, which is used to ensure that the center of the shaft trajectory coincides with the electrical center of the sensor; w x (k a ) and w y (k a ) is the displacement amplitude coefficient, whose function is to make and The amplitude remains at T0 and does not change with rotational speed or time.

[0118] Combining the above equations, we can obtain the corrected displacement coordinate vector at time k:

[0119]

[0120] in This represents the floor operation. The corrected rotor trajectory is as follows: Figure 4 As shown in part c.

[0121] The output Ω of the initial estimation module e for:

[0122]

[0123] Where Ω e (0) = 0, ζ is the sampling interval of the speed estimator, ζ c This is an auxiliary counting parameter, and its initial value is also 0. H A (k) and H A The angle between (k-ζ) is Δθ. To ensure that Δθ is within the range of 0 to 1, we need to ensure that Δθ is within the range of 0 to 1. Between these two values, ζ is generally taken as 2.

[0124] In summary, the initial estimation module first filters the displacement signal using a digital first-order low-pass filter to reduce the impact of noise and harmonic components on the invention; secondly, the module adaptively adjusts the bias and amplitude of the filtered displacement signal; finally, based on the corresponding formula, the initial estimate of the rotational speed can be obtained. However, Ω e The error is too large to be applied directly; a dual-loop filter module and a real-time reference averaging module are still needed to adjust Ω. e Further processing will be carried out.

[0125] Furthermore, in one embodiment of the present invention, the dual-loop filtering module is further configured to:

[0126] An improved sliding filter technique is used to perform initial filtering on the initial speed estimate:

[0127]

[0128]

[0129] Where L1 represents the length of the sliding memory, and the symbol corresponding to its memory element is... χ 1 (k) is a pointer to the sliding memory; This represents the result of the initial filtering; k represents the discrete time.

[0130] An improved jitter filtering technique is used to perform a second filtering on the initial speed estimate after the first filtering:

[0131]

[0132]

[0133] Where L2 represents the length of the jump memory, and the symbol corresponding to its memory element is... χ 2 (k) is a pointer to jumping memory. χ represents the result of the second filtering. 2 (0) = 0,

[0134] Specifically, although the initial estimation module calculates Ω e Previously, the displacement signal was filtered, biased, and its amplitude adjusted. However, the corrected displacement signal still contains some noise and harmonic interference. Therefore, the output Ω of the initial estimation module... e There will be noise, that is, Ω e There is an error between the actual rotational speed and the actual rotational speed. The function of the dual-loop filter module is to reduce the error by Ω without affecting the real-time performance of the algorithm. e This reduces errors and improves algorithm accuracy.

[0135] like Figure 5 The diagram shows the structural principle of the dual-loop filter module. This module consists of a first-stage sliding filter and a second-stage bouncing filter, and has the advantages of low computational complexity and high real-time performance.

[0136] like Figure 5 As shown, the first-order filter consists of a sliding memory and processing units. The length of the sliding memory is L1, and the symbol corresponding to each memory element is... The output value Ω of the initial estimation module e (k) will be copied cyclically to the corresponding memory element in the sliding memory:

[0137]

[0138] Where, χ 1 (k) is a pointer to the sliding memory. The first-level filter processing module performs the following functions:

[0139]

[0140] like Figure 5 As shown, the second-order filter consists of hopping memory and corresponding processing units. The hopping memory has a length of L2, and the corresponding memory elements are represented by symbols. This indicates the output after passing through a first-stage filter. It will enter the second-level filter:

[0141]

[0142] Where, χ 2 (k) is a pointer to jumping memory, χ 2 (0) = 0. If χ2 (k) Stop updating; the speed estimator will stop speed estimation. Therefore, starting and pausing the speed estimator can be controlled by controlling χ. 2 (k) is used to implement this. The processing unit of the jitter filter performs the following functions:

[0143]

[0144] in The size of the sliding memory length L1 and the jumping memory length L2 determines the noise resistance of the present invention. The larger L1+L2 is, the stronger the noise resistance of the algorithm, but it may sacrifice the real-time performance of the algorithm.

[0145] Due to its unique structure, the dual-loop filter module can improve optimization accuracy while maintaining system real-time performance, and its noise resistance can be adjusted by regulating the memory length of the dual-loop filter. However, the output value of the dual-loop filter may contain gross errors, therefore a real-time reference averaging module is needed for further adjustment.

[0146] Furthermore, in one embodiment of the present invention, the real-time reference averaging module is further configured to:

[0147] By setting a threshold, gross errors in the filtered rotational speed values ​​are identified and removed in real time. The judgment condition is as follows:

[0148]

[0149] Where τ1, τ2, τ3, and τ4 are cached variables, and τ1(0) = 0, τ2(0) = 0, τ3(0) = 0, and τ4(0) = 0; T e To determine the threshold, its value is set to 1Hz; k b (k b =1,2,3…) are counting parameters; if the judgment condition is true, then let If the stated judgment condition is not met, then let

[0150] After identifying and eliminating gross errors, the algorithm accuracy is further adjusted using the following filters:

[0151]

[0152] The output memory length is L3, and its memory elements consist of symbolic elements. express;

[0153] Finally, the output speed Ω of the electromagnetic bearing system is determined. final .

[0154] Specifically, harmonic interference and controller undersampling may cause Contains gross errors. If used directly... Imbalance control can easily compromise system safety. The real-time reference averaging module identifies and eliminates gross errors in real time, further improving the accuracy of optimization. Figure 6 This displays a schematic diagram of the real-time reference averaging module. For example... Figure 6 As shown, the real-time reference averaging module first identifies and eliminates gross errors, and its judgment criteria are as follows:

[0155]

[0156] Where τ1, τ2, τ3, and τ4 are cached variables, and τ1(0) = 0, τ2(0) = 0, τ3(0) = 0, and τ4(0) = 0; T e To determine the threshold, its value can be set to 1Hz; k b (k b =1,2,3…) are counting parameters. If the above condition is true, then let If the above judgment conditions are not met, then let

[0157] like Figure 6 As shown, after identifying and eliminating gross errors, the real-time reference averaging module can further adjust the algorithm's accuracy:

[0158]

[0159] The output memory length is L3, and its memory elements consist of symbolic elements. This means that the size of L3 affects the real-time performance of the optimization; the larger L3 is, the worse the real-time performance. Therefore, the size of L3 is much smaller than the sizes of L1 and L2.

[0160] In summary, the real-time reference averaging module can identify and remove gross errors in real time, and can further adjust the accuracy of the algorithm to make the algorithm output value Ω more precise. final To meet the corresponding engineering requirements.

[0161] To achieve the above objectives, a third aspect of the present invention provides a computer device, characterized in that it includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the method for online estimation of the rotational speed of an electromagnetic bearing system as described above.

[0162] To achieve the above objectives, a fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the method for online estimation of the rotational speed of an electromagnetic bearing system as described above.

[0163] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0164] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0165] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for online estimation of the rotational speed of an electromagnetic bearing system, characterized in that, Includes the following steps: An initial estimate of the rotational speed of the electromagnetic bearing system is performed to obtain an initial estimated value; specifically, the initial estimate of the rotational speed of the electromagnetic bearing system includes: The displacement signal of the electromagnetic bearing system is filtered using a low-pass filter; Obtain the peaks and troughs of the filtered displacement signal at intervals L. a The peak and trough values ​​are updated once at each sampling point. The displacement offset coefficient and displacement amplitude coefficient are calculated based on the peak value and trough value. The displacement offset coefficient is used to perform axis center trajectory centering processing on the filtered displacement signal. The displacement amplitude coefficient is used to perform amplitude normalization processing on the filtered displacement signal so that it moves around the origin center while keeping the amplitude constant. Based on the angle difference of the rotor of the electromagnetic bearing system at different times after alignment with the shaft center trajectory and amplitude normalization, the initial estimated value of the rotational speed is calculated. The initial speed estimate is filtered using a dual-loop filtering method; the dual-loop filtering method includes a cascaded sliding filter and a jumping filter, specifically including: An improved sliding filter technique is used to perform initial filtering on the initial speed estimate: ， ， in, The length of the sliding memory is represented by the symbol of its corresponding memory element. , It is a pointer to sliding memory; This indicates the result of the initial filtering. Represents discrete time; An improved jitter filtering technique is used to perform a second filtering on the initial speed estimate after the first filtering: ， ， in, The length of the jump memory is indicated by the symbol of the corresponding memory element. , It is a pointer that jumps through memory. This represents the result of the second filtering. ; The filtered rotational speed value is identified in real time and gross errors are eliminated. Then, the accuracy is further adjusted using a filter to obtain the rotational speed of the electromagnetic bearing system.

2. The method according to claim 1, characterized in that, The real-time identification and error removal of the filtered rotational speed value includes: By setting a threshold, gross errors in the filtered rotational speed values ​​are identified and removed in real time. The judgment condition is as follows: ， in, , , and For cached variables, and , , and ; To determine the threshold, its value is set to 1 Hz; Let be the counting parameter; if the judgment condition is true, then let . If the stated judgment condition is not met, then let ; After identifying and eliminating gross errors, the algorithm accuracy is further adjusted using the following filters: ， The output memory length is Its memory elements are composed of symbols express; Finally, the rotational speed of the electromagnetic bearing system is output. .

3. A device for online estimation of the rotational speed of an electromagnetic bearing system, characterized in that, include: The initial estimation module is used to make an initial estimate of the rotational speed of the electromagnetic bearing system and obtain an initial estimated value of the rotational speed. The displacement signal of the electromagnetic bearing system is filtered using a low-pass filter; Obtain the peaks and troughs of the filtered displacement signal at intervals L. a The peak and trough values ​​are updated once at each sampling point. The displacement offset coefficient and displacement amplitude coefficient are calculated based on the peak value and trough value. The displacement offset coefficient is used to perform axis center trajectory centering processing on the filtered displacement signal. The displacement amplitude coefficient is used to perform amplitude normalization processing on the filtered displacement signal so that it moves around the origin center while keeping the amplitude constant. Based on the angle difference of the rotor of the electromagnetic bearing system at different times after alignment with the shaft center trajectory and amplitude normalization, the initial estimated value of the rotational speed is calculated. A dual-loop filtering module is used to filter the initial speed estimate using a dual-loop filtering method; the dual-loop filtering method includes a cascaded sliding filter and a jumping filter, specifically including: An improved sliding filter technique is used to perform initial filtering on the initial speed estimate: ， ， in, The length of the sliding memory is represented by the symbol of its corresponding memory element. , It is a pointer to sliding memory; This indicates the result of the initial filtering. Represents discrete time; An improved jitter filtering technique is used to perform a second filtering on the initial speed estimate after the first filtering: ， ， in, The length of the jump memory is indicated by the symbol of the corresponding memory element. , It is a pointer that jumps through memory. This represents the result of the second filtering. ; The real-time reference averaging module is used to identify and eliminate gross errors in the filtered rotational speed value in real time, and then use the filter to further adjust its accuracy, thereby obtaining the rotational speed of the electromagnetic bearing system.

4. The apparatus according to claim 3, characterized in that, The real-time reference averaging module is also used for: By setting a threshold, gross errors in the filtered rotational speed values ​​are identified and removed in real time. The judgment condition is as follows: ， in, , , and For cached variables, and , , and ; To determine the threshold, its value is set to 1 Hz; Let be the counting parameter; if the judgment condition is true, then let . If the stated judgment condition is not met, then let ; After identifying and eliminating gross errors, the algorithm accuracy is further adjusted using the following filters: ， The output memory length is Its memory elements are composed of symbols express; Finally, the rotational speed of the electromagnetic bearing system is output. .

5. A computer device, characterized in that, The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method for online estimation of the rotational speed of an electromagnetic bearing system as described in any one of claims 1-2.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for online estimation of the rotational speed of an electromagnetic bearing system as described in any one of claims 1-2.

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