A twin control method for magnetically driven rotor systems oriented towards fault scenarios

Abstract

This invention provides a digital twin control method for a magnetically driven rotor system oriented towards fault scenarios. First, based on the dynamic mechanism of the magnetically driven rotor system, a static digital twin model is established in digital space to characterize the system's dynamic response behavior when the physical system parameters remain constant. Then, key parameters of the magnetically driven rotor in the actual physical system are identified in real time and injected into the constructed static digital twin model, transforming it into a dynamic digital twin model capable of reflecting changes in the physical system's state in real time. Finally, for situations involving sensor and controller failures in the physical system, the digital twin model ensures that the magnetically driven rotor system can still operate stably under fault conditions. This invention effectively solves the problems of control lag and loss of control under faults caused by the disconnect between the model and the physical system state, significantly improving the system's fault-tolerant control capability and operational stability when sensors and controllers fail.

Description

Technical Field

[0001] This invention relates to a fault control method for a magnetically driven rotor system, and more particularly to a twin control method for a magnetically driven rotor system oriented towards fault scenarios, belonging to the fields of magnetically driven control technology and digital twins. Background Technology

[0002] This section provides only background information relevant to this disclosure and is not necessarily prior art.

[0003] As high-end rotating equipment continues to develop towards higher speed, higher precision, and higher reliability, the magnetically driven rotor system, as its core component, is crucial to the overall performance and safety of the equipment. Under complex operating conditions, potential failures of key components such as sensors and controllers are a major threat to system stability; therefore, achieving efficient fault control is key to ensuring the safe and reliable operation of the equipment.

[0004] Currently, fault control methods for magnetically driven rotor systems have many limitations. Existing modeling methods are mostly based on the system's initial state or fixed parameters, making it difficult to dynamically adapt to time-varying system parameters caused by component aging, environmental disturbances, etc., during actual operation. The constructed models gradually become disconnected from the magnetically driven rotor system, resulting in insufficient accuracy in mapping the system's true state. Simultaneously, traditional control methods fail to fully utilize real-time system data for online model updates, limiting the real-time performance and accuracy of fault control strategies. Therefore, how to construct a dynamic model that evolves synchronously with the physical system and accurately maps it, and on this basis, achieve fast and reliable fault-tolerant control under fault conditions, has become a bottleneck problem that urgently needs to be overcome in this technical field.

[0005] With the continuous development of artificial intelligence and big data, digital twins provide solutions for model building and virtual-real interaction. Therefore, it is of great significance to introduce digital twin technology and design a precise, effective and easy-to-implement fault control method for magnetic rotor systems to make up for the shortcomings of existing technologies.

[0006] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] Purpose of the invention: The technical problem to be solved by the present invention is to provide a twin control method for magnetically driven rotor systems oriented towards fault scenarios, addressing the shortcomings of the existing technology.

[0008] To address the aforementioned technical problems, this invention discloses a twin control method for a magnetically driven rotor system oriented towards fault scenarios, comprising the following steps:

[0009] Step 1: Establish a static digital twin model in the digital space to characterize the dynamic response behavior of the system when the physical system parameters remain unchanged;

[0010] Step 2: Identify the key parameters of the magnetic rotor in the actual physical system in real time, and inject the identified key parameters into the static digital twin model constructed in Step 1, so that it can be transformed into a dynamic digital twin model that can reflect the changes in the state of the physical system in real time.

[0011] Step 3: In the case of sensor failure in the physical system, a dynamic digital twin model is used to replace the physical object, so that the magnetic rotor system can maintain stable operation in the case of sensor failure.

[0012] Step 4: In the event of controller failure in the physical system, a dynamic digital twin model is used to directly control the physical object, so that the magnetic rotor system can maintain stable operation in the event of controller failure.

[0013] Furthermore, in step 1, a static digital twin model of the magnetic rotor system in digital space is constructed, including a dynamic model of the radial magnetic bearing-rotor system.

[0014] Furthermore, in step 1, PID control is used to model the controller in the digital space.

[0015] Furthermore, in step 2, the key parameters are current stiffness and displacement stiffness.

[0016] In step 2, the specific process of identifying the parameters of the magnetic power rotor system is as follows: by observing the displacement, current and acceleration of the magnetic power rotor in real time, the parameters to be estimated are obtained recursively, and finally the current stiffness and displacement stiffness are calculated.

[0017] In step 2, the recursive least squares method is used to continuously update the parameter estimates using new sampled data.

[0018] Furthermore, in step 3, a sensor fault detection module is set up, which calculates in real time the difference between the physical sensor output value and the corresponding estimated value of the sensor output of the dynamic digital twin model.

[0019] In step 3, when the location of the sensor fault flag is detected... Then, immediately cut off the signal transmission between the physical sensor and the controller, and replace the faulty module with the corresponding estimated value of the digital twin model sensor output to calculate the physical sensor output value in real time.

[0020] Furthermore, in step 4, two controller fault judgment conditions are set:

[0021] Condition 1: When the deviation between the controller output in the physical space and the controller output in the digital space exceeds the error threshold and continues to exceed the error threshold for a certain period of time, wherein the certain period of time is not less than the larger of twice the synchronization period of the digital twin model and the voltage ripple decay time of the Buck circuit, and not greater than five times the larger value.

[0022] Condition 2: When the controller output in the physical space is continuously in a saturated state, i.e., at the maximum or minimum voltage.

[0023] In step 4, when a controller malfunction is detected, the controller flag is set. It immediately cuts off the output of the fault controller through an electronic switch and connects the output of the digital controller to the power amplifier to ensure the stable operation of the magnetic rotor system in the physical space.

[0024] Beneficial effects:

[0025] A high-fidelity dynamic digital twin model is constructed based on mechanism modeling and parameter identification methods. By deeply integrating real-time parameter identification with the mechanism-based digital twin model, a high-fidelity digital twin model capable of dynamic evolution is built. When the physical system encounters sensor or controller failures, this dynamic digital twin model can serve as a reliable state benchmark and control compensation basis for fault-tolerant control, enhancing the system's fault-tolerant control capability and ensuring stable operation under fault conditions. This method effectively solves the problem that traditional models are difficult to adapt to time-varying conditions due to fixed parameters, realizing online dynamic updates of the model and improving the system's adaptive performance. At the same time, the method provided by this invention has strong generalization and can be applied to the autonomous health management of various equipment. Attached Figure Description

[0026] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, and the advantages of the present invention in the above and / or other aspects will become clearer.

[0027] Figure 1 This is a schematic diagram of the method flow provided by the present invention.

[0028] Figure 2 This is a schematic diagram of the static digital twin model of the magnetic power rotor system provided by the present invention.

[0029] Figure 3 This is the displacement response diagram of the magnetically driven rotor system and the digital twin model after parameter identification provided by the present invention.

[0030] Figure 4 This is a diagram of sensor fault and digital twin model takeover provided by the present invention.

[0031] Figure 5This is a controller fault and digital twin model takeover diagram provided by the present invention. Detailed Implementation

[0032] This invention provides a twin control method for magnetically driven rotor systems oriented towards fault scenarios, addressing the problem that existing digital models of magnetically driven rotor systems cannot accurately reflect the current state and effectively cope with multiple fault modes. The proposed solution is specific and reliable, capable of dynamically updating the digital twin model of the magnetically driven rotor system and effectively controlling fault states, thus providing effective support for the maintenance and management of magnetically driven rotor systems.

[0033] The technical solution of this invention is as follows: A high-fidelity dynamic digital twin model is constructed based on mechanism modeling and parameter identification methods. When faced with sensor or controller failures, this dynamic digital twin model can serve as a reliable state reference and control compensation basis for fault-tolerant control. Figure 1 As shown, Figure 1 The method flowchart provided by this invention specifically includes the following steps:

[0034] Step 1: Based on the dynamic mechanism of the magnetic rotor system, establish a static digital twin model in digital space to characterize the dynamic response behavior of the system when the physical system parameters remain unchanged. A schematic diagram of the static digital twin model is shown below. Figure 2 As shown, the system includes the controlled object, controller, power amplifier, and sensors. Since the rotor dynamic response is significantly radial, a mechanistic model of the radial direction is performed in digital space. Based on Newton's second law, the dynamic model of the radial magnetic bearing-rotor system can be obtained as follows:

[0035]

[0036] in, It is the rotor mass. and It is the displacement acceleration of the rotor's geometric center in the X and Y directions. and These are the forces acting on the magnetic rotor in the X and Y directions, and the electromagnetic force, respectively. It mainly depends on the current intensity in the electromagnet coil. and the distance between the magnet and the rotor , can be represented as:

[0037]

[0038] in, The permeability of free space, For the stator core area, For bias current, The number of coil turns. It is a radial air gap.

[0039] The magnitude of the magnetic force is determined by the magnetic field strength between the stator and rotor. The control current in the coil and the air gap length primarily affect the magnetic field strength. When the rotor is stably suspended at the center of the radial air gap, the electromagnetic force it experiences reaches equilibrium with other forces. In this state, the distance between the magnetic bearing and the rotor is significantly smaller than the radial air gap. At this equilibrium position, to simplify the analysis, the electromagnetic force can be Taylor-expanded, retaining only the linear terms, and linearized into current stiffness. and displacement stiffness Combination forms:

[0040]

[0041] in, As an intermediate variable, current stiffness and displacement stiffness for

[0042]

[0043] in, Let be the angle between the two magnetic pole forces, therefore Equation 1.1 can be rewritten as:

[0044]

[0045] in, and These are the displacement stiffness in the X and Y directions, respectively. and These are the current stiffness in the X and Y directions, respectively. and These are the rotor displacements in the X and Y directions, respectively. and These are the control currents in the X and Y directions, respectively.

[0046] Since the planar radial two-degree-of-freedom suspension is independently controlled, taking a single-degree-of-freedom case as an example, PID control is typically used. The controller in the digital space is modeled, and its transfer function... It can be represented as

[0047]

[0048] in, This is the proportional control coefficient. The integral control coefficient, The differential control coefficient, The filtering time constant is Let be the complex variable after the Laplace transform.

[0049] In digital space, a power amplifier is represented by a first-order inertial element, and its transfer function is... It can be represented as

[0050]

[0051] in, This is the time constant of the switching power amplifier.

[0052] Step 2: Identify the key parameters of the magnetic rotor system in the actual physical space in real time, and inject the identified key parameters into the static digital twin model constructed in Step 1, so that it can be transformed into a dynamic digital twin model that can reflect the changes in the physical system state in real time.

[0053] Taking a single channel as an example, based on the static digital twin model obtained in step 1, the rotor dynamics equation can be expressed as:

[0054]

[0055] in, The equivalent mass of this planar rotor is... This refers to the displacement of the magnetically driven rotor in physical space. The displacement acceleration of the magnetically driven rotor in physical space. To control the current in physical space. This represents the actual displacement stiffness of the magnetically driven rotor system in physical space. This represents the actual current stiffness of the magnetic rotor system in physical space.

[0056] Current stiffness and displacement stiffness are core parameters characterizing the coupling relationship between force and displacement, and force and current in an electromagnetic bearing-rotor system. This reflects the system's resistance to external disturbances and directly affects the rotor's support stiffness and natural frequency; while This reflects the gain characteristics of the electromagnetic actuator, determining the efficiency of the control current in generating electromagnetic force. In practical systems, factors such as material properties, assembly errors, magnetic circuit saturation, and temperature rise effects affect the efficiency of the electromagnetic force. and These parameters may deviate from their theoretical design values, and may even change slowly depending on the operating conditions. Therefore, accurate identification of these two key parameters is fundamental to improving the accuracy of digital twin models and achieving high-precision active control and condition monitoring.

[0057] First, for the equation Divide both sides by After sorting, we get:

[0058]

[0059] in, , The parameter to be estimated is, i.e. .

[0060] The input vector is That is, the real-time acquisition of the magnetic rotor displacement and current in the physical space, and the output vector. This refers to rotor acceleration. (Through real-time observation) and recursive estimation Finally, reverse calculation and .

[0061] In physical space, a magnetically driven rotor system can be represented as:

[0062]

[0063] in, for The transpose of .

[0064] To achieve online real-time identification, the Recursive Least Squares (RLS) method is employed. This method continuously updates parameter estimates using new sampled data without repeatedly processing all historical data, resulting in high computational efficiency and meeting the real-time requirements of digital twin models. Furthermore, the RLS algorithm incorporates a forgetting factor to attenuate the influence of old data, thereby better tracking the time-varying characteristics of parameters and ensuring that the identification results remain consistent with the current system state.

[0065] Definition of the first The time error is:

[0066]

[0067] in, For the first The physical space magnetic rotor acceleration measured at constant time. For the first The parameter estimates at time 1.

[0068] Parameter gradient descent update formula:

[0069]

[0070] in, The gain matrix (which controls the correction magnitude) must minimize the parameter estimation error.

[0071] Gain matrix Derivation of the optimal solution:

[0072]

[0073] in, For the first The covariance matrix at time t reflects the confidence level of the parameter estimate. Forgetting factor, .

[0074] Covariance matrix update formula (for the next iteration):

[0075]

[0076] Obtained from identification and Back-engineering target parameters:

[0077]

[0078]

[0079] The estimated current stiffness and displacement stiffness The parameters are passed to the magnetically driven rotor system in digital space, i.e., the static digital twin model. This model is rewritten as a dynamic digital twin model, which can be represented as follows:

[0080]

[0081] in, For the acceleration of the magnetically driven rotor in digital space, This represents the magnetic rotor displacement in digital space. This refers to the control current of the magnetically driven rotor in digital space.

[0082] Step 3: In the case of sensor failure in the physical space, a digital twin model is used to replace the physical object, so that the magnetic rotor system can still operate stably even in the case of sensor failure.

[0083] A physical space closed loop typically includes a controller, a controlled object, a sensor, a power amplifier, and an actuator. When a sensor malfunctions, the sensor output may exhibit various modes such as drift or high noise, which will directly lead to a significant increase in the vibration of the magnetic rotor, or even instability.

[0084] Configure sensor fault detection module This module calculates the output values ​​of the physical sensors in real time. Corresponding estimated value of the sensor output of the dynamic digital twin model The difference between them, i.e., the residual :

[0085]

[0086] When the magnetically driven rotor system operates normally in physical space, due to the high synchronization between the digital twin model and the physical magnetically driven rotor system, the residual... It remains within a very small range. When the physical sensor malfunctions, An anomalous mode has emerged, leading to Significantly increased. When the module detects that the residual continuously exceeds the safety threshold. And it has exceeded the scheduled time. It can eliminate external transient interference, that is

[0087]

[0088] in, The function is defined as: when hour, ;when hour, .

[0089] When satisfied ,in, If the value is very close to 1, it can be determined that the sensor has malfunctioned, and a sensor fault flag can be set. Upon detecting a sensor fault flag at position 1, immediately cut off the signal transmission between the physical sensor and the controller, and switch the digital twin model sensor output... Replacement fault As an input to the physical controller, it ensures the stable operation of the physical closed-loop system.

[0090] Step 4: In the case of controller failure in the physical space, the physical object is directly controlled using a digital twin model, so that the magnetic rotor system can still operate stably even in the event of controller failure.

[0091] A controller malfunction in the physical space will cause the magnetic rotor system to become directly unstable, resulting in serious consequences. A complete controller output should be within the expected range and its dynamic changes should be reasonable. Two judgment conditions should be set:

[0092] Condition 1: When the controller in the physical space outputs... With controller output in digital space The deviation between them exceeds the error threshold And it continues to exceed the error threshold for a period of time. ,Right now

[0093] , lasting longer than

[0094] in, The Buck circuit voltage ripple decay time is the larger of the following: no less than twice the synchronization period of the digital twin model and no more than five times the larger value. It can be represented as

[0095]

[0096] in, The engineering attenuation coefficient, This is the circuit's filter cutoff frequency.

[0097] Condition 2: When the controller output in the physical space is continuously in a saturated state, i.e., at the maximum or minimum voltage.

[0098] When all of the above conditions are met, the controller is determined to be faulty, and the controller flag is set. And immediately cut off the output of the fault controller via electronic switch, and cut off the output of the digital controller. Connect a power amplifier to ensure the stable operation of the magnetic rotor system in the physical space.

[0099] Figure 3 To determine the displacement response results of the magnetically driven rotor system and its digital twin model after parameter identification, a parameter identification method between the magnetically driven rotor system and its digital twin model is established according to step 2. Figure 3 (a) shows the simulation results of the physical object of the magnetically driven rotor. Figure 3 (b) shows the simulation results of the digital twin model of the magnetically driven rotor, and the two are highly consistent. The experimental results demonstrate that the proposed parameter identification method can accurately identify key parameters and effectively synchronize the states of the physical system and the digital twin model.

[0100] Figure 4Based on the sensor fault mode and digital twin model takeover results, perform sensor fault judgment according to step 3. Figure 4 (a) shows the simulation results of the instability of the magnetically driven rotor system under sensor failure conditions. Figure 4 (b) shows that the system can still run stably after the simulated digital twin model is connected.

[0101] Figure 5 Based on the controller fault mode and digital twin model control results, perform controller fault judgment according to step 4. Figure 5 (a) shows the simulation results of the instability of the magnetically driven rotor system under controller failure conditions. Figure 5 (b) shows that the system can still run stably after the simulated digital twin model is connected. Figure 4 and Figure 5 Experimental results show that the proposed fault control method for magnetically driven rotor systems based on digital twins can effectively address sensor and controller failures and ensure stable system operation.

[0102] In summary, this invention provides a twin control method for magnetically driven rotor systems oriented towards fault scenarios. It constructs a dynamic digital twin model that evolves synchronously with and is deeply coupled to the magnetically driven rotor system. By providing a highly reliable virtual sensing and compensation mechanism under fault conditions, it achieves fault-tolerant control of the system. The method provided by this invention has significant advantages and is applicable to modeling and maintenance management under different physical objects and multiple fault modes.

[0103] In its specific implementation, this application provides a computer storage medium and a corresponding data processing unit. The computer storage medium is capable of storing a computer program, which, when executed by the data processing unit, can run the invention's content regarding a twin control method for a magnetically driven rotor system oriented towards fault scenarios, as well as some or all of the steps in various embodiments. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0104] Those skilled in the art will clearly understand that the technical solutions in the embodiments of the present invention can be implemented using computer programs and their corresponding general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of computer programs, i.e., software products. These computer program software products can be stored in a storage medium and include several instructions to cause a device containing a data processing unit (which may be a personal computer, server, microcontroller, MCU, or network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments of the present invention.

[0105] This invention provides a twin control method for a magnetically driven rotor system oriented towards fault scenarios. Many methods and approaches exist for implementing this technical solution; the above description is merely a preferred embodiment of the invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.

Claims

1. A twin control method for a magnetically driven rotor system oriented towards fault scenarios, characterized in that, Includes the following steps: Step 1: Establish a static digital twin model in the digital space to characterize the dynamic response behavior of the system when the physical system parameters remain unchanged; Step 2: Identify the key parameters of the magnetic rotor in the actual physical system in real time, and inject the identified key parameters into the static digital twin model constructed in Step 1, so that it can be transformed into a dynamic digital twin model that can reflect the changes in the state of the physical system in real time. Step 3: In the case of sensor failure in the physical system, a dynamic digital twin model is used to replace the physical object, so that the magnetic rotor system can maintain stable operation in the case of sensor failure. Step 4: In the event of controller failure in the physical system, a dynamic digital twin model is used to directly control the physical object, so that the magnetic rotor system can maintain stable operation in the event of controller failure.

2. The twin control method for a magnetically driven rotor system oriented towards fault scenarios according to claim 1, characterized in that, In step 1, a static digital twin model of the magnetic rotor system in digital space is constructed, including a dynamic model of the radial magnetic bearing-rotor system.

3. The twin control method for a magnetically driven rotor system oriented towards fault scenarios according to claim 1, characterized in that, In step 1, PID control is used to model the controller in the digital space.

4. The twin control method for a magnetically driven rotor system oriented towards fault scenarios according to claim 1, characterized in that, In step 2, the key parameters are current stiffness and displacement stiffness.

5. The twin control method for a magnetically driven rotor system oriented towards fault scenarios according to claim 4, characterized in that, In step 2, the specific process of identifying the parameters of the magnetic power rotor system is as follows: by observing the displacement, current and acceleration of the magnetic power rotor in real time, the parameters to be estimated are obtained recursively, and finally the current stiffness and displacement stiffness are calculated.

6. The twin control method for a magnetically driven rotor system oriented towards fault scenarios according to claim 5, characterized in that, In step 2, the recursive least squares method is used to continuously update the parameter estimates using new sampled data.

7. The twin control method for a magnetically driven rotor system oriented towards fault scenarios according to claim 1, characterized in that, In step 3, a sensor fault detection module is set up. This module calculates the difference between the physical sensor output value and the corresponding estimated value of the sensor output of the dynamic digital twin model in real time.

8. The twin control method for a magnetically driven rotor system oriented towards fault scenarios according to claim 7, characterized in that, In step 3, when the location of the sensor fault mark is detected... Then, immediately cut off the signal transmission between the physical sensor and the controller, and replace the faulty module with the corresponding estimated value of the digital twin model sensor output to calculate the physical sensor output value in real time.

9. The twin control method for a magnetically driven rotor system oriented towards fault scenarios according to claim 1, characterized in that, In step 4, two controller fault judgment conditions are set: Condition 1: When the deviation between the controller output in the physical space and the controller output in the digital space exceeds the error threshold and continues to exceed the error threshold for a certain period of time, wherein the certain period of time is not less than the larger of twice the synchronization period of the digital twin model and the voltage ripple decay time of the Buck circuit, and not greater than five times the larger value. Condition 2: When the controller output in the physical space is continuously in a saturated state, i.e., at the maximum or minimum voltage.

10. A twin control method for a magnetically driven rotor system oriented towards fault scenarios according to claim 9, characterized in that, In step 4, after determining that a controller malfunction has occurred, the controller flag is set. It immediately cuts off the output of the fault controller through an electronic switch and connects the output of the digital controller to the power amplifier to ensure the stable operation of the magnetic rotor system in the physical space.

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