Satellite maglev control system fault detection method and system based on mode conversion

Abstract

The invention provides a satellite maglev control system fault detection method and system based on mode conversion, and the method comprises the steps: constructing a first-order mode conversion matrix A at moments before and after a parameter according to a telemetering parameter state and the longest state maintenance time in historical telemetering data in a mode of uniformly quantifying the telemetering data, on the basis of the matrix A, the change trend of telemetering parameters in two modal transformations is extracted, the quantization series division error of the first-order modal transformation is eliminated, so that a second-order modal transformation matrix B is constructed, and fault detection is carried out on telemetering data generated in the actual test of the magnetic levitation control system according to the matrix A and the matrix B. Meanwhile, kinetic model parameter data is adopted as backup parameters of satellite telemetering, and fault detection is assisted according to the difference relation between the main telemetering parameters and the backup telemetering parameters. According to the method, the time sequence characteristic information in the telemetry data of the magnetic levitation control system can be effectively utilized, and a new feasible method is provided for satellite fault analysis and diagnosis.

Description

Technical Field

[0001] This invention relates to the field of satellite fault detection, and more specifically, to a fault detection method and system for satellite magnetic levitation control systems based on mode conversion. Background Technology

[0002] Currently, with high-resolution Earth observation, laser communication, and high-precision mapping satellites placing extremely high demands on pointing accuracy and stability, the "ultra-precise and ultra-stable" control of satellites has become a key factor to consider during the design process. To meet ultra-high pointing accuracy (better than...) ) and ultra-high attitude stability (better than To meet the satellite design requirements of dual-super performance, a certain satellite adopts a satellite platform design method of "dynamic and static spatial isolation between vibration source and payload, and master-slave collaborative control". The magnetic levitation control system is used to isolate the payload compartment and the platform compartment, eliminating the interference of micro-vibrations generated by moment gyroscopes, thrusters and other components on the payload imaging accuracy, thus realizing the dual-super performance of the satellite platform.

[0003] Because satellite maglev control systems require high precision, they also place higher demands on the reliability of satellite attitude control. Therefore, any abnormal disturbance to the satellite's attitude can cause the maglev control system to fail, rendering the satellite unusable. Telemetry data characterizes all satellite operational information. Analyzing and processing this telemetry data to extract useful features and promptly detect potential faults is crucial, allowing for preventative damage before it becomes too great.

[0004] Currently, various technical methods exist in the field of satellite fault detection, among which threshold detection is widely used. This method sets upper and lower thresholds and judgment rules for telemetry channels based on prior knowledge. However, the setting of thresholds and rules relies on human experience, resulting in low sensitivity and accuracy of fault detection. Often, obvious manifestations only appear after the fault range has expanded, making it difficult to meet the early fault warning requirements of maglev control systems. Meanwhile, data-driven fault detection methods such as neural networks, set membership estimation, and Bayesian networks are also applied to satellite fault detection. However, the calculation process of these methods is often complex, requiring extensive data cleaning of the original telemetry data in practical engineering applications. This can easily lead to misclassification of data that is far from normal telemetry data or occurs with low frequency as faulty data. Furthermore, the calculation process only utilizes partial information such as distance and frequency of occurrence in the telemetry data, failing to fully explore the deeper features contained within the data.

[0005] In recent years, with the rapid improvement of computing power, it has become possible to extract patterns from massive amounts of telemetry data and detect telemetry anomalies. Satellite telemetry data can be transformed into a series of time-series data. Utilizing its high correlation with time, the telemetry data can be quantized and converted into modal data. First-order and second-order mode transformation matrices are then established based on the data characteristics, and the information in the matrices is used for subsequent fault detection. Therefore, applying the mode transformation method to fault detection in satellite magnetic levitation control systems is of great significance for improving satellite fault analysis and diagnosis capabilities. Summary of the Invention

[0006] In view of the deficiencies in the existing technology, the purpose of this invention is to provide a fault detection method and system for satellite magnetic levitation control systems based on mode conversion.

[0007] The present invention provides a fault detection method for a satellite magnetic levitation control system based on mode conversion, comprising: performing fault detection by a fault detection method based on mode conversion and / or a fault detection method based on backup parameters; if a fault is detected by at least one method at any time, it is determined that a fault has occurred. The fault detection method based on mode transformation includes: constructing a first-order mode transformation matrix A and a second-order mode transformation matrix B using primary telemetry parameters; detecting whether the current primary telemetry parameters conform to the mode transformation relationship corresponding to the first-order mode transformation matrix A or the second-order mode transformation matrix B; if they do not conform, it is determined that a fault has occurred. The fault detection method based on backup parameters includes: constructing a judgment rule using the maximum difference between the primary and backup telemetry parameters, detecting whether the current difference between the primary and backup telemetry parameters exceeds the maximum difference in the judgment rule, and if it does, determining that a fault has occurred. The primary telemetry parameters include: the relative position Z of the two cabins of the satellite maglev control system, the readings of the eddy current displacement sensor and the laser displacement sensor, and the attitude angle Z used for control of the attitude and orbit control subsystem; The backup telemetry parameters include: the relative displacement between the two compartments (Dd) of the dynamic model, the eddy current output (Dd), the laser output (Dd), and the dlx inertial attitude angle (Zd).

[0008] Preferably, the fault detection method based on mode conversion includes: Step S1.1: By uniformly quantizing the primary telemetry parameters into modal data, first-order mode transformation information and second-order mode transformation information are extracted, and first-order mode transformation matrix A and second-order mode transformation matrix B are constructed based on the above features; Step S1.2: Check whether the mode transformation relationship of the current primary telemetry parameter measured data conforms to the mode transformation relationship corresponding to the first-order mode transformation matrix A or the second-order mode transformation matrix B. If it does not conform, it is determined that a fault has occurred.

[0009] Preferably, step S1.1 includes: Step S1.1.1: By uniformly quantizing the training data of the primary telemetry parameters into modal data, if the first... The first mode and the first If the modes can be converted to each other, then set the parameter to 1 at the corresponding position, that is... ,otherwise The mode transition is directional. and Since they cannot be equivalently substituted, a first-order mode transformation matrix A is constructed. Step S1.1.2: If in the first... The first mode and the first If the transitions between two consecutive modes maintain an upward trend, then set the parameter to 1 at the corresponding position. If both the preceding and following conversions maintain a downward trend, then If the previous trend turned into an upward trend and the subsequent trend turned into a downward trend, then If the previous trend turned into a downtrend and the subsequent trend turned into an uptrend, then If the trend remains unchanged, then In second-order mode transition and They cannot be equivalently substituted, thus constructing a first-order mode transformation matrix B.

[0010] Preferably, the fault detection method based on mode transition further includes: extracting the longest duration of a mode by comparing the time during which a mode does not transition in the first-order mode transition matrix A; detecting whether the duration of the mode in the current primary telemetry parameter measured data exceeds the longest time that the mode can be maintained; if it does, it is determined that a fault has occurred.

[0011] Preferably, the fault detection method based on backup parameters includes: Step S2.1: By obtaining the correspondence between the dynamic model and the on-board telemetry parameters, calculate the difference between each pair of telemetry parameters, and set the maximum value of the difference and its correspondence as a rule; Step S2.2: Obtain the correspondence between the dynamic model and the on-board telemetry parameters from the rule base, compare the difference between the measured data of the primary and backup telemetry parameters with the rule base, and check whether it exceeds the maximum difference limit of the rule base. If it exceeds, it is determined that a fault has occurred.

[0012] The present invention provides a fault detection system for a satellite magnetic levitation control system based on mode conversion, comprising: performing fault detection by a fault detection module based on mode conversion and / or a fault detection module based on backup parameters; if a fault is detected by at least one method at any time, it is determined that a fault has occurred. The fault detection module based on mode conversion is used to construct a first-order mode conversion matrix A and a second-order mode conversion matrix B using the primary telemetry parameters, and to detect whether the current primary telemetry parameters conform to the mode conversion relationship corresponding to the first-order mode conversion matrix A or the second-order mode conversion matrix B. If they do not conform, it is determined that a fault has occurred. The fault detection module based on backup parameters is used to construct a judgment rule using the maximum difference between the primary and backup telemetry parameters, and to detect whether the current difference between the primary and backup telemetry parameters exceeds the maximum difference in the judgment rule. If it does, it is determined that a fault has occurred. The primary telemetry parameters include: the relative position Z of the two cabins of the satellite maglev control system, the readings of the eddy current displacement sensor and the laser displacement sensor, and the attitude angle Z used for control of the attitude and orbit control subsystem; The backup telemetry parameters include: the relative displacement between the two compartments (Dd) of the dynamic model, the eddy current output (Dd), the laser output (Dd), and the dlx inertial attitude angle (Zd).

[0013] Preferably, the fault detection module based on mode conversion includes: Module M1.1: By uniformly quantizing the primary telemetry parameters into modal data, first-order mode transformation information and second-order mode transformation information are extracted, and first-order mode transformation matrix A and second-order mode transformation matrix B are constructed based on the above features; Module M1.2: Detects whether the mode transformation relationship of the current master telemetry parameter measured data conforms to the mode transformation relationship corresponding to the first-order mode transformation matrix A or the second-order mode transformation matrix B. If it does not conform, it is determined that a fault has occurred.

[0014] Preferably, module M1.1 includes: Module M1.1.1: By uniformly quantizing the training data of the primary telemetry parameters into modal data, if the first... The first mode and the first If the modes can be converted to each other, then set the parameter to 1 at the corresponding position, that is... ,otherwise The mode transition is directional. and Since they cannot be equivalently substituted, a first-order mode transformation matrix A is constructed. Module M1.1.2: If in the... The first mode and the first If the transitions between two consecutive modes maintain an upward trend, then set the parameter to 1 at the corresponding position. If both the preceding and following conversions maintain a downward trend, then If the previous trend turned into an upward trend and the subsequent trend turned into a downward trend, then If the previous trend turned into a downtrend and the subsequent trend turned into an uptrend, then If the trend remains unchanged, then In second-order mode transition and They cannot be equivalently substituted, thus constructing a first-order mode transformation matrix B.

[0015] Preferably, the fault detection module based on mode transition further includes: extracting the longest duration of a mode by comparing the time during which a mode does not transition in the first-order mode transition matrix A; detecting whether the duration of the mode in the current primary telemetry parameter measured data exceeds the longest time that the mode can be maintained; if it does, it is determined that a fault has occurred.

[0016] Preferably, the fault detection module based on backup parameters includes: Module M2.1: By obtaining the correspondence between the dynamic model and the on-board telemetry parameters, calculate the difference between each pair of telemetry parameters, and set the maximum value of the difference and its correspondence as a rule; Module M2.2: Obtains the correspondence between the dynamic model and the on-board telemetry parameters from the rule base, compares the difference between the measured data of the primary and backup telemetry parameters with the rule base, and checks whether it exceeds the maximum difference limit of the rule base. If it does, it is determined that a fault has occurred.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. Compared with threshold detection methods, the modal conversion method of this invention can fully mine the temporal information of telemetry data and obtain the changing trend of telemetry data by constructing first-order and second-order modal conversion matrices, thereby improving the accuracy of fault detection. Compared with intelligent algorithms such as neural networks, the modal matrix construction process is simple, and the fault detection sensitivity can be improved with less computing resources by comparing the main and backup telemetry parameters. 2. The present invention provides a fault detection method for a satellite maglev control system based on mode conversion, which aims to enhance the fault detection capability of the satellite maglev control system during testing and ensure the safety of satellite testing. 3. This invention can effectively utilize the timing characteristic information in the telemetry data of the maglev control system, providing a new and feasible method for satellite fault analysis and diagnosis. Attached Figure Description

[0018] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart of the fault detection method based on mode conversion; Figure 2 This is a flowchart of the modal transition fault detection method; Figure 3 This is a flowchart of the backup parameter fault detection method. Detailed Implementation The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0019] Example 1 The present invention provides a fault detection method for a satellite magnetic levitation control system based on mode conversion, comprising: performing fault detection by a fault detection method based on mode conversion and / or a fault detection method based on backup parameters; if a fault is detected by at least one method at any time, it is determined that a fault has occurred. The fault detection method based on mode transformation includes: constructing a first-order mode transformation matrix using primary telemetry parameter data. A and second-order mode transformation matrix B The system checks whether the telemetry parameter conforms to the mode transformation relationship corresponding to the first or second order mode transformation matrix. If it does not conform, it is determined that a fault has occurred. The fault detection method based on backup parameters constructs a judgment rule using the maximum difference between the primary and backup telemetry parameters. It detects whether the difference between the primary and backup data of the telemetry parameter exceeds the maximum difference in the judgment rule. If it does, it is determined that a fault has occurred.

[0020] The primary telemetry parameters include: the relative position Z of the two cabins of the satellite maglev control system, the readings of the eddy current displacement sensor and the laser displacement sensor, and the attitude angle Z used for control of the attitude and orbit control subsystem; The backup telemetry parameters include: the relative displacement between the two compartments (Dd) of the dynamic model, the eddy current output (Dd), the laser output (Dd), and the dlx inertial attitude angle (Zd).

[0021] Specifically, the fault detection method based on mode conversion includes a learning process and a monitoring process; the learning process involves uniformly quantizing telemetry parameter training data into modal data, extracting first-order mode conversion information and second-order mode conversion information, and constructing a first-order mode conversion matrix based on the above features. Aand second-order mode transformation matrix B The monitoring process detects whether the measured data of the telemetry parameter conforms to the mode transformation relationship corresponding to the first-order or second-order mode transformation matrix. If it does not conform, it is determined that a fault has occurred.

[0022] Specifically, the first-order mode transformation matrix A This is achieved by uniformly quantizing the training data of the primary telemetry parameters into modal data, if the first... The first mode and the first If the modes can be converted to each other, then set the parameter to 1 at the corresponding position, that is... ,otherwise The mode transition is directional. and Since they cannot be equivalently substituted, a first-order mode transformation matrix A is constructed. The second-order mode transition matrix B If in the first The first mode and the first If the transitions between two consecutive modes maintain an upward trend, then set the parameter to 1 at the corresponding position. If both the preceding and following conversions maintain a downward trend, then If the previous trend turned into an upward trend and the subsequent trend turned into a downward trend, then If the previous trend turned into a downtrend and the subsequent trend turned into an uptrend, then If the trend remains unchanged, then In second-order mode transition and They cannot be equivalently substituted, thus constructing the first-order mode transformation matrix B. The fault detection method based on mode transition further includes: extracting the longest duration of a mode by comparing the time during which a mode does not transition in the first-order mode transition matrix A; detecting whether the duration of the mode in the current primary telemetry parameter measured data exceeds the longest duration that the mode can be maintained; if it does, it is determined that a fault has occurred.

[0023] The first-order mode transformation matrix A and second-order mode transformation matrix B The telemetry parameters for the satellite maglev control system consist of four telemetry parameters: the relative position Z of the two cabins, the readings of the eddy current displacement sensor and the laser displacement sensor, and the attitude angle Z used for control of the attitude and orbit control subsystem.

[0024] The fault detection method based on backup parameters includes a learning process and a monitoring process. The learning process obtains the correspondence between the dynamic model and the on-board telemetry parameters, calculates the difference between each pair of telemetry parameters, and sets the maximum difference and its correspondence as a rule. The monitoring process obtains the correspondence between the dynamic model and the on-board telemetry parameters from the rule base, compares the difference between the measured data of the primary and backup telemetry parameters with the rule base, and detects whether it exceeds the maximum difference limit of the rule base. If it exceeds the limit, a fault is determined to have occurred.

[0025] The present invention also provides a fault detection system for a satellite maglev control system based on mode conversion. The fault detection system for a satellite maglev control system based on mode conversion can be implemented by executing the process steps of the fault detection method for a satellite maglev control system based on mode conversion. That is, those skilled in the art can understand the fault detection method for a satellite maglev control system based on mode conversion as a preferred embodiment of the fault detection system for a satellite maglev control system based on mode conversion.

[0026] Example 2 Example 2 is a preferred example of Example 1. According to the present invention, a fault detection method for a satellite magnetic levitation control system based on mode conversion is provided, such as... Figure 1 As shown, the process includes three steps: fault detection based on mode transformation, fault detection based on backup parameters, and fusion-improved fault detection. The fault detection method based on mode transformation constructs a first-order mode transformation matrix using primary telemetry parameter data. A and second-order mode transformation matrix B The method detects whether the telemetry parameter conforms to the mode transformation relationship corresponding to the first or second order mode transformation matrix. If it does not conform, it is determined that a fault has occurred. The fault detection method based on backup parameters constructs a judgment rule using the maximum difference between the primary and backup telemetry parameters. It detects whether the difference between the primary and backup data of the telemetry parameter exceeds the maximum difference in the judgment rule. If it exceeds the maximum difference, it is determined that a fault has occurred. The fusion improved fault detection method uses the mode transformation fault detection method to detect the primary telemetry parameter and the backup parameter fault detection method to detect all telemetry parameters with corresponding backup relationships. If at least one method detects a fault at a certain time, it is determined that a fault has occurred.

[0027] like Figure 2 As shown, the steps for constructing a first-order mode transformation matrix are as follows: The telemetry parameters of the satellite magnetic levitation control system are uniformly quantized into 100 orders of magnitude modes. The number of rows in the matrix is ​​set to the total number of quantization levels. If the i-th mode and the j-th mode can be mutually converted, then a first-order mode transformation matrix is ​​set. AThe corresponding positional parameter is set to 1, otherwise it is set to 0, thus constructing the first-order mode transition matrix. Because mode transitions are directional, the matrix... A It is asymmetric, that is... and They cannot be equivalently substituted. Furthermore, the first-order mode transition matrix... A The longest modal duration is obtained during the construction process and stored in the first-order mode transition matrix. A On the diagonal; The steps for constructing a second-order mode transition matrix are as follows: Second-order mode transitions are divided into four cases: a trend-maintaining state where two consecutive transitions maintain an upward (or downward) trend, a trend-changing state where the previous transition was an upward (or downward) trend, and the subsequent transition is a downward (or upward) trend. The corresponding transition relationship numbers are assigned sequentially from 1 to 4. The second-order mode transition matrix is ​​obtained by comparing two adjacent first-order mode transitions with the above four second-order mode transition cases. B Specifically, if any of the four mode transition relationships appears in the telemetry parameters in the training data, the corresponding transition relationship index is added to the matrix. B If it does not occur in the corresponding position, then the matrix will be... B The values ​​at the corresponding positions are set to 0, thus constructing the second-order mode transition matrix. Because mode transitions are directional, the matrix... B It is asymmetric, that is... and They cannot be substituted for each other.

[0028] Mode transition fault detection steps: Based on the construction steps of the first-order mode transition matrix and the second-order mode transition matrix, the first-order and second-order mode transition matrices of four telemetry parameters of the satellite maglev control system—the relative position Z of the two cabins, the readings of the eddy current displacement sensor and the laser displacement sensor, and the attitude angle Z of the attitude and orbit control subsystem—are extracted from the training data and used as the basis for fault judgment. If a mode transition that does not conform to the basis occurs during satellite testing, it can be determined that a fault has occurred.

[0029] The system checks whether the duration of the current primary telemetry parameter measured data mode exceeds the maximum duration that mode can be maintained. If it does, a fault is determined to have occurred.

[0030] like Figure 3As shown, the backup parameter fault detection steps are as follows: The relative position Z of the two modules in the satellite maglev control system, the readings of the eddy current displacement sensor and the laser displacement sensor, and the attitude angle Z used for control of the attitude and orbit control subsystem are selected as primary telemetry parameters. The relative displacement Z of the two modules (D), the eddy current output (D), the laser output (D), and the dlx inertial attitude angle Z from the dynamic model are selected as corresponding backup telemetry parameters. The maximum value of the difference between the primary and backup telemetry parameters is obtained based on the training data and used as the basis for fault judgment. If the difference between the primary and backup telemetry parameters exceeds the range during satellite testing, a fault can be determined. The difference between the primary and backup telemetry parameters includes: the difference between the relative position Z of the two modules and the relative displacement Z of the two modules (D), the difference between the reading of the eddy current displacement sensor and the eddy current output (D), the difference between the reading of the laser displacement sensor and the laser output (D), and the difference between the attitude angle Z used for control and the dlx inertial attitude angle Z.

[0031] During satellite testing, the method in the mode conversion fault detection step is used to detect the primary telemetry parameters, and the method in the backup parameter fault detection step is used to detect all telemetry parameters that have corresponding backup relationships in the dynamic model. If at least one method detects a fault at a certain moment, it can be determined that a fault has occurred.

[0032] In summary, this invention provides an excellent solution for fault detection in satellite maglev control systems. The mode-transformation-based fault detection method fully leverages the time-dependent characteristics of satellite telemetry parameters, significantly improving fault detection sensitivity while minimizing algorithm complexity to ensure rapid fault detection. Furthermore, a backup parameter-based fault detection method assists in fault detection within the maglev control system, effectively enhancing the method's accuracy. This method is applicable to the testing of satellite maglev control systems and is of great significance for ensuring safety during satellite testing.

[0033] Those skilled in the art will understand that, in addition to implementing the system, apparatus, and their modules provided by this invention in purely computer-readable program code, the same program can be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system, apparatus, and their modules provided by this invention can be considered a hardware component, and the modules included therein for implementing various programs can also be considered structures within the hardware component; alternatively, modules for implementing various functions can be considered both software programs implementing the method and structures within the hardware component.

[0034] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A method for detecting faults in a satellite maglev control system based on modal transformation, the method comprising: The method comprises the following steps: The fault detection is performed by the modal transformation-based fault detection method and / or the backup parameter-based fault detection method, and it is determined that a fault occurs when at least one method detects a fault at any time; The modal transformation-based fault detection method comprises the following steps: a first-order modal transformation matrix A and a second-order modal transformation matrix B are constructed by using main telemetry parameters, and it is determined that a fault occurs if the current main telemetry parameters do not conform to the modal transformation relationship corresponding to the first-order modal transformation matrix A or the second-order modal transformation matrix B; The backup parameter-based fault detection method comprises the following steps: a determination rule is constructed by using the maximum difference between main and backup telemetry parameters, and it is determined that a fault occurs if the difference between the current main and backup telemetry parameters exceeds the maximum difference in the determination rule; The main telemetry parameters comprise the relative position Z between two cabins of a satellite magnetic suspension control system, the readings of an electric eddy current displacement sensor and a laser displacement sensor, and the attitude angle Z for control of an attitude and orbit control subsystem; The backup telemetry parameters comprise the relative displacement Z of the two cabins of a dynamic model, the electric eddy current output, the laser output, and the dlx inertial attitude angle Z.

2. The modal transformation based satellite maglev control system fault detection method of claim 1, wherein, The modal transformation-based fault detection method comprises the following steps: Step S1.1: first-order modal transformation information and second-order modal transformation information are extracted by uniformly quantizing the main telemetry parameters into modal data, and a first-order modal transformation matrix A and a second-order modal transformation matrix B are constructed based on the above features; Step S1.2: it is determined that a fault occurs if the modal transformation relationship of the current main telemetry parameter measurement data does not conform to the modal transformation relationship corresponding to the first-order modal transformation matrix A or the second-order modal transformation matrix B.

3. The modal transformation based satellite maglev control system fault detection method of claim 2, wherein, The step S1.1 comprises the following steps: Step S1.1.1: Transforming the training data of the main fraction of telemetry parameters into modal data by uniform quantization, if the first modal and the second modal can be converted into each other, set the parameter at the corresponding position to 1, i.e. , otherwise , wherein the modal conversion has directionality, and cannot be replaced equivalently, thereby constructing a first-order modal conversion matrix A; and cannot be replaced equivalently, thereby constructing a first-order modal conversion matrix A; Step S1.1.2: If the two previous transitions between the first and the second modal keep an ascending trend, then set the parameter at the corresponding position to 1, i.e. If the two previous transitions keep a descending trend, then If the previous transition is an ascending trend and the next transition is a descending trend, then If the previous transition is a descending trend and the next transition is an ascending trend, then If the trend keeps unchanged, then The second order modal transition cannot be replaced equivalently between and , thus the first order modal transition matrix B is constructed.

4. The modal transformation based satellite maglev control system fault detection method of claim 1, wherein, The modal transformation-based fault detection method further comprises the following steps: the longest duration of a modal is extracted by comparing the time during which the modal is maintained without transformation in the first-order modal transformation matrix A; and it is determined that a fault occurs if the modal duration of the current main telemetry parameter measurement data exceeds the longest duration that the modal can maintain.

5. The modal transformation based satellite maglev control system fault detection method of claim 1, wherein, The backup parameter-based fault detection method comprises the following steps: Step S2.1: the maximum difference between the difference of each pair of telemetry parameters and the corresponding relationship between the dynamic model and the telemetry parameters on the satellite is set as a rule by obtaining the corresponding relationship between the dynamic model and the telemetry parameters on the satellite and calculating the difference of each pair of telemetry parameters; Step S2.2: the corresponding relationship between the dynamic model and the telemetry parameters on the satellite is obtained from a rule library, the difference between the main and backup telemetry parameter measurement data is compared with the rule library, and it is determined that a fault occurs if the difference exceeds the maximum difference limit of the rule library.

6. A modal transformation based satellite maglev control system fault detection system, characterized in that, The method comprises the following steps: The fault detection is performed by the modal transformation-based fault detection module and / or the backup parameter-based fault detection module, and it is determined that a fault occurs when at least one method detects a fault at any time; The modal transformation-based fault detection module is configured to construct a first-order modal transformation matrix A and a second-order modal transformation matrix B by using the main telemetry parameters, detect whether the current main telemetry parameters conform to the modal transformation relationship corresponding to the first-order modal transformation matrix A or the second-order modal transformation matrix B, and determine that a fault occurs if the current main telemetry parameters do not conform to the modal transformation relationship corresponding to the first-order modal transformation matrix A or the second-order modal transformation matrix B. The backup parameter-based fault detection module is configured to construct a determination rule by using the maximum difference between the main and backup telemetry parameters, detect whether the difference between the current main and backup telemetry parameters exceeds the maximum difference in the determination rule, and determine that a fault occurs if the difference between the current main and backup telemetry parameters exceeds the maximum difference in the determination rule. The main telemetry parameters include the relative position Z of two cabins of a satellite magnetic suspension control system, the electric eddy current displacement sensor reading, the laser displacement sensor reading, and the control attitude angle Z of an orbit control subsystem. The backup telemetry parameters include the relative displacement Z of two cabins of a dynamic model, the electric eddy current output, the laser output, and the dlx inertial attitude angle Z.

7. The modal transformation based satellite maglev control system fault detection system of claim 6, wherein, The modal transformation-based fault detection module includes: Module M1.1: first-order modal transformation information and second-order modal transformation information are extracted by uniformly quantizing the main telemetry parameters into modal data, and the first-order modal transformation matrix A and the second-order modal transformation matrix B are constructed based on the above features. Module M1.2: whether the modal transformation relationship of the current main telemetry parameter measured data conforms to the modal transformation relationship corresponding to the first-order modal transformation matrix A or the second-order modal transformation matrix B is detected, and it is determined that a fault occurs if the modal transformation relationship of the current main telemetry parameter measured data does not conform to the modal transformation relationship corresponding to the first-order modal transformation matrix A or the second-order modal transformation matrix B.

8. The modal transformation based satellite maglev control system fault detection system of claim 7, wherein, The modal transformation-based fault detection module includes: Module M1.1.1: If the first and the second modal can be converted to each other, set the parameter at the corresponding position to 1, i.e. , otherwise , wherein the modal conversion has directionality, and cannot be replaced equivalently, thereby constructing a first-order modal conversion matrix A; Module M1.1.2: If the two successive transitions between the first and the second modal remain in an ascending trend, then the parameter is set to 1 at the corresponding position, i.e. if the two successive transitions remain in a descending trend, then the parameter is set to -1 at the corresponding position, i.e. if the previous transition was in an ascending trend and the next transition was in a descending trend, then the parameter is set to 0 at the corresponding position, i.e. if the previous transition was in a descending trend and the next transition was in an ascending trend, then the parameter is set to 0 at the corresponding position, i.e. if the trend remains unchanged, then the parameter is set to 0 at the corresponding position, i.e. , the second order modal transition matrix B cannot be replaced equivalently between and , thus constructing a first order modal transition matrix B.

9. The modal transformation based satellite maglev control system fault detection system of claim 6, wherein, The modal transformation-based fault detection module further includes that the longest duration of a mode is extracted by comparing the time during which the mode in the first-order modal transformation matrix A is maintained without transformation, and it is determined that a fault occurs if the modal duration of the current main telemetry parameter measured data exceeds the longest duration that the mode can maintain.

10. The modal transformation based satellite maglev control system fault detection system of claim 6, wherein, The backup parameter-based fault detection module includes: Module M2.1: the maximum difference between each pair of telemetry parameters is calculated by obtaining the corresponding relationship between the dynamic model and the telemetry parameters on the satellite, and the maximum difference and its corresponding relationship are set as a rule; Module M2.2: the corresponding relationship between the dynamic model and the telemetry parameters on the satellite is obtained from the rule library, the difference between the main and backup telemetry parameter measured data is compared with the rule library, it is detected whether the difference exceeds the maximum difference limit of the rule library, and it is determined that a fault occurs if the difference exceeds the maximum difference limit of the rule library.

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