Radio telescope active surface system state monitoring and fault diagnosis system and method

Abstract

The invention relates to the technical field of state monitoring and fault diagnosis of industrial automatic control systems, and provides a radio telescope active surface system state monitoring and fault diagnosis system for solving the technical problem that an existing telescope active surface system lacks multi-level equipment state monitoring and comprehensive fault diagnosis functions. Comprising a bottom-layer equipment execution layer, a middle control layer and a top-layer control layer which are in communication connection with one another, and the bottom-layer equipment execution layer obtains a primary diagnosis conclusion; the intermediate control layer obtains an intermediate diagnosis conclusion; and the top control layer obtains a fusion diagnosis conclusion. According to the method, fault and failure points of an actuator, controllers of all levels, power supply, communication and the like are accurately recognized and positioned through hierarchical diagnosis and superior fusion, high-reliability comprehensive decision and fault processing / recovery are achieved on a top control layer, and the diagnosis accuracy and the response speed are improved.

Description

Technical Field

[0001] This invention relates to the field of status monitoring and fault diagnosis technology for industrial automation control systems, and in particular to a system and method for status monitoring and fault diagnosis of an active surface system for a radio telescope. Background Technology

[0002] Radio telescopes with active surface systems consist of dozens to hundreds or even thousands of actuators (specially designed servo electric cylinders or electro-hydraulic cylinders, integrating control and drive modules, and equipped with various types of sensors) to compensate for changes in the reflector surface shape caused by the weight of the antenna back frame structure, temperature deformation, and panel manufacturing errors. The active surface system has a large number of actuators and requires supporting subsystems or equipment such as power distribution and networking. The system is large, complex in structure, and has numerous interfaces; any failure in any number of devices will reduce the receiving efficiency of the antenna reflector.

[0003] Existing active surface telescope systems generally employ alarm mechanisms based on fixed thresholds. Fault diagnosis heavily relies on the personal experience and knowledge of maintenance personnel, resulting in slow response times and a high risk of misdiagnosis. Furthermore, due to the hierarchical coupling of power supply, network, and controllers, as well as the coupling between actuators, data from a single level or individual component is often incomplete, leading to insufficient comprehensive fault diagnosis information and high rates of misdiagnosis and missed diagnosis. In other words, existing active surface telescope systems lack multi-level equipment status monitoring and comprehensive fault diagnosis capabilities, or primarily rely on independent equipment self-testing and centralized data processing and analysis, lacking a systematic analysis of fault types and locations. The lack of hierarchical resource matching for system controller hardware and multi-dimensional sensor integration makes it difficult to meet the dual requirements of timeliness and accuracy. Summary of the Invention

[0004] To address the aforementioned problems, this invention aims to provide a system and method for monitoring the status and diagnosing faults in the active surface system of a radio telescope. Through hierarchical diagnosis and fusion with higher levels, it accurately identifies and locates the failure points of actuators and each level, achieving highly reliable fusion diagnostic conclusions at the top control layer, thereby improving diagnostic accuracy and response speed.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: This application discloses a condition monitoring and fault diagnosis system for an active surface system of a radio telescope, including: The underlying device execution layer is used to acquire data from the actuator itself, extract features from the acquired data, generate primary features, perform primary diagnosis on the primary features, and obtain a primary diagnostic conclusion. The intermediate control layer is used to receive data from the underlying device execution layer, and to acquire voltage signals and vibration data from the actuator. It extracts features from the data to generate intermediate features. It also monitors the network of the intermediate control layer to obtain diagnostic conditions. Combining the diagnostic conditions, intermediate features, and primary diagnostic conclusions, it obtains intermediate diagnostic conclusions. The top-level control layer receives data from the bottom-level device execution layer and the intermediate control layer, and acquires the voltage signal and vibration data of the actuator. It then extracts features from the obtained data to generate top-level features. Combining the primary diagnostic conclusions, intermediate diagnostic conclusions, and top-level features, it obtains a fusion diagnostic conclusion.

[0006] Furthermore, the underlying device execution layer includes: The underlying data acquisition module is used to acquire data from within the actuator itself; The underlying external sensor is used to acquire data on the external temperature of the actuator itself; The underlying feature extraction module is used to extract features from the data acquired by the underlying data acquisition module and the underlying external sensors to generate primary features. The underlying diagnostic module performs primary diagnostics on the primary features to obtain primary diagnostic conclusions.

[0007] Furthermore, the actuator itself contains data on position, temperature, voltage, and current.

[0008] Furthermore, the intermediate control layer includes: The middle-layer data acquisition module is used to receive data acquired by the underlying device execution layer; The middle layer external sensor is used to acquire the voltage signal inside the actuator and the vibration data of the telescope reflector back frame structure at the actuator installation position; The mid-layer monitoring module is used to monitor the network of the intermediate control layer and obtain diagnostic conditions; The mid-level feature extraction module is used to acquire data from the underlying device execution layer and the mid-level external sensors, and to extract features from the data to generate mid-level features; The intermediate-level diagnostic module is used to combine diagnostic conditions, intermediate-level features, and primary-level diagnostic conclusions to obtain intermediate-level diagnostic conclusions.

[0009] Furthermore, the top-level control layer includes: The top-level data acquisition module is used to receive data from the underlying device execution layer and the intermediate control layer; Top-level external sensors are used to acquire the voltage signal of the actuator's power input and the vibration data of the telescope reflector back frame structure at the actuator's mounting position; The top-level feature extraction module is used to acquire data from the underlying device execution layer, the intermediate control layer, and the top-level external sensors, and to extract features from the data to generate top-level features. The top-level diagnostic module is used to combine primary diagnostic conclusions, intermediate diagnostic conclusions, and top-level features to obtain a fusion diagnostic conclusion.

[0010] Furthermore, the top-level control layer also includes a human-machine interface device module, which is used to monitor the status or diagnose faults of the top-level data acquisition module and the top-level external sensors, as well as to store data and execute computing tasks.

[0011] Furthermore, Intermediate features include: generating primary features, frequency domain primary features, and sector coupling features from data obtained from the underlying device execution layer, actuator voltage signals, and vibration data; Top-level features include: generating primary features, time-domain features, frequency-domain features, system coupling features, and time-series features from data obtained from the underlying device execution layer and intermediate control layer, actuator voltage signals, and vibration data.

[0012] This application also discloses a method for condition monitoring and fault diagnosis of an active surface system of a radio telescope, including the following steps: Step 1: Obtain the actuator's own data, extract features from the acquired data to generate primary features, perform primary diagnosis on the primary features, and obtain a primary diagnostic conclusion. Step 2: Receive data from the execution layer of the underlying device, and obtain the voltage signal and vibration data of the actuator. Extract features from the obtained data to generate intermediate features; and use the network for monitoring the intermediate control layer to obtain diagnostic conditions. Combine the diagnostic conditions, intermediate features, and primary diagnostic conclusions to obtain intermediate diagnostic conclusions. Step 3: Receive data from the underlying device execution layer and the intermediate control layer, and obtain the voltage signal and vibration data of the actuator. Extract features from the obtained data to generate top-level features. Combine the primary diagnostic conclusion, intermediate diagnostic conclusion, and top-level features to obtain a fusion diagnostic conclusion.

[0013] Furthermore, step 2 includes: Step 2-1: Receive data obtained from the underlying device execution layer; Step 2-2: Obtain the voltage signal inside the actuator and the vibration data of the telescope reflector back frame structure at the actuator installation location; Steps 2-3: Monitor the network of the intermediate control layer to obtain diagnostic conditions; Steps 2-4: Receive data from the underlying device execution layer and the middle layer external sensors, and extract features from the data to generate intermediate features; Steps 2-5: Combining diagnostic criteria, intermediate characteristics, and primary diagnostic conclusions, we arrive at an intermediate diagnostic conclusion.

[0014] Furthermore, step 3 includes: Step 3-1: Receive data from the underlying device execution layer and the intermediate control layer; Step 3-2: Obtain the voltage signal of the actuator's power input and the vibration data of the telescope reflector back frame structure at the actuator's installation location; Step 3-3: Collect data from the bottom-level device execution layer, the intermediate control layer, and the top-level external sensors, and extract features from the data to generate top-level features; Steps 3-4: Combine the primary diagnostic conclusion, intermediate diagnostic conclusion, and top-level features to obtain the fusion diagnostic conclusion.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This application's system adopts a three-tiered hierarchical diagnostic architecture comprising a low-level device execution layer, an intermediate control layer, and a top-level control layer, progressively achieving precise fault location and handling. By acquiring, processing, and analyzing multi-dimensional sensor data and status information in real time, it achieves comprehensive adaptive data acquisition. Feature extraction progresses from primary features to sector-coupled features, and then to time-series features, progressively deepening to achieve multi-dimensional feature fusion. The diagnostic process supports automatic and manual decision-making interaction, ensuring high reliability and flexibility in diagnosis.

[0016] The comprehensiveness and accuracy of fault detection are improved through hierarchical feature processing and diagnostic combinations. In particular, the top layer of the system enhances fault prediction and type identification capabilities by integrating global multi-source data and historical information. In terms of response speed, the bottom layer can handle emergency faults and execute immediately, while the middle and top layers perform step-by-step analysis of complex faults, forming a rapid, multi-layered fault handling closed loop. This is especially suitable for large active surface radio telescope systems requiring high reliability and real-time response, ensuring continuous stability and precise control of the system under complex operating conditions. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the three-layer control structure of the present invention.

[0018] Figure 2 This is a flowchart of the execution status monitoring and fault diagnosis of the top, middle and bottom layers of this invention.

[0019] Figure 3 This is a task flowchart for fault diagnosis and handling in the top, middle and bottom layers of this invention.

[0020] Figure 4 This invention covers the tasks of top-level, middle-level, and bottom-level data acquisition, data processing, diagnosis, and information uploading.

[0021] Figure 5 This invention provides the diagnostic decision logic for hierarchical inference and comprehensive decision-making in active surface system fault diagnosis. Detailed Implementation

[0022] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0023] Please see Figures 1-5 This application discloses a status monitoring and fault diagnosis system for an active surface system of a radio telescope, comprising: The underlying device execution layer is used to acquire data from the actuator itself, extract features from the acquired data, generate primary features, perform primary diagnosis on the primary features, and obtain a primary diagnostic conclusion. The intermediate control layer is used to receive data from the underlying device execution layer, and to acquire voltage signals and vibration data from the actuator. It extracts features from the data to generate intermediate features. It also monitors the network of the intermediate control layer to obtain diagnostic conditions. Combining the diagnostic conditions, intermediate features, and primary diagnostic conclusions, it obtains intermediate diagnostic conclusions. The top-level control layer receives data from the bottom-level device execution layer and the intermediate control layer, and acquires the voltage signal and vibration data of the actuator. It then extracts features from the obtained data to generate top-level features. Combining the primary diagnostic conclusions, intermediate diagnostic conclusions, and top-level features, it obtains a fusion diagnostic conclusion.

[0024] The underlying device execution layer, the intermediate control layer, and the top control layer are interconnected, and the communication connection is mainly based on high-speed industrial Ethernet protocols such as EtherCAT or commercial Ethernet protocols such as TCP / IP and UDP, or a hybrid construction is also possible; It can communicate with external sensors at various levels using fieldbuses such as Modbus and CANopen, or industrial Ethernet such as EtherCAT and Profinet, through the built-in conversion or external coupling communication module of the underlying device execution layer. External sensors at various levels can also communicate using custom communication protocols, but they need to be converted to the backbone network protocol through gateway devices such as protocol conversion modules and coupling modules.

[0025] Furthermore, the external sensors connected to the bottom-level device execution layer are mainly temperature sensors, which acquire the temperature data of the actuator housing, that is, the temperature data of the interface between the environment and the machine body; the sensors connected to the middle control layer and the top control layer through the network of the middle layer and the actuator are force sensors and vibration sensors, which acquire the voltage signal and vibration data.

[0026] Among them, the vibration data obtained by the intermediate control layer refers to the vibration data of the telescope reflector back frame structure at the actuator installation position, and the data obtained by the force sensor is the voltage signal inside the actuator; the data obtained by the force sensor of the top control layer is the voltage signal related to the power input of the actuator.

[0027] Specifically, the underlying device execution layer includes: The underlying data acquisition module is used to acquire data from within the actuator itself; The underlying external sensor is used to acquire data on the external temperature of the actuator itself; The underlying feature extraction module is used to extract features from the data acquired by the underlying data acquisition module and the underlying external sensors to generate primary features. The underlying diagnostic module performs primary diagnostics on the primary features to obtain primary diagnostic conclusions.

[0028] It is important to note that, apart from actuators, other devices in the underlying device execution layer possess certain status detection and diagnostic capabilities, and can upload status data or self-diagnose fault codes. The requirements for status monitoring and fault diagnosis at this layer are as follows: for integrated, non-customizable devices, only their open functions and computing / storage capabilities are utilized. For customizable devices such as actuators, data processing and fault diagnosis utilize the margin beyond the main control functions guaranteed within the control cycle of their own control chip.

[0029] The actuator is a specially designed servo electric cylinder or electro-hydraulic cylinder, integrating control and drive modules, and equipped with various types of sensors to compensate for changes in the reflective surface shape caused by the weight of the antenna back frame structure, temperature deformation, and panel manufacturing errors. A small number of external sensors can be connected to the actuator, but this must comply with the actuator's hardware interface design requirements. Alternatively, external sensors can be directly connected to the network where the actuator resides, and their information can be read by the actuator or by an upper-level intermediate controller. This can be simplified to allow the actuator interface to be directly connected to external sensors that are read and processed by the actuator's MCU, and also to external sensors connected to the network where the actuator resides, whose information is still read by the actuator (the object on which the sensor is installed and measured).

[0030] The actuator itself contains data on position, temperature, voltage, and current.

[0031] The intermediate control layer includes: The middle-layer data acquisition module is used to receive data acquired by the underlying device execution layer; The middle layer external sensor is used to acquire the voltage signal inside the actuator and the vibration data of the telescope reflector back frame structure at the actuator installation position; The mid-layer monitoring module is used to monitor the network of the intermediate control layer and obtain diagnostic conditions; The specific diagnostic criteria are as follows: Some faults can cause force coupling due to the mechanical structure that connects the actuators, resulting in a certain degree of mutual influence. For example, an actuator that cannot move after a fault may cause deviations in the movement displacement of surrounding normal actuators due to the nearby mechanical structure. In the initial diagnosis, this deviation may be judged as a fault. However, by associating the initial conclusion of this actuator with the coupling relationship of the sector and the current movement position of another truly faulty actuator, more comprehensive diagnostic criteria can be provided, leading to a relatively more accurate intermediate diagnostic conclusion.

[0032] The network of the mid-level monitoring module adopts industrial Ethernet, industrial bus or other network protocols. It has certain status monitoring and diagnostic functions and provides status codes or fault codes to the mid-level controller of the intermediate control layer. These codes can be used as diagnostic conditions for abnormal or faulty data communication between the actuators connected to the mid-level controller and the intermediate level.

[0033] The mid-level feature extraction module is used to acquire data from the underlying device execution layer and the mid-level external sensors, and to extract features from the data to generate mid-level features; Intermediate features include: primary features, frequency domain primary features, and sector coupling features generated from data acquired by the underlying device execution layer, voltage signals acquired by external sensors, and vibration data; The intermediate-level diagnostic module is used to combine diagnostic conditions, intermediate-level features, and primary-level diagnostic conclusions to obtain intermediate-level diagnostic conclusions.

[0034] Specifically, the intermediate control layer includes the following devices or sensors: intermediate controller, external sensors, intermediate network devices, and edge computing devices; there are multiple of the above devices or sensors.

[0035] Specifically, the intermediate control layer primarily consists of a mid-level controller, but also includes external mid-level sensors with autonomous data acquisition and communication capabilities as an additional extension for system status monitoring. It also includes mid-level network devices for communication between the mid-level controller and actuators, as well as external mid-level sensors. Furthermore, it includes edge computing devices that may be added to the mid-level controller and actuators as additional extensions for mid-level data processing and fault diagnosis.

[0036] The middle-layer network device is connected to both the middle-layer controller and the middle-layer external sensors. The edge computing device is added between the middle-layer controller and the actuator and is connected to both.

[0037] It should be noted that the top-level controller and several middle-level controllers form a network, which can contain devices such as external sensors. Each middle-level controller and several actuators below it form a network, which can also be connected to the bottom-level external sensors.

[0038] The intermediate controller is configured to collect data from mid-layer network devices and mid-layer external sensors, as well as data uploaded by actuators.

[0039] The intermediate control layer is capable of implementing signal thresholding and logic diagnostics, as well as executing complex diagnostic algorithms with a certain amount of data. For devices that cannot be further developed, this layer's ability to perform status monitoring and fault diagnosis utilizes only its open functionalities and computing and storage capabilities. For data processing and fault diagnosis by the intermediate controller, it uses a margin beyond the main control functions within its control cycle. Edge computing devices, whether shared or dedicated to monitoring and diagnostics, can have their load percentage allocated as needed.

[0040] The top-level control layer includes: The top-level data acquisition module is used to receive data from the underlying device execution layer and the intermediate control layer; Top-level external sensors are used to acquire the voltage signal of the actuator's power input and the vibration data of the telescope reflector back frame structure at the actuator's mounting position; The top-level feature extraction module is used to acquire data from the underlying device execution layer, the intermediate control layer, and the top-level external sensors, and to extract features from the data to generate top-level features. The top-level diagnostic module is used to combine primary diagnostic conclusions, intermediate diagnostic conclusions, and top-level features to obtain a fusion diagnostic conclusion.

[0041] The top-level control layer also includes a human-machine interface device module, which is used to monitor the status or diagnose faults of the top-level data acquisition module and the top-level external sensors, as well as to store data and execute computing tasks.

[0042] The top-level control layer includes the following devices and sensors: top-level controller, database, computing server equipment, human-machine interface device module, power distribution module, top-level network equipment, and top-level external sensors; The top-level network device module is communicatively connected to both the top-level control layer and the intermediate control layer.

[0043] The human-machine interface (HMI) device module is essential for system control operations, while the database and computing server are not essential. They are used for top-level complex data processing and status information storage. Depending on the different requirements of the system's status monitoring and fault diagnosis data integrity and complexity, the storage and computing tasks can be replaced by the HMI computer or top-level controller in the HMI device module, but the performance is worse than that of providing a database and computing server.

[0044] The top-level controller is configured to collect data from external sensors, electronic modules, network devices, and data uploaded from the intermediate control layer and the database.

[0045] The top-level control layer's capability for condition monitoring and fault diagnosis requires that, for integrated equipment that cannot be further developed, only its open functions and computing and storage capabilities be utilized. For top-level controller data processing and fault diagnosis, a margin beyond ensuring the fulfillment of the main control functions within its control cycle should be used. Database and computing server equipment can be dedicated to data storage and computation tasks for condition monitoring or fault diagnosis.

[0046] This application also discloses a method for condition monitoring and fault diagnosis of an active surface system of a radio telescope, including the following steps: Step 1: Obtain the actuator's own data, extract features from the acquired data to generate primary features, perform primary diagnosis on the primary features, and obtain a primary diagnostic conclusion. Step 2: Receive data from the execution layer of the underlying device, and obtain the voltage signal and vibration data of the actuator. Extract features from the obtained data to generate intermediate features; and use the network for monitoring the intermediate control layer to obtain diagnostic conditions. Combine the diagnostic conditions, intermediate features, and primary diagnostic conclusions to obtain intermediate diagnostic conclusions. Step 3: Receive data from the underlying device execution layer and the intermediate control layer, and obtain the voltage signal and vibration data of the actuator. Extract features from the obtained data to generate top-level features. Combine the primary diagnostic conclusion, intermediate diagnostic conclusion, and top-level features to obtain a fusion diagnostic conclusion.

[0047] In addition, changes can be made by manual diagnostic decisions. Based on the fusion diagnostic conclusions, diagnostic and maintenance suggestions can be proposed, top-level fusion decision inference information can be generated, and the final decision can be output.

[0048] It should be noted that in steps 1 to 3, the data collected at each level is decompressed or converted and normalized according to physical quantities to obtain normalized data at each level. Furthermore, the controller or edge computing / computing server processing device in the normalized data at each level performs outlier removal and missing value filling locally, and filters according to the data processing needs to obtain the processed data at each level. Only then is feature extraction performed on the data obtained at each level.

[0049] Furthermore, when reporting the features corresponding to data at each level as status information layer by layer, compression encoding is required to report only the differences and diagnostic inferences, thereby reducing network load. The reason is that actuators transmit their status information and post-fault inferences to higher-level controllers or computers for further diagnosis. This status information involves data from multiple sensors, resulting in a large data volume and consuming significant network resources. Therefore, targeted compression of the uploaded status detection data is considered to reduce network load.

[0050] Step 2 includes: Step 2-1: Receive data obtained from the underlying device execution layer; Step 2-2: Obtain the voltage signal inside the actuator and the vibration data of the telescope reflector back frame structure at the actuator installation location; Steps 2-3: Monitor the network of the intermediate control layer to obtain diagnostic conditions; Steps 2-4: Receive data from the underlying device execution layer and the middle layer external sensors, and extract features from the data to generate intermediate features; Steps 2-5: Combining diagnostic criteria, intermediate characteristics, and primary diagnostic conclusions, we arrive at an intermediate diagnostic conclusion.

[0051] Step 3 includes: Step 3-1: Receive data from the underlying device execution layer and the intermediate control layer; Step 3-2: Obtain the voltage signal of the actuator's power input and the vibration data of the telescope reflector back frame structure at the actuator's installation location; Step 3-3: Collect data from the bottom-level device execution layer, the intermediate control layer, and the top-level external sensors, and extract features from the data to generate top-level features; Steps 3-4: Combine the primary diagnostic conclusion, intermediate diagnostic conclusion, and top-level features to obtain the fusion diagnostic conclusion.

[0052] It should be noted that the hierarchical diagnostic process from the initial diagnostic conclusion to the intermediate diagnostic conclusion and then to the fusion diagnostic conclusion in steps 1 to 3 is as follows: The underlying device execution layer employs detection methods such as threshold detection, logical judgment, and discrete device status polling. It generates underlying inference information in triplets of '[underlying inference, confidence level, basis]'. It then uploads the underlying inference information and underlying state data.

[0053] Intermediate Control Layer: Combining actuator results with sector network / sensor monitoring data, it employs primary detection methods such as threshold detection, logical judgment, and discrete device status polling, as well as time-domain and frequency-domain feature threshold detection methods. It generates intermediate layer inference information in triplets of '[intermediate layer inference, confidence level, basis]'. It uploads 'bottom-level inference information, intermediate-level inference information, bottom-level state data, and intermediate-level supplementary state data'.

[0054] Top-level control layer: This layer performs fusion diagnosis on all data information, including bottom-level and intermediate-level status data, inference information, and other system information. It employs primary detection methods such as threshold detection, logical judgment, and discrete device status polling, as well as time-domain and frequency-domain feature detection methods and time-series model features. Furthermore, manual diagnostic decisions can be made to modify the data. Based on the fusion diagnosis conclusions, it proposes diagnostic and maintenance suggestions such as "immediate repair / delayed repair / continued observation." It generates top-level fusion decision inference information in the form of a triple '[top-level inference, confidence level, basis]'. Finally, it outputs the final decision (including fault location, severity assessment, equipment fault handling, and maintenance recommendations).

[0055] Troubleshooting and Confirmation / Correction At the lowest level (lower device execution layer): Emergency faults are executed immediately after local self-diagnosis and decision-making. The decision is updated and executed after confirmation by the two levels above. General faults are executed after receiving the top-level diagnostic decision. For example, this might involve immediately shutting down the actuator motor drive circuit power output to reduce drive current and prevent further damage.

[0056] Intermediate Control Layer: For lower-level faults: The reported fault is further diagnosed by combining it with other data from this layer, and then uploaded to the top layer. At this level: Emergency faults are executed immediately after local diagnostic decision-making, and updated and executed upon receiving the final decision from the top layer; general faults are executed after receiving the top-level diagnostic decision. For example, switching redundant communication links.

[0057] Top-level control layer: All emergency and general faults at this level are immediately issued and executed after the top-level diagnostic decision, but can be further modified manually. For fault diagnosis at lower levels, the decision is fed back to each lower level, enabling dynamic correction of the lower-level inferences. Example

[0058] The underlying device execution layer uses a TI TMS320F28377D MCU, combined with a built-in 3-axis accelerometer, temperature sensor, current sensor, voltage sensor, and linear displacement sensor for actuator control feedback.

[0059] Processing logic: Perform simple diagnostics on the sensor data built into the actuator, including threshold detection (position deviation > 0.1%, current deviation > 5%, voltage deviation > 10%) and logic judgment (whether the actual operating status matches the control command).

[0060] The CPU computing power, RAM temporary storage, and Flash storage capacity of the actuators used for condition monitoring and fault diagnosis shall not exceed 70% of their margin beyond the main control functions. The computing power of all actuators shall account for less than 5% of the total system capacity, and the data storage capacity shall account for less than 1% of the total system capacity.

[0061] Middle control layer: Uses x86 embedded controllers in conjunction with EtherCAT industrial Ethernet and related network equipment.

[0062] Processing logic: Aggregate 32 diagnostic sub-items from the actuator and calculate the diagnosis using the overall data from the intermediate control layer.

[0063] The CPU computing power, RAM temporary storage, and Flash storage capacity of the mid-level controllers used for status monitoring and fault diagnosis shall not exceed 50% of their margin beyond the main control functions. The total computing power of all mid-level controllers and edge computing devices shall account for less than 5% of the total system capacity, and the data storage capacity shall account for less than 5% of the total system capacity.

[0064] Top-level control layer: Beckhoff x86 embedded controller with EtherCAT industrial Ethernet and commercial Ethernet TCP / IP.

[0065] Processing logic: Collect data and diagnostic items from actuators, mid-level controllers, external sensors, electronic modules, and network devices at each level, and obtain historical data from the database to form time-series data. Calculate and utilize the overall system data to perform a comprehensive diagnosis.

[0066] The CPU computing power, RAM temporary storage, and Flash storage capacity of the top-level controller used for status monitoring and fault diagnosis shall not exceed 50% of its margin beyond the main control functions. The total computing power of the top-level controller, computing server, and database shall account for no less than 90% of the total system capacity, and the data storage capacity shall account for no less than 95% of the total system capacity.

[0067] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A state monitoring and fault diagnosis system for an active surface system of a radio telescope, characterized in that, The system comprises: a bottom device execution layer for obtaining data of the actuator itself, performing feature extraction on the obtained data, generating primary features, performing primary diagnosis on the primary features, and obtaining a primary diagnosis conclusion; a middle control layer for receiving the data obtained by the bottom device execution layer, obtaining voltage signals and vibration data of the actuator, performing feature extraction on the obtained data, and generating middle-level features; a network for monitoring the middle control layer to obtain a diagnosis condition, and combining the diagnosis condition, the middle-level features, and the primary diagnosis conclusion to obtain a middle-level diagnosis conclusion; a top control layer for receiving the data obtained by the bottom device execution layer and the middle control layer, obtaining voltage signals and vibration data of the actuator, performing feature extraction on the obtained data, generating top-level features, and combining the primary diagnosis conclusion, the middle-level diagnosis conclusion, and the top-level features to obtain a fusion diagnosis conclusion.

2. The state monitoring and fault diagnosis system for an active surface system of a radio telescope according to claim 1, characterized in that, The bottom device execution layer comprises: a bottom data acquisition module for acquiring data inside the actuator itself; a bottom external sensor for acquiring data of the temperature outside the actuator itself; a bottom feature extraction module for performing feature extraction on the data acquired by the bottom data acquisition module and the bottom external sensor to generate primary features; a bottom diagnosis module for performing primary diagnosis on the primary features to obtain a primary diagnosis conclusion.

3. The state monitoring and fault diagnosis system for an active surface system of a radio telescope according to claim 2, characterized in that, The data inside the actuator itself includes position, temperature, voltage, and current data.

4. The state monitoring and fault diagnosis system for an active surface system of a radio telescope according to claim 3, characterized in that, The middle control layer comprises: a middle data acquisition module for receiving the data obtained by the bottom device execution layer; a middle external sensor for acquiring voltage signals inside the actuator and vibration data of a telescope mirror back structure at the installation position of the actuator; a middle monitoring module for monitoring the network of the middle control layer to obtain a diagnosis condition; a middle feature extraction module for acquiring the data of the bottom device execution layer and the middle external sensor, and performing feature extraction on the data to generate middle-level features; a middle diagnosis module for combining the diagnosis condition, the middle-level features, and the primary diagnosis conclusion to obtain a middle-level diagnosis conclusion.

5. The state monitoring and fault diagnosis system for an active surface system of a radio telescope according to claim 4, characterized in that, The top control layer comprises: a top data acquisition module for receiving the data obtained by the bottom device execution layer and the middle control layer; a top external sensor for acquiring voltage signals of a power supply input of the actuator and vibration data of a telescope mirror back structure at the installation position of the actuator; a top feature extraction module for acquiring the data of the bottom device execution layer, the middle control layer, and the top external sensor, and performing feature extraction on the data to generate top-level features; a top diagnosis module for combining the primary diagnosis conclusion, the middle-level diagnosis conclusion, and the top-level features to obtain a fusion diagnosis conclusion.

6. The state monitoring and fault diagnosis system for an active surface system of a radio telescope according to claim 5, characterized in that, The top control layer further comprises a human-machine interface device module for state monitoring or fault diagnosis of the top data acquisition module and the top external sensor, as well as data storage and execution of computing tasks.

7. The system according to claim 6, wherein the middle-level features comprise primary features, frequency domain primary features, and sector coupling features generated from the data obtained by the bottom device execution layer, the voltage signals and the vibration data of the actuator. ​ The top-level features include: the data obtained by the bottom device execution layer and the intermediate control layer, the voltage signal of the actuator and the vibration data of the primary features, the time domain, the frequency domain features and the system coupling features, and the time series features.

8. A method for monitoring and diagnosing the state of an active surface system of a radio telescope, characterized in that, The method comprises the following steps: Step 1: obtaining the data of the actuator itself, performing feature extraction on the obtained data to generate primary features, performing primary diagnosis on the primary features, and obtaining a primary diagnosis conclusion; Step 2: receiving the data obtained by the bottom device execution layer, obtaining the voltage signal of the actuator and the vibration data, performing feature extraction on the obtained data to generate intermediate features, and monitoring the network of the intermediate control layer to obtain a diagnosis condition, combining the diagnosis condition, the intermediate features and the primary diagnosis conclusion to obtain an intermediate diagnosis conclusion; Step 3: receiving the data obtained by the bottom device execution layer and the intermediate control layer, obtaining the voltage signal of the actuator and the vibration data, performing feature extraction on the obtained data to generate top-level features, combining the primary diagnosis conclusion, the intermediate diagnosis conclusion and the top-level features to obtain a fusion diagnosis conclusion.

9. The method of claim 8, wherein the method further comprises: Step 2 includes: Step 2-1: receiving the data obtained by the bottom device execution layer; Step 2-2: obtaining the voltage signal inside the actuator and the vibration data of the telescope reflector back frame structure at the installation position of the actuator; Step 2-3: monitoring the network of the intermediate control layer to obtain a diagnosis condition; Step 2-4: receiving the data of the bottom device execution layer and the intermediate external sensor, and performing feature extraction on the data to generate intermediate features; Step 2-5: combining the diagnosis condition, the intermediate features and the primary diagnosis conclusion to obtain an intermediate diagnosis conclusion.

10. The method of claim 9, wherein the method further comprises: Step 3 includes: Step 3-1: receiving the data obtained by the bottom device execution layer and the intermediate control layer; Step 3-2: obtaining the voltage signal of the power input of the actuator and the vibration data of the telescope reflector back frame structure at the installation position of the actuator; Step 3-3: the data of the bottom device execution layer, the intermediate control layer and the top external sensor, and performing feature extraction on the data to generate top-level features; Step 3-4: combining the primary diagnosis conclusion, the intermediate diagnosis conclusion and the top-level features to obtain a fusion diagnosis conclusion.

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