Space ground measurement and control system health management knowledge base storage system

Abstract

The application provides a space ground TT&C system health management knowledge base storage system, comprising: a device model sub-knowledge base, used for completing the normalized storage of model knowledge, structure knowledge and measurement point knowledge related to different types of space ground TT&C system devices; a fault diagnosis sub-knowledge base, used for completing the normalized storage of fault diagnosis knowledge related to different types of space ground TT&C system devices; and an evaluation and prediction sub-knowledge base, used for completing the normalized storage of health state evaluation and prediction knowledge related to different types of space ground TT&C system devices. The application can uniformly and normatively organize and construct and store the health management knowledge base of various types of space ground TT&C system equipment, realize the standardized design of space ground TT&C system equipment basic model knowledge, fault diagnosis knowledge and evaluation and prediction knowledge, support the standardized construction of new research equipment health management knowledge base and the rapid reuse of existing knowledge of similar equipment.

Description

Technical Field

[0001] This invention relates to the field of aerospace ground telemetry and control, and in particular to a knowledge base storage system for health management of aerospace ground telemetry and control systems. Background Technology

[0002] With the rapid development of the aerospace field in recent years, spacecraft and their supporting ground tracking and control systems have entered a period of rapid construction and development. A large number of newly developed ground tracking and control systems have been put into use to support the rapidly expanding needs of space missions. Health management technology, accompanying the technological updates and development of aerospace ground tracking and control systems, has now become one of the fundamental subsystems, responsible for various health status management functions such as monitoring the operational status of equipment and components at all levels of the system, fault detection and diagnosis, health status assessment, and health trend prediction. However, from an engineering practice perspective, some problems still exist.

[0003] Aerospace ground telemetry and control systems are complex, with numerous devices and components. Apart from mechanical equipment such as antenna drive equipment and electronic devices like batteries that exhibit significant degradation characteristics, other electronic or electromechanical coupling equipment often fails suddenly due to the lack of significant degradation characteristics. This makes data-based health management techniques ineffective in practical engineering applications. In contrast, expert rule-based health management technology analyzes equipment operating and fault mechanisms to build a health management knowledge base. Driven by an inference engine, it achieves automated and intelligent functions such as health status identification, fault analysis and location, and maintenance decision-making, offering advantages in efficiency and accuracy.

[0004] In the field of health management technology for aerospace ground telemetry and control systems, due to the relatively short application and development time of the technology, and the independent design and development of various equipment models, there is currently no standardized and unified design method for health management knowledge bases. This makes it difficult to apply the relevant health knowledge accumulated by similar equipment products to other products, resulting in knowledge loss or distortion of knowledge during migration. There is an urgent need to propose a storage method for health management knowledge bases that can be adapted to multiple types of aerospace ground telemetry and control systems. Summary of the Invention

[0005] To address the problems existing in the prior art, a knowledge base storage system for health management of aerospace ground telemetry and control systems is provided. This system stores different types of knowledge involved in aerospace ground telemetry and control systems by constructing equipment model sub-knowledge bases, fault diagnosis sub-knowledge bases, and assessment and prediction sub-knowledge bases.

[0006] The technical solution adopted in this invention is as follows: A knowledge base storage system for health management of aerospace ground telemetry and control systems, comprising:

[0007] The equipment model sub-knowledge base is used to standardize the storage of model knowledge, structural knowledge, and measurement point knowledge related to different types of aerospace ground telemetry and control system equipment;

[0008] The fault diagnosis sub-knowledge base is used to standardize the storage of fault diagnosis knowledge related to different types of aerospace ground telemetry and control system equipment;

[0009] The assessment and prediction sub-knowledge base is used to standardize the storage of health status assessment and prediction knowledge related to different types of aerospace ground telemetry and control system equipment.

[0010] Furthermore, the model knowledge in the equipment model sub-knowledge base is stored in a model knowledge base table. The fields of the model knowledge base table include equipment name, equipment code, deployment location, station type, research and development unit, management unit, equipment status, and responsible person.

[0011] Furthermore, the structural knowledge in the equipment model sub-knowledge base is stored by dividing the hardware equipment into four levels: system, subsystem, complete machine, and unit. The subsystem includes antenna feed subsystem, transmission subsystem, receiving subsystem, baseband subsystem, and time and frequency subsystem. The complete machine and unit are defined according to the specific composition of each equipment model.

[0012] Furthermore, the measurement point knowledge in the equipment model sub-knowledge base is stored using a measurement point knowledge base table; the measurement point knowledge includes equipment status data types and their decision conditions, task status data types and their decision conditions, and calibration test data types and their decision conditions; the fields of the measurement point knowledge base table include measurement point name, measurement point code, measurement point type, equipment node to which the measurement point belongs, parameter unit, working system, normal range, attention range, abnormal range, and enumerated values.

[0013] Furthermore, the fault diagnosis sub-knowledge base includes fault diagnosis knowledge such as testable D-matrix, expert rules, FMEA knowledge, fault trees, and fault cases; the testable D-matrix is ​​constructed based on the correlation between fault modes and test points; the expert rules use a production rule representation method to describe the reasoning relationship between fault modes and test points; the FMEA knowledge is stored in an FMEA knowledge base table; the fault tree uses a tree structure to identify abnormal equipment fault events and their causes; and the fault cases are stored in a fault case database table.

[0014] Furthermore, each row of the testability D matrix represents a fault mode, and each column represents one or more coupled test points; the elements in the matrix take values ​​of 0 or 1, where 1 indicates that the fault mode in the row corresponds to an abnormal state of the test point in the corresponding column, and 0 indicates that the fault mode in the row corresponds to a normal state of the test point in the corresponding column.

[0015] Furthermore, the fields of the FMEA knowledge base table include faulty equipment node, severity, equipment model, fault code, fault mode, fault cause, fault rate, fault detection method, impact on local function, impact on subsystem function, impact on system function, and handling suggestions.

[0016] Furthermore, the fault tree decomposes the causes of abnormal equipment failure events step by step through a tree structure, where the root node is the top event and the direct cause of the top event, and so on, until the bottom event that cannot be expanded or does not need to be expanded is the leaf node.

[0017] Furthermore, the fields of the fault case database table include case number, case name, equipment model, faulty whole machine / unit, faulty equipment type, affiliated unit, fault mode, fault phenomenon, fault time, occurrence stage, fault frequency, severity, diagnostic process, mechanism analysis, maintenance type, maintenance personnel, maintenance measures, zeroing time, learning from one case to another, and other matters.

[0018] Furthermore, the health status assessment and prediction knowledge in the assessment and prediction sub-knowledge base includes an indicator system, a health measurement model, a health rating strategy, and lifespan indicators. The indicator system includes task capability indicators and equipment status indicators, and a tree-structured indicator system is constructed based on these indicators, with each node assigned a weight, and the weights at the same level sum to 1. The health measurement model quantifies the health status of the underlying units of the equipment, and calculates the final system and subsystem quantitative scores by weighting the scores of the units and the indicator system at each level. The health rating strategy configures the mapping relationship from the final system quantitative score to the qualitative graded evaluation. The lifespan indicators include rated lifespan indicators, effective lifespan indicators, and statistical lifespan indicators, used to provide feedback on the lifespan of the equipment.

[0019] Compared with existing technologies, the beneficial effects of adopting the above technical solution are as follows: This invention can organize, construct, and store the health management knowledge base of various aerospace ground telemetry and control system equipment in a unified and standardized manner, realize the standardized design of basic model knowledge, fault diagnosis knowledge, and assessment and prediction knowledge of aerospace ground telemetry and control system equipment, support the standardized construction of health management knowledge bases for newly developed equipment, and facilitate the rapid reuse of existing knowledge for similar equipment. Currently, the technical achievements of this invention have been demonstrated and applied to multiple sets of equipment in the aerospace ground telemetry and control station network. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the knowledge base storage system for health management of aerospace ground telemetry and control system proposed in this invention. Detailed Implementation

[0021] The embodiments of this application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar modules or modules having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application. Rather, the embodiments of this application include all variations, modifications, and equivalents falling within the spirit and scope of the appended claims.

[0022] To address the shortcomings of existing technologies and meet the health management needs of aerospace ground-based telemetry and control systems, this invention proposes a knowledge base storage system for health management in aerospace ground-based telemetry and control systems. This system constructs sub-knowledge bases for equipment models, fault diagnosis, and assessment and prediction to achieve standardized storage of health management knowledge for aerospace ground-based telemetry and control systems. The specific solution is as follows:

[0023] like Figure 1 As shown, a knowledge base storage system for health management of aerospace ground telemetry and control systems includes:

[0024] The equipment model sub-knowledge base is used to standardize the storage of model knowledge, structural knowledge, and measurement point knowledge related to different types of aerospace ground telemetry and control system equipment;

[0025] The fault diagnosis sub-knowledge base is used to standardize the storage of fault diagnosis knowledge related to different types of aerospace ground telemetry and control system equipment;

[0026] The assessment and prediction sub-knowledge base is used to standardize the storage of health status assessment and prediction knowledge related to different types of aerospace ground telemetry and control system equipment.

[0027] In this embodiment, the storage system is mainly divided into the three parts mentioned above, and each part will be described in detail below.

[0028] The first part, the equipment model sub-knowledge base, mainly focuses on the organization, identification, and storage of model characteristic knowledge such as development information, performance requirements, equipment composition and structure, and monitoring point deployment of aerospace ground telemetry and control systems. This provides telemetry and control system model information support for fault diagnosis and health status assessment. Furthermore, the knowledge involved is divided into three categories: model knowledge, structural knowledge, and measurement point knowledge.

[0029] In one embodiment, model knowledge mainly involves the standardized design of basic common information for different types of aerospace ground telemetry and control systems, and is represented by a model knowledge base table. The fields of the model knowledge base table are shown in the table below:

[0030] Equipment name Equipment code Deployment site Station type Research unit Management unit Equipment status Responsible person

[0031] The station types include fixed stations, semi-fixed stations, ship-mounted stations, and vehicle-mounted stations, and the equipment status includes normal operation, maintenance, and upgrades.

[0032] In one embodiment, structural knowledge mainly involves the standardized design of the composition structure of different types of aerospace ground telemetry and control systems, divided into four levels: system, subsystem, complete unit, and unit. Among them, the hardware equipment subsystem includes the antenna feed subsystem, transmitting subsystem, receiving subsystem, baseband subsystem, and time and frequency subsystem, while the complete unit and unit are defined according to the specific composition of each equipment model.

[0033] The measurement point knowledge mainly sets status discrimination rules for three types of monitoring point data reported by different types of aerospace ground telemetry and control systems: equipment status data and its judgment conditions, mission status data and its judgment conditions, and calibration test data and its judgment conditions. These rules are stored in a measurement point knowledge base table, the fields of which are shown in the table below:

[0034]

[0035] The measurement point types include numerical measurement points, enumerated measurement points, and Boolean measurement points. Numerical measurement points have normal, warning, and abnormal ranges set, and the current status is determined based on the range of the data reported by the measurement point. Enumerated measurement points have their value range set by an enumerated value field. Boolean measurement points limit the reported data to 0 or 1, where 0 represents normal equipment and 1 represents abnormal equipment.

[0036] The second part, the fault diagnosis sub-knowledge base, is used to standardize and represent fault diagnosis knowledge involved in different types of aerospace ground telemetry and control systems. In this embodiment, it includes testability D matrix, expert rules, FMEA knowledge, fault tree, and fault cases.

[0037] Specifically, the testability D matrix is ​​constructed based on the correlation between fault modes and measurement points, describing the correlation between these modes and measurement points. Each row of the matrix represents a fault mode, and each column represents one or more coupled measurement points. Elements in the matrix take values ​​of 0 or 1; 1 indicates that the fault mode in that row corresponds to an abnormal state at the measurement point in that column, and 0 indicates that the fault mode in that row corresponds to a normal state at the measurement point in that column. By judging the state of the measurement points in the matrix, fault modes that satisfy all measurement point states can be matched and isolated.

[0038] Expert rules use a production rule representation method to describe the reasoning relationship between failure modes and measurement points. The premise of each rule is a specified state of one or more coupled measurement points, where the relationships between these points can be AND, OR, or NOT logical relationships. The conclusion of each rule is the failure mode.

[0039] FMEA knowledge is used to represent failure modes and their effects. In this embodiment, an FMEA knowledge base table is used to store these, and its fields are shown below:

[0040]

[0041] Among them, the faulty equipment node is at the whole machine or unit level.

[0042] In this embodiment, the severity level is divided into four levels based on the final impact of the fault on the function and performance of the measurement and control system, as follows:

[0043] Class I (catastrophic): Faults that cause system tasks to fail;

[0044] Class II (Critical): Faults that cause the loss of multiple system functions, but do not lead to task failure;

[0045] Category III (Medium): Faults that cause loss of single function, functional degradation, delay, or degradation of the system;

[0046] Category IV (Mild): Does not affect system functionality, but may lead to performance degradation and unplanned maintenance or repair.

[0047] Failure rate: Qualitatively provides the probability of a failure occurring, including 4 levels, as follows:

[0048] Level 1: High probability of occurrence under all operating conditions;

[0049] Level 2: Prone to occur under specific working conditions;

[0050] Level 3: Normal;

[0051] Level 4: Very rare.

[0052] Furthermore, in this embodiment, the fault tree decomposes the causes of abnormal equipment failure events step by step through a tree structure. The root node is the top event, and the direct causes of the top event, such as hardware failure, human error, input abnormality, environmental factors, etc., are the second-level events. Then, it expands step by step until the bottom events that cannot be expanded or do not need to be expanded are the leaf nodes.

[0053] It should be noted that the fault tree as a whole (root node) must be associated with device nodes, typically at the system, subsystem, or overall machine level. Individual fault event nodes (subsequent nodes) within the fault tree can also be associated with device nodes, typically at the overall machine or unit level. Each fault node within the fault tree can be associated with test points to enable automated reasoning. If no associated tests are available, subsequent troubleshooting paths must be manually specified. Nodes can also have auxiliary troubleshooting information added for manual confirmation during path selection.

[0054] In one embodiment, the fault cases are standardized and defined for typical fault cases of various types of aerospace ground telemetry and control system equipment, and stored in a fault case database table. The fields and field descriptions are as follows.

[0055]

[0056] The frequency of failures includes high frequency, triggering under specific conditions, and occasional occurrence; the severity includes task failure, equipment de-rated, and general.

[0057] Finally, the third part, the assessment and prediction sub-knowledge base, is used to standardize, organize, represent, and store health status assessment and prediction knowledge related to the aerospace ground telemetry and control system. In this embodiment, it mainly includes information such as indicator system, health measurement model, health rating strategy, lifespan index, and lifespan distribution function.

[0058] Specifically, the indicator system mainly includes two categories: task capability indicators and equipment status indicators. In this embodiment, task capability indicators include EIRP, G / T value, EIRP stability, system acquisition time, angle measurement accuracy, velocity measurement accuracy, distance measurement accuracy, telemetry bit error rate, and telemetry command error. The equipment status indicators adopt a five-level structure: system, subsystem, whole machine, unit, and measurement point. A tree-structured indicator system is constructed based on the task capability indicators and equipment status indicators, and each node is weighted, with the sum of the weights at the same level being 1.

[0059] The health quantification model quantifies and scores task capability indicators and the health status of underlying equipment units, with scores ranging from 0 to 100. Based on unit scores and the indicator system, a weighted calculation is performed at each level to ultimately obtain the system's quantitative score.

[0060] The corresponding health rating strategy is configured by mapping the calculated quantitative score to the qualitative grading evaluation. In this embodiment, it is divided into 5 levels, as follows:

[0061] Level 5: Indicates that the equipment is in perfect health and has no malfunctions, corresponding to a score of 95-100.

[0062] Level 4: Indicates that the equipment is in basically healthy condition, with individual units having faults (not single point of failure), but the overall function is normal and the main indicators are qualified, corresponding to a score of 80-94 points;

[0063] Level 3: Indicates that the equipment is in a sub-healthy state, such as a slight downgrade in some indicators, but the equipment can operate normally, corresponding to a score of 60-80 points;

[0064] Level 2: Indicates a significant deterioration in equipment condition, such as downgrades in multiple indicators, corresponding to a comprehensive score of 40-60 points;

[0065] Level 1: Indicates equipment failure or malfunction, corresponding to a comprehensive score of 40 or below.

[0066] Lifespan indicators include rated lifespan indicators, effective lifespan indicators, and statistical lifespan indicators. Rated lifespan indicators are provided by the equipment manufacturer and reflect the theoretical lifespan of the equipment. Effective lifespan indicators are calculated based on the equipment's lifespan distribution function and reflect the effective usage time of the equipment. Statistical lifespan indicators are obtained based on the average lifespan of similar equipment and reflect the average usable time of the equipment.

[0067] This invention supports the effective application of knowledge-based health management by standardizing and storing basic model knowledge, fault diagnosis knowledge, and assessment and prediction knowledge of aerospace ground tracking and control system equipment. Currently, the technological achievements of this invention have been demonstrated and applied to multiple sets of equipment in the aerospace ground tracking and control network.

[0068] It should be noted that, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "set" and "connection" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances. The accompanying drawings in the embodiments are used to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0069] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A knowledge base storage system for health management in aerospace ground telemetry and control systems, characterized in that, include: The equipment model sub-knowledge base is used to standardize the storage of model knowledge, structural knowledge, and measurement point knowledge related to different types of aerospace ground telemetry and control system equipment; The fault diagnosis sub-knowledge base is used to standardize the storage of fault diagnosis knowledge related to different types of aerospace ground telemetry and control system equipment; The assessment and prediction sub-knowledge base is used to standardize the storage of health status assessment and prediction knowledge related to different types of aerospace ground telemetry and control system equipment; The fault diagnosis sub-knowledge base includes fault diagnosis knowledge such as testable D-matrix, expert rules, FMEA knowledge, fault trees, and fault cases. The testable D-matrix is ​​constructed based on the correlation between fault modes and test points. The expert rules use a production rule representation method to describe the reasoning relationship between fault modes and test points. The FMEA knowledge is stored in an FMEA knowledge base table. The fault tree uses a tree structure to identify abnormal equipment fault events and their causes. The fault cases are stored in a fault case database table; Each row of the testability D matrix represents a fault mode, and each column represents one or more coupled test points. The elements in the matrix take the value 0 or 1. 1 indicates that the fault mode in the row corresponds to the abnormal state of the test point in the corresponding column, and 0 indicates that the fault mode in the row corresponds to the normal state of the test point in the corresponding column. The fields in the FMEA knowledge base table include faulty equipment node, severity, equipment model, fault code, fault mode, fault cause, fault rate, fault detection method, impact on local function, impact on subsystem function, impact on system function, and handling suggestions. The health status assessment and prediction sub-knowledge base includes an indicator system, a health quantification model, a health rating strategy, and lifespan indicators. The indicator system comprises task capability indicators and equipment status indicators, and a tree-structured indicator system is constructed based on these indicators, with each node assigned a weight, and the weights at the same level sum to 1. The health quantification model quantifies the health status of the underlying equipment units, and calculates the final system and subsystem quantitative scores by weighting the unit scores and indicator system at each level. The health rating strategy configures the mapping relationship from the final system quantitative score to a qualitative grading evaluation. The lifespan indicators include rated lifespan indicators, effective lifespan indicators, and statistical lifespan indicators, used to provide feedback on the equipment's lifespan.

2. The aerospace ground telemetry and control system health management knowledge base storage system according to claim 1, characterized in that, The model knowledge in the equipment model sub-knowledge base is stored in a model knowledge base table. The fields of the model knowledge base table include equipment name, equipment code, deployment location, station type, research and development unit, management unit, equipment status, and responsible person.

3. The aerospace ground telemetry and control system health management knowledge base storage system according to claim 1 or 2, characterized in that, The structural knowledge in the equipment model sub-knowledge base is stored by dividing hardware equipment into four levels: system, subsystem, complete machine, and unit. The subsystem includes antenna feed subsystem, transmission subsystem, receiving subsystem, baseband subsystem, and time and frequency subsystem. The complete machine and unit are defined according to the specific composition of each equipment model.

4. The aerospace ground telemetry and control system health management knowledge base storage system according to claim 3, characterized in that, The measurement point knowledge in the equipment model sub-knowledge base is stored in the measurement point knowledge base table; the measurement point knowledge includes equipment status data type and its judgment conditions, task status data type and its judgment conditions, and calibration test data type and its judgment conditions; the fields of the measurement point knowledge base table include measurement point name, measurement point code, measurement point type, equipment node to which the measurement point belongs, parameter unit, working system, normal range, attention range, abnormal range, and enumerated values.

5. The aerospace ground telemetry and control system health management knowledge base storage system according to claim 1, characterized in that, The fault tree decomposes the causes of abnormal equipment failure events step by step through a tree structure. The root node is the top event, which is the direct cause of the top event. The tree expands step by step until the bottom event, which cannot be expanded or does not need to be expanded, is the leaf node.

6. The aerospace ground telemetry and control system health management knowledge base storage system according to claim 1, characterized in that, The fields in the fault case database table include case number, case name, equipment model, faulty whole machine / unit, faulty equipment type, affiliated unit, fault mode, fault phenomenon, fault time, occurrence stage, fault frequency, severity, diagnostic process, mechanism analysis, maintenance type, maintenance personnel, maintenance measures, zeroing time, learning from one case to another, and other matters.

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