Electric propulsion health assessment and management method based on hierarchical assessment system

Abstract

The invention provides an electric propulsion health assessment and management method based on a hierarchical assessment system. The electric propulsion health assessment and management method comprises the following steps: step 1, constructing an electric propulsion health tree model: layering electric propulsion step by step into an equipment layer, a module layer, a component layer and a particle layer; 2, calculating a particle layer health index: respectively obtaining individual functions of particle health performance according to the characteristics of each particle, and calculating a particle utilization rate, namely the particle health index, according to the individual functions; determining health classification of each particle according to the particle health index; 3, calculating health indexes of other layers: determining weight proportions of each particle, each component and each module, and calculating a component layer health index, a module layer health index and an equipment layer health index in sequence according to weighted average; 4, calculating the remaining life percentage; and 5, outputting the health condition. The real-time performance, the specialty and the universality of electric propulsion health detection can be guaranteed, and the reliable operation of electric propulsion before service and in service is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of spacecraft, and in particular to an electric propulsion health assessment and management method based on a hierarchical assessment system. Background Art

[0002] As a power supply device, electric propulsion is an important component of spacecraft. Its reliability and safety are related to whether the entire system can operate normally. With the development of the aerospace industry, the number of on-orbit electric propulsion has increased dramatically. It is difficult to conduct real-time detection of electric propulsion problems and lifespans by relying solely on manual experience and regular testing.

[0003] The existing Chinese invention patent application with application number CN202311264316.7 discloses a multi-mode ion electric propulsion health management method and device based on reinforcement learning. The method includes: obtaining a basic data set for ion electric propulsion health management; determining the key failure modes of the ion electric propulsion health management based on the basic data set; constructing a degradation model for the ion electric propulsion health management based on the key failure modes; training the reinforcement learning model for the ion electric propulsion health management based on the degradation model; determining the optimal control strategy for the ion electric propulsion health management based on the reinforcement learning model and the strategy iteration algorithm. The multi-mode ion electric propulsion autonomous health management method based on reinforcement learning in this application can improve the on-orbit intelligence level of electric propulsion when human intervention is limited or even unable to accept ground instructions, realize timely response and autonomous decision-making of multi-mode ion electric propulsion for deep space exploration to emergencies, extend service life, and improve use efficiency and service reliability.

[0004] The Chinese invention patent with application number CN202410940217.4 discloses a fault diagnosis method and system for a lightweight electric propulsion system, which relates to the field of fault diagnosis technology, including determining fault causality and component dependency based on historical data and structural data, constructing a fault tree and a directional graph, constructing an initial graph model, mapping nodes to a low-dimensional dense vector space, determining node embedded representations, and generating a fault knowledge graph; based on the node embedded representation, searching for corresponding neighbor embedded representations, extracting node high-level feature representations by performing convolution operations on nodes and neighbors, determining fault association relationships, and extracting fault importance representations through pooling operations, and constructing a fault association feature extraction network; inputting real-time monitoring data of the lightweight electric propulsion system into the fault diagnosis model, extracting fault association features and importance features corresponding to the real-time monitoring data, and making classification decisions through a fault classifier in combination with the fault knowledge graph to determine the fault diagnosis results.

[0005] The above-mentioned solution 1 mainly builds a degradation model for ion electric propulsion, trains a learning model, conducts autonomous fault judgment and autonomous isolation, and does not send up the results of the current electric propulsion fault.

[0006] The above-mentioned Scheme 2 confirms the classification result of the fault, but does not make a prediction for the service life of the electric propulsion. Whether the service life can be reached is a question that electric propulsion users are very concerned about.

[0007] Therefore, the ideas and methods of health detection of modern electric propulsion need to be further improved to ensure the real-time, professional and extensive health detection of electric propulsion and to ensure the reliable operation of electric propulsion before and during service. Summary of the invention

[0008] The present invention provides an electric propulsion health assessment and management method based on a hierarchical assessment system, which can ensure the real-time, professional and extensive nature of electric propulsion health detection and ensure the reliable operation of electric propulsion before and during service.

[0009] In order to solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides an electric propulsion health assessment and management method based on a hierarchical assessment system, comprising the following steps: Step 1: Construct the electric propulsion health tree model: The electric propulsion is layered into equipment layer, module layer, component layer and particle layer; Step 2: Calculate the particle layer health index: According to the characteristics of each particle, the individual function of the particle health performance is obtained respectively, and the particle utilization rate, i.e., the particle health index, is calculated according to the individual function of each particle; and the health classification of each particle is determined according to the particle health index; Step 3: Calculate the health index of other layers: Determine the weight of each particle, each component, and each module, and calculate the component layer health index, module layer health index, and device layer health index in turn according to the weighted average; Step 4: Calculate the remaining life percentage: Calculate the remaining gas content of the gas cylinder, the status of the ROM in the PPU, and the health of the CPU, and determine the proportion of the above-mentioned influencing nodes. The remaining life percentage of the equipment layer is calculated by the weighted average of the remaining life percentages of the above-mentioned nodes; Step 5: Health status output: Get the code, health classification, health index of each layer, the remaining life of the whole machine, whether there is a serious fault in each layer and the layer code of the serious fault By adopting the above scheme: This application can decompose the electric propulsion layer by layer and element by element, perform health assessment and fault diagnosis on each layer and element respectively, and finally confirm the health score through the score calculation model, and diagnose the fault status and processing suggestions through the fault library.

[0010] The present invention provides an electric propulsion health assessment and management method based on a hierarchical assessment system. Further, in step 1, the health status of the equipment layer is determined by the module layer, the health status of the module layer is determined by the component layer, and the health status of the component layer is determined by the particle layer; the weighted average of the health indexes of each particle constitutes the health index of the component, the weighted average of the health indexes of each component constitutes the health index of the module, and the weighted average of the health indexes of each module constitutes the equipment health index.

[0011] By adopting the above solution: the health of the electric propulsion system is decomposed layer by layer, and the health calculation of electric propulsion is achieved.

[0012] The present invention provides an electric propulsion health assessment and management method based on a hierarchical assessment system. Further, the equipment layer is electric propulsion; the module layer includes gas cylinders, pipelines, and PPU; the PPU module layer includes various power supplies, EEPROM, FLASH, and chip operating conditions; the EEPROM component layer includes the number of read and write times and utilization rate; the FLASH component layer includes the number of read and write times, utilization rate, and the number of bad blocks; and the chip component layer includes CPU utilization rate, operating time, backup status, and alarms.

[0013] By adopting the above scheme: the main factors affecting the health of electric propulsion are determined for electric propulsion, and the health index of the component layer, module layer, and equipment layer can be calculated layer by layer based on the above influencing factors.

[0014] The present invention provides an electric propulsion health assessment and management method based on a hierarchical assessment system. Further, the health classification includes: One type can be used normally without any extra attention and can be used with normal operation steps; Category II: limited use, requiring supplementary operations, or available under special conditions; Category 3: unusable, obsolete products that no longer provide functionality.

[0015] By adopting the above scheme: it is determined whether there is a serious fault in the node layer, that is, three types of nodes, and the node code of the serious fault is obtained.

[0016] The present invention provides an electric propulsion health assessment and management method based on a hierarchical assessment system. Further, for particles in Class I and Class III health conditions, there is no need to calculate the health index. For Class II health conditions, it is necessary to calculate the health index to show the current severity of the situation.

[0017] By adopting the above solution, the computational complexity of the first and third types of nodes is saved.

[0018] The present invention provides an electric propulsion health assessment and management method based on a hierarchical assessment system. Further, in step 3, according to the working dependencies of the nodes in each component, the components in each module, and the modules in the device, a calculation method of the node health of the nodes, components, and modules is determined, and the calculation method of the node health includes and, or, and; When the calculation method of node health is sum, the proportion of node health index is integrated at the same time, and the weighted average calculation is performed to obtain the component health index, module health index and device health index; When the calculation method of node health is or, if there is a node health index of Class III, then the corresponding component is Class III. If they are all Class II, then a weighted average calculation is performed to obtain the component health index, module health index, and device health index; When the calculation method of node health is and, all the contact health indices are all Class 3, and the components are Class 3. If they are all Class 2, a weighted average calculation is performed to obtain the health index of the corresponding level.

[0019] By adopting the above solution: the health index of the device layer can be accurately calculated by the above method.

[0020] The present invention provides an electric propulsion health assessment and management method based on a hierarchical assessment system. Further, the remaining life percentage is the percentage of the remaining reliable service life to the total service life.

[0021] The present invention provides an electric propulsion health assessment and management method based on a hierarchical assessment system. Further, in step 2, the influencing factor of the normal operation of each particle is determined according to the characteristics of each particle, and the individual function of the utilization rate or occupancy rate of each particle is obtained, and the particle utilization rate is calculated according to the individual function of each particle.

[0022] The present invention provides a system, comprising: Health index configuration file, including total number of layers, node code, number of subnodes, subnode code, node health calculation method, node health ratio and node health classification. Node health calculation methods include sum, or, and, where 1 is sum, 2 is or, and 3 is and; Remaining life profile, number of influencing factors, node code, calculated remaining life and percentage of this node; A configuration file reading module is used to read a health index configuration file and a remaining life configuration file; The particle layer health calculation module calculates the health classification and health index of the particle layer according to the calculation method of each particle in the health index configuration file; The health calculation module of other layers calculates the health score of the current node according to the health index configuration file and the node health calculation method; The remaining life calculation module obtains the weighted average of the remaining life of each node and the weighted coefficient in the remaining life configuration file; The health status output module transmits the health status and remaining life of each node to the entire satellite through telemetry.

[0023] A system of the present invention further transmits a telemetry table including the code of each node, the health classification of the node, the health index of the node, the remaining life of the whole machine, whether there is a serious fault, and the node code of the serious fault.

[0024] Compared with the prior art, the present invention has the following beneficial effects: The health of the electric propulsion system is decomposed layer by layer through the concept and practice of the health tree, and the flexible configuration of the health tree is realized through the configuration method. Through this method, the health calculation of the electric propulsion system is realized, and the results are sent to the entire satellite in a timely manner, so that the entire satellite can understand the current health status and classification of the electric propulsion in real time.

[0025] The present invention will be further described below in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a decomposition diagram of the health tree of the present invention; Figure 2 It is a layer-by-layer derivation diagram of the final health status of the present invention; Figure 3 This is a detailed health tree breakdown diagram for the electric propulsion of the present invention. DETAILED DESCRIPTION

[0027] Embodiment 1: Reference Figure 1-Figure 3 The present invention discloses an electric propulsion health assessment and management method based on a hierarchical assessment system, comprising the following steps: Step 1: Construct the electric propulsion health tree model: The equipment health of the electric propulsion system can be decomposed layer by layer. The most important thing is the health of the equipment layer. However, when it is unhealthy, we need to know the cause of the unhealthy state. Therefore, the electric propulsion is layered step by step into the equipment layer, module layer, component layer and particle layer. The health of the equipment layer is determined by the module layer, the health of the module layer is determined by the component layer, and the health of the component layer is determined by the particle layer. The weighted average of the health indexes of each particle constitutes the health index of the component, the weighted average of the health indexes of each component constitutes the health index of the module, and the weighted average of the health indexes of each module constitutes the equipment health index.

[0028] The equipment layer is electric propulsion; the module layer includes gas cylinders, pipelines, and PPU. PPU is Paralleling and Protection Unit PPU, a physical operation processor; the PPU module layer includes various power supplies, EEPROM, FLASH, chip operation status and other components. EEPROM is Electrically Erasable Programmable Read-Only Memory, and FLASH is a type of storage chip; the EEPROM component layer includes read and write times and usage rate; the FLASH component layer includes read and write times, usage rate, and number of bad blocks; the chip component layer includes CPU usage rate, running time, backup status, and alarms. CPU is Central Processing Unit / Processor. The health index of the gas cylinder is determined by the high-pressure sensor, and the health index of the pipeline is determined by the low-pressure sensor.

[0029] Step 2: Calculate the health index of the particle layer: The health classification of each particle is determined based on the particle health index. The index range of the health classification is determined based on the requirements or needs for the normal operation of each project. The health classification includes: One type can be used normally without any extra attention and can be used with normal operation steps; Category II: limited use, requiring supplementary operations, or available under special conditions; Category 3: unusable, obsolete products that no longer provide functionality.

[0030] For particles in the first and third health conditions, there is no need to calculate the health index. For particles in the second health condition, the health index needs to be calculated to show the current severity of the situation.

[0031] The calculation method of the health index of the particle layer is different for each particle. The influencing factors of the normal operation of each particle are determined according to the characteristics of each particle, and the individual function of the utilization rate or occupancy rate of each particle is obtained. The particle utilization rate is calculated according to the individual function of each particle, that is, the particle health index is obtained.

[0032] Example: The usage rate of EEPROM read and write times. For example, if the current EEPROM write times is 10,000 times, read the current EEPROM write times and divide it by 10,000 to get the current read and write times usage health value; The CPU usage is calculated by setting the CPU over-usage threshold, such as 75%, and obtaining the current CPU usage N, (N-75%) / 75%, which is the current CPU over-usage situation and also indicates the health index of the CPU usage.

[0033] Step 3: Calculate the health index of other layers: According to the working dependencies of nodes in each component, components in each module and modules in the device, the calculation method of node health of nodes, components and modules is determined. The calculation methods of node health include sum, or, and; the weights of particles in each component layer and the weights of each component in the module layer are determined.

[0034] When the calculation method of node health is sum, the proportion of node health index is integrated at the same time, and the weighted average calculation is performed to obtain the component health index, module health index and device health index; When the calculation method of node health is or, if there is a node health index of Class III, then the corresponding component is Class III. If they are all Class II, then a weighted average calculation is performed to obtain the component health index, module health index, and device health index; When the calculation method of node health is and, all the contact health indices are all Class 3, and the components are Class 3. If they are all Class 2, a weighted average calculation is performed to obtain the health index of the corresponding level.

[0035] Step 4: Calculate the remaining life percentage: The remaining life percentage refers to the percentage of the remaining reliable service life to the total service life, which is mainly determined by the remaining gas content in the gas cylinder, the status of the ROM in the PPU, and the health of the CPU. The proportion of the above-mentioned influencing nodes is determined by the weighted average calculation of the remaining life percentages of the above-mentioned nodes.

[0036] Step 5: Health status output: Obtain the code of each node, the health classification of the node, the health index of the node, the remaining life of the whole machine, whether there is a serious fault, whether there are three types of nodes, and the node code of the serious fault.

[0037] Embodiment 2: This embodiment provides a system for running the above method, including: The health index profile includes: a) Total number of layers, Layers b) Node

[0038] The remaining life profile includes: a) Number of influencing factors: b) Each node

[0039] The specific life calculation method of each node involved varies from individual to individual, and the individual calculation method can be used.

[0040] Configuration file reading module: used to read the health index configuration file and the remaining life configuration file; each configuration file is a txt file, which is edited by the host computer, and the host computer communicates with the CPU, and then is downloaded and stored in the CPU; when the CPU is initialized, it will read the configuration file. If the configuration file is missing, it will run according to the default configuration; the result of reading the configuration file is stored in the structure.

[0041] The particle layer health calculation module calculates the health classification and health index of the particle layer according to the calculation method of each particle in the health index configuration file.

[0042] The health calculation module of other layers obtains the number of sub-nodes Sub_node_num according to the health index configuration file, and calculates the health score of the current node according to the sub-node code Sub_node_code, the health index Node_score and the health classification Node_class of the sub-node and the node health calculation method Node_cal_alth.

[0043] If Node_cal_alth is the sum, then the node health proportion Node_coff is also taken into consideration to perform weighted average calculation.

[0044] If Node_cal_alth is or, then one of them is in Class 3, and the parent node is in Class 3. If both are in Class 2, it does not affect the result and is calculated by the score.

[0045] If Node_cal_alth is and, then all are of the third category, and the parent node is the third category. If all are of the second category, then the score is calculated.

[0046] The remaining life calculation module obtains the weighted average of the remaining life of each node and the weighted coefficient in the remaining life configuration file.

[0047] The health status output module transmits the health status and remaining life of each node to the entire satellite through telemetry.

[0048] The transmitted telemetry table includes the code of each node, the health classification of the node, the health index of the node, the remaining life of the whole machine, whether there is a serious fault (three types of nodes), and the node code of the serious fault.

[0049] The embodiments described above are merely descriptions of preferred implementation modes of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. An electric propulsion health assessment and management method based on a hierarchical assessment system, characterized in that: The following steps are involved: Step 1: Construct the electric propulsion health tree model: The electric propulsion is layered into equipment layer, module layer, component layer and particle layer; Step 2: Calculate the particle layer health index: According to the characteristics of each particle, the individual function of the particle health performance is obtained respectively, and the particle utilization rate, i.e., the particle health index, is calculated according to the individual function of each particle; and the health classification of each particle is determined according to the particle health index; Step 3: Calculate the health index of other layers: Determine the weight of each particle, each component, and each module, and calculate the component layer health index, module layer health index, and device layer health index in turn according to the weighted average; Step 4: Calculate the remaining life percentage: Calculate the remaining life percentage of the following influencing nodes: the remaining gas content of the gas cylinder, the status of the ROM in the PPU, and the health of the CPU; determine the proportion of the above influencing nodes, and calculate the remaining life percentage of the equipment layer by the weighted average of the remaining life percentage of the above influencing nodes; Step 5: Health status output: Obtain the code, health classification, health index of each layer, the remaining life of the whole machine, whether there is a serious fault in each layer, and the layer code of the serious fault.

2. The electric propulsion health assessment and management method based on a hierarchical assessment system according to claim 1, characterized in that: In step 1, the health of the device layer is determined by the module layer, the health of the module layer is determined by the component layer, and the health of the component layer is determined by the particle layer; the weighted average of the health indexes of each particle constitutes the health index of the component, the weighted average of the health indexes of each component constitutes the health index of the module, and the weighted average of the health indexes of each module constitutes the device health index.

3. The electric propulsion health assessment and management method based on a hierarchical assessment system according to claim 1, characterized in that: The equipment layer is electric propulsion; the module layer includes gas cylinders, pipelines, and PPU; the PPU module layer includes various power supplies, EEPROM, FLASH, and chip operating status; the EEPROM component layer includes the number of read and write times and utilization rate; the FLASH component layer includes the number of read and write times, utilization rate, and the number of bad blocks; the chip component layer includes CPU utilization rate, operating time, backup status, and alarms.

4. The electric propulsion health assessment and management method based on a hierarchical assessment system according to claim 1, characterized in that: Health categories include: One type can be used normally without any extra attention and can be used with normal operation steps; Category II: limited use, requiring supplementary operations, or available under special conditions; Category 3: unusable, obsolete products that no longer provide functionality.

5. The electric propulsion health assessment and management method based on a hierarchical assessment system according to claim 1, characterized in that: For particles in the first and third health conditions, there is no need to calculate the health index. For particles in the second health condition, the health index needs to be calculated to show the current severity of the situation.

6. The electric propulsion health assessment and management method based on a hierarchical assessment system according to claim 1, characterized in that: In step 3, according to the working dependencies of the nodes in each component, the components in each module, and the modules in the device, the calculation method of the node health of the nodes, components, and modules is determined, and the calculation method of the node health includes and, or, and; When the calculation method of node health is sum, the proportion of node health index is integrated at the same time, and the weighted average calculation is performed to obtain the component health index, module health index and device health index; When the calculation method of node health is or, if there is a node health index of Class III, then the corresponding component is Class III. If they are all Class II, then a weighted average calculation is performed to obtain the component health index, module health index, and device health index; When the calculation method of node health is and, all the contact health indices are all Class 3, and the components are Class 3. If they are all Class 2, a weighted average calculation is performed to obtain the health index of the corresponding level.

7. The electric propulsion health assessment and management method based on a hierarchical assessment system according to claim 1, characterized in that: The remaining life percentage is the percentage of the remaining reliable service life to the total service life.

8. The electric propulsion health assessment and management method based on a hierarchical assessment system according to claim 1, characterized in that: In step 2, the influencing factor of the normal operation of each particle is determined according to the characteristics of each particle, and the individual function of the utilization rate or occupancy rate of each particle is obtained, and the particle utilization rate is calculated according to the individual function of each particle.

9. A system for implementing any of the methods of claims 1 to 8, characterized in that: include: Health index configuration file, including total number of layers, node code, number of subnodes, subnode code, node health calculation method, node health ratio and node health classification. Node health calculation methods include sum, or, and, where 1 is sum, 2 is or, and 3 is and; Remaining life profile, number of influencing factors, node code, calculated remaining life and percentage of this node; A configuration file reading module is used to read a health index configuration file and a remaining life configuration file; The particle layer health calculation module calculates the health classification and health index of the particle layer according to the calculation method of each particle in the health index configuration file; The health calculation module of other layers calculates the health score of the current node according to the health index configuration file and the node health calculation method; The remaining life calculation module obtains the weighted average of the remaining life of each node and the weighted coefficient in the remaining life configuration file; The health status output module transmits the health status and remaining life of each node to the entire satellite through telemetry.

10. A system according to claim 9, characterized in that: The transmitted telemetry table includes the code of each node, the health classification of the node, the health index of the node, the remaining life of the whole machine, whether there is a serious fault, and the node code of the serious fault.

Images