CN113886951A - Aircraft health management system and method - Google Patents
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Abstract
The invention discloses an aircraft health management system and method, which relate to the technical field of position testing and comprise the following steps: the database management module is used for storing fault threshold diagnosis standards of a plurality of types of aircrafts; the data acquisition module is used for acquiring, analyzing and outputting actual operation data of the aircraft; the comprehensive analysis application module is used for processing the actual operation data and the fault threshold diagnosis standard according to the fault diagnosis model to obtain a fault diagnosis result; and if the fault diagnosis result indicates that the aircraft does not have faults, respectively processing the actual operation data according to the fault prediction model and the health degree evaluation model to obtain a fault prediction result and a health evaluation result. The invention can realize the functions of operation data management, fault diagnosis, fault prediction, health level evaluation and the like of aircrafts with different models and different communication modes, thereby realizing the health management of the whole life cycle of the aircraft equipment.
Description
Technical Field
The invention relates to the technical field of position testing, in particular to an aircraft health management system and method.
Background
The situation maintenance in the civil field is introduced in the end of the 20 th century and the 90 th year in China as a strategic equipment guarantee strategy, the purpose of the strategy is to monitor the equipment state in real time or near real time and determine the optimal maintenance time according to the actual state of the equipment so as to improve the availability and the task reliability of the equipment.
Meanwhile, rapid development of information technologies and advanced technologies such as high-speed data acquisition, large-capacity data storage, high-speed data transmission and processing, information fusion, Micro-Electro-Mechanical systems (MEMS), and networks means that more data storage and processing functions are allowed to be completed in equipment to eliminate the need for processing information by relying on ground stations too much, and conditions are created for improving the fault Prediction and Health Management (PHM) capability. In recent years, PHM technology has attracted much attention from various countries, and various ways are actively adopted by all parties to accelerate the development and utilization of the dual-purpose technology for military and civil use.
The missile weapon fault diagnosis and health management technical research is highly emphasized abroad. For example, the influence of the temperature, humidity, impact, vibration, electromagnetism and other environments of the sections of different missions of the missile weapon on the performance of the missile is researched, and the PHM management of the missile weapon is realized by monitoring the environmental stress of the different missions of the missile weapon.
China does not pay enough attention to guarantee capability, but in recent years, the improvement of the PHM capability of equipment such as aircrafts and the like is increasingly paid more attention, so that related research and development are needed.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to overcome the defects in the prior art and provides an aircraft health management system and method, which can be used for carrying out fault diagnosis, fault prediction and health management on an aircraft.
In order to achieve the above purposes, the technical scheme adopted by the invention is as follows:
an aircraft health management system, the aircraft comprising a plurality of components, the types of the plurality of components comprising a component and a part associated with the component; the aircraft health management system comprises:
the data acquisition module is used for acquiring, analyzing and outputting actual operation data of the aircrafts of multiple models;
the comprehensive analysis application module is used for training the supervised label data through a logistic regression model to obtain a health degree evaluation model and selecting a plurality of parts of the part in a specific fault mode as key variables, acquiring real-time operation data of the key variables in a certain time period in the specific fault mode through a fault injection test, and training the real-time operation data to obtain a fault prediction model;
the comprehensive analysis application module is also used for processing actual operation data according to a prestored fault diagnosis model to obtain a fault diagnosis result; and if the fault diagnosis result indicates that the aircraft does not have faults, respectively processing the actual operation data according to the health degree evaluation model and the fault prediction model to obtain a health evaluation result and a fault prediction result.
Preferably, the actual operation data includes instruction data, operation trajectory data, attitude data, and environment data.
Preferably, the aircraft health management system further comprises:
the system comprises a database management module, a fault diagnosis module and a fault prediction module, wherein the database management module is used for storing actual operation data, management data, fault threshold diagnosis standards, a fault diagnosis model, a health degree evaluation model and a fault prediction model of a plurality of types of aircrafts;
the management data includes model data and system configuration data.
Preferably, the comprehensive analysis application module is further configured to respectively construct a fault diagnosis model based on a fault tree according to the management data of each model of aircraft, where the fault diagnosis model includes a component diagnosis model of each component associated with the aircraft.
Preferably, when the comprehensive analysis application module judges that the aircraft has a fault, the comprehensive analysis application module positions a fault component by using a comparison result of the actual operation data and the fault threshold diagnosis standard, calls a corresponding component diagnosis model according to the fault component, sequentially performs transverse traversal and longitudinal traversal on a fault tree associated with the component diagnosis model to obtain a plurality of parts, positions the fault component from the plurality of parts by using the comparison result of the actual operation data and the fault threshold diagnosis standard, and generates the fault diagnosis result according to the fault component and the fault component.
Preferably, the data acquisition module comprises a CPCI case for connecting to a data output interface of the aircraft;
the mode of acquiring actual operation data by the data acquisition module comprises real-time acquisition and offline import.
A method of health management of an aircraft, the aircraft comprising a plurality of components, the types of the plurality of components comprising a component and a part associated with the component; the aircraft health management system comprises:
configuring fault diagnosis models of a plurality of types of aircrafts in advance;
taking actual operation data of an aircraft in normal operation and any part in fault as supervised label data, and training the supervised label data through a logistic regression model to obtain a health degree evaluation model;
selecting a plurality of parts of the component in a specific fault mode as key variables, acquiring real-time operation data of the key variables in a certain time period in the specific fault mode through a fault injection test, and training the real-time operation data to obtain a fault prediction model;
acquiring, analyzing and outputting actual operation data of a plurality of types of aircrafts;
processing actual operation data according to a prestored fault diagnosis model to obtain a fault diagnosis result; and if the fault diagnosis result indicates that the aircraft does not have faults, respectively processing the actual operation data according to the health degree evaluation model and the fault prediction model to obtain a health evaluation result and a fault prediction result.
Preferably, the aircraft health management method further comprises:
storing actual operation data, management data, fault threshold diagnosis standards, fault diagnosis models, health degree evaluation models and fault prediction models of a plurality of types of aircrafts;
the management data includes model data and system configuration data.
Preferably, a fault tree-based fault diagnosis model is constructed based on the management data of each model of aircraft, and the fault diagnosis model comprises a component diagnosis model of each component associated with the aircraft
Preferably, the processing of the actual operation data according to the pre-stored fault diagnosis model specifically includes:
when the fault of the aircraft is judged, a fault part is positioned by using a comparison result of actual operation data and a fault threshold diagnosis standard, a corresponding part diagnosis model is called according to the fault part, a fault tree related to the part diagnosis model is sequentially subjected to transverse traversal and longitudinal traversal to obtain a plurality of parts, a fault part is positioned from the plurality of parts by using the comparison result of the actual operation data and the fault threshold diagnosis standard, and a fault diagnosis result is generated according to the fault part and the fault part.
Compared with the prior art, the invention has the advantages that:
the functions of operation data management, fault diagnosis, health level assessment, fault prediction and the like of aircrafts with different models and different communication modes can be realized, and the health management of the aircrafts is realized.
Drawings
FIG. 1 is a block schematic diagram of an aircraft health management system in an embodiment of the invention.
FIG. 2 is a schematic flow chart of a method for health management of an aircraft according to an embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
The aircraft comprises a plurality of parts, the types of the parts comprise parts and parts related to the parts, namely the working state of each part is influenced by the parts, the parts can be parts forming the parts or parts connected or contacted with the parts, and health assessment needs to be carried out on the parts in combination with the health degree of the parts related to the parts when the parts are subjected to health management. The same component can be classified as either a component or a part.
As shown in fig. 1, the present application provides an aircraft health management system comprising:
and the data acquisition module 1 is used for acquiring, analyzing and outputting actual operation data of the aircrafts of multiple models. The communication modes of the aircrafts with multiple models can be different, and the actual operation data is required to be analyzed and then output to the comprehensive analysis application module 3.
And the comprehensive analysis application module 3 is used for taking actual operation data or fault injection test data of the aircraft in normal operation and any part in fault as supervised label data and training the supervised label data through a logistic regression model to obtain a health degree evaluation model. For example, the health degree of the aircraft in normal operation is set to 1, the health degree of any part of the aircraft in fault is set to 0, the parameter estimation of the health degree estimation model is completed by combining the actual operation data acquired under different operation states, a logistic regression model and a supervised label data combined maximum likelihood estimation algorithm, and then the health degree estimation model is obtained.
The comprehensive analysis application module 3 is also used for selecting a plurality of parts of the component in a specific fault mode as key variables, acquiring real-time operation data of the key variables in a certain time period in the specific fault mode through a fault injection test, and training the real-time operation data to obtain a fault prediction model. For example, a specific fault mode is artificially manufactured through a fault injection test, monitoring variables with definite time-varying evolution are screened, actual operation data of the monitoring variables obviously change along with time and are key variables causing component faults, an evolution rule of the key variables changing along with time under the specific fault mode is obtained based on a cyclic neural network technology, and real-time operation data of the key variables in a certain time period is trained to obtain a fault prediction model.
The comprehensive analysis application module 3 is further configured to process actual operation data according to a pre-stored fault diagnosis model to obtain a fault diagnosis result. And if the fault diagnosis result indicates that the aircraft does not have faults, respectively processing the actual operation data according to the health degree evaluation model and the fault prediction model to obtain a health evaluation result and a fault prediction result. For example, whether a component enters a fault latency period can be judged by using a fault prediction model to realize fault prediction, and the actual operation data for training the fault prediction model can be from the actual historical data of the aircraft or from the actual operation data of a plurality of aircraft of the same model.
The actual operation data comprises instruction data, operation track data, attitude data and environment data.
In this embodiment, the comprehensive analysis application module 3 configures importance and failure probability (or referred to as failure probability) of relevant parts for the healthy operation of the component, constructs an initial health degree evaluation model according to two parameters of the importance and the failure probability, and then quantitatively evaluates health degree scores of aircraft equipment based on supervised label data of normal scenes and failure scenes by using a logistic steinrichia machine learning algorithm, divides health grades, realizes a multi-level health evaluation function of key components, single aircraft or aircraft clusters, and provides data input for equipment scheduling use and maintenance plans.
For the key components, the failure prediction can be carried out by utilizing a neural network technology and the like, and a basis is provided for the subsequent prediction and maintenance process. The comprehensive analysis application module 3 determines the current states of the key components and the single machine by comparing and analyzing the current actual operation data of the key components and the historical state change mode by using a large amount of historical data, fault information and related prediction models and knowledge of the equipment, judges whether the current states of the key components and the single machine enter a fault latency period or not and realizes the prediction of the fault.
In a preferred embodiment, the same model of aircraft or aircraft component can be used for lateral comparison analysis, and historical comparison analysis can be performed for a certain aircraft component.
The method comprises the steps of transversely counting and analyzing the common problems and differences of health index monitoring parameters of aircrafts with similar states, comparing actual operation data of different models, the same component or a single aircraft, and providing various commonly used algorithms by a system, wherein the analysis comprises the steps of carrying out weighted average, maximum and minimum value taking and the like on selected parameters in the actual operation data.
The longitudinal statistical analysis equipment is used for analyzing the health change trend of the equipment and performing actual operation data analysis on components or single machines of the same model, wherein the actual operation data analysis comprises time series data characteristic analysis and frequency domain characteristic analysis. The time series data characteristic analysis comprises analysis of mean value, maximum value, standard deviation, margin factor, peak factor, waveform factor, pulse factor and the like; the frequency domain characteristic analysis comprises a linear amplitude spectrum curve, a logarithmic amplitude spectrum curve and a short-time Fourier transform analysis result.
The aircraft health management system can realize the functions of operation data management, fault diagnosis, fault prediction, health level evaluation and the like of aircrafts of different models and different communication modes, and realize the health management of the aircrafts.
In a preferred embodiment, the aircraft health management system further comprises:
and the database management module 2 is used for storing actual operation data, management data, fault threshold diagnosis standards, a fault diagnosis model, a health degree evaluation model and a fault prediction model of a plurality of types of aircrafts. The management data comprises model data, system configuration data and the like, and can support the integrated development function of the openness and the expansibility of the aircraft health management system.
The comprehensive analysis application module 3 respectively constructs fault diagnosis models based on fault trees according to the management data of each model of aircraft, each fault diagnosis model comprises a component diagnosis model of each component related to the corresponding aircraft, and the comprehensive analysis application module 3 calls the component diagnosis model of which component is called when the component is diagnosed to have a fault. The fault diagnosis result, the fault prediction result and the health evaluation result obtained by the comprehensive analysis application module 3 are stored and updated in the database management module 2.
In this embodiment, the database management module 2 establishes a configuration management database for information such as important equipment models, manufacturers, configuration model names, model product structures, model acquisition, monitoring parameters, spare parts, technical data, security resources and the like according to an equipment product structure tree of each model of aircraft, so as to realize dynamic association configuration of model basic information, model product structures and actual operation data, and provide data support for state evaluation and health management. And functions of importing, exporting, inquiring and the like of management data of aircrafts of different models are supported.
In a preferred embodiment, the data acquisition module 1 includes a software part and a hardware part, the software part is data interface software, and the hardware part is a CPCI chassis.
The data interface software is respectively connected with the database management module 2 and the comprehensive analysis application module 3, and the data interface software, the database management module 2 and the comprehensive analysis application module 3 can be respectively arranged on three servers or an industrial personal computer. The actual operation data received by the CPCI case is processed by the data interface software and then sent to the database management module 2 and the comprehensive analysis application module 3. The data interface software can decode actual operation data according to a configured analysis protocol, and functions of data acquisition, data import, data analysis, parameter monitoring, data storage, data transmission and the like are realized.
The aircraft health management system is connected with a data output interface of the aircraft through the CPCI case so as to receive actual operation data output by the aircraft in the test process. The CPCI case comprises an A/D interface, an Ethernet interface, an I/O interface, a 1533 interface, a CAN bus interface and an expansion interface.
In this embodiment, the aircraft health management system provides standard interfaces such as DB9 externally, and includes various communication modes such as CAN and 1553B internally, and the communication protocol may be configured in data interface software, that is, provides multi-bus data protocol dynamic configuration, and supports different front-end physical signal accesses. The aircraft or the ground test equipment matched with the aircraft is connected to the aircraft health management system through the patch cable.
In a preferred embodiment, when the comprehensive analysis application module 3 determines that the aircraft has a fault, the fault component is located by using the comparison result of the actual operation data and the fault threshold diagnosis standard, the corresponding component diagnosis model is called according to the fault component, a plurality of parts are obtained by sequentially performing transverse traversal and longitudinal traversal on the fault tree associated with the fault component, the fault component is located from the plurality of parts by using the comparison result of the actual operation data and the fault threshold diagnosis standard, and the fault diagnosis result is generated according to the fault component and the fault component.
In this embodiment, in the early-stage comprehensive analysis application module 3, a fault tree based on a specific aircraft is constructed and a corresponding fault diagnosis model is generated according to the complete fault tree information combing and fault logic relationship combing and the established rule according to the mature fault tree.
After the comprehensive analysis application module 3 collects the actual operation data of the aircraft, the actual operation data and the fault threshold diagnosis standard are firstly compared according to the threshold rule to judge whether the aircraft has faults or not, if so, the fault tree is inquired according to the logic rule to obtain a plurality of parts which can cause the parts to have faults, and then the parts are judged to have faults according to the threshold rule, so that the corresponding relation between the fault parts and the fault parts is determined, and the fault diagnosis results such as the fault mode, the fault position, the fault influence and the like are obtained. When the fault tree is inquired, the fault tree is traversed transversely firstly, and then the fault tree is traversed longitudinally.
As shown in fig. 2, the present application further discloses an aircraft health management method comprising:
and step S1, configuring fault diagnosis models of the aircrafts of multiple models in advance.
And S2, taking actual operation data of the aircraft in normal operation and any part in fault as supervised label data, and training the supervised label data through a logistic regression model to obtain a health degree evaluation model.
And step S3, selecting a plurality of parts of the component in a specific fault mode as key variables, acquiring real-time operation data of the key variables in a certain time period in the specific fault mode through a fault injection test, and training the real-time operation data to obtain a fault prediction model.
And step S4, collecting, analyzing and outputting actual operation data of the aircrafts of multiple models.
Step S5, processing actual operation data according to a prestored fault diagnosis model to obtain a fault diagnosis result; and if the fault diagnosis result indicates that the aircraft does not have faults, respectively processing the actual operation data according to the health degree evaluation model and the fault prediction model to obtain a health evaluation result and a fault prediction result.
In this embodiment, the aircraft health management system and method formed by the hardware and software can realize the functions of parameter and file management, fault diagnosis, key single-machine fault prediction, health analysis and the like of aircrafts of different models and different communication modes, and can be used as equipment in a technical position to realize automatic health management of the aircrafts and other equipment. The system can serve military products such as missiles and the like, and can also serve aircraft equipment such as carrier rockets, small aircrafts and the like.
The present invention is not limited to the above-described embodiments, and it will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the principle of the present invention, and such modifications and improvements are also considered to be within the scope of the present invention. Those not described in detail in this specification are within the skill of the art.
Claims (10)
1. An aircraft health management system, the aircraft comprising a plurality of components, the types of the plurality of components comprising a component and a part associated with the component; characterized in that said aircraft health management system comprises:
the data acquisition module is used for acquiring, analyzing and outputting actual operation data of the aircrafts of multiple models;
the comprehensive analysis application module is used for training the supervised label data through a logistic regression model to obtain a health degree evaluation model and selecting a plurality of parts of the part in a specific fault mode as key variables, acquiring real-time operation data of the key variables in a certain time period in the specific fault mode through a fault injection test, and training the real-time operation data to obtain a fault prediction model;
the comprehensive analysis application module is also used for processing actual operation data according to a prestored fault diagnosis model to obtain a fault diagnosis result; and if the fault diagnosis result indicates that the aircraft does not have faults, respectively processing the actual operation data according to the health degree evaluation model and the fault prediction model to obtain a health evaluation result and a fault prediction result.
2. The aircraft health management system of claim 1, wherein the actual operational data comprises command data, operational trajectory data, attitude data, and environmental data.
3. The aircraft health management system of claim 1, further comprising:
the system comprises a database management module, a fault diagnosis module and a fault prediction module, wherein the database management module is used for storing actual operation data, management data, fault threshold diagnosis standards, a fault diagnosis model, a health degree evaluation model and a fault prediction model of a plurality of types of aircrafts;
the management data includes model data and system configuration data.
4. The aircraft health management system of claim 3, wherein the integrated analysis application module is further configured to separately build a fault tree-based fault diagnosis model from the management data for each model of aircraft, the fault diagnosis model comprising a component diagnosis model for each component associated with the aircraft.
5. The aircraft health management system of claim 4, wherein the analysis-by-synthesis application module, when determining that the aircraft is malfunctioning, locates a malfunctioning component using the comparison of the actual operational data and the malfunction threshold diagnostic criteria, invokes a corresponding component diagnostic model based on the malfunctioning component, performs a transverse traversal and a longitudinal traversal in sequence on the fault tree associated with the component diagnostic model to obtain a plurality of parts, locates a malfunctioning component from the plurality of parts using the comparison of the actual operational data and the malfunction threshold diagnostic criteria, and generates the malfunction diagnostic result based on the malfunctioning component and the malfunctioning component.
6. The aircraft health management system of claim 1, wherein the data acquisition module comprises a CPCI chassis for interfacing with a data output interface of the aircraft;
the mode of acquiring actual operation data by the data acquisition module comprises real-time acquisition and offline import.
7. A method of health management of an aircraft, the aircraft comprising a plurality of components, the types of the plurality of components comprising a component and a part associated with the component; characterized in that said aircraft health management system comprises:
configuring fault diagnosis models of a plurality of types of aircrafts in advance;
taking actual operation data of an aircraft in normal operation and any part in fault as supervised label data, and training the supervised label data through a logistic regression model to obtain a health degree evaluation model;
selecting a plurality of parts of the component in a specific fault mode as key variables, acquiring real-time operation data of the key variables in a certain time period in the specific fault mode through a fault injection test, and training the real-time operation data to obtain a fault prediction model;
acquiring, analyzing and outputting actual operation data of a plurality of types of aircrafts;
processing actual operation data according to a prestored fault diagnosis model to obtain a fault diagnosis result; and if the fault diagnosis result indicates that the aircraft does not have faults, respectively processing the actual operation data according to the health degree evaluation model and the fault prediction model to obtain a health evaluation result and a fault prediction result.
8. The aircraft health management method of claim 7, wherein the aircraft health management method further comprises:
storing actual operation data, management data, fault threshold diagnosis standards, fault diagnosis models, health degree evaluation models and fault prediction models of a plurality of types of aircrafts;
the management data includes model data and system configuration data.
9. The aircraft health management method of claim 8, wherein the fault tree-based fault diagnosis model is constructed separately from management data for each model of aircraft, the fault diagnosis model comprising a component diagnosis model for each component associated with the aircraft.
10. The aircraft health management method according to claim 9, wherein processing the actual operational data according to the pre-stored fault diagnosis model specifically comprises:
when the fault of the aircraft is judged, a fault part is positioned by using a comparison result of actual operation data and a fault threshold diagnosis standard, a corresponding part diagnosis model is called according to the fault part, a fault tree related to the part diagnosis model is sequentially subjected to transverse traversal and longitudinal traversal to obtain a plurality of parts, a fault part is positioned from the plurality of parts by using the comparison result of the actual operation data and the fault threshold diagnosis standard, and a fault diagnosis result is generated according to the fault part and the fault part.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114688926A (en) * | 2022-03-22 | 2022-07-01 | 中国运载火箭技术研究院 | Carrier rocket health detection verification evaluation system and method |
| CN116227086A (en) * | 2023-03-23 | 2023-06-06 | 中国航空发动机研究院 | Aeroengine gas circuit fault simulation method |
| CN116448189A (en) * | 2023-06-13 | 2023-07-18 | 北京神导科技股份有限公司 | Test equipment of supporting facility of flight command system |
| CN117408668A (en) * | 2023-08-07 | 2024-01-16 | 长龙(杭州)航空维修工程有限公司 | Maintenance method, system, equipment and storage medium based on aircraft health management |
| CN117422888A (en) * | 2023-09-13 | 2024-01-19 | 长龙(杭州)航空维修工程有限公司 | Aircraft performance evaluation method and system |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114688926A (en) * | 2022-03-22 | 2022-07-01 | 中国运载火箭技术研究院 | Carrier rocket health detection verification evaluation system and method |
| CN114688926B (en) * | 2022-03-22 | 2024-06-04 | 中国运载火箭技术研究院 | Carrier rocket health detection verification evaluation system and method |
| CN116227086A (en) * | 2023-03-23 | 2023-06-06 | 中国航空发动机研究院 | Aeroengine gas circuit fault simulation method |
| CN116227086B (en) * | 2023-03-23 | 2023-11-24 | 中国航空发动机研究院 | Aeroengine gas circuit fault simulation method |
| CN116448189A (en) * | 2023-06-13 | 2023-07-18 | 北京神导科技股份有限公司 | Test equipment of supporting facility of flight command system |
| CN116448189B (en) * | 2023-06-13 | 2023-09-08 | 北京神导科技股份有限公司 | Test equipment of supporting facility of flight command system |
| CN117408668A (en) * | 2023-08-07 | 2024-01-16 | 长龙(杭州)航空维修工程有限公司 | Maintenance method, system, equipment and storage medium based on aircraft health management |
| CN117408668B (en) * | 2023-08-07 | 2024-05-10 | 长龙(杭州)航空维修工程有限公司 | Maintenance method, system, equipment and storage medium based on aircraft health management |
| CN117422888A (en) * | 2023-09-13 | 2024-01-19 | 长龙(杭州)航空维修工程有限公司 | Aircraft performance evaluation method and system |
| CN117422888B (en) * | 2023-09-13 | 2024-05-10 | 长龙(杭州)航空维修工程有限公司 | Aircraft performance evaluation method and system |
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