CN106649727A - Database construction method used for fault detection of unmanned aerial vehicle flight control system - Google Patents
Database construction method used for fault detection of unmanned aerial vehicle flight control system Download PDFInfo
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- CN106649727A CN106649727A CN201611205514.6A CN201611205514A CN106649727A CN 106649727 A CN106649727 A CN 106649727A CN 201611205514 A CN201611205514 A CN 201611205514A CN 106649727 A CN106649727 A CN 106649727A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
Abstract
The invention discloses a database construction method used for the fault detection of an unmanned aerial vehicle flight control system, and belongs to the technical field of the fault detection of an unmanned aerial vehicle. Normal unmanned aerial vehicle real-time flight control data screened through a judging rule is blended into historical data; whether unmanned aerial vehicle flight control data of an unknown fault at a future moment fails or not is judged through the judging rule trained according to the historical data; a reference database formed by normal historical data is dynamically updated so as to guarantee the real-time update of the judging rule and guarantee the real-time characteristic performance of the reference database formed by the normal historical data; and the judging rule which is dynamically updated can effectively detect various exceptional forms including isolated point exception, context exception, accumulation exception and the like in the unmanned aerial vehicle flight control data so as to effectively improve the recall ratio and the accuracy of fault detection and simultaneously lower a false alarm rate.
Description
Technical field
The invention discloses a kind of database construction method for UAV Flight Control System fault detect, belongs to nothing
The technical field of man machine fault's detection.
Background technology
In recent years, as prognostic and health management technology (PHM) concept is to the introducing of unmanned plane total management system,
Unmanned plane safety management is information-based, intelligentized demand is increasingly improved.The core of unmanned plane safety management --- safety prison
Control and abnormity early warning, can carry out overall monitor to the health status of unmanned plane, to improving unmanned plane security performance, ensureing maintenance
Efficiency, reduction life cycle are significant.The flight control system of unmanned plane as unmanned plane core component, by various
The subsystems such as sensor, network link, performs device are constituted, and fly the real-time flight state parameter and handle of control sensor survey aircraft
Data feedback is to flight-control computer.Flight-control computer resolves Flight Control Law so as to complete unmanned machine automatic drive
Aerial mission, but flight control system easily breaks down because its working environment is complex.
Divide according to research object, it is current problematic for unmanned plane security monitoring and this spy of fault pre-alarming both at home and abroad
It is different that research is concentrated mainly on the winged control Transducer fault detection of the unmanned plane network communication link safety of broad sense, unmanned plane, unmanned plane
The aspects such as the analysis of Chang Hangwei.
In the last few years the domestic researcher about colleges and universities and scientific research institutions exported number to system for flight control computer sensor
Extensive research has been done according to abnormal state, hardware failure detection.The research teams of Northwestern Polytechnical University are to based on improved master
The methods such as meta analysis method flight control system fault detect, improved discrete wavelet-optimization limit study fault detect are studied；
Nanjing Aero-Space University Jiang Bin professor team is detected for the adaptive failure of unmanned plane, fault-tolerant navigation scheduling algorithm has carried out one
Series of studies；BJ University of Aeronautics ＆ Astronautics Zhong Maiying professor team is for being based on EKF, volume Kalman filtering
System for flight control computer fault detection method studied；Additionally, the R＆D institution such as National University of Defense Technology is also to nothing
The many aspects such as man machine fault's detection are studied.This series of research is established for the fault detect of system for flight control computer
Theoretical foundation and technical foundation.
External colleges and universities and research institution also fly control failure problems to unmanned plane and conduct extensive research, and Kim etc. is proposed
A kind of unmanned plane is subject to the abnormal flight detection framework under cyber-attack, hardware attack, sensor fraud and wireless attack.
Cork and Walker devise the unmanned plane abnormal data of the winged control sensing data state analysis estimated based on IMM-UKF
Detection method.Lin etc. and Khalastchi etc. is by the flying quality to unmanned plane, pipeline data, engine data, servo data
The state of stream is monitored and analyzed, and sets up abnormal flight judgment models.Mitchell and Chen lead in network link data
Domain proposes a kind of self adaptation unmanned plane behavior criteria for classifying based on intruding detection system.Birnbaum etc. is devised based on real
When recursive least-squares system flight dynamic and fly the detection method of control parameter monitoring unmanned plane health status, afterwards
Birnbaum etc. is to the flight position of the real-time output of system for flight control computer, multidate information, temporal information and default unmanned plane
Flight plan relevant information is matched to realize the monitoring of unmanned plane state.Additionally, there is some to fly for broad sense aircraft
The research of control fault detect.
For above research contents, from the point of view of its theoretical, methods and techniques route, we can draw the core of fault detect
Center algorithm can be divided into following three major types：Modelling (model based), knowledge experience method (knowledge based) and data
Driving method (data driven).
In recent years, with machine learning and the fast development of data mining, data-driven method progressively becomes fault diagnosis and grinds
The focus studied carefully and direction.The fault diagnosis method of data-driven is extensively applied in the fault detect in each field, with system operation mistake
Based on the data of journey, by various data process＆analysis methods the information implied in data, completion system data are excavated
Abnormality detection.Khalastchi etc. proposes automatic robot's airborne sensor abnormality detection side of real-time online data-driven
Case.Beghi etc. is proposed based on the air-conditioning equipment water chilling unit system failure detection schemes of data-driven.Yin etc. proposes base
In the wind turbine system method for diagnosing faults of data-driven.Additionally, the field such as automation, machinery is also widely studied based on number
Method according to driving.Therefore, the method for data-driven because the characteristics of its is flexible and changeable in system for flight control computer fault detect
With huge potentiality and value.
On the whole, understand that current modelling and knowledge experience method has the disadvantages that according to domestic and foreign literature analysis：
(1) it is difficult to detect unknown failure；(2) it is relatively specific for the system of low dimensional.This is complicated to fly control for unmanned plane
System, the method based on model can not possibly obtain each details of complicated mechanism model；The side of another aspect knowledge based experience
Method needs long-term Heuristics accumulation, there is the defect for not adapting to unmanned plane abnormality detection demand complicated and changeable.Therefore, when
The front fault detect research generally existing based on modelling and Qualitative Knowledge empirical method computationally intensive, fault model and assumes not conforming to
The problems such as reason, flexible compliance difference.Fault detect application prospect based on data-driven extensively, but still has key technical problem to have
It is to be solved, wherein, effective extraction (failure is normal) for data characteristics and it is used as fault detect reference data
Effective structure in storehouse is based on the key problem in data-driven fault detect.
The content of the invention
The goal of the invention of the present invention is the deficiency for above-mentioned background technology, there is provided one kind is used for UAV Flight Control
The database construction method of system failure detection, extracts normal data feature and realizes system for flight control computer by real-time online
The dynamic construction in fault-detection data storehouse, the fault detect for solving system for flight control computer is special to every kind of failure because being difficult to
The extraction levied and the technical problem for not adapting to abnormality detection demand.
The present invention is adopted the following technical scheme that for achieving the above object：
A kind of database construction method for UAV Flight Control System fault detect, gathers system for flight control computer
Data of lasting offline under current time are trained by the real-time observed data under current time using intelligent algorithm
Obtain the breakdown judge rule under current time and normal historical data is constituted under filtering out current time reference database, adopt
Judge whether is real-time observed data failure under current time with the breakdown judge rule under current time, under current time
The reference database that normally historical data is constituted under current time is updated when real-time observed data is normal, offline history number is updated
According to, again and again, according to renewal after offline historical data train breakdown judge rule in real time and filter out in real time
The reference database that normal historical data is constituted, sees in real time under processing current time according to the breakdown judge rule for training in real time
Survey the reference database that the normal historical data of result online updating of data is constituted.
As the further prioritization scheme of database construction method, real-time sight of the system for flight control computer under current time
Survey packet to contain：Three-axis gyroscope, GPS, accelerometer, barometrical measurement data.
As the further prioritization scheme of database construction method, when the real-time observed data under current time is normal more
The reference database that normal historical data is constituted under new current time, concrete grammar is：With the time series with length of window
The reference database that normally historical data is constituted under current time is represented, using the sliding window model with the length of window
The end data in the time series with length of window are rejected, adds the real-time observed data under current time with window
The seasonal effect in time series end of length.
Used as the further prioritization scheme of database construction method, the breakdown judge rule under using current time judges
Real-time observed data failure under current time whether before, see in real time under first removing current time using Kalman filtering algorithm
The noise surveyed in data.
As the further prioritization scheme of database construction method, using including but not limited to artificial neural network, decision-making
The intelligent algorithm of tree is trained the breakdown judge method obtained under current time to the data of lasting offline under current time
Then and filter out the reference database that normal historical data is constituted under current time.
The present invention adopts above-mentioned technical proposal, has the advantages that：The present invention is directed to system for flight control computer failure
Detection proposes the database construction method for data-driven, trains the historical data base at each moment each to obtain in real time
When the breakdown judge rule inscribed and inscribe the reference database that normal historical data is constituted when filtering out each, using per for the moment
The breakdown judge rule inscribed processes the real-time observed data inscribed during correspondence, by the normal number in the real-time flight control data of unmanned plane
According to incorporating reference database and then updating historical data base, the reference database and failure of normal historical structure are updated by dynamic
Judging rules, it is ensured that the real-time characteristic of the reference database that normal historical data is constituted, using the normal of dynamic renewal
The reference database that historical data is constituted can, context abnormal to the isolated point in unmanned plane flight control data be abnormal, add up different
Various anomaly patterns such as normal are effectively detected, so as to effectively increase the recall ratio and accuracy of fault detect, while drop
Low false alarm rate.
Description of the drawings
Fig. 1 is the flow chart of system for flight control computer fault-detection data storehouse dynamic construction.
Specific embodiment
The technical scheme invented is described in detail with reference to Fig. 1.To solve the failure inspection of system for flight control computer
Survey because being difficult to not adapt to the technical problem of abnormality detection demand to the extraction of every kind of fault signature, the present invention is directed to nothing
Man-machine flight control system fault detect proposes the database construction method for data-driven, and the history at each moment is trained in real time
The breakdown judge rule inscribed when database is to obtain each simultaneously inscribes the reference that normal historical data is constituted when filtering out each
Database, using it is each when the breakdown judge rule inscribed process correspondence when the real-time observed data inscribed, unmanned plane is real-time
Normal data in flight control data incorporates reference database and then updates historical data base, and by dynamic normal historical structure is updated
Reference database and breakdown judge rule, it is ensured that the real-time characteristic of the reference database that normal historical data is constituted.
The renewal of reference database and breakdown judge rule under current time t+1：
(1) data that system for flight control computer is exported under current time t+1 are gathered, including：Three-axis gyroscope, GPS, plus
The measurement data such as speedometer, barometer, are labeled as matrix xt+1, xt+1It is included as normal data, known fault data and unknown event
The blended data of barrier data, using Kalman filtering algorithm to real-time observed data x under current timet+1Pre-processed to go
Except noise, it is to avoid noise data disturbs abnormality detection and caused false alarm, real-time monitored under pretreated current time is marked
Data are
(2) using including but not limited to artificial neural network, decision tree intelligent algorithm under current time t+1
The off-line data under data training current time is lasted offline, is obtained the breakdown judge rule under current time t+1 and is filtered out
The reference database that normal historical data is constituted under current time t+1, marks what normal historical data under current time t+1 was constituted
Reference database is the time series with length of window：{xt'-q+1,...,xt'+1, wherein, t represents the moment, and q represents window
Length, due to the t0 moment, that is, first moment does not have the input of online data, so now normal data is constituted
Reference database is exactly the reference database of real-time online normal data composition；
(3) real-time monitored number under pretreated current time is judged using the breakdown judge rule under current time t+1
According toWhether is failure, is judgingWhen normal, adopt length of window for q sliding window model rejecting length of window time
Sequence { x't-q+1,...,x't+1In end data x't+1, willIt is added on length of window seasonal effect in time series end to obtain：That is, updating the reference database that normal historical data is constituted under current time t+1 isEnter
And updating the off-line data under current time is used for the training of subsequent time.
Subsequent time and it is each thereafter when inscribe the renewal of reference database and breakdown judge rule：
Offline historical data after according to renewal trains in real time breakdown judge rule and filters out in real time and normally goes through
The reference database that history data are constituted, according to real-time observed data under the breakdown judge rule process current time for training in real time
The reference database that constitutes of the normal historical data of result online updating.
Claims (5)
1. a kind of database construction method for UAV Flight Control System fault detect, it is characterised in that gather nobody
Real-time observed data of the machine flight control system under current time, using intelligent algorithm to lasting number offline under current time
According to the ginseng for being trained the breakdown judge rule that obtains under current time and normal historical data is constituted under filtering out current time
Database is examined, judges whether real-time observed data failure under current time to be using the breakdown judge rule under current time,
The reference database that normally historical data is constituted under current time is updated when real-time observed data under current time is normal, is updated
Offline historical data, again and again, according to renewal after offline historical data train breakdown judge rule and real in real time
When filter out the reference database that normal historical data is constituted, when processing current according to the breakdown judge rule for training in real time
Inscribe the reference database that the normal historical data of result online updating of real-time observed data is constituted.
2. a kind of database construction method for UAV Flight Control System fault detect according to claim 1, its
It is characterised by, real-time observed data of the system for flight control computer under current time is included：Three-axis gyroscope, GPS, acceleration
Meter, barometrical measurement data.
3. a kind of database construction method for UAV Flight Control System fault detect according to claim 1, its
It is characterised by, the reference of normal historical data composition under current time is updated when the real-time observed data under current time is normal
Database, concrete grammar is：The ginseng that normally historical data is constituted under current time is represented with the time series with length of window
Database is examined, the end in the time series with length of window is rejected using the sliding window model with the length of window
Data, add the real-time observed data under current time at the seasonal effect in time series end with length of window.
4. a kind of database construction method for UAV Flight Control System fault detect according to claim 1, its
Be characterised by, the breakdown judge rule under using current time judge real-time observed data failure under current time whether it
Before, first using the noise in real-time observed data under Kalman filtering algorithm removal current time.
5. a kind of database construction method for UAV Flight Control System fault detect according to claim 1, its
Be characterised by, using including but not limited to artificial neural network, decision tree intelligent algorithm to offline under current time
Last data and be trained the breakdown judge rule that obtains under current time and normal historical data structure under filtering out current time
Into reference database.
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| CN107272654A (en) * | 2017-07-21 | 2017-10-20 | 南京航空航天大学 | A kind of data clusters dimension reduction method for system for flight control computer fault detect |
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| CN113514109A (en) * | 2021-08-18 | 2021-10-19 | 广东新创华科环保股份有限公司 | Unmanned aerial vehicle flight fault detection method and system |
| CN113850138B (en) * | 2021-08-25 | 2024-03-22 | 哈尔滨工业大学 | Unmanned aerial vehicle sensor fault detection method, system and device based on data driving |
| CN116882751A (en) * | 2023-07-19 | 2023-10-13 | 江苏锐天智能科技股份有限公司 | Equipment analysis system and method based on multi-source heterogeneous data fusion |
| CN116882751B (en) * | 2023-07-19 | 2024-02-06 | 江苏锐天智能科技股份有限公司 | Equipment analysis system and method based on multi-source heterogeneous data fusion |
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