CN106649727B - Database construction method for fault detection of unmanned aerial vehicle flight control system - Google Patents
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Abstract
The invention discloses a database construction method for unmanned aerial vehicle flight control system fault detection, and belongs to the technical field of unmanned aerial vehicle fault detection. The invention fuses the normal real-time flight control data of the unmanned aerial vehicle screened by the judgment rule into the historical data, judges whether the unmanned aerial vehicle with unknown fault at the future moment has fault or not according to the judgment rule trained by the historical data, and dynamically updates the reference database formed by the normal historical data, thereby ensuring the real-time updating of the judgment rule and the real-time characteristic of the reference database formed by the normal historical data.
Description
Technical Field
The invention discloses a database construction method for unmanned aerial vehicle flight control system fault detection, and belongs to the technical field of unmanned aerial vehicle fault detection.
Background
In recent years, with the introduction of a fault Prediction and Health Management (PHM) concept to an integrated management system for an unmanned aerial vehicle, demands for informatization and intellectualization of safety management of the unmanned aerial vehicle have been increasing. The core part of the safety management of the unmanned aerial vehicle, namely safety monitoring and abnormity early warning, can comprehensively monitor the health state of the unmanned aerial vehicle, and has important significance for improving the safety performance of the unmanned aerial vehicle, guaranteeing the maintenance efficiency and reducing the service life. The flight control system of the unmanned aerial vehicle is used as a core component of the unmanned aerial vehicle and comprises subsystems such as various sensors, network links, execution devices and the like, wherein the flight control sensors measure real-time flight state parameters of the aircraft and feed back data to a flight control computer. The flight control computer solves the flight control law so as to complete the flight task of automatic driving of the unmanned aerial vehicle, but the flight control system is very easy to break down due to the complex working environment.
According to research objects, the current research at home and abroad aiming at the special problem of unmanned aerial vehicle safety monitoring and fault early warning mainly focuses on the aspects of generalized unmanned aerial vehicle network communication link safety, unmanned aerial vehicle flight control sensor fault detection, analysis of abnormal behaviors of unmanned aerial vehicles and the like.
Researchers in related colleges and scientific research institutions in China in recent years make extensive researches on detection of abnormal state of output data of a sensor of an unmanned aerial vehicle flight control system and hardware faults. Research teams of northwest industry university research the fault detection of flight control systems based on improved principal component analysis, improved discrete wavelet-optimized extreme learning fault detection and other methods; a series of researches are conducted by teaching teams of Nanjing aerospace university ginger and teaching aiming at algorithms of adaptive fault detection, fault-tolerant navigation and the like of the unmanned aerial vehicle; the Beijing university of aerospace Schmidt and McLeng professor team researches the unmanned aerial vehicle flight control system fault detection method based on extended Kalman filtering and volume Kalman filtering; in addition, research units such as national defense science and technology university also study multiple aspects such as unmanned aerial vehicle fault detection. The series of researches lay a theoretical foundation and a technical foundation for fault detection of the unmanned aerial vehicle flight control system.
Foreign colleges and universities and research institutions also carry out extensive research on the problem of flight control faults of the unmanned aerial vehicle, and Kim and the like provide an abnormal flight detection framework for the unmanned aerial vehicle under network attack, hardware attack, sensor fraud and wireless attack. An unmanned aerial vehicle abnormal data detection method based on flight control sensor data state analysis estimated by IMM-UKF is designed by Cork and Walker. Lin and Khalatchi and the like establish an abnormal flight judgment model by monitoring and analyzing the states of flight data, pipeline data, engine data and servo data streams of the unmanned aerial vehicle. Mitchell and Chen put forward a self-adaptive unmanned aerial vehicle behavior partitioning standard based on an intrusion detection system in the field of network link data. Birnbaum and the like design flight dynamics based on a real-time recursive least square system and a detection method of flight control parameters to monitor the health state of the unmanned aerial vehicle, and then Birnbaum and the like match flight position, dynamic information, time information and preset unmanned aerial vehicle flight plan related information output by an unmanned aerial vehicle flight control system in real time to realize monitoring of the state of the unmanned aerial vehicle. In addition, there have been some studies directed to generalized aircraft flight control fault detection.
For the research content, from the theory, method and technical route, we can find that the core algorithms of fault detection can be divided into the following three major categories: model based, knowledge-empirical based, and data-driven.
In recent years, with the rapid development of machine learning and data mining, data-driven methods have gradually become a hotspot and direction of fault diagnosis research. The data-driven fault diagnosis method is widely applied to fault detection in various fields, and based on data in the system operation process, information implicit in the data is mined through various data processing and analyzing methods to complete abnormal detection of the system data. Khalatchi et al propose a real-time online data-driven scheme for detecting the abnormality of an onboard sensor of an automatic robot. Beghi et al propose data-driven based fault detection schemes for chiller systems of air conditioning units. Yin et al propose a data-driven based wind turbine system fault diagnosis method. In addition, methods based on data driving are also widely studied in the fields of automation, machinery, and the like. Therefore, the data driving method has great potential and value in the fault detection of the unmanned plane flight control system due to the flexible and changeable characteristics.
Generally, according to domestic and foreign literature analysis, the current model method and knowledge-experience method have the following disadvantages: (1) difficulty in detecting unknown faults; (2) is more suitable for a low-dimensional system. Aiming at the unmanned aerial vehicle flight control complex system, each detail of a complex mechanism model cannot be obtained by a model-based method; on the other hand, the method based on knowledge experience needs long-term experience knowledge accumulation, and has the defect that the method cannot meet the requirement of complex and variable anomaly detection of the unmanned aerial vehicle. Therefore, the problems of large calculation amount, unreasonable fault model assumption, poor flexibility and adaptability and the like generally exist in the current fault detection research based on a model method and a qualitative knowledge-experience method. The data-driven fault detection has a wide application prospect, but key technical problems remain to be solved, wherein effective extraction (fault or normal) of data features and effective construction of a reference database for fault detection are core problems in data-driven fault detection.
Disclosure of Invention
The invention aims to provide a database construction method for unmanned aerial vehicle flight control system fault detection, which aims to overcome the defects of the background technology, realizes the dynamic construction of the unmanned aerial vehicle flight control system fault detection database by extracting normal data features on line in real time, and solves the technical problem that the fault detection of the unmanned aerial vehicle flight control system cannot adapt to the abnormal detection requirement because the extraction of each fault feature is difficult to realize.
The invention adopts the following technical scheme for realizing the aim of the invention:
a database construction method for detecting faults of an unmanned aerial vehicle flight control system includes the steps of collecting real-time observation data of the unmanned aerial vehicle flight control system at the current moment, training off-line duration data at the current moment by adopting an artificial intelligence algorithm to obtain a fault judgment rule at the current moment and screening out a reference database formed by normal historical data at the current moment, judging whether the real-time observation data at the current moment are faulty or not by adopting a fault judgment rule at the current moment, updating the reference database formed by the normal historical data at the current moment when the real-time observation data at the current moment are normal, updating the off-line historical data, repeatedly training the fault judgment rule in real time according to the updated off-line historical data and screening out the reference database formed by the normal historical data in real time, processing the result of the real-time observation data at the current moment according to the real-time trained fault judgment rule, and updating the reference database formed by the normal historical data on line according to the A database.
As a further optimization scheme of the database construction method, real-time observation data of the unmanned aerial vehicle flight control system at the current moment comprises the following steps: and measuring data of a three-axis gyroscope, a GPS, an accelerometer and a barometer.
As a further optimization scheme of the database construction method, a reference database formed by normal historical data at the current time is updated when real-time observation data at the current time are normal, and the specific method is as follows: and representing a reference database formed by normal historical data at the current moment by using a time sequence with a window length, eliminating tail data in the time sequence with the window length by using a sliding window model with the window length, and adding real-time observation data at the tail of the time sequence with the window length at the current moment.
As a further optimization scheme of the database construction method, before judging whether the real-time observation data at the current time has a fault by adopting a fault judgment rule at the current time, the noise in the real-time observation data at the current time is removed by adopting a Kalman filtering algorithm.
As a further optimization scheme of the database construction method, an artificial intelligence algorithm including but not limited to an artificial neural network and a decision tree is adopted to train offline duration data at the current time to obtain a fault judgment rule at the current time, and a reference database formed by normal historical data at the current time is screened out.
By adopting the technical scheme, the invention has the following beneficial effects: the invention provides a database construction method for data driving aiming at the fault detection of an unmanned aerial vehicle flight control system, which trains a historical database at each moment in real time to obtain a fault judgment rule at each moment and screens out a reference database formed by normal historical data at each moment, adopts the fault judgment rule at each moment to process real-time observation data at corresponding moment, fuses normal data in the real-time flight control data of the unmanned aerial vehicle into the reference database to further update the historical database, ensures the real-time characteristic of the reference database formed by the normal historical data by dynamically updating the reference database formed by the normal history and the fault judgment rule, and can effectively detect various abnormal forms such as isolated point abnormality, context abnormality, accumulation abnormality and the like in the flight control data of the unmanned aerial vehicle by utilizing the reference database formed by the dynamically updated normal historical data, therefore, the recall ratio and the accuracy of fault detection are effectively improved, and the false alarm rate is reduced.
Drawings
Fig. 1 is a flow chart of dynamic construction of a fault detection database of an unmanned aerial vehicle flight control system.
Detailed Description
The technical solution of the invention is explained in detail with reference to fig. 1. The invention provides a data-driven database construction method for solving the technical problem that fault detection of an unmanned aerial vehicle flight control system cannot adapt to abnormal detection requirements due to the fact that extraction of each fault feature is difficult to achieve, a historical database at each moment is trained in real time to obtain a fault judgment rule at each moment and a reference database formed by normal historical data at each moment is screened out, real-time observation data at corresponding moments are processed by adopting the fault judgment rule at each moment, normal data in the real-time flight control data of the unmanned aerial vehicle are merged into the reference database to further update the historical database, and real-time characteristics of the reference database formed by the normal historical data are guaranteed by dynamically updating the reference database and the fault judgment rule formed by the normal history.
Updating a reference database and a fault judgment rule at the current time t + 1:
(1) gather the data that unmanned aerial vehicle flight control system exported under current moment t +1, include: measurement data of three-axis gyroscope, GPS, accelerometer, barometer, etc., marked as matrix xt+1，xt+1Mixed data including normal data, known fault data and unknown fault data, and Kalman filtering algorithm is adopted to obtain real-time observation data x at current momentt+1Preprocessing is carried out to remove noise, false alarm caused by noise data interference abnormal detection is avoided, and preprocessed real-time observation data at the current moment are marked as
(2) Training offline data at the current time by adopting an artificial intelligence algorithm including but not limited to an artificial neural network and a decision tree for the offline duration data at the current time t +1 to obtain a fault judgment rule at the current time t +1, screening out a reference database consisting of normal historical data at the current time t +1, and marking a reference number consisting of the normal historical data at the current time t +1The database is a time series with a window length: { xt'-q+1,...,xt'+1T represents time, q represents the length of the window, and since no online data is input at time t0, that is, at the first time, the reference database formed by the normal data is the reference database formed by the real-time online normal data;
(3) judging the preprocessed real-time observation data at the current moment by adopting a fault judgment rule at the current moment t +1Whether it is faulty or not is judgedIn normal condition, a time sequence { x 'of window length is rejected by adopting a sliding window model with the window length of q't-q+1,...,x't+1Tail data x 'in }'t+1Will beThe addition at the end of the window length time series yields:that is, the reference database composed of the normal history data at the current time t +1 is updated toAnd then updating the offline data at the current moment for training at the next moment.
Updating the reference database and the failure judgment rule at the next moment and at each moment after the next moment:
training a fault judgment rule in real time according to the updated offline historical data, screening a reference database formed by normal historical data in real time, and processing the result of real-time observation data at the current moment according to the fault judgment rule trained in real time to update the reference database formed by the normal historical data on line.
Claims (4)
1. A database construction method for unmanned aerial vehicle flight control system fault detection is characterized in that,
collecting real-time observation data of an unmanned aerial vehicle flight control system at the current moment, training off-line duration data at the current moment by adopting an artificial intelligence algorithm to obtain a fault judgment rule at the current moment and screening out a reference database consisting of normal historical data at the current moment, judging whether the real-time observation data at the current moment is faulty or not by adopting the fault judgment rule at the current moment, expressing the reference database consisting of the normal historical data at the current moment by using a time sequence with a window length, eliminating tail data in the time sequence with the window length by adopting a sliding window model with the window length, adding the real-time observation data at the current moment to the tail of the time sequence with the window length, updating the off-line historical data, and repeatedly training out the fault judgment rule in real time according to the updated off-line historical data and screening out the reference database consisting of the normal historical data in real time, and processing the result of real-time observation data at the current moment according to a real-time trained fault judgment rule, and updating a reference database formed by normal historical data on line.
2. The method for constructing the database for detecting the faults of the flight control system of the unmanned aerial vehicle according to claim 1, wherein the real-time observation data of the flight control system of the unmanned aerial vehicle at the current moment comprises: and measuring data of a three-axis gyroscope, a GPS, an accelerometer and a barometer.
3. The method as claimed in claim 1, wherein before determining whether the real-time observation data at the current time has a fault according to the fault determination rule at the current time, noise in the real-time observation data at the current time is removed by using a kalman filter algorithm.
4. The method for constructing the database for detecting the faults of the flight control system of the unmanned aerial vehicle according to claim 1, wherein an artificial intelligence algorithm including but not limited to an artificial neural network and a decision tree is adopted to train offline duration data at the current time to obtain a fault judgment rule at the current time, and a reference database consisting of normal historical data at the current time is screened out.
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