CN116184986B - Unmanned aerial vehicle fault detection method and system based on flight control log - Google Patents
Unmanned aerial vehicle fault detection method and system based on flight control log Download PDFInfo
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Abstract
The invention discloses an unmanned aerial vehicle fault detection method and system based on a flight control log, comprising the following steps: acquiring a flight control log record of the unmanned aerial vehicle; detecting the unmanned aerial vehicle by using a fault detection model according to the flight control log record; generating detection information, judging whether the unmanned aerial vehicle has faults or not, and outputting a detection report; wherein, when utilizing the fault detection model to detect unmanned aerial vehicle, include: and judging whether the unmanned aerial vehicle has a vibration problem or not by detecting the acceleration value of the unmanned aerial vehicle. The method is used for solving the problems of low efficiency, high cost and inconsistent analysis conclusion of the existing flight control log, thereby achieving the purposes of automatically analyzing the unmanned aerial vehicle flight control log and accurately detecting the unmanned aerial vehicle faults.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle fault detection, in particular to an unmanned aerial vehicle fault detection method and system based on a flight control log.
Background
The unmanned aerial vehicle refers to an unmanned aerial vehicle, and is an unmanned aerial vehicle operated by using radio remote control equipment and a self-contained program control device. Compared with manned plane, it has the advantages of small size, low cost, convenient use, etc.
The failure of the unmanned aerial vehicle mainly comes from the following two aspects:
(1) Is influenced by the environment in the flying process;
(2) Problems arise during the manufacturing process.
Therefore, unmanned aerial vehicle needs to be guaranteed at all stages of production and assembly, and as long as a link has a problem, the flight control system of the unmanned aerial vehicle can possibly generate faults.
In practical production and application, various faults possibly occurring in the unmanned aerial vehicle need to be detected, and the detection adopts analysis of a flight control log of the unmanned aerial vehicle. At present, the unmanned aerial vehicle flight control log analysis method needs to manually analyze one flight control log content, so that problems are found, a large amount of manpower and material resources are required to be spent, the diagnosis cost is high, the overall working efficiency is not high, in addition, the conclusions obtained by different specialists are different, and the diagnosis efficiency is difficult to improve.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an unmanned aerial vehicle fault detection method and system based on a flight control log, which are used for solving the problems of low efficiency, high cost and inconsistent analysis conclusion of the existing flight control log analysis method, thereby achieving the purposes of automatically analyzing the unmanned aerial vehicle flight control log and accurately detecting unmanned aerial vehicle faults.
In order to solve the problems, the technical scheme adopted by the invention is as follows:
a unmanned aerial vehicle fault detection method based on a flight control log comprises the following steps:
acquiring a flight control log record of the unmanned aerial vehicle;
detecting the unmanned aerial vehicle by using a fault detection model according to the flight control log record;
generating detection information, judging whether the unmanned aerial vehicle has faults or not, and outputting a detection report;
wherein, when utilizing the fault detection model to detect unmanned aerial vehicle, include:
and judging whether the unmanned aerial vehicle has a vibration problem or not by detecting the acceleration value of the unmanned aerial vehicle.
As a preferred embodiment of the present invention, when the unmanned aerial vehicle is detected by using a fault detection model, the method further includes:
judging whether the GPS sensor has a problem or not by analyzing the number of satellites and the satellite positioning precision error;
judging whether the electronic speed regulator has a problem or not by detecting whether the current of the unmanned aerial vehicle rotor wing has overload or too low phenomenon;
judging whether a position estimation problem exists according to the estimated position of the unmanned aerial vehicle and the actual position of the unmanned aerial vehicle detected by the GPS sensor;
judging whether the attitude control loop has a hysteresis problem or not by analyzing the size of the following hysteresis time;
and judging the connection state of the unmanned aerial vehicle and the ground terminal by counting the number value of the heartbeat packet data received by the unmanned aerial vehicle.
In a preferred embodiment of the present invention, when judging whether or not there is a problem with a GPS sensor, the method includes:
detecting the number of satellites received in the GPS sensor;
if the number of the received satellites is within a first satellite number preset range, reporting that the GPS sensor positioning accuracy is good;
if the number of the received satellites is within the preset range of the number of the second satellites, reporting that the positioning accuracy of the GPS sensor is general;
if the number of the received satellites is within a third satellite number preset range, reporting that the GPS sensor positioning accuracy is poor;
if the received change value of the satellite number at the next moment in comparison with the satellite number at the last moment is larger than a preset value, reporting that the GPS sensor has the satellite loss phenomenon;
detecting satellite positioning accuracy errors of the GPS sensor;
if the satellite positioning precision error is in the first satellite precision preset range, reporting that the positioning precision is normal;
if the satellite positioning precision error is in the second satellite precision preset range, reporting that the positioning precision is general;
and if the satellite positioning precision error is in the third satellite precision preset range, reporting that the positioning precision is poor.
In a preferred embodiment of the present invention, when judging whether or not there is a problem with the electronic governor, the method includes:
acquiring current of a rotor wing of the unmanned aerial vehicle, and reporting a current overload warning if the current exceeds a first current preset value;
if the current is lower than a second current preset value, reporting an electric regulation protection warning and triggering electric regulation protection;
reporting a low current warning if the current is below a third current preset value;
and acquiring the power of the unmanned aerial vehicle rotor wing, and reporting an over-power warning if the power exceeds a power preset value.
As a preferred embodiment of the present invention, when determining whether the unmanned aerial vehicle has a vibration problem, the method includes:
capturing raw X-, Y-, and Z-axis accelerometer values from the IMU;
filtering an original acceleration value by high-pass filtering at 5 Hz, eliminating the motion of the unmanned aerial vehicle to obtain updated acceleration values of an X axis, a Y axis and a Z axis, and creating diff_x, diff_y and diff_z for the X axis, the Y axis and the Z axis;
acquiring the difference values between the updated acceleration values of the X axis, the Y axis and the Z axis and the diff_x, diff_y and diff_z;
squaring the difference, filtering by using 2 Hz high-pass filtering, and obtaining a filtered X-axis difference value, a filtered Y-axis difference value and a filtered Z-axis difference value;
square root is carried out on the filtered X-axis difference value, the filtered Y-axis difference value and the filtered Z-axis difference value to obtain an X-axis standard deviation, a Y-axis standard deviation and a Z-axis standard deviation;
judging whether the unmanned aerial vehicle has a vibration problem according to the X-axis standard deviation, the Y-axis standard deviation and the Z-axis standard deviation.
As a preferred embodiment of the present invention, the condition for determining whether the unmanned aerial vehicle has a vibration problem includes:
if the standard deviation of the X axis and the standard deviation of the Y axis are both beyond the preset value of the vibration index, reporting the balance problem of the motor bearing or the support column;
if the standard deviation of the X axis or the standard deviation of the Y axis exceeds the preset value of the vibration index, reporting the installation problem of the flight control;
and if the Z-axis standard deviation exceeds the preset vibration index value, reporting a propeller or motor problem.
In a preferred embodiment of the present invention, when judging whether or not there is a problem in position estimation, the method includes:
acquiring the angular rate of the unmanned aerial vehicle from an IMU, and acquiring the angular position of the unmanned aerial vehicle according to the angular rate;
acquiring acceleration of the unmanned aerial vehicle from the IMU, converting the angular position by using a coordinate system of the north east of the slave machine body according to the acceleration, and correcting the angular position according to gravity to obtain an estimated angular position;
obtaining an estimated speed of the unmanned aerial vehicle according to the acceleration, obtaining an estimated position of the unmanned aerial vehicle according to the estimated speed and the estimated angular position by using an extended Kalman filter;
detecting the actual position of the unmanned aerial vehicle according to the GPS sensor, and acquiring a variance value of the estimated position and the actual position;
and if the variance value is larger than a position estimation error preset value, judging that the estimated position is not consistent with the actual position.
In a preferred embodiment of the present invention, when determining whether or not there is a hysteresis problem in the attitude control loop, the method includes:
judging whether attitude data of the unmanned aerial vehicle exist or not, if so, acquiring the attitude angle and the angular speed of the unmanned aerial vehicle;
acquiring the following lag time of the attitude angle and the angular speed by using a time lag cross-correlation method;
judging whether the following lag time is larger than a preset lag time value, if so, reporting that the gesture control loop has a lag problem.
In a preferred embodiment of the present invention, when judging a connection state between a unmanned aerial vehicle and a ground terminal, the method includes:
judging whether heartbeat packet data of the unmanned aerial vehicle exist or not, if so, counting the number of the heartbeat packet data received by the unmanned aerial vehicle from the ground terminal within ten seconds;
if the number is smaller than the first heartbeat packet preset value, the report signal is weaker;
if the number is smaller than the second heartbeat packet preset value, the report signal is very weak.
Unmanned aerial vehicle fault detection system based on flight control log includes:
a data acquisition unit: the method comprises the steps of acquiring a flight control log record of an unmanned aerial vehicle;
a fault detection unit: the unmanned aerial vehicle detection system is used for detecting the unmanned aerial vehicle by using a fault detection model according to the flight control log record;
analysis and output unit: the unmanned aerial vehicle detection system is used for generating detection information, judging whether the unmanned aerial vehicle has faults or not, and outputting a detection report;
wherein, when utilizing the fault detection model to detect unmanned aerial vehicle, include:
and judging whether the unmanned aerial vehicle has a vibration problem or not by detecting the acceleration value of the unmanned aerial vehicle.
Compared with the prior art, the invention has the beneficial effects that:
(1) The invention can automatically analyze the flight control log of the unmanned aerial vehicle, thereby effectively improving the analysis efficiency;
(2) According to the invention, an expert does not need to spend a great deal of time for analysis, so that the analysis efficiency is further improved, the analysis cost is reduced, and the consistency of analysis results is ensured;
(3) The invention enables maintenance personnel to quickly and intuitively know the operation condition of unmanned aerial vehicle flight control, and can judge the faults of the unmanned aerial vehicle in time, thereby greatly improving the fault detection efficiency.
The invention is described in further detail below with reference to the drawings and the detailed description.
Drawings
FIG. 1 is a flow chart of the detection of a GPS sensor of an unmanned aerial vehicle according to an embodiment of the present invention;
FIG. 2-is a flow chart of electronic governor detection of the unmanned aerial vehicle according to an embodiment of the present invention;
FIG. 3 is a flow chart of vibration problem detection for an unmanned aerial vehicle according to an embodiment of the present invention;
FIG. 4 is a flow chart of a position estimation detection of a drone according to an embodiment of the present invention;
FIG. 5-is a flow chart of the attitude control loop detection of the unmanned aerial vehicle according to an embodiment of the present invention;
FIG. 6 is a flow chart of connection status detection of the unmanned aerial vehicle according to an embodiment of the present invention;
fig. 7-is a step diagram of a unmanned aerial vehicle fault detection method based on a flight control log according to an embodiment of the present invention.
Detailed Description
The unmanned aerial vehicle fault detection method based on the flight control log provided by the invention, as shown in fig. 7, comprises the following steps:
step S1: acquiring a flight control log record of the unmanned aerial vehicle;
step S2: detecting the unmanned aerial vehicle by using a fault detection model according to the flight control log record;
step S3: generating detection information, judging whether the unmanned aerial vehicle has faults or not, and outputting a detection report;
wherein, when utilizing the fault detection model to detect unmanned aerial vehicle, include:
and judging whether the unmanned aerial vehicle has a vibration problem or not by detecting the accelerometer value of the unmanned aerial vehicle.
In the step S2, when the unmanned aerial vehicle is detected by using the fault detection model, the method further includes:
judging whether the GPS sensor has a problem or not by analyzing the number of satellites and the satellite positioning precision error;
judging whether the electronic speed regulator has a problem or not by detecting whether the rotor current of the unmanned aerial vehicle has overload or too low phenomenon;
judging whether a position estimation problem exists according to the estimated position of the unmanned aerial vehicle and the actual position of the unmanned aerial vehicle detected by the GPS sensor;
judging whether the attitude control loop has a hysteresis problem or not by analyzing the size of the following hysteresis time;
and judging the connection state of the unmanned aerial vehicle and the ground terminal by counting the number of the heartbeat packet data received by the unmanned aerial vehicle.
Further, when judging whether there is a problem with the GPS sensor, it includes:
detecting the number of satellites received in the GPS sensor;
if the number of the received satellites is within a first satellite number preset range, reporting that the GPS sensor positioning accuracy is good;
if the number of the received satellites is within the preset range of the number of the second satellites, reporting that the positioning accuracy of the GPS sensor is general;
if the number of the received satellites is within a third satellite number preset range, reporting that the GPS sensor positioning accuracy is poor;
if the received change value of the satellite number at the next moment in comparison with the satellite number at the last moment is larger than a preset value, reporting that the GPS sensor has the satellite loss phenomenon;
detecting satellite positioning accuracy errors of the GPS sensor;
if the satellite positioning precision error is in the first satellite precision preset range, reporting that the positioning precision is normal;
if the satellite positioning precision error is within the second satellite precision preset range, reporting the positioning precision generally;
if the satellite positioning precision error is in the third satellite precision preset range, reporting that the positioning precision is poor.
Further, the first satellite number preset range is 75% -100%, the second satellite number preset range is 50% -75%, the third satellite number preset range is 0-50%, the preset value is 0-50%, the first satellite precision preset range is less than 0.6 m, the second satellite precision preset range is 0.6-1 m, and the third satellite precision preset range is greater than 1 m.
Specifically, the GPS sensor is an important sensor for achieving unmanned aerial vehicle positioning, and plays a key role in normal flight of the unmanned aerial vehicle. A position error from the GPS sensor may cause the drone to think that it is suddenly in the wrong position and to fly actively to correct the perceived error, taking it off the normal trajectory. The GPS sensor diagnosis judges whether the GPS sensor has problems or not by analyzing the satellite quantity and satellite positioning precision, and the detection flow is as follows:
as shown in fig. 1, firstly, detecting the number of satellites received by a GPS sensor, wherein the GPS has 24 satellites in total, and if the number of the received satellites is within a preset range (more than 75%) of the first number of satellites, the number of the received satellites is considered to be good, and the reported positioning quality is good; if the number of the received satellites is within the preset range (50% -75%) of the second number of satellites, the number of the received satellites is considered to be general, and the reported positioning quality is considered to be general; if the number of received satellites is within a third predetermined range (less than 50%), then the received satellites are considered poor and poor positioning quality is reported. Then detecting satellite positioning precision errors of the GPS, and reporting that the positioning precision is normal if the satellite positioning precision errors are in a first satellite precision preset range (less than 0.6 meter); if the satellite positioning accuracy error is within a second satellite accuracy preset range (0.6-1 m), reporting the positioning accuracy generally; if the satellite positioning accuracy error is within the third satellite accuracy preset range (greater than 1 meter), the positioning accuracy is reported to be poor. And then detecting whether the searched satellites have the satellite loss phenomenon, judging the satellite quantity change at the next moment and the last moment, and reporting that the satellite loss phenomenon exists if the satellite quantity at the next moment is less than the last moment by more than 50%.
Further, when judging whether the electronic governor has a problem, the method includes:
acquiring current of a rotor wing of the unmanned aerial vehicle, and reporting a current overload warning if the current exceeds a first current preset value;
if the current is lower than the second current preset value, reporting an electric regulation protection warning and touching electric regulation protection;
reporting a low current warning if the current is below a third current preset value;
and acquiring the power of the unmanned aerial vehicle rotor wing, and reporting an over-power warning if the power exceeds a power preset value.
Further, the first current preset value is 99%, the second current preset value is 50%, the third current preset value is 1%, and the power preset value is 2500w.
Specifically, the electric control refers to an electronic speed regulator, which is used for controlling the rotating speed of a signal motor, if the sensor fails, the unmanned aerial vehicle is easy to cause the explosion, the electric control diagnosis is mainly carried out by detecting whether overload and over-low phenomenon exist in the unmanned aerial vehicle electric power, and the detection flow is as follows:
as shown in fig. 2, firstly, judging whether the rotor wing of the unmanned aerial vehicle is overloaded, and if the current exceeds a first current preset value (99%), reporting a current overload warning; judging whether the electric power tone protection is touched, if the current suddenly drops to a second current preset value (50%), reporting an electric power tone protection warning, and touching the electric power tone protection; it is then determined if the current is too low and if the current is below a third current preset value (1%), a low warning is reported.
Detecting whether the power is too high, wherein the power is the product of current and voltage, the power is normally around a power preset value (2000 w), and if the power exceeds the power preset value (2500 w), reporting an over-power warning.
Further, when judging whether the unmanned aerial vehicle has a vibration problem, the method comprises the following steps:
capturing raw X-, Y-and Z-axis acceleration values from the IMU;
filtering an original acceleration value by high-pass filtering at 5 Hz, eliminating the motion of the unmanned aerial vehicle to obtain updated acceleration values of an X axis, a Y axis and a Z axis, and creating diff_x, diff_y and diff_z for the X axis, the Y axis and the Z axis;
acquiring the difference values between the updated acceleration values of the X axis, the Y axis and the Z axis and diff_x, diff_y and diff_z;
squaring the difference, filtering with 2 Hz high-pass filter, and obtaining a filtered X-axis difference, Y-axis difference and Z-axis difference;
square root is carried out on the filtered X-axis difference value, Y-axis difference value and Z-axis difference value to obtain an X-axis standard deviation, a Y-axis standard deviation and a Z-axis standard deviation;
judging whether the unmanned aerial vehicle has a vibration problem or not according to the X-axis standard deviation, the Y-axis standard deviation and the Z-axis standard deviation.
Further, judging whether the unmanned aerial vehicle has the vibration problem condition includes:
if the standard deviation of the X axis and the standard deviation of the Y axis are both beyond the preset value of the vibration index, reporting the balance problem of the motor bearing or the support column;
if the standard deviation of the X axis or the standard deviation of the Y axis exceeds a preset value of the vibration index, reporting the installation problem of the flight control;
if the Z-axis standard deviation exceeds the preset vibration index value, a propeller or motor problem is reported.
Further, the preset vibration index value is 20m/s 2 。
Specifically, as shown in fig. 3, the vibration level of the unmanned aerial vehicle is calculated by the standard deviation of the accelerometer output, and the algorithm for calculating the vibration level is as follows:
capturing the original X, Y and Z axis acceleration values from the IMU, high pass filtering the original 5 hz values to eliminate the motion of the drone and create diff_x, diff_y, diff_z for X, Y and Z axes, calculating the difference between the updated acceleration X, Y, Z values and diff_x, diff_y, diff_z, squaring the above differences, filtering at 2 hz, and then calculating the square root, obtaining X, Y and the standard deviation of the Z axis.
When the vibration is subjected to fault elimination, the problem is found by taking the axis of the vibration as a sending point:
standard deviation of accelerometer output in m/s 2 In units of. Normally should be below 15m/s 2 But occasionally reaches 20m/s 2 Is a normal level of (c). If X and Y axesExceeds the preset vibration index value (20 m/s) 2 ) Reporting the motor bearing or strut balancing problem; if the X or Y axis exceeds the preset vibration index value (20 m/s 2 ) Reporting the installation problem of the flight control; if the Z axis exceeds the preset vibration index (20 m/s 2 ) The propeller or motor problem is reported.
Further, when judging whether there is a problem of position estimation, it includes:
acquiring the angular rate of the unmanned aerial vehicle from the IMU, and acquiring the angular position of the unmanned aerial vehicle according to the angular rate;
acquiring acceleration of the unmanned aerial vehicle from the IMU, converting the angular position of the auxiliary machine body by using a north-east coordinate system according to the acceleration, and correcting the angular position according to gravity to obtain an estimated angular position;
obtaining an estimated speed of the unmanned aerial vehicle according to the acceleration, obtaining an estimated position of the unmanned aerial vehicle according to the estimated speed and the estimated angular position, and obtaining the estimated position of the unmanned aerial vehicle by using an extended Kalman filter;
detecting the actual position of the unmanned aerial vehicle according to the GPS sensor, and acquiring a variance value of the estimated position and the actual position;
if the variance value is larger than the position estimation error preset value, judging that the estimated position is not consistent with the actual position.
Further, the position estimation error preset value is 0.3.
Specifically, extended Kalman Filter (EKF) is an extended form of standard kalman filter under the nonlinear condition, and the EKF algorithm performs taylor expansion on a nonlinear function, omits Gao Jiexiang, retains first-order terms of expansion terms, so as to realize linearization of the nonlinear function, and finally approximates state estimation values and variance estimation values of a computing system through the kalman filter algorithm to filter signals.
Using an Extended Kalman Filter (EKF) to estimate the position, speed and angular direction of the drone from gyroscopes and accelerometers, the flow of state prediction by the filter is as follows, as shown in fig. 4:
(1) Calculating an angular position according to the IMU angular rate;
(2) Converting the angular position of the slave machine body by using a north-east coordinate system according to the IMU acceleration, and correcting the angular position according to the gravity;
(3) Calculating a speed according to the acceleration;
(4) The position is calculated from the velocity.
And evaluating according to the estimated unmanned aerial vehicle position and the actual position detected by the GPS sensor, calculating variance, and if the EKF variance value is larger than a position estimation error preset value (0.3), indicating that the estimated position is inconsistent with the actual detected position.
Further, in judging whether or not there is a hysteresis problem in the attitude control loop, as shown in fig. 5, the method includes:
judging whether attitude data of the unmanned aerial vehicle exist or not, and if so, acquiring an attitude angle and an angular speed of the unmanned aerial vehicle;
acquiring the following lag time of the attitude angle and the angular velocity by using a Time Lag Cross Correlation (TLCC);
judging whether the following lag time is larger than a preset lag time value, if so, reporting that the gesture control loop has a lag problem.
Still further, the hysteresis time preset is 0.2 seconds.
Specifically, a Time Lag Cross Correlation (TLCC) is used to calculate the lag correlation of the expected and actual values of the attitude angle and angular velocity. TLCC is obtained by shifting one time-series vector step by step and repeatedly calculating the correlation between two signals. If the peak of the correlation is centered, this means that the correlation is highest for both time series at this time. However, if one signal is guiding another signal, the peak value of the correlation may be located on different coordinate values, and the difference value of the two coordinates on the time axis is calculated, so that the following lag time of the two curves is obtained, and in general, the following lag time is less than 0.2 seconds, which has little influence on the unmanned aerial vehicle, and more than 0.2 seconds may cause the unmanned aerial vehicle to have a large gesture motion, which easily causes the unmanned aerial vehicle to be out of control. Judging whether the following lag time is excessive or not, and if the following lag time is greater than a preset value (0.2 seconds) of the lag time, reporting that the gesture control loop has a lag problem.
Further, when judging the connection state between the unmanned aerial vehicle and the ground, as shown in fig. 6, the method includes:
judging whether heartbeat packet data of the unmanned aerial vehicle exist or not, if so, counting the number of the heartbeat packet data received by the unmanned aerial vehicle from the ground terminal within ten seconds;
if the number is smaller than the first heartbeat packet preset value, the report signal is weaker;
if the number is smaller than the second heartbeat packet preset value, the report signal is very weak.
Further, the first heartbeat packet preset value is 7, and the second heartbeat packet preset value is 4.
Specifically, the heartbeat packet is a heartbeat packet which is called by the ground end and is used for detecting the connection state of the unmanned aerial vehicle and the ground end because the ground end sends a message to the unmanned aerial vehicle at a certain time interval similar to the heartbeat.
The ground terminal sends a heartbeat packet to the unmanned aerial vehicle every second, statistics is carried out on heartbeat packet data received by the unmanned aerial vehicle in ten seconds, the heartbeat packet data which should be received by the unmanned aerial vehicle in ten seconds under the condition that signals are normal is 10, if the heartbeat packet data received by the unmanned aerial vehicle in ten seconds is lower than a first heartbeat packet preset value (7), the report signal is weaker, and if the heartbeat packet data is lower than a second heartbeat packet preset value (4), the report signal is very weak.
The unmanned aerial vehicle fault detection system based on the flight control log provided by the invention comprises the following components:
a data acquisition unit: the method comprises the steps of acquiring a flight control log record of an unmanned aerial vehicle;
a fault detection unit: the unmanned aerial vehicle detection method comprises the steps of detecting an unmanned aerial vehicle by using a fault detection model according to flight control log records;
analysis and output unit: the unmanned aerial vehicle detection system is used for generating detection information, judging whether the unmanned aerial vehicle has faults or not, and outputting a detection report;
wherein, when utilizing the fault detection model to detect unmanned aerial vehicle, include:
and judging whether the unmanned aerial vehicle has a vibration problem or not by detecting the acceleration value of the unmanned aerial vehicle.
Compared with the prior art, the invention has the beneficial effects that:
(1) The invention can automatically analyze the flight control log of the unmanned aerial vehicle, thereby effectively improving the analysis efficiency;
(2) According to the invention, an expert does not need to spend a great deal of time for analysis, so that the analysis efficiency is further improved, the analysis cost is reduced, and the consistency of analysis results is ensured;
(3) The invention enables maintenance personnel to quickly and intuitively know the operation condition of unmanned aerial vehicle flight control, and can judge the faults of the unmanned aerial vehicle in time, thereby greatly improving the fault detection efficiency.
The above embodiments are only preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, but any insubstantial changes and substitutions made by those skilled in the art on the basis of the present invention are intended to be within the scope of the present invention as claimed.
Claims (8)
1. The unmanned aerial vehicle fault detection method based on the flight control log is characterized by comprising the following steps of:
acquiring a flight control log record of the unmanned aerial vehicle;
detecting the unmanned aerial vehicle by using a fault detection model according to the flight control log record;
generating detection information, judging whether the unmanned aerial vehicle has faults or not, and outputting a detection report;
wherein, when utilizing the fault detection model to detect unmanned aerial vehicle, include:
judging whether the unmanned aerial vehicle has a vibration problem or not by detecting the acceleration value of the unmanned aerial vehicle;
when judging whether the unmanned aerial vehicle has the vibrations problem, include:
capturing acceleration values of an original X axis, a Y axis and a Z axis from the IMU;
carrying out high-pass filtering processing on the original acceleration value, and obtaining a difference value between the high-pass filtered acceleration value and the original acceleration value;
performing high-pass filtering processing on the square of the difference value and square root, thereby obtaining standard deviations of an X axis, a Y axis and a Z axis;
reporting a first unmanned aerial vehicle problem according to a standard deviation of an X axis, a Y axis or a Z axis;
the second drone problem is reported according to any combination of standard deviations of X-axis, Y-axis and Z-axis.
2. The unmanned aerial vehicle fault detection method based on the flight control log according to claim 1, wherein when the unmanned aerial vehicle is detected using a fault detection model, further comprising:
judging whether the GPS sensor has a problem or not by analyzing the number of satellites and the satellite positioning precision error;
judging whether the electronic speed regulator has a problem or not by detecting whether the current of the unmanned aerial vehicle rotor wing has overload or too low phenomenon;
judging whether a position estimation problem exists according to the estimated position of the unmanned aerial vehicle and the actual position of the unmanned aerial vehicle detected by the GPS sensor;
judging whether the attitude control loop has a hysteresis problem or not by analyzing the size of the following hysteresis time;
and judging the connection state of the unmanned aerial vehicle and the ground terminal by counting the number value of the heartbeat packet data received by the unmanned aerial vehicle.
3. The unmanned aerial vehicle fault detection method based on the flight control log according to claim 2, wherein when judging whether there is a problem with the GPS sensor, comprising:
detecting the number of satellites received in the GPS sensor;
if the number of the received satellites is within a first satellite number preset range, reporting that the GPS sensor positioning accuracy is good;
if the number of the received satellites is within the preset range of the number of the second satellites, reporting that the positioning accuracy of the GPS sensor is general;
if the number of the received satellites is within a third satellite number preset range, reporting that the GPS sensor positioning accuracy is poor;
if the received change value of the satellite number at the next moment in comparison with the satellite number at the last moment is larger than a preset value, reporting that the GPS sensor has the satellite loss phenomenon;
detecting satellite positioning accuracy errors of the GPS sensor;
if the satellite positioning precision error is in the first satellite precision preset range, reporting that the positioning precision is normal;
if the satellite positioning precision error is in the second satellite precision preset range, reporting that the positioning precision is general;
and if the satellite positioning precision error is in the third satellite precision preset range, reporting that the positioning precision is poor.
4. The unmanned aerial vehicle fault detection method based on the flight control log according to claim 2, wherein when judging whether the electronic governor has a problem, comprising:
acquiring current of a rotor wing of the unmanned aerial vehicle, and reporting a current overload warning if the current exceeds a first current preset value;
if the current is lower than a second current preset value, reporting an electric regulation protection warning and triggering electric regulation protection;
reporting a low current warning if the current is below a third current preset value;
and acquiring the power of the unmanned aerial vehicle rotor wing, and reporting an over-power warning if the power exceeds a power preset value.
5. The unmanned aerial vehicle fault detection method based on the flight control log according to claim 2, wherein when judging whether there is a position estimation problem, comprising:
acquiring the angular rate of the unmanned aerial vehicle from an IMU, and acquiring the angular position of the unmanned aerial vehicle according to the angular rate;
acquiring acceleration of the unmanned aerial vehicle from the IMU, converting the angular position by using a coordinate system of the north east of the slave machine body according to the acceleration, and correcting the angular position according to gravity to obtain an estimated angular position;
obtaining an estimated speed of the unmanned aerial vehicle according to the acceleration, obtaining an estimated position of the unmanned aerial vehicle according to the estimated speed and the estimated angular position by using an extended Kalman filter;
detecting the actual position of the unmanned aerial vehicle according to the GPS sensor, and acquiring a variance value of the estimated position and the actual position;
and if the variance value is larger than a position estimation error preset value, judging that the estimated position is not consistent with the actual position.
6. The unmanned aerial vehicle fault detection method based on the flight control log according to claim 2, wherein when judging whether the attitude control loop has a hysteresis problem, comprising:
judging whether attitude data of the unmanned aerial vehicle exist or not, if so, acquiring the attitude angle and the angular speed of the unmanned aerial vehicle;
acquiring the following lag time of the attitude angle and the angular speed by using a time lag cross-correlation method;
judging whether the following lag time is larger than a preset lag time value, if so, reporting that the gesture control loop has a lag problem.
7. The unmanned aerial vehicle fault detection method based on the flight control log according to claim 2, wherein when judging the connection state of the unmanned aerial vehicle and the ground terminal, comprising:
judging whether heartbeat packet data of the unmanned aerial vehicle exist or not, if so, counting the number of the heartbeat packet data received by the unmanned aerial vehicle from the ground terminal within ten seconds;
if the number is smaller than the first heartbeat packet preset value, the report signal is weaker;
if the number is smaller than the second heartbeat packet preset value, the report signal is very weak.
8. Unmanned aerial vehicle fault detection system based on flight control log, characterized by comprising:
a data acquisition unit: the method comprises the steps of acquiring a flight control log record of an unmanned aerial vehicle;
a fault detection unit: the unmanned aerial vehicle detection system is used for detecting the unmanned aerial vehicle by using a fault detection model according to the flight control log record;
analysis and output unit: the unmanned aerial vehicle detection system is used for generating detection information, judging whether the unmanned aerial vehicle has faults or not, and outputting a detection report;
wherein, when utilizing the fault detection model to detect unmanned aerial vehicle, include:
judging whether the unmanned aerial vehicle has a vibration problem or not by detecting the acceleration value of the unmanned aerial vehicle;
when judging whether the unmanned aerial vehicle has the vibrations problem, include:
capturing acceleration values of an original X axis, a Y axis and a Z axis from the IMU;
carrying out high-pass filtering processing on the original acceleration value, and obtaining a difference value between the high-pass filtered acceleration value and the original acceleration value;
performing high-pass filtering processing on the square of the difference value and square root, thereby obtaining standard deviations of an X axis, a Y axis and a Z axis;
reporting a first unmanned aerial vehicle problem according to a standard deviation of an X axis, a Y axis or a Z axis;
the second drone problem is reported according to any combination of standard deviations of X-axis, Y-axis and Z-axis.
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