CN105915294B - Unmanned aerial vehicle onboard transmitter failure prediction technique and system - Google Patents
Unmanned aerial vehicle onboard transmitter failure prediction technique and system Download PDFInfo
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- CN105915294B CN105915294B CN201610442514.1A CN201610442514A CN105915294B CN 105915294 B CN105915294 B CN 105915294B CN 201610442514 A CN201610442514 A CN 201610442514A CN 105915294 B CN105915294 B CN 105915294B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/10—Monitoring; Testing of transmitters
- H04B17/15—Performance testing
- H04B17/17—Detection of non-compliance or faulty performance, e.g. response deviations
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/10—Monitoring; Testing of transmitters
- H04B17/15—Performance testing
- H04B17/18—Monitoring during normal operation
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/10—Monitoring; Testing of transmitters
- H04B17/15—Performance testing
- H04B17/19—Self-testing arrangements
Abstract
The invention discloses a kind of unmanned aerial vehicle onboard transmitter failure prediction techniques, including：Generate the failure response signal based on airborne transmitter；Fisrt fault signal is obtained in failure response signal；Wherein, Fisrt fault signal includes the signal of one or more of power signal, frequency signal and spectrum signal；Characteristic parameter is extracted in Fisrt fault signal；Wherein, characteristic parameter includes peak bandwidth and/or phase noise value；Failure predication is carried out according to characteristic parameter.Above-mentioned unmanned aerial vehicle onboard transmitter failure prediction technique more accurately can carry out failure predication to unmanned aerial vehicle onboard transmitter.Invention additionally discloses a kind of unmanned aerial vehicle onboard transmitter failure forecasting systems.
Description
Technical field
The present invention relates to unmanned plane failure predication technical fields, more particularly to a kind of unmanned aerial vehicle onboard transmitter unmanned plane
Failure prediction method and system.
Background technology
The wireless software download subsystem (abbreviation data catenary system) of unmanned plane, the important composition portion as unmanned plane equipment
Point, it is the bridge for connecting ground handling operator and unmanned plane.As the critical component of data catenary system, airborne transmitter completes phase
Modulation and the launch mission of instruction and information are closed, how is health status, if be that unmanned plane executes task there are potential faults
Before need key problems-solving.And it is accurate and perfect not enough to the prediction technique of airborne transmitter health status at present.
Invention content
It is more smart the technical problem to be solved by the present invention is in view of the above shortcomings of the prior art, provide a kind of prediction result
True unmanned aerial vehicle onboard transmitter failure prediction technique and system.
In order to solve the above technical problems, the technical solution used in the present invention is：A kind of unmanned aerial vehicle onboard transmitter failure
Prediction technique includes the following steps：
Generate the failure response signal based on airborne transmitter；
Fisrt fault signal is obtained in the failure response signal；Wherein, the Fisrt fault signal includes power letter
Number, the signal of one or more of frequency signal and spectrum signal；
Characteristic parameter is extracted in the Fisrt fault signal；Wherein, the characteristic parameter includes peak bandwidth and/or phase
Position noise figure；
Failure predication is carried out according to the characteristic parameter.
Preferably, according to default pumping signal, the failure response signal based on airborne transmitter is generated.
Preferably, further include after the progress failure predication step according to the characteristic parameter：
The result of failure predication is classified and stored.
Invention additionally discloses a kind of unmanned aerial vehicle onboard transmitter failure forecasting systems, including fault-signal generation module, letter
Number acquisition module, communication module and failure predication module；Wherein：
The fault-signal generation module, for according to preset failure type and fault degree, generating and being based on launched by airplane
The failure response signal of machine；
The signal acquisition module for receiving the failure response signal, and acquires Fisrt fault signal, and by institute
It states Fisrt fault signal and is sent to the communication module；The Fisrt fault signal includes power signal, frequency signal and frequency spectrum
The signal of one or more of signal；
The communication module, for the Fisrt fault signal to be sent to the failure predication module；
The failure predication module, for carrying out failure predication according to the Fisrt fault signal.
Preferably, further include excitation module；
The excitation module for generating default pumping signal, and is sent to the fault-signal generation module.
Preferably, the excitation module includes waveform generator and D.C. regulated power supply；
The waveform generator is connect with the fault-signal generation module；
The D.C. regulated power supply is powered for the fault-signal generation module.
Preferably, the failure predication module includes characteristic parameter extraction unit and failure predication unit；
The characteristic parameter extraction unit for extracting characteristic parameter in the Fisrt fault signal, and is sent to institute
State failure predication unit；The characteristic parameter includes peak bandwidth and/or phase noise value；
The failure predication unit, for carrying out failure predication according to the characteristic parameter.
Preferably, the signal acquisition module includes one or more of frequency meter, power meter and frequency spectrograph；Wherein：
The frequency meter for the frequency acquisition signal in the Fisrt fault signal, and is sent to the communication module；
The power meter for acquiring power signal in the Fisrt fault signal, and is sent to the communication module；
The frequency spectrograph for acquiring spectrum signal in the Fisrt fault signal, and is sent to the communication module.
Preferably, further include prediction result management module；
The prediction result management module, the prediction result for generating the failure predication module are classified and are deposited
Storage.
Preferably, the communication module includes one or more of USB interface, GBIP interfaces and RS232 serial ports.
It is using advantageous effect caused by above-mentioned technical proposal：Above-mentioned unmanned aerial vehicle onboard transmitter failure prediction technique
And system, identifying object of the airborne transmitter as failure predication technology is chosen, first is acquired after extracting Fisrt fault signal
Characteristic parameter in fault-signal, and collected characteristic parameter is sent into failure prediction method and obtains prediction conclusion, to
Realize airborne transmitter failure predication Proof-Of Principle overall process, failure predication false alarm rate and false alarm rate are relatively low, failure predication knot
Fruit is more accurate.
Description of the drawings
Fig. 1 is the structural schematic diagram of airborne transmitter in the prior art；
Fig. 2 is the structural schematic diagram of unmanned aerial vehicle onboard transmitter failure forecasting system one embodiment of the present invention；
Fig. 3 is the structural schematic diagram of signal acquisition module in Fig. 2；
Fig. 4 is the structural schematic diagram of failure predication module in Fig. 2；
Fig. 5 is the structural schematic diagram of failure predication unit in Fig. 4；
Fig. 6 is the structural schematic diagram that module is encouraged in Fig. 2；
Fig. 7 is the concrete structure schematic diagram of unmanned aerial vehicle onboard transmitter failure forecasting system of the present invention；
Fig. 8 is the flow diagram of unmanned plane fault method one embodiment the present invention is based on airborne transmitter；
Fig. 9 is the frequency values of 2 output interface of reference clock when always referring to clock frequency offset failure；
Figure 10 is the frequency values of intermediate frequency output interface when always referring to clock frequency offset failure；
Figure 11 is the failure predication running software interface based on STSCKF；
Frequency spectrum of the intermediate frequency output interface in Data=0 when Figure 12 is modulation circuit failure；
Frequency spectrum of the intermediate frequency output interface in Data=255 when Figure 13 is modulation circuit failure；
Intermediate frequency output spectrum peak-to-peak value bandwidth when Figure 14 is modulation circuit failure；
Figure 15 is the failure predication running software interface based on ImSAP-ELM；
Figure 16 is the failure predication running software interface based on MPELM；
Frequency spectrum of the intermediate frequency output interface in Data=0 when Figure 17 is loop filter failure；
Frequency spectrum of the intermediate frequency output interface in Data=247 when Figure 18 is loop filter failure；
The normalization phase noise value that intermediate frequency exports when Figure 19 is loop filter failure；
Figure 20 is the failure predication running software interface based on BAFOS-ELM.
Specific implementation mode
The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
Airborne transmitter is mainly used to realize the work(such as the image of codec unit, the modulation of telemetered signal, frequency conversion and transmitting
Energy.Shown in Figure 1, airborne transmitter is mainly by sequentially connected 10MHz reference signals source module 100 ', phaselocked loop and oscillation
The part such as device module 200 ' and mixer module 300 ' forms.The present invention is flat in order to be tested using airborne transmitter failure predication
Platform verifies the validity of unmanned plane failure prediction method, needs airborne transmitter that can generate failure, and can utilize measuring instrument
Device acquires Fault characteristic parameters, and finally collected fault message is sent into failure prediction method, obtains prediction conclusion, to
Realize airborne transmitter failure predication Proof-Of Principle overall process.
Referring to Fig. 2, in one embodiment, unmanned aerial vehicle onboard transmitter failure forecasting system includes fault-signal generation module
100, signal acquisition module 200, communication module 300 and failure predication module 400.Wherein, fault-signal generation module 100 is used
According to preset failure type and fault degree, the failure response signal based on airborne transmitter is generated.Signal acquisition module
200, the failure response signal for receiving the generation of fault-signal generation module 100, and Fisrt fault signal is therefrom acquired, and
Fisrt fault signal is sent to communication module 300.Communication module 300, for Fisrt fault signal to be sent to failure predication
Module 400.Failure predication module 400, for carrying out failure predication according to Fisrt fault signal.In the present embodiment, the first event
Barrier signal may include the signal of one or more of power signal, frequency signal and spectrum signal.
Specifically, fault-signal generation module 100 can simulate the physical fault of airborne transmitter, according to the difference of setting
Fault type and fault degree export corresponding response signal, provide for the verification of failure predication technology and are predicted object.Failure
Signal generation module 100 is mainly used for verifying the validity of prediction technique, and failure is arranged according to destructive mode, can be to equipment
Performance have an impact.In addition, fault degree caused by destructive test is each is different, it is unfavorable for comparing distinct methods identical
Estimated performance under fault condition.For this purpose, the circuit composed structure of fault-signal generation module 100 and practical airborne transmitter base
This is identical, and original device of transmitter, the value by adjusting different adjustable devices is replaced to carry out mould just with some adjustable devices
Quasi- different type and different degrees of failure.Adjustable device selects digital adjustable type, and can be changed according to digital controlled signal can
Adjust the parameter value of device.
In the present embodiment, fault-signal generation module 100 is also known as direct fault location model machine, and the trouble unit that can be arranged is covered
All comprising modules in airborne transmitter, main trouble unit include crystal oscillator, capacitance, amplifier, voltage conversion unit,
Modulation circuit unit, loop filter, voltage conversion unit, bandpass filter and local oscillator unit etc..Wherein, crystal oscillator, capacitance and
Amplifier is the trouble unit under 10MHz reference signal source modules.Voltage conversion unit, modulation circuit unit and loop filter
For the trouble unit under phase-locked loop module.Voltage conversion unit and bandpass filter are the trouble unit under oscillator module.This
The unit that shakes is the trouble unit under mixer module.
Referring to Fig. 3, in one embodiment, signal acquisition module 200 may include frequency meter 210, power meter 220 and frequency
One or more of spectrometer 230.Wherein, frequency meter 210 for the frequency acquisition signal in Fisrt fault signal, and are sent to
Communication module 300.Power meter 220 for acquiring power signal in Fisrt fault signal, and is sent to communication module 300.Frequently
Spectrometer 230 for acquiring spectrum signal in Fisrt fault signal, and is sent to communication module 300.In the present embodiment, frequency
The model Agilent 53132A that meter 210 uses.The model Agilent E4416A that power meter 220 uses.Frequency spectrograph 230
The model Agilent N9320B of use.
Certainly, in other embodiments, signal acquisition module 200 can also be as needed, including frequency meter 210, power
Other measuring instruments other than meter 220 and frequency spectrograph 230, are not restricted this.
As a kind of embodiment, communication module 300 may include USB (Universal Serial Bus, general string
Row bus) interface, GBIP (General-Purpose Interface Bus, general purpose interface bus) interfaces and RS232 serial ports
One or more of.
Referring to Fig. 4, in one embodiment, failure predication module 400 may include characteristic parameter extraction unit 410 and therefore
Hinder predicting unit 420.Characteristic parameter extraction unit 410 for extracting characteristic parameter in Fisrt fault signal, and is sent to event
Hinder predicting unit 420.Failure predication unit 420, for carrying out failure predication according to characteristic parameter.Wherein, characteristic parameter can be with
Including peak bandwidth and/or phase noise value.Peak bandwidth can extract in spectrogram, and phase noise value can be according to difference
The power calculation of frequency point obtains.
Referring to Fig. 5, in one embodiment, failure predication unit 420 may include that prediction under model known conditions is single
It is pre- under prediction subelement 422 and fault data part known conditions under member 421, degeneration overall process fault data known conditions
Survey subelement 423.Failure predication unit 420 can carry out event according to the characteristic parameter extracted for different priori conditions
Barrier prediction.Priori conditions include mainly two major classes：(correspond to the prediction subelement under model known conditions known to mathematical model
421), mathematical model is unknown (corresponds to the prediction subelement 422 and fault data under degeneration overall process fault data known conditions
Prediction subelement 423 under the known conditions of part).
Different failure prediction methods is used to carry out failure predication for different priori conditions, specially：(1) mould
The failure prediction method used under type known conditions for：STSCKF (the strong tracking SCKF of the Multiple fading factor), OS-ELM (pass through sequence
Extreme learning machine) and χ2It examines.(2) failure prediction method used under degeneration overall process fault data known conditions for：
ImSAP-ELM (improved sensitivity pruning ELM) and MPELM (more class probability ELM).(3) fault data part known conditions
The lower failure prediction method used for：BAFOS-ELM (sequence resampling Bootstrap and AFOS-ELM are passed through in fusion).
Preferably, unmanned aerial vehicle onboard transmitter failure forecasting system can also include excitation module 500.Wherein, excited modes
Block 500 is sent to fault-signal generation module 100 for generating pumping signal.Fault-signal generation module 100 is according to excitation
Signal generates corresponding failure response signal in conjunction with the different faults type and fault degree of setting.Wherein, pumping signal can
Think modulated signal.
Referring to Fig. 6, in one embodiment, excitation module 500 may include waveform generator 510 and D.C. regulated power supply
520.Waveform generator 510 is connect with fault-signal generation module 100.D.C. regulated power supply 520 is fault-signal generation module
100 power supplies.Wherein, the model that waveform generator 510 uses can be Agilent 33220A.
Preferably, unmanned aerial vehicle onboard transmitter failure forecasting system can also include prediction result management module 600.In advance
Results management module 600 is surveyed, the prediction result for generating failure predication module 400 is classified and stored.For example, prediction
Prediction result can be divided by results management module 600 according to certain standard：Well, preferably, it is general, poor and excessively poor
Etc. several grades.Then classification storage is carried out to these prediction results, a data is established for maintenance scheme decision-making and management in the future
Infrastructural support.
Referring to Fig. 7, unmanned aerial vehicle onboard transmitter failure forecasting system is illustrated with a specific example below.This reality
Apply in example, unmanned aerial vehicle onboard transmitter failure forecasting system include airborne transmitter direct fault location model machine 710, excitation module 500,
Signal acquisition module 200, communication interface 720 and main control computer 730.
Wherein, airborne transmitter direct fault location model machine 710 is according to the practical circuit composition equipped of airborne transmitter and function
Structure, transmitter original device is replaced using some adjustable devices, and inhomogeneity is simulated by adjusting the value of different adjustable devices
Type and different degrees of failure.And in order to improve the efficiency and accuracy of fault setting, adjustable device selects digital adjustable type,
The parameter value of adjustable device can be changed according to digital controlled signal.
The external output interface of airborne transmitter direct fault location model machine 710 includes reference clock output interface, intermediate frequency output
Interface, radio frequency output interface, excitation input interface and communication interface etc..Wherein, reference clock output interface may include reference
3 output interface of 1 output interface of clock, 2 output interface of reference clock and reference clock.Three reference clock output interfaces are used for
The output of 10MHz reference signal source modules.Intermediate frequency output interface is used for the output of phase-locked loop module.Radio frequency output interface is for passing through
Output after mixer module.Excitation input interface is that airborne transmitter direct fault location model machine 710 provides DC power supply, and adjusts
The input etc. of signal processed.Communication interface controls the value of internal adjustable device for being connect with main control computer 730.
It includes DC power supply and signal source 3320A to encourage module 500.DC power supply is airborne transmitter direct fault location model machine
The direct current of 710 offers ± 12V.Signal source 3320A is that airborne transmitter direct fault location model machine 710 provides pumping signal.Excitation
Module 500 is by encouraging input interface to be connect with airborne transmitter direct fault location model machine 710.
Signal acquisition module 200 includes frequency meter 53132A, power meter E4416A and frequency spectrograph N9320B.Signal acquisition mould
Block 200 acquisition power signal, frequency signal and frequency from the failure response signal that airborne transmitter failure injection model machine 710 generates
Spectrum signal.Frequency meter 53132A and power meter E4416A is connect by USB interfaces with communication interface 720, frequency spectrograph N9320B
It is connect with communication interface 720 by gpib interface.
Connection is communicated between communication interface 720 and main control computer 730.Airborne transmitter direct fault location model machine 710 passes through
RS232 serial ports is connect with communication interface 720.In addition, airborne transmitter direct fault location model machine 710 and power meter E4416A it
Between attenuator can also be set so that power meter E4416A can more accurately acquire power signal.
In the present embodiment, main control computer 730 controls airborne transmitter failure injection model machine 710 and different faults occurs, directly
Galvanic electricity source and signal source are that airborne transmitter direct fault location model machine 710 provides pumping signal.Airborne transmitter direct fault location model machine
710 output interfaces are connected to different meter device (power meter, frequency meter and frequency spectrograph), and output signal is read using measuring instrument
Characteristic parameter.After starting prediction, according to the trouble unit of selection, main control computer 730 sends out control instruction automatically, gradually
Increase the fault degree of airborne transmitter direct fault location model machine 710.And the instruction that main control computer 730 is sent out is for failure predication
Method is unknown, i.e., prediction technique does not know that 710 actual fault degree of airborne transmitter direct fault location model machine and failure become
Gesture, to ensure the objectivity and accuracy tested prediction technique.
Above-mentioned unmanned aerial vehicle onboard transmitter failure forecasting system chooses verification of the airborne transmitter as failure predication technology
Object extracts the characteristic parameter in acquisition Fisrt fault signal after Fisrt fault signal, and collected characteristic parameter is sent
Enter and obtain prediction conclusion in failure prediction method, to realize that airborne transmitter failure predication Proof-Of Principle overall process, failure are pre-
It surveys false alarm rate and false alarm rate is relatively low, failure predication result is more accurate.
Based on same inventive concept, the present invention also proposes a kind of unmanned aerial vehicle onboard transmitter failure prediction technique, repetition
Place repeats no more.
Referring to Fig. 8, in one embodiment, unmanned aerial vehicle onboard transmitter failure prediction technique may comprise steps of：
S801 generates the failure response signal based on airborne transmitter according to preset failure type and fault degree.
Wherein it is possible to generate failure response signal using airborne transmitter direct fault location model machine.It can be to airborne transmitter
Direct fault location model machine sends pumping signal, and airborne transmitter direct fault location model machine generates failure response signal according to pumping signal.
Since airborne transmitter direct fault location model machine is described in detail in unmanned aerial vehicle onboard transmitter failure forecasting system, therefore herein not
It repeats again.
S802 obtains Fisrt fault signal in failure response signal.
In the present embodiment, Fisrt fault signal is obtained from failure response signal.Wherein, Fisrt fault signal may include
The signal of one or more of power signal, frequency signal and spectrum signal.It is corresponding, can by power meter, frequency meter and
Frequency spectrograph obtains power signal, frequency signal and spectrum signal from failure response signal.
S803 extracts characteristic parameter in Fisrt fault signal.
Wherein, characteristic parameter may include peak bandwidth and/or phase noise value.Peak bandwidth can carry in spectrogram
It takes, phase noise value can be obtained according to the power calculation of different frequent points.
S804 carries out failure predication according to the characteristic parameter extracted.
In the present embodiment, failure predication can be carried out for different priori conditions according to the characteristic parameter extracted.First
It includes two major classes to test condition mainly：(corresponding to the prediction technique under model known conditions), mathematical model known to mathematical model are not
Know and (corresponds to the prediction under prediction technique and fault data part known conditions under degeneration overall process fault data known conditions
Method).
Different failure prediction methods is used to carry out failure predication for different priori conditions, specially：(1) mould
The failure prediction method used under type known conditions for：STSCKF (the strong tracking SCKF of the Multiple fading factor), OS-ELM (pass through sequence
Extreme learning machine) and χ2It examines.(2) failure prediction method used under degeneration overall process fault data known conditions for：
ImSAP-ELM (improved sensitivity pruning ELM) and MPELM (more class probability ELM).(3) fault data part known conditions
The lower failure prediction method used for：BAFOS-ELM (sequence resampling Bootstrap and AFOS-ELM are passed through in fusion).
1. based on the technical identification of STSCKF failure predications (STSCKF is a kind of failure prediction method).
Using total data with reference under clock frequency offset failure as test data.The failure is by 10MHz with reference to brilliant
Shake frequency shift (FS) caused by performance degradation, influence airborne transmitter reference clock 1 export, the output of reference clock 2, reference clock
The frequency values of 3 outputs and intermediate frequency output interface signal.Main control computer send serial ports instruct Data, due to reference clock 1 output,
The frequency that the output of reference clock 2, reference clock 3 export is identical.For this purpose, measuring the output of reference clock 2 and intermediate frequency using frequency meter
The frequency values of output interface, as shown in Figure 9 and Figure 10.With the increase of Data numerical value it can be seen from Fig. 9 and Figure 10, two
The signal frequency of output interface shifts upwards.The power output of 1 interface of reference clock is acquired using power meter.
Main control computer sends a serial ports at interval of Δ t=1min and instructs Data, controls frequency offset, and Data
Value at increasing trend.It is utilized respectively frequency meter and power meter frequency acquisition value and performance number.Failure predication runnable interface is as schemed
Shown in 11.
During the figure of the upper ends Figure 11 indicates time of failure prediction, modified χ2The detection function D of inspectionk+5|k, in
Between two width figures indicate respectively abort situation prediction during, modified χ2It examines to frequency shift fault model and gain reduction event
Hinder the D of modelk+5|k, two width figures of lower end indicate fault parameter R respectivelyfreWith probability of malfunction predicted value.Prediction result shows to be carried
Failure predication can be effectively performed in the STSCKF methods gone out.
2. the failure predication technical identification based on ImSAP-ELM (ImSAP-ELM is a kind of failure prediction method).
Using the data under phase-locked loop module modulation circuit failure as test data.The failure can influence intermediate frequency output and connect
Mouth spectral peak peak bandwidth Wfre.Signal source is enabled to generate 2Vpp, 1kHz square-wave signals, as the input modulating signal of modulation circuit,
Main control computer sends serial ports and instructs Data.When Figure 12 and Figure 13 show Data=0 and Data=255, the frequency of intermediate frequency output
Spectrum.As can be seen that spectral peak peak bandwidth W under different DatafreDifference chooses WfreFor Fault characteristic parameters, its calculating is defined
Formula is
Wfre=fre2-fre1 (1)
In formula, fre1For the frequency of first spectrum peak point, fre2For the frequency of second spectrum peak point.
Data is enabled to change successively between 0~255, the intermediate frequency output interface W measured using frequency spectrographfreValue is such as Figure 14 institutes
Show.It can be seen that WfreValue become smaller with the increase of Data.
Modulation circuit failure can make the W of modulation circuitfreIt changes, main control computer is sent at interval of Δ t=1min
One time serial ports instruction Data controls modulation circuit adjustable resistance, and the value of Data is at increasing trend.Intermediate frequency is measured using frequency spectrograph
The W of output interfacefre, by WfreAs the characteristic parameter based on ImSAP-ELM failure predications.Failure predication runnable interface such as Figure 15
It is shown.Wherein, the figure of upper end indicates that the predicted value of spectral peak peak bandwidth, the figure of lower end indicate probability of malfunction prediction in Figure 15
Value.Prediction result demonstrates the feasibility based on ImSAP-ELM failure prediction methods.
3. the failure predication technical identification based on MPELM (MPELM is a kind of failure prediction method).
Using the data under 2 failure of reference clock as test data.Assuming that its frequency of occurrences offset simultaneously and power decline
Subtract failure, computer sends once command Data1 and Data2 every Δ t=1min.What wherein Data1 was used to control crystal oscillator can
Resistance is adjusted, Data2 controls the pad value of adjustable attenuator, and the value of Data1 and Data2 is in increasing trend.It is utilized respectively frequency
Meter and power meter measure the frequency values and performance number of 2 interface of reference clock, and the parameter value measured is sent into MPELM prediction algorithms
In, failure predication runnable interface is as shown in Figure 16.Wherein, the figure of upper end indicates that test sample adheres to 4 kinds of degenerate states separately in Figure 16
Probabilistic estimated value, the figure of lower end indicates probability of malfunction predicted value, and prediction result demonstrates the validity of MPELM methods and correct
Property.
4. the failure predication technical identification based on BAFOS-ELM (BAFOS-ELM is a kind of failure prediction method).
Using the data under loop filter failure as test data.The failure will increase IF output signal noise,
It is 0~247 that main control computer, which sends serial ports and instructs Data, Data value ranges,.Figure 17 and Figure 18 show Data=0 and
When Data=247, intermediate frequency output spectrum.As can be seen that phase when phase noise when Data=247 is higher than Data=0 is made an uproar
Sound.Phase noise is extracted as characteristic parameter using direct frequency spectrograph method, calculation formula is as follows,
PN=Pssb-P0-10lg(1.2RBW)+2.5 (2)
In formula, P0For power at peak value, PssbFor away from the power at peak value 10kHz frequency deviations, RBW is resolution bandwidth.
It enables Data change successively between 0~247, the phase noise of intermediate frequency output interface is measured using frequency spectrograph, because
The time series fluctuation being made of phase noise is larger, is unfavorable for accurately being predicted.For this purpose, using Wavelet-denoising Method, and
Phase noise is normalized, obtained waveform is as shown in figure 19.As can be seen that increasing of the phase noise value with Data
Add and becomes larger.
Main control computer sends a serial ports at interval of Δ t=1min and instructs Data, control loop filter resistor value, and
The value of Data is at increasing trend.Prediction technique based on BAFOS-ELM needs the phase noise of intermediate frequency output interface, utilizes frequency spectrum
Instrument measures phase noise, has this parameter as fault signature and substitutes into BAFOS-ELM algorithms, failure predication runnable interface is as schemed
Shown in 20.Wherein, the figure of upper end indicates that the prediction mean value of phase noise, upper and lower bound, the figure of lower end indicate failure in Figure 20
The prediction mean value of probability, upper and lower bound, prediction result illustrate the validity of BAFOS-ELM methods.
Further, unmanned aerial vehicle onboard transmitter failure prediction technique can also include：
The result of failure predication is classified and is stored by step S805.
For example, prediction result can be divided into according to certain standard：Well, preferably, it is general, poor and excessively poor etc.
Several grades.Then classification storage is carried out to these prediction results, a data base is established for maintenance scheme decision-making and management in the future
Plinth is supported.
Above-mentioned unmanned aerial vehicle onboard transmitter failure prediction technique chooses verification of the airborne transmitter as failure predication technology
Object extracts the characteristic parameter in acquisition Fisrt fault signal after Fisrt fault signal, and collected characteristic parameter is sent
Enter and obtain prediction conclusion in failure prediction method, to realize airborne transmitter failure predication Proof-Of Principle overall process, prediction knot
Fruit is more accurate.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
All any modification, equivalent and improvement etc., should all be included in the protection scope of the present invention made by within refreshing and principle.
Claims (10)
1. a kind of unmanned aerial vehicle onboard transmitter failure prediction technique, which is characterized in that include the following steps：
Generate the failure response signal based on airborne transmitter；
Fisrt fault signal is obtained in the failure response signal；Wherein, the Fisrt fault signal includes power signal, frequency
The signal of one or more of rate signal and spectrum signal；
Characteristic parameter is extracted in the Fisrt fault signal；Wherein, the characteristic parameter includes that peak bandwidth and/or phase are made an uproar
Sound value；
Failure predication is carried out according to the characteristic parameter；
Wherein, described to be according to characteristic parameter progress failure predication：
According to the characteristic parameter extracted, failure predication is carried out for different priori conditions；Priori conditions include mathematical model
Known and two class of mathematics unknown-model；The known prediction technique corresponding under model known conditions of mathematical model, mathematical model is not
Know corresponding to the prediction under the prediction technique and fault data part known conditions under degeneration overall process fault data known conditions
Method；
The failure prediction method used under the model known conditions includes：The strong tracking SCKF of the Multiple fading factor, sequence pole is passed through
Limit learning machine method and χ2Method of inspection；
The failure prediction method used under the degeneration overall process fault data known conditions includes：Sensitivity pruning ELM methods and
More class probability ELM methods；
The failure prediction method used under the known conditions of the fault data part includes：Sequence resampling Bootstrap is passed through in fusion
With AFOS-ELM methods.
2. unmanned aerial vehicle onboard transmitter failure prediction technique according to claim 1, which is characterized in that according to default excitation
Signal generates the failure response signal based on airborne transmitter.
3. unmanned aerial vehicle onboard transmitter failure prediction technique according to claim 1, which is characterized in that described according to institute
Stating characteristic parameter progress failure predication step further includes later：
The result of failure predication is classified and stored.
4. a kind of unmanned aerial vehicle onboard transmitter failure forecasting system, which is characterized in that adopted including fault-signal generation module, signal
Collect module, communication module and failure predication module；Wherein：
The fault-signal generation module, for according to preset failure type and fault degree, generating based on airborne transmitter
Failure response signal；
The signal acquisition module for receiving the failure response signal, and acquires Fisrt fault signal, and by described
One fault-signal is sent to the communication module；The Fisrt fault signal includes power signal, frequency signal and spectrum signal
One or more of signal；
The communication module, for the Fisrt fault signal to be sent to the failure predication module；
The failure predication module, for carrying out failure predication according to the Fisrt fault signal；
Wherein, the failure predication module is specifically used for：
Failure predication is carried out for different priori conditions；Priori conditions include known to mathematical model and mathematics unknown-model two
Class；The known prediction technique corresponding under model known conditions of mathematical model, mathematical model is unknown to correspond to the event of degeneration overall process
Hinder the prediction technique under the prediction technique and fault data part known conditions under data known conditions；
The failure prediction method used under the model known conditions includes：The strong tracking SCKF of the Multiple fading factor, sequence pole is passed through
Limit learning machine method and χ2Method of inspection；
The failure prediction method used under the degeneration overall process fault data known conditions includes：Sensitivity pruning ELM methods and
More class probability ELM methods；
The failure prediction method used under the known conditions of the fault data part includes：Sequence resampling Bootstrap is passed through in fusion
With AFOS-ELM methods.
5. unmanned aerial vehicle onboard transmitter failure forecasting system according to claim 4, which is characterized in that further include excited modes
Block；
The excitation module for generating default pumping signal, and is sent to the fault-signal generation module.
6. unmanned aerial vehicle onboard transmitter failure forecasting system according to claim 5, which is characterized in that the excitation module
Including waveform generator and D.C. regulated power supply；
The waveform generator is connect with the fault-signal generation module；
The D.C. regulated power supply is powered for the fault-signal generation module.
7. unmanned aerial vehicle onboard transmitter failure forecasting system according to claim 4, which is characterized in that the failure predication
Module includes characteristic parameter extraction unit and failure predication unit；
The characteristic parameter extraction unit for extracting characteristic parameter in the Fisrt fault signal, and is sent to the event
Hinder predicting unit；The characteristic parameter includes peak bandwidth and/or phase noise value；
The failure predication unit, for carrying out failure predication according to the characteristic parameter.
8. unmanned aerial vehicle onboard transmitter failure forecasting system according to claim 4, which is characterized in that the signal acquisition
Module includes one or more of frequency meter, power meter and frequency spectrograph；Wherein：
The frequency meter for the frequency acquisition signal in the Fisrt fault signal, and is sent to the communication module；
The power meter for acquiring power signal in the Fisrt fault signal, and is sent to the communication module；
The frequency spectrograph for acquiring spectrum signal in the Fisrt fault signal, and is sent to the communication module.
9. the unmanned aerial vehicle onboard transmitter failure forecasting system according to claim 4 to 8 any one, which is characterized in that
It further include prediction result management module；
The prediction result management module, the prediction result for generating the failure predication module are classified and are stored.
10. the unmanned aerial vehicle onboard transmitter failure forecasting system according to claim 4 to 8 any one, which is characterized in that
The communication module includes one or more of USB interface, GBIP interfaces and RS232 serial ports.
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