CN112182773A - Online identification method for aircraft steering engine fault based on linear frequency modulation Z transformation - Google Patents
Online identification method for aircraft steering engine fault based on linear frequency modulation Z transformation Download PDFInfo
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Abstract
The application discloses aircraft steering engine fault on-line identification method based on linear frequency modulation Z transformation, including: selecting a concerned frequency band aiming at a state space model of the longitudinal short-period motion of the aircraft; calculating the linear frequency modulation Z transformation of each frequency point in the concerned frequency band; calculating the estimated value of the parameter to be identified at the previous moment, and iterating to obtain the variance estimated value of the parameter to be identified; judging whether the variance estimation value of the parameter to be identified is less than or equal to a set threshold value; when the variance estimation value is less than or equal to a set threshold value, the steering engine system is considered not to be in fault, and parameters to be identified are updated to obtain an identification result; and when the variance estimation value is larger than the set threshold value, the steering engine system is considered to have a fault at the moment k, and the identification is restarted. The method and the device can effectively reduce the influence of measurement noise, avoid numerical differential errors, quickly change sensitive parameters, realize online identification of aircraft faults, and have strong universality on general aircrafts.
Description
Technical Field
The invention belongs to the technical field of aircraft control, and particularly relates to an online identification method for aircraft steering engine faults based on linear frequency modulation Z transformation.
Background
For an aircraft with an air rudder as an actuating mechanism, the effectiveness or non-effectiveness of the air rudder is a key factor influencing the success or failure of flight. In the flight process of the aircraft, in order to ensure the effectiveness of a control command, the control capability of the air rudder needs to be analyzed and estimated according to real-time flight data, so that a corresponding strategy is formulated to ensure the flight safety of subsequent tasks. During the flight of the aircraft, the control capability of the aircraft is damaged when the air rudder fails. However, for a power system, the reliability of the power system cannot be improved in a hardware redundancy mode, and when unexpected faults occur in the power system and a control mechanism, the current control system lacks self-adaptive capacity and autonomy and cannot autonomously process abnormal conditions.
At present, in the conventional time domain identification method, a better identification result can be obtained under the condition of small measurement noise, but when the measurement noise is large, the identification effect is poor due to the amplification effect of numerical differentiation on the measurement noise. The frequency domain recursive identification method has the advantages of strong noise suppression capability, small calculation amount, fixed memory requirement and simplicity and convenience in signal differentiation processing. However, accumulation of a certain amount of data is required to reduce spectrum leakage, so that the real-time performance is poor, and the conventional frequency domain identification method assumes that parameters are fixed in the identification process, so that the adaptability to the time-varying situation of the parameters is relatively poor.
Aiming at the problem that the identification result is influenced by noise and has poor adaptability in the prior art, no effective method is available.
Disclosure of Invention
In order to solve the defects in the prior art and solve the problem of identifying the faults of the steering engine of the aircraft system, the application provides an online identification method for the faults of the steering engine of the aircraft based on linear frequency modulation Z transformation. A large sliding time window needs to be preset, and under the condition that the parameter change is not large, a default time window is used, so that large data volume can be utilized, and the denoising capability and stability of the identification algorithm are improved. The method for identifying the faults of the steering engine of the aircraft on line based on the linear frequency modulation Z transformation not only can effectively reduce the influence of measurement noise, but also can quickly sense parameter change, enhances the adaptability of the frequency domain identification method to the fault situation of the aircraft, can realize the online identification of the faults of the aircraft, and has stronger universality to general aircraft.
An aircraft steering engine fault online identification method based on linear frequency modulation Z transformation comprises the following steps:
selecting a concerned frequency band aiming at a state space model of the longitudinal short-period motion of the aircraft;
calculating chirp Z transformation of each frequency point in the concerned frequency band for each moment on the basis of recursive chirp Z transformation based on a sliding time window;
and calculating the estimated value of the parameter to be identified at the previous moment based on the linear frequency modulation Z transformation result, and iterating to obtain the variance estimated value of the parameter to be identified.
Judging whether the variance estimation value of the parameter to be identified is less than or equal to a set threshold value;
when the variance estimation value of the parameter to be identified at the moment k is less than or equal to a set threshold value, updating the parameter to be identified by utilizing a recursive linear frequency modulation Z transformation result to obtain an identification result of the parameter to be identified; and if the variance estimation value of the parameter to be identified is greater than the set threshold value, the steering engine system at the moment k is considered to have a fault, and identification needs to be restarted in order to improve the identification precision of the linear frequency modulation Z transformation. Since the accurate value of the parameter to be identified is unknown, the parameter identification result is kept as the value of the previous beat, and the parameter identification is restarted at the next moment, that is, when k is equal to k + 1. And when the variance estimation value of the parameter to be identified is less than or equal to the set threshold value, the steering engine system is considered not to be in fault, and the identification result is used for replacing the parameter to be identified.
The frequency band of interest is [ f ]0,f1) Selecting M frequencies distributed at equal intervals in the concerned frequency bandRate point, i.e. fi＝f0+ i Δ f, where i ═ 0, 1., M-1, i denotes the ith frequency signal, and Δ f denotes the frequency interval between two adjacent frequency points.
The iteration obtains the parameter to be identified (namely the air rudder effect) and the variance estimation value thereof, and the steps are as follows:
step S101: inputting online identification information as a sequence;
step S102: judging whether the length of the accumulated time sequence is less than or equal to a preset length threshold L or not, namely whether the beat number of the data subjected to linear frequency modulation Z conversion is less than or equal to the preset length threshold L or not;
step S103: if yes, accumulating the data of each beat in the time window, solving a chirp Z conversion value of each frequency point, calculating sleep time so that length is equal to length +1, and going to step S105;
step S104: if not, the accumulated time sequence length is L +1, the beat of data of the time window which is added first needs to be removed, the latest beat of data is added, the beat number of the data subjected to the linear frequency modulation Z conversion is ensured to be equal to a preset length threshold value L, and the step S109 is carried out;
step S105: judging whether the moment is less than the dormancy time;
step S106: if the variance estimation value is less than the sleep time, the variance estimation value of the current moment is equal to the initial variance estimation value
The identification result of the parameter to be identified is equal to the initial value of the parameter estimation
Go to step S102;
step S107: if the time is not less than the sleep time, judging whether the time is equal to the sleep time;
step S108: if the variance estimation value is equal to the sleep time, the variance estimation value of the current moment is equal to the initial variance estimation value
And calculating the identification result of the parameter to be identified
Wherein (·)HIndicating the conjugate transpose, go to step S102;
step S109: if not, calculating a variance estimation value of the parameter to be identified;
step S110: judging whether the variance estimation value is larger than a variance threshold value;
step S111: if the variance estimation value is larger than the variance threshold value, restarting the linear frequency modulation Z transformation, assigning the identification result of the parameter to be identified at the previous moment to the current moment, and turning to the step S102;
step S112: and if the variance estimation value is less than or equal to the variance threshold value, calculating and outputting the identification result of the parameter to be identified at the current moment.
The sleep time ts0Making the parameter value of the parameter to be identified at [ i delta t, i delta t + ts0) In time, the parameter estimation value of the previous moment is used continuously without change, and the system does not judge variance change any more, thereby ensuring that the system is not restarted again in the sleep time.
The online identification information includes: attitude angular velocity, angle of attack, air rudder deflection.
The parameter to be identified is the rudder effect of the air rudder.
The time window is a time-varying window, the data in the window is increased from 1 to L, and L data are kept unchanged.
The beneficial effect that this application reached:
the method and the device have the advantages that the pneumatic parameters of the air rudder are calculated by adopting a frequency domain method based on linear frequency modulation Z transformation, the influence of measurement noise can be effectively reduced, numerical differential errors are avoided, parameter changes can be quickly sensed, the online identification of aircraft faults is realized, and the method and the device have strong universality on general aircrafts.
Drawings
FIG. 1 is a flow chart of an online identification method for faults of an aircraft steering engine based on linear frequency modulation Z transformation according to an embodiment of the application;
fig. 2 is a flowchart illustrating an iterative process of obtaining a parameter to be identified and a variance estimation value thereof according to an embodiment of the present application.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
An online identification method for faults of an aircraft steering engine based on linear frequency modulation Z transformation is disclosed, as shown in figure 1, and comprises the following steps:
step S1: selecting a concerned frequency band aiming at a state space model of the longitudinal short-period motion of the aircraft; the embodiment selects a concerned frequency band far away from the noise, and further weakens the influence of the noise, thereby obtaining better identification effect.
Step S2: on the basis of a sliding-time-window-based recursive chirp Z-transform, for each time tkK Δ t, where Δ t represents a sampling time and k represents a time beat, calculating a chirp-Z transform for each frequency point in the frequency band of interest;
wherein, ω isz1Representing the angular velocity of the aircraft about the Z1 axis of the body, alpha representing the angle of attack of the aircraft,
indicating the pitch rudder deflection of the aircraft air rudder.
Step S3: based on the result of the linear frequency modulation Z transformation, calculating the estimated value of the parameter to be identified at the previous moment (k-1) delta t
And iterating to obtain variance estimation values of the parameters to be identified.
The variance estimate is formulated as follows:
wherein λ is the forgetting factor of variance, the initial value
Can be set empirically, and e2(k) Calculated by the following equation:
wherein, (.)HWhich represents the transpose of the conjugate,
step S4: judging whether the variance estimation value of the parameter to be identified is less than or equal to a set threshold value
Step S5: when the variance estimation value of the parameter to be identified at the moment K is less than or equal to the variance estimation value K at the last momenttDouble, i.e.
And in time, the steering engine system is not considered to be in fault, the linear frequency modulation Z conversion result is utilized to update the parameters to be identified, and the identification result of the parameters to be identified is obtained:
due to the fact that
Then
Step S6: and if the variance estimation value of the parameter to be identified is greater than the set threshold value, the steering engine system at the moment k is considered to have a fault, and identification needs to be restarted in order to improve the identification precision of the linear frequency modulation Z transformation. Since the accurate value of the parameter to be identified is unknown, the parameter identification result is kept as the value of the previous beat, and the parameter identification is restarted at the next moment, that is, when k is equal to k + 1.
The frequency band of interest is[f0,f1) Selecting M frequency points, i.e. f, equally spaced within said frequency band of interesti＝f0+ i Δ f, where i ═ 0, 1., M-1, denotes the ith frequency signal, and Δ f denotes the frequency spacing of two adjacent frequency points.
The air vane aerodynamic parameters include: the attitude angular velocity, the attack angle and the pitching rudder deflection of the pitching channel.
The iteration obtains the parameter to be identified and the variance estimation value thereof, as shown in fig. 2, the steps are as follows:
step S101: inputting online identification information as a sequence;
step S102: judging whether the length of the accumulated time sequence is less than or equal to a preset length threshold L or not, namely whether the beat number of the data subjected to linear frequency modulation Z conversion is less than or equal to the preset length threshold L or not;
step S103: if yes, accumulating and solving the chirp Z conversion value of each frequency point, calculating the sleep time to enable length to be equal to length +1, and going to step S105;
step S104: if not, the accumulated time sequence length is L +1, the beat of data of the time window which is added first needs to be removed, the latest beat of data is added, the beat number of the data subjected to the linear frequency modulation Z conversion is ensured to be equal to the preset length threshold value L, and the step S109 is carried out;
step S105: judging whether the moment is less than the dormancy time;
step S106: if the variance estimation value is less than the sleep time, the variance estimation value of the other current moment is equal to the initial variance estimation value
The identification result of the parameter to be identified is equal to the initial value of the parameter estimation
Go to step S102;
step S107: if the time is not less than the sleep time, judging whether the time is equal to the sleep time;
step S108: if the time equals to the sleep time, the other current timeThe variance estimate at that moment is equal to the initial variance estimate
And calculating the identification result of the parameter to be identified
Go to step S102;
step S109: if not, calculating a variance estimation value of the parameter to be identified;
step S110: judging whether the variance estimation value is larger than a variance threshold value;
step S111: if the variance estimation value is larger than the variance threshold value, restarting the linear frequency modulation Z transformation, assigning the identification result of the parameter to be identified at the previous moment to the current moment, and turning to the step S102;
step S112: and if the variance estimation value is less than or equal to the variance threshold value, calculating and outputting the identification result of the parameter to be identified at the current moment.
The sleep time ts0Making the parameter value of the parameter to be identified at [ i delta t, i delta t + ts0) In time, the parameter estimation value of the previous moment is used continuously without change, and the system does not judge variance change any more, thereby ensuring that the system is not restarted again in the sleep time.
The online identification information includes: attitude angular velocity, angle of attack, air rudder deflection.
The parameter to be identified is the rudder effect of the air rudder.
The time window refers to a window for data processing, i.e., data in the window is processed without concern for data outside the window. The window changes with time, the number of data in the window is increased from 1 to L at first, and then the number of data in the window is kept unchanged at L.
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.
Claims (8)
1. An aircraft steering engine fault online identification method based on linear frequency modulation Z transformation is characterized by comprising the following steps:
selecting a concerned frequency band aiming at a state space model of the longitudinal short-period motion of the aircraft;
calculating chirp Z transformation of each frequency point in the concerned frequency band for each moment on the basis of recursive chirp Z transformation based on a sliding time window;
calculating an estimated value of a parameter to be identified at the previous moment based on a linear frequency modulation Z transformation result, and iterating to obtain a variance estimated value of the parameter to be identified;
judging whether the variance estimation value of the parameter to be identified is less than or equal to a set threshold value;
when the variance estimation value of the parameter to be identified at the moment k is smaller than or equal to a set threshold value, the steering engine system is considered not to be in fault, and the parameter to be identified is updated by using a linear frequency modulation Z transformation result to obtain an identification result of the parameter to be identified;
and when the variance estimation value of the parameter to be identified is larger than the set threshold value, the steering engine system is considered to have a fault at the moment k, and the identification is restarted.
2. The method for identifying the faults of the aircraft steering engine based on the chirp Z transform as claimed in claim 1, wherein the method comprises the following steps: the reboot recognition process is as follows: the recognition result is kept as the value of the previous beat, and the parameter recognition is restarted at the next moment, namely when k is equal to k + 1.
3. The method for identifying the faults of the aircraft steering engine based on the chirp Z transform as claimed in claim 1, wherein the method comprises the following steps: the frequency band of interest is [ f ]0，f1) Selecting M frequency points, i.e. f, equally spaced within said frequency band of interesti＝f0+ i Δ f, where i ═ 0, 1., M-1, i denotes the ith frequency signal, and Δ f denotes the frequency interval between two adjacent frequency points.
4. The method for identifying the faults of the aircraft steering engine based on the chirp Z transform as claimed in claim 1, wherein the method comprises the following steps: the iteration obtains the parameter to be identified and the variance estimation value thereof, and the calculation steps are as follows:
step S101: inputting online identification information as a sequence;
step S102: judging whether the length of the accumulated time sequence is less than or equal to a preset length threshold L or not, namely whether the beat number of the data subjected to linear frequency modulation Z conversion is less than or equal to the preset length threshold L or not;
step S103: if yes, accumulating and solving the chirp Z conversion value of each frequency point, calculating the sleep time to enable length to be equal to length +1, and going to step S105;
step S104: if not, the accumulated time sequence length is L +1, the beat of data of the time window which is added first needs to be removed, the latest beat of data is added, the beat number of the data subjected to the linear frequency modulation Z conversion is ensured to be equal to a preset length threshold value L, and the step S109 is carried out;
step S105: judging whether the moment is less than the dormancy time;
step S106: if the variance estimation value is less than the sleep time, the variance estimation value of the current moment is equal to the initial variance estimation value
Making the identification result of the parameter to be identified equal to the initial value of the parameter estimation
Go to step S102;
step S107: if the time is not less than the sleep time, judging whether the time is equal to the sleep time;
step S108: if the variance estimation value is equal to the sleep time, the variance estimation value of the current moment is equal to the initial variance estimation value
And calculating the identification result of the parameter to be identified
Go to step S102;
step S109: if not, calculating a variance estimation value of the parameter to be identified;
step S110: judging whether the variance estimation value is larger than a variance threshold value;
step S111: if the variance estimation value is larger than the variance threshold value, restarting the linear frequency modulation Z transformation, assigning the identification result of the parameter to be identified at the previous moment to the current moment, and turning to the step S102;
step S112: and if the variance estimation value is less than or equal to the variance threshold value, calculating and outputting the identification result of the parameter to be identified at the current moment.
5. The method for identifying the faults of the aircraft steering engine based on the chirp Z transform as claimed in claim 4, wherein the method comprises the following steps: the sleep time ts0Making the parameter value of the parameter to be identified at [ i delta t, i delta t + ts0) Within the time, no change occurs, the parameter estimation value of the previous moment is used, and the system does not judge the variance change any more.
6. The method for identifying the faults of the aircraft steering engine based on the chirp Z transform as claimed in claim 4, wherein the method comprises the following steps: the online identification information includes: attitude angular velocity, angle of attack, air rudder deflection.
7. The method for identifying the faults of the aircraft steering engine based on the chirp Z transform as claimed in claim 4, wherein the method comprises the following steps: the parameter to be identified is the rudder effect of the air rudder.
8. The method for identifying the faults of the aircraft steering engines based on the chirp Z transform as claimed in claim 4, wherein the time window is a time-varying window, the data in the window is increased from 1 to L, and the L data are kept unchanged.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106649909A (en) * | 2016-08-29 | 2017-05-10 | 成都飞机工业（集团）有限责任公司 | Dual-redundancy compensation type empennage control plane fault state control method |
| CN111176263A (en) * | 2020-01-23 | 2020-05-19 | 北京航天自动控制研究所 | Online aircraft thrust fault identification method based on BP neural network |
| CN111241626A (en) * | 2020-01-02 | 2020-06-05 | 南京航空航天大学 | Unmanned aerial vehicle arresting landing real-time simulation method based on rapid prototyping |
| CN111258302A (en) * | 2020-01-23 | 2020-06-09 | 北京航天自动控制研究所 | Aircraft thrust fault online identification method based on LSTM neural network |
| CN111538309A (en) * | 2020-04-03 | 2020-08-14 | 浙江大学 | Industrial process plant-level oscillation detection method based on multivariate nonlinear frequency modulation modal decomposition |
| CN111678699A (en) * | 2020-06-18 | 2020-09-18 | 山东大学 | Early fault monitoring and diagnosing method and system for rolling bearing |
-
2020
- 2020-10-16 CN CN202011106405.5A patent/CN112182773A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106649909A (en) * | 2016-08-29 | 2017-05-10 | 成都飞机工业（集团）有限责任公司 | Dual-redundancy compensation type empennage control plane fault state control method |
| CN111241626A (en) * | 2020-01-02 | 2020-06-05 | 南京航空航天大学 | Unmanned aerial vehicle arresting landing real-time simulation method based on rapid prototyping |
| CN111176263A (en) * | 2020-01-23 | 2020-05-19 | 北京航天自动控制研究所 | Online aircraft thrust fault identification method based on BP neural network |
| CN111258302A (en) * | 2020-01-23 | 2020-06-09 | 北京航天自动控制研究所 | Aircraft thrust fault online identification method based on LSTM neural network |
| CN111538309A (en) * | 2020-04-03 | 2020-08-14 | 浙江大学 | Industrial process plant-level oscillation detection method based on multivariate nonlinear frequency modulation modal decomposition |
| CN111678699A (en) * | 2020-06-18 | 2020-09-18 | 山东大学 | Early fault monitoring and diagnosing method and system for rolling bearing |
Non-Patent Citations (1)
| Title |
|---|
| 钟鸿豪 等: "基于CZT 的变时间窗时变参数在线辨识方法及应用", 《航天控制》, vol. 37, no. 6, pages 20 - 22 * |
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