CN112746875B - Active control system and method for complex vibration of rotor shaft system of aircraft engine - Google Patents
Active control system and method for complex vibration of rotor shaft system of aircraft engine Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
- F01D—NON-POSITIVE DISPLACEMENT MACHINES OR ENGINES, e.g. STEAM TURBINES
- F01D25/00—Component parts, details, or accessories, not provided for in, or of interest apart from, other groups
- F01D25/04—Antivibration arrangements
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
- F01D—NON-POSITIVE DISPLACEMENT MACHINES OR ENGINES, e.g. STEAM TURBINES
- F01D21/00—Shutting-down of machines or engines, e.g. in emergency; Regulating, controlling, or safety means not otherwise provided for
- F01D21/003—Arrangements for testing or measuring
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
- F01D—NON-POSITIVE DISPLACEMENT MACHINES OR ENGINES, e.g. STEAM TURBINES
- F01D25/00—Component parts, details, or accessories, not provided for in, or of interest apart from, other groups
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
- F01D—NON-POSITIVE DISPLACEMENT MACHINES OR ENGINES, e.g. STEAM TURBINES
- F01D5/00—Blades; Blade-carrying members; Heating, heat-insulating, cooling or antivibration means on the blades or the members
- F01D5/02—Blade-carrying members, e.g. rotors
- F01D5/10—Anti- vibration means
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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Abstract
The invention provides an active control system and method for complex vibration of an aircraft engine rotor shafting. In the active control system, a rotor speed sensor is used to measure the rotor speed; the vibration sensor is used for measuring the vibration of the rotor shaft in the horizontal direction and the vertical direction; the vibration estimation module in the controller comprises a rotor rotating speed model and an EKF (extended Kalman filter), receives a rotor rotating speed sensor signal and a vibration sensor signal, estimates the vibration signal of the rotor, and outputs a rotor rotating speed filtering signal and a vibration signal estimation parameter according to the vibration signal estimation and the rotor rotating speed sensor signal; and the fuel control module controls the fuel output quantity as a self-adaptive parameter according to the rotor speed filtering signal and the vibration signal estimation parameter. The active control system can realize complex vibration suppression, so that the vibration level of a controlled object can meet requirements in different flight states, and the aims of improving the reliability and safety of an engine, reducing noise and prolonging the service life of the engine are fulfilled.
Description
Technical Field
The invention provides a control system and a control method for rotor shafting vibration of an aircraft engine.
Background
GTF (geared turbofan engine) is a design that employs a low pressure turbine to drive a fan through a high power gearbox. The gear transmission system ensures that the low-pressure compressor and the low-pressure turbine rotate at high speed and simultaneously enables the fan to rotate at ideal low speed. Under the condition of the same temperature ratio and pressure ratio, the diameter can be increased by reducing the rotating speed of the fan, so that the bypass ratio is greatly improved, and the propulsion efficiency is improved. Thus, the GTF configuration reduces engine noise as well as fuel consumption.
GTF engines drive fans through a gear system, and these parts together form a complex mechanical torsional vibration system. In the working process, the complex vibration characteristics are generated at the end of an engine due to the comprehensive influence of factors such as aerodynamic excitation of components such as a fan and the like, unbalanced excitation, friction excitation, time-varying rigidity and the like in a transmission system. Since the bearings of the various components in the drive train are somewhat flexible, transverse vibrations become non-negligible, and torsional and transverse vibrations produce a strong coupling effect due to the meshing action between the gears, which is essentially a bending-torsional coupled vibration characteristic.
The basic modes of vibration control can be divided into active control and passive control. The passive control realizes the effective inhibition of vibration through additional components, mainly comprises a vibration isolator, a dynamic vibration absorber, a damping vibration absorber and the like, and has the defects of high maintenance cost of the components and narrow control frequency band. The gear box transmission coupling dynamic system of the GTF engine has extreme complexity and strong nonlinearity, the vibration mode of the system, particularly the vibration frequency at the engine end, can change obviously along with the change of flight conditions, and the change frequency band is large. In the design of the GTF engine, although the vibration is restrained by means of coupling flexible coupling and the like, the satisfactory effect cannot be achieved when the engine works in a large range.
The active control mainly adopts the implementation of a control theory to realize the vibration control of the controlled object. For example, torsional vibration control of a helicopter is usually realized by serially connecting a notch filter in a feedback path of fuel control, and the pulsation of fuel flow is suppressed by filtering out the rotational speed pulsation superposed with a specific torsional vibration frequency, namely, the self-excited vibration is avoided by cutting off the energy input of fuel. The notch filter is generally designed for constant coefficients, and a method of connecting a plurality of filters in series is adopted to filter torsional vibration signals in different states, such as states of ground, air slow-moving vehicles and the like. Different from a turboshaft engine with constant rotating speed, the rotating speed of an output shaft of the GTF engine has a large variation range, and aerodynamic loads applied to the output shaft of the GTF engine under different flight conditions also vary greatly, so that the vibration frequency at the engine end changes remarkably, and therefore, a constant-coefficient filter design is not suitable.
Disclosure of Invention
The GTF turbofan engine with a large bypass ratio is influenced by factors such as unbalanced excitation of a transmission system, time-varying rigidity of gear meshing and the like, and all parts in the transmission system have certain elasticity for supporting, so that a complex bending-torsion coupling vibration characteristic is generated at the end of the engine. This vibration can couple with the fuel regulation system of the engine, further causing coupled dynamic stability problems, and in severe cases may form unstable self-excited vibrations.
The bending-torsional coupled vibration frequency in a GTF engine can vary with changes in flight conditions and flight conditions, with wide frequency variations, which present challenges to the design of active control systems. When the frequency changes beyond the design frequency band, the control system performance is significantly degraded due to the strong nonlinearity of the system, and even oscillation dispersion occurs.
Therefore, an object of the present invention is to provide an active control system and method for complex vibration of an aircraft engine rotor shaft system, which can meet the vibration level of a controlled object under different flight conditions.
An active control system for complex vibration of an aircraft engine rotor shaft system, comprising a rotor speed sensor for measuring rotor speed, a vibration sensor for measuring vibration of the rotor shaft in horizontal and vertical directions, and a controller, comprising:
the vibration estimation module comprises a rotor rotating speed model and an extended Kalman filter, receives a rotor rotating speed sensor signal and a vibration sensor signal, carries out vibration signal estimation on the rotor, and outputs a rotor rotating speed filtering signal and a vibration signal estimation parameter according to the vibration signal estimation and the rotor rotating speed sensor signal; and
and the fuel control module is used for controlling the fuel output quantity as a self-adaptive parameter according to the rotor rotating speed filtering signal and the vibration signal estimation parameter.
In one embodiment of the active control system, the input of the rotor speed model is a rotor speed sensor signal, a vibration sensor signal, and a torsional vibration signal estimation output by an extended kalman filter, the rotor speed model is obtained by constructing a simplified model of the low-pressure rotor speed of the engine and then linearly superposing the rotor speed and the torsional vibration speed, the torsional vibration speed comprises a component caused by the torsional vibration angular displacement of the rotor determined by the torsional vibration signal estimation and a component caused by the bending-torsional coupling determined by the vibration sensor signal, and the rotor speed model outputs a rotor speed signal estimation and a bending-torsional coupling vibration estimation; the vibration estimation module inputs residual signals estimated by the rotor speed sensor signals and the rotor speed signals into an extended Kalman filter for iterative computation by calculating the residual signals, and the extended Kalman filter outputs torsional vibration signal estimation and vibration parameters thereof; the vibration estimation module is also used for removing the sum of the torsional vibration signal and the bending-torsional coupling vibration estimation from the rotor speed sensor signal to obtain a rotor speed filtering signal; and the fuel control module comprises a self-adaptive controller, and the fuel control module controls the fuel output amount of the fuel according to the rotor speed filtering signal and the vibration signal estimation parameter as self-adaptive parameters.
In one embodiment of the active control system, for a two-shaft aircraft engine, the rotor speed sensor comprises:
the high-pressure rotor rotating speed sensor is used for measuring the rotating speed of the high-pressure rotor; the low-pressure rotor rotating speed sensor is used for measuring the rotating speed of the low-pressure rotor; the vibration sensor is used for measuring the vibration of the low-pressure rotor shaft in the horizontal direction and the vertical direction; the vibration estimation module comprises a low-pressure rotor rotating speed model and an extended Kalman filter, wherein the input of the low-pressure rotor rotating speed model is a high-pressure rotor rotating speed sensor signal, a vibration sensor signal and a torsional vibration signal estimation output by the extended Kalman filter, the low-pressure rotor rotating speed model constructs a simplified model of the rotating speed of the low-pressure rotor of the engine by taking the high-pressure rotor rotating speed sensor signal as the input, then the rotating speed of the low-pressure rotor and the torsional vibration rotating speed are linearly superposed, the torsional vibration rotating speed comprises a component caused by the torsional vibration angular displacement of the low-pressure rotor determined by the torsional vibration signal estimation and a component caused by bending-torsion coupling determined by the vibration sensor signal, and the low-pressure rotor rotating speed model outputs a low-pressure rotor rotating speed signal estimation and a bending-torsion coupling vibration estimation; the vibration estimation module inputs the residual error signals estimated by the low-pressure rotor rotating speed sensor signals and the low-pressure rotor rotating speed signals into an extended Kalman filter for iterative calculation, and the extended Kalman filter outputs torsional vibration signal estimation and vibration parameters thereof; the vibration estimation module is also used for removing the sum of the torsional vibration signal and the bending coupling vibration estimation from the low-pressure rotor rotating speed sensor signal to obtain a low-pressure rotor rotating speed filtering signal; the fuel control module, it includes: and the self-adaptive controller is used for controlling the fuel output amount of the fuel according to the low-pressure rotor rotating speed filtering signal and the vibration signal estimation parameter as self-adaptive parameters.
In one embodiment of the active control system, the adaptive controller uses the estimated vibration signal parameters including frequency and/or amplitude as adaptive parameters and limits the fuel fluctuation, thereby suppressing vibration by injecting energy and avoiding fuel circuit oscillation with large amplitude.
In one embodiment of the active control system, the fuel control module further comprises a self-excited vibration protection logic system, wherein the self-excited vibration protection logic system comprises an adaptive band-pass filter and a fuel fluctuation peak-to-peak value calculation module; the self-adaptive band-pass filter selects the vibration frequency estimation output by the extended Kalman filter as the center frequency of the band-pass filter, and accordingly, the filter coefficient is selected in a self-adaptive mode to realize filtering output of the fuel quantity signal with the slowly-variable frequency quantity; the fuel fluctuation peak-peak value calculation module calculates the peak-peak value of fuel fluctuation by using the vibration frequency estimation parameter and taking an average value in one vibration period or a plurality of vibration periods, and outputs the fuel fluctuation amplitude; and the self-excited vibration protection logic system also executes protection logic, and immediately cuts off the fuel if judging that the fuel fluctuation amplitude exceeds a set fluctuation threshold value or if judging that the vibration amplitude estimation exceeds a set vibration amplitude threshold value, otherwise, normally outputs the fuel quantity.
In one embodiment of the active control system, the self-excited vibration protection logic system is configured to perform the steps of: comparing the fuel fluctuation amplitude with a fuel fluctuation threshold value to output a first logic signal; comparing the estimation of the vibration amplitude with a threshold value of the vibration amplitude to output a second logic signal; performing an and logic operation on the first logic signal and the second logic signal to output a third logic signal; and outputting a result of multiplying the fuel quantity by the third logic signal as the fuel quantity.
The aircraft engine is a GTF engine or other type of engine.
An active control method for complex vibration of an aircraft engine rotor shafting comprises the following steps
Acquiring a rotor rotating speed signal;
acquiring vibration signals of a rotor shaft in the horizontal direction and the vertical direction;
according to the rotor rotating speed signal and the vibration signal, a rotor rotating speed model and an extended Kalman filter are used for estimating the vibration signal of the rotor, and a rotor rotating speed filtering signal and a vibration signal estimation parameter are output according to the vibration signal estimation and the rotor rotating speed signal;
and controlling the fuel output quantity by taking the rotor speed filtering signal and the vibration signal estimation parameter as self-adaptive parameters.
One or more embodiments of the active control method set the input of a rotor speed model as a rotor speed signal, a vibration signal, and a torsional vibration signal estimate output by an extended kalman filter, the rotor speed model builds a simplified model of the engine rotor speed, and then linearly superimposes the rotor speed and the torsional vibration speed, the torsional vibration speed includes a component caused by the torsional vibration angle displacement of the rotor determined by the torsional vibration signal estimate and a component caused by the bending-torsion coupling determined by the vibration signal, the rotor speed model outputs a rotor speed signal estimate and a bending-torsion coupling vibration estimate; inputting a residual signal estimated by calculating a rotor rotating speed signal and the rotor rotating speed signal into an extended Kalman filter for iterative calculation, wherein the extended Kalman filter outputs a torsional vibration signal estimation and a vibration parameter thereof; and removing the sum of the torsional vibration signal and the bending-torsional coupling vibration estimation from the rotor speed sensor signal to obtain a rotor speed filtering signal.
One or more embodiments of the active control method determine the fuel quantity of the fuel output according to the rotor speed filtering signal and the vibration signal estimation parameter as the self-adaptive parameter; selecting the vibration frequency estimation output by the extended Kalman filter as the center frequency of the band-pass filter by using a self-adaptive band-pass filter, and accordingly, adaptively selecting a filter coefficient to realize filtering output of the fuel quantity signal with slowly-variable frequency quantity; calculating the peak-peak value of fuel fluctuation by using a vibration frequency estimation parameter and an averaging method in one vibration period or a plurality of vibration periods, and outputting the fuel fluctuation amplitude; and if the fuel oil fluctuation amplitude is judged to exceed the set fluctuation threshold value or if the vibration amplitude estimation is judged to exceed the set vibration amplitude threshold value, immediately cutting off the fuel oil, otherwise, normally outputting the fuel oil quantity.
The EKF design in the aforementioned systems and methods requires that the frequency content in the vibration signal be substantially accurately estimated. The bending-torsional coupled vibration in a GTF engine or other types of engines is a non-stationary random process, and spectral estimation methods such as short-time FFT and the like for stationary signals are not suitable, because when the engine works in a transition state, analysis frequency mutation is caused by rotation speed mutation, and further a designed adaptive controller fails.
Adaptive filters designed for noise cancellation in the field of communications often require a known reference signal as the filter input; when no reference signal exists, a plurality of beats of delay of a process signal (a signal to be filtered) are adopted to construct the reference signal, and the selection of delay beats needs to meet the condition that the autocorrelation coefficient of a noise signal is approximate to zero. Since the kink-coupled vibration signal is not available in advance and therefore the known reference signal is not available, the latter method, like the aforementioned short-time FFT, tends to introduce a considerable time delay, which deteriorates or even renders the vibration suppression unacceptable.
The bending couple vibration frequency in a GTF engine can vary with changes in flight conditions and flight conditions, with wide frequency variations, which present challenges to vibration estimator design. When the frequency changes beyond the design frequency band, the performance of the conventional estimation method is significantly degraded due to strong nonlinearity of the system, and even oscillation divergence occurs.
The control system and method utilize existing engine control variables and sensor configurations to enable complex vibration suppression, such as active control systems and methods of bending-torsional coupled vibration in GTF engines, to meet the vibration levels of the controlled object under different flight conditions. The purposes of improving the reliability and safety design of the engine, reducing noise and prolonging the service life of the engine are achieved.
The control system and the control method utilize a rotating speed sensor measuring signal and a vibration sensor measuring signal, a simplified low-pressure rotor rotating speed model with superimposed vibration characteristics is constructed, and an Extended Kalman Filter (EKF) is utilized to estimate the natural vibration frequency and amplitude of bending-torsion coupled vibration, so that the design of the self-adaptive control system and the self-adaptive control method for vibration suppression is realized.
In some embodiments, active control of system vibration is accomplished by design of a vibration signal estimator and an adaptive controller. In order to improve the dynamic response of a closed-loop system, the vibration signal with slow-changing parameters is estimated and then directly subtracted from the low-pressure rotor rotating speed signal, namely, the superposed vibration interference is filtered, and the normal dynamic state of the system is completely reserved. The vibration estimation adopts an Extended Kalman Filter (EKF) design, and can realize the self-adaptive filtering function of the non-stationary random signal under the condition that the signal statistic is unknown. The method comprises the steps of constructing a simplified low-pressure rotor rotating speed model with superposed vibration characteristics, utilizing a rotating speed sensor measuring signal and a vibration sensor measuring signal to estimate natural vibration frequency and amplitude needing active suppression, and using the natural vibration frequency and amplitude as the input of an adaptive controller.
In some embodiments, the EKF filtering method based on the simplified low-pressure rotor speed model can realize the optimal estimation of bending-torsion coupling vibration frequency and amplitude of the GTF engine, can ensure the real-time performance of the algorithm, and solves the problems of inaccurate estimation and larger time delay caused by the fact that a reference signal cannot be obtained in the traditional method.
In some embodiments, the EKF filtering design can realize vibration spectrum estimation in a wide frequency range, is suitable for an engine working in a transition state, and solves the difficulty of accurately estimating the signal characteristics of the bending-torsional coupling vibration nonlinear and non-stationary process.
In some embodiments, the adaptive controller uses an internal model control method, and uses the estimated parameters of the EKF output, such as frequency, amplitude, etc., as the adaptive parameters. And the fuel quantity fluctuation is limited, so that the vibration can be inhibited by properly injecting energy, and the fuel circuit oscillation with larger amplitude is avoided.
In some embodiments, the protection logic monitors both the vibration amplitude estimate and the fuel quantity fluctuation amplitude, and when either amplitude exceeds a set threshold, the fuel supply will be immediately cut off, preventing further self-excited vibration of the system.
Geared Turbofan (GTF) engines are considered to be one of the major power systems of large passenger aircraft that can meet future energy saving and environmental protection requirements. Taking an UltraFan engine of rochon as an example, the UltraFan engine is planned to be in service in 2025, is a large commercial turbofan engine adopting GTF technology, and aims to achieve the purpose that the fuel consumption is improved by at least 25 percent compared with the existing engine. However, the low-pressure rotor gear shaft system of the GTF engine is a very complex bending-torsional coupling vibration system, has very complex vibration characteristics, and mechanical vibration can be further coupled with a fuel regulation system, which brings about problems of safety and reliability. Compared with passive control, the active control does not need to increase hardware, so that a series of troublesome problems such as higher system cost, performance degradation of system vibration suppression caused by hardware aging, reliability, maintenance and the like are avoided. Therefore, the active control system and the active control method are one of effective designs for the low-pressure rotor shaft system of the GTF engine, and can be used for inhibiting complex vibration of other large-scale rotating machinery rotor shaft systems.
Drawings
The above and other features, properties and advantages of the present invention will become more apparent from the following description taken in conjunction with the accompanying drawings and examples, in which:
FIG. 1 is a system diagram of an active control system for complex vibration of an aircraft engine rotor shafting in accordance with one or more embodiments.
FIG. 2 is a functional block diagram of active vibration control of the control system.
FIG. 3 is a block diagram of a vibration estimation module.
FIG. 4 is a block diagram of a low pressure rotor speed model.
Fig. 5 is a block diagram of an adaptive controller.
Fig. 6 is a block diagram of self-excited vibration protection logic.
FIG. 7 is a graph of measured signals and estimated values of low pressure rotor speed.
Fig. 8 is a graph of actual values as well as estimated values of the vibration signal.
Fig. 9 is a graph of the actual value and the estimated value of the vibration frequency.
Fig. 10 is a graph of actual values as well as estimated values of vibration amplitudes.
Detailed Description
The following discloses a variety of different implementation or examples implementing the subject technology. Specific examples of components and arrangements are described below to simplify the disclosure, but these are by way of example only and are not limiting as to the scope of the invention. For example, if a first feature is formed on or above a second feature in a description that follows, this may include embodiments in which the first and second features are formed in direct contact, and may also include embodiments in which additional features may be formed between the first and second features, such that there may be no direct contact between the first and second features. Additionally, reference numerals and/or letters may be repeated among the various examples throughout this disclosure. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. Further, when a first element is described as being coupled or coupled to a second element, the description includes embodiments in which the first and second elements are directly coupled or coupled to each other, as well as embodiments in which one or more additional intervening elements are added to indirectly couple or couple the first and second elements to each other.
Fig. 1 shows an active control system for complex vibration of an aircraft engine rotor shafting. The aircraft engine 1 may have a single-shaft or double-shaft or triple-shaft structure, and in fig. 1, the double shafts are taken as an example and are respectively a low-pressure rotor shaft and a high-pressure rotor shaft. The rotation speed sensors 2 are respectively used for measuring the rotation speeds of the low-pressure rotor shaft and the high-pressure rotor shaft, the number of the rotation speed sensors 2 is not limited to two, and the number of the rotation speed sensors can be more than two, and the rotation speed sensors can be but not limited to a magnetic-sensing type sensor, a laser type sensor, a magnetoelectric type sensor or a capacitance type sensor. The vibration sensor 3 monitors the vibration of the low-pressure rotor shaft. The rotation speed signal measured by the rotation speed sensor 2 is sent to the controller 4, and the vibration sensor 3 can be monitored by a separate computing unit and also can be sent to the controller 4 for monitoring. The controller 4 calculates the fuel instruction required by the rotation speed control and the vibration control according to the low-pressure rotor rotation speed instruction (taking the slow vehicle as an example) given by the rotation speed regulation control plan of the system and the measured values of the sensors 2 and 3, and sends the fuel instruction to the fuel system 5 after the fuel instruction is integrated. The fuel system 5 is composed of accessories such as an oil tank, a fuel pump, a fuel metering device and the like, and provides corresponding fuel quantity according to a fuel instruction to a combustion chamber to burn and then push a turbine to do work so as to adjust the rotating speed of the engine.
Fig. 2 shows a functional block diagram of the active vibration control. Wherein the controller 4 comprises a vibration estimation module 41 and a fuel control module 42, the vibration estimation module 41 estimates vibration according to sensor signals, such as signals of rotation speed sensors and signals of vibration sensors, thereby outputting estimated rotation speed signals, i.e. rotation speed filtering signals, and vibration signals, and also outputting vibration parameter estimates, including vibration frequency estimation and vibration amplitude estimation. These estimates are output to the fuel control module 42, and fuel commands are output by the fuel control module 42 to the fuel system 5 based on these estimates.
According to the traditional control method, a rotating speed sensor measuring signal directly enters a fuel control module to realize fuel control, and a vibration signal superposed on the rotating speed measuring signal also enters a fuel control loop, namely a combustion regulation loop, so that self-excited vibration can be generated after positive feedback is formed. The method firstly filters the measuring signal of the rotating speed sensor (through EKF filtering), then uses the filtered signal as a feedback signal to control, can avoid self-excited vibration to a certain extent, and is matched with the subsequent protection logic, thereby avoiding the disaster by double insurance.
As shown in fig. 3, the vibration estimation module 41 includes a rotor speed model and an EKF filter 410. The rotor speed model is a low-pressure rotor speed model 411 in which the high-pressure rotor speed is superimposed in a two-shaft aircraft engine, and this superimposition may be cancelled in a single-shaft aircraft engine. The EKF filter 410 is configured to construct a simplified low-pressure rotor rotation speed model superimposed with vibration characteristics, estimate vibration and rotation speed of a low-pressure rotor rotating shaft by using EKF, and extract vibration parameters such as vibration frequency and vibration amplitude.
The low-pressure rotor speed model shown in FIG. 4 is obtained by dividing the high-pressure rotor speed n H Constructing a simplified model of the low-pressure rotor speed of the engine as input, i.e. by n H Determining n L Then the low-pressure rotor is rotated at a speed n L Linearly superposed with the torsional rotation speed. The torsional rotation speed includes a component d caused by the torsional displacement of the low-pressure rotor T And a component due to the flexural coupling g (.). n is a radical of an alkyl radical H Variable d of g () for high-pressure rotor speed sensor signal X ，d Y Determined from the vibration sensor signal. Rotor speed n in rotor speed model for single-shaft aircraft engine L The determination may be made in other ways, such as by the amount of fuel output. Rotor speed n in the rotor speed model for a three-shaft aircraft engine L Can be determined by measurements of the medium and high pressure shafts.
Thus, in FIG. 3, the input to the low pressure rotor speed model is the high pressure rotor speed sensor signal, corresponding to n in FIG. 4 H X-axis and Y-axis vibration sensor signals, corresponding to d in FIG. 4 X ，d Y And the estimation of the torsional vibration signal output by the EKF filter, corresponding to d in FIG. 4 T . The low-pressure rotor speed signal estimate and the estimate of the bending-torsional coupled vibration are output by the operation shown in fig. 4. Residual signals estimated by calculating the low-pressure rotor rotating speed sensor signals and the low-pressure rotor rotating speed signals are input into an EKF filter for iterative calculation, and torsional vibration signal estimation and vibration parameters thereof, such as amplitude and frequency, are output. And subtracting the vibration signal estimation (the sum of the torsional vibration signal estimation and the bending-torsional coupling vibration estimation) from the low-pressure rotor rotating speed sensor signal to obtain a low-pressure rotor rotating speed filtering signal, namely filtering the superposed vibration interference amount, wherein the normal dynamic state of the system is completely reserved.
In the following, an implementation of the EKF filter is described, taking the engine as a GTF engine as an example, and based on GTF engine dynamics model analysis, it can be known that the low-pressure rotor output speed model can be approximately equivalent to a linear superposition of the engine speed and the torsional vibration speed without considering the vibration. The torsional rotation speed includes a component due to the torsional displacement of the low-pressure rotor and a component due to the bending-torsional coupling, and thus, a simplified low-pressure rotation speed model block diagram is shown in fig. 4.
The method comprises the steps of (I) analyzing main vibration modes and vibration characteristics of bending-torsion coupled vibration of the GTF engine, extracting vibration signal characteristics and modeling the vibration signal characteristics as interference, wherein the general form of the vibration signal characteristics is as shown in an equation (1).
In the formula of omega 0 For fundamental frequency, M represents M order vibration mode before interception as main vibration mode consideration, A k Is the amplitude of the resonant frequency of the k-th order,
is the phase of the k-th order resonant frequency. In the design, the interference model of the formula (1) needs to be further simplified according to the result of vibration analysis, so that the problem of insufficient observability caused by limited measurement signals of the sensor is solved.
And (II) constructing a simplified low-pressure rotor rotating speed model superposed with vibration characteristics. According to the analysis of the GTF engine dynamics model, the low-pressure rotor output rotating speed model can be approximately equivalent to the linear superposition of the engine rotating speed without considering vibration and the torsional vibration rotating speed. The torsional rotation speed includes a component due to the torsional displacement of the low pressure rotor and a component due to the bending-torsional coupling. The simplified low-pressure rotation speed model is as follows.
The state equation of the system is shown as formula (2).
The sensor measurement equation is as in equation (3 a).
y＝n L +d T +g(d X ,d Y ) + v formula (3a)
n L Is the low pressure rotor speed. n is H For high pressure rotor speed, it is assumed that the low pressure rotor mechanical vibrations have a negligible effect on the high pressure rotor. And y is the measurement output of the rotating speed sensor. d is a radical of T As a torsional vibration component, it can be expressed by formula (1); d X The signal of the X-axis vibration sensor is used for measuring the vibration component of the rotor shaft in the horizontal direction; d Y And g (.) is a coupling function of bending vibration to angular displacement in a torsion direction. w and v are the system process noise and the sensor measurement noise, respectively.
When there is no vibration sensor to measure,
y＝n L +d′ T + v formula (3b)
D 'in formula (3 b)' T The vibration mode increases for the total component of the torsional vibration in the bending-torsional coupling, but it can also be expressed by equation (1).
And (III) designing an extended Kalman filter EKF to realize optimal estimation on the frequency and amplitude of the interference (vibration) signal. And after the frequency and the amplitude of the signal to be estimated are taken as the amplification state of the system, the state update equation of the EKF is shown as the formula (4).
The nonlinear measurement updating equation of EKF is shown as the formula (5a)
y＝n L +d T +g(d X ,d Y ) Formula (5a)
The filter gains require the computation of C and D matrices. Since the superposition relationship between the rotation speed signal and the torsional vibration signal is assumed, the C matrix is easily obtained, and the matrix elements are 0 and 1. The D matrix is also obtained by Taylor expansion.
To this end, we can estimate the state and unknown parameters of the system according to the recursion equation of EKF.
When there is no vibration sensor measurement, the D-matrix becomes a 0-matrix. The measurement update equation is as in equation (5 c).
y＝n L +d′ T Formula (5c)
(IV) construction of the simulation model equations (1) - (5) are appropriately simplified as follows.
n L And n H The input of fuel oil quantity is generally a second-order link, n L -n H The dynamic compensation of (a) can be generally approximated in the form of a lead/lag element, and for simplicity, it is assumed that the relationship between the two can be expressed as a first-order inertia element, as in equation (6).
The state equation is as in equation (7). Tau is the time constant of the first order inertia element.
Discretizing the formula (7) to obtain a formula (7b), T s Is the sampling time of a discrete system. For convenience of representation, n is uniformly expressed in the following formula L Is denoted by n, n H And is denoted as u.
Assuming that the vibration signal is a sinusoidal signal with unknown frequency and amplitude, the interference model of formula (1) is further simplified to obtain formula (8).
d(t)＝A 0 sin (ω t) equation (8)
The frequency of the signal to be estimated is taken as the state of amplification of the system, which can be described by a random walk process since it is slowly varying.
Selecting the EKF state vector as:
wherein
Is an estimate of the sinusoidal vibration signal and,
is its quadrature signal.
Assuming that the input of EKF has no vibration sensor measurement signal, the non-linear measurement update equation of EKF can be simplified from equation (5c) to equation (10)
y (k +1) ═ n (k +1) + d (k +1) formula (10)
The measurement matrix C is as in equation (11) and the D matrix is a 0 matrix.
C ═ 1010 formula (11)
Up to this point, the state and unknown parameters of the system can be mapped according to the recursion equation of EKF, i.e.
And (6) estimating. After obtaining the estimation of the vibration in-phase and orthogonal signals, the amplitude A 0 (k) The estimated value of (c) can be expressed as formula (12).
And (V) according to the formula derivation in the formula (IV), establishing an EKF and simulating, wherein the obtained simulation results are shown in fig. 7 to fig. 10.
As shown in fig. 5, the rotational speed regulation control plan gives a low-pressure spool rotational speed setting signal according to the operating point of the engine. The unit 422 receives the low-pressure rotor speed setting signal and the low-pressure rotor speed filtering signal, calculates a control error, and sends the result to the adaptive controller 424. In one embodiment of the adaptive controller 424, which uses the internal model principle to add the unstable pole of the sinusoidal signal to the open loop transfer function, the zero pole configuration can be used to control the unit design and stabilize the system. However, the design brings the problem of fuel control quantity pulsation, and when the fluctuation amplitude is within a certain range, the fluctuation amplitude can not be considered, because the system really needs a certain energy input to suppress vibration; when the amplitude of the fluctuation exceeds the threshold value, it is indicated that positive feedback may be developing in the fuel regulation circuit, and therefore the fuel supply should be cut off immediately.
As shown in fig. 6, the fuel control module 42 further includes an adaptive band-pass filter 43, and the adaptive band-pass filter 43 performs band-pass filtering according to the estimated vibration frequency range to obtain a fluctuation signal of the fuel control amount. And calculating the peak-to-peak value of the vibration according to the vibration frequency estimated value output by the EKF filter 410, and immediately cutting off the fuel if the peak-to-peak value of the fuel fluctuation signal exceeds a set threshold value. The self-excited vibration protection logic simultaneously monitors the vibration amplitude estimator and also immediately shuts off fuel when its peak-to-peak value exceeds a set threshold. Detailed description of the preferred embodimentsfigure 6 is a functional block diagram of the self-excited vibration protection logic of the fuel regulation circuit. The adaptive band-pass filter 43 outputs fuel fluctuation according to the fuel quantity output by the adaptive controller 424 and the estimation of the vibration frequency output by the EKF filter 410, and the peak-peak value calculation module 435 calculates the peak-peak value of vibration according to the estimation value of the vibration frequency output by the EKF filter 410 and outputs the amplitude of the fluctuation. The magnitude of the fuel fluctuation is compared with a fuel fluctuation threshold at the comparing unit 431 and a first logic signal is output, for example, the first logic signal is 0, which indicates that the fuel fluctuation exceeds the fluctuation threshold, and the first logic signal is 1, which indicates that the fuel fluctuation does not exceed the fluctuation threshold. The estimate of the vibration amplitude output by the EKF filter 410 is also compared with a threshold value of the vibration amplitude at the comparing unit 432 to output a second logic signal, for example, the second logic signal is 0, which indicates that the vibration amplitude exceeds the vibration amplitude threshold value, and the second logic signal is 1, which indicates that the vibration amplitude does not exceed the vibration amplitude threshold value. The controller 4 performs an and operation on the first logic signal and the second logic signal at the and gate 433 to output a third logic signal. Finally, the fuel quantity is multiplied by the third logic signal at the multiplication unit 434, and then a control fuel output quantity is output.
The comparison means and the operation means may be implemented by a combination of a program language and a processor, or may be implemented by circuit calculation.
FIGS. 7-10 are simulation results of vibration estimation. The low-pressure rotor speed measurement signal, as shown by the dotted line in fig. 7, has superimposed thereon a sine wave vibration simulation signal of 10Hz and 60rpm in amplitude and measurement noise. Through EKF estimation output, an optimal estimation result of the vibration signal and parameters (frequency and amplitude) thereof under noise is given, and a solid line shows the estimation result of the vibration signal, so that the vibration signal can be separated from a measurement signal, and a low-pressure rotor rotating speed signal is reconstructed. To make the simulation more general, the output of the vibration sensor is not used in the simulation, considering that not all engine controllers will collect vibration signals.
Fig. 8 is a simulation result of vibration signal estimation. The solid line is the actual value of the torsional vibration, which is a sine wave vibration simulation signal at a frequency of 10Hz and an amplitude of 60 rpm. The dashed line is the vibration signal estimate of the EKF, which is seen to substantially reproduce the actual value of the vibration.
Fig. 9 is a simulation result of vibration frequency estimation. The solid line is the actual frequency value of a white noise signal with a certain energy superimposed thereon, and the average value is 2 π × 10 rad/s. The dotted line is an estimated value, and it can be seen in the figure that after a short initial estimation error, the value rapidly converges to a mean value of 2 π × 10rad/s, which achieves a relatively accurate estimation of the vibration frequency.
Fig. 10 is a simulation result of vibration amplitude estimation. The solid line is the actual value of the vibration amplitude, which is 60 rpm. The dashed line is the estimated value, and it can be seen that after the filter converges, the estimation error is substantially less than 2rpm, and in the design of the vibration threshold of fig. 6, the influence of the estimation error on the design threshold should be considered, that is, a proper margin is reserved.
Although the present invention has been disclosed in terms of the preferred embodiment, it is not intended to limit the invention, and variations and modifications may be made by one skilled in the art without departing from the spirit and scope of the invention. Therefore, any modifications, equivalent changes and modifications made to the above embodiments according to the technical essence of the present invention are within the scope of the present invention defined by the claims.
Claims (8)
1. Active control system of aeroengine rotor shafting complex vibration, its characterized in that includes:
a rotor speed sensor for measuring the rotor speed;
the vibration sensor is used for measuring the vibration of the rotor shaft in the horizontal direction and the vertical direction;
a controller, comprising:
the vibration estimation module comprises a rotor rotating speed model and an extended Kalman filter, receives a rotor rotating speed sensor signal and a vibration sensor signal, carries out vibration signal estimation on the rotor, and outputs a rotor rotating speed filtering signal and a vibration signal estimation parameter according to the vibration signal estimation and the rotor rotating speed sensor signal;
the fuel control module is used for controlling the fuel output quantity as a self-adaptive parameter according to the rotor rotating speed filtering signal and the vibration signal estimation parameter;
the input of the rotor speed model is a rotor speed sensor signal, a vibration sensor signal and a torsional vibration signal estimation output by an extended Kalman filter,
the rotor speed model is characterized in that a simplified model of the low-pressure rotor speed of the engine is constructed, then the rotor speed and the torsional vibration speed are linearly superposed, the torsional vibration speed comprises a component caused by the torsional vibration angular displacement of the rotor estimated and determined according to the torsional vibration signal and a component caused by the bending-torsion coupling determined according to the vibration sensor signal,
the rotor speed model outputs rotor speed signal estimation and bending coupling vibration estimation;
the vibration estimation module inputs the residual signal estimated by the rotor speed sensor signal and the rotor speed signal into an extended Kalman filter for iterative calculation, and the extended Kalman filter outputs a torsional vibration signal estimation and a vibration parameter thereof;
the vibration estimation module is also used for removing the sum of the torsional vibration signal and the bending-torsional coupling vibration estimation from the rotor speed sensor signal to obtain a rotor speed filtering signal;
the fuel control module, it includes:
and the self-adaptive controller is used for controlling the fuel output amount of the fuel according to the rotor speed filtering signal and the vibration signal estimation parameter as self-adaptive parameters.
2. The active control system of claim 1, wherein the rotor speed sensor comprises:
the high-pressure rotor rotating speed sensor is used for measuring the rotating speed of the high-pressure rotor; and
the low-pressure rotor rotating speed sensor is used for measuring the rotating speed of the low-pressure rotor;
the vibration sensor is used for measuring the vibration of the low-pressure rotor shaft in the horizontal direction and the vertical direction;
the vibration estimation module comprises a rotor speed model which is a low-pressure rotor speed model, wherein
The input of the low-pressure rotor rotating speed model is a high-pressure rotor rotating speed sensor signal, a vibration sensor signal and a torsional vibration signal estimation output by an extended Kalman filter,
the low-pressure rotor speed model constructs a simplified model of the low-pressure rotor speed of the engine by taking a high-pressure rotor speed sensor signal as an input, and then linearly superposes the low-pressure rotor speed and the torsional vibration speed, wherein the torsional vibration speed comprises a component caused by the torsional vibration angular displacement of the low-pressure rotor estimated and determined by the torsional vibration signal and a component caused by bending-torsion coupling determined by the vibration sensor signal,
the low-pressure rotor rotating speed model outputs low-pressure rotor rotating speed signal estimation and bending-torsion coupling vibration estimation;
the vibration estimation module inputs the residual error signals estimated by the low-pressure rotor rotating speed sensor signals and the low-pressure rotor rotating speed signals into an extended Kalman filter for iterative calculation, and the extended Kalman filter outputs torsional vibration signal estimation and vibration parameters thereof;
the vibration estimation module is also used for removing the sum of the torsional vibration signal and the bending coupling vibration estimation from the low-pressure rotor rotating speed sensor signal to obtain a low-pressure rotor rotating speed filtering signal;
the fuel control module, it includes:
and the self-adaptive controller is used for controlling the fuel output amount of the fuel according to the low-pressure rotor rotating speed filtering signal and the vibration signal estimation parameter as self-adaptive parameters.
3. The active control system of claim 1 or 2, wherein said adaptive controller uses said vibration signal estimation parameters including frequency or/and amplitude as adaptive parameters and limits fuel quantity fluctuations, whereby vibrations are suppressed by injection of energy and large amplitude fuel circuit oscillations are avoided.
4. The active control system of claim 1 or 2, wherein the fuel control module further comprises a self-excited vibration protection logic system comprising an adaptive band pass filter, a fuel fluctuation peak-to-peak calculation module;
the self-adaptive band-pass filter selects the vibration frequency estimation output by the extended Kalman filter as the center frequency of the band-pass filter, and accordingly, the filter coefficient is selected in a self-adaptive mode to realize filtering output of the fuel quantity signal with the slowly-variable frequency quantity;
the fuel fluctuation peak-peak value calculation module calculates the peak-peak value of fuel fluctuation by using the vibration frequency estimation parameter and taking an average value in one vibration period or a plurality of vibration periods, and outputs the fuel fluctuation amplitude;
and the self-excited vibration protection logic system also executes protection logic, and immediately cuts off the fuel if judging that the fuel fluctuation amplitude exceeds a set fluctuation threshold value or if judging that the vibration amplitude value estimation exceeds a set vibration amplitude threshold value, otherwise, normally outputs the fuel quantity.
5. The active control system of claim 4, wherein the self-excited vibration protection logic system is configured to perform the steps of:
comparing the fuel fluctuation amplitude with a fuel fluctuation threshold value to output a first logic signal;
comparing the estimation of the vibration amplitude with a threshold value of the vibration amplitude to output a second logic signal;
performing an and logic operation on the first logic signal and the second logic signal to output a third logic signal;
and outputting the result of multiplying the fuel quantity by the third logic signal as the fuel quantity.
6. The active control system of claim 4, wherein the aircraft engine is a GTF engine.
7. Active control method of complex vibrations of an aircraft engine rotor shaft, characterized in that an active control system according to any of claims 1 to 6 is used, the control method comprising the following steps:
acquiring a rotor rotating speed signal;
acquiring vibration signals of a rotor shaft in the horizontal direction and the vertical direction;
according to the rotor rotating speed signal and the vibration signal, a rotor rotating speed model and an extended Kalman filter are utilized to carry out vibration signal estimation on the rotor, and a rotor rotating speed filtering signal and a vibration signal estimation parameter are output according to the vibration signal estimation and the rotor rotating speed signal;
taking the rotor speed filtering signal and the vibration signal estimation parameter as a self-adaptive parameter to control the fuel output quantity;
the input of the rotor rotating speed model is a rotor rotating speed signal, a vibration signal and a torsional vibration signal estimation output by an extended Kalman filter,
the rotor speed model is obtained by constructing a simplified model of the engine rotor speed and then linearly superposing the rotor speed and the torsional vibration speed, wherein the torsional vibration speed comprises a component caused by the torsional vibration angular displacement of the rotor determined by the estimation of the torsional vibration signal and a component caused by the bending-torsional coupling determined by the vibration signal,
the rotor speed model outputs rotor speed signal estimation and bending-torsion coupling vibration estimation;
inputting a residual signal estimated by calculating a rotor rotating speed signal and the rotor rotating speed signal into an extended Kalman filter for iterative computation, wherein the extended Kalman filter outputs a torsional vibration signal estimation and vibration parameters thereof;
and removing the sum of the torsional vibration signal and the bending coupling vibration estimation from the rotor rotating speed sensor signal to obtain a rotor rotating speed filtering signal.
8. The active control method of claim 7,
determining the fuel quantity of fuel output according to the rotor rotating speed filtering signal and the vibration signal estimation parameter as a self-adaptive parameter;
selecting the vibration frequency estimation output by the extended Kalman filter as the center frequency of the band-pass filter by using a self-adaptive band-pass filter, and accordingly, adaptively selecting a filter coefficient to realize filtering output of the fuel quantity signal with slowly-variable frequency quantity;
calculating the peak-peak value of fuel fluctuation by using the vibration frequency estimation parameters and a method of taking an average value in one vibration period or a plurality of vibration periods, and outputting the fuel fluctuation amplitude;
and if the fuel oil fluctuation amplitude is judged to exceed the set fluctuation threshold value or if the vibration amplitude value is judged to exceed the set vibration amplitude threshold value, immediately cutting off the fuel oil, otherwise, normally outputting the fuel oil quantity.
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