WO2024050883A1 - 一种针对航空发动机传感器故障诊断的容错软硬混杂控制方法 - Google Patents

一种针对航空发动机传感器故障诊断的容错软硬混杂控制方法 Download PDF

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WO2024050883A1
WO2024050883A1 PCT/CN2022/121216 CN2022121216W WO2024050883A1 WO 2024050883 A1 WO2024050883 A1 WO 2024050883A1 CN 2022121216 W CN2022121216 W CN 2022121216W WO 2024050883 A1 WO2024050883 A1 WO 2024050883A1
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sensor
fault
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hard
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孙希明
孙涛
杨航
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Dalian University of Technology
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D18/00—Testing or calibrating apparatus or arrangements provided for in groups G01D1/00 - G01D15/00
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/24—Pc safety
    • G05B2219/24065—Real time diagnostics
    • 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
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Definitions

  • the invention proposes a fault-tolerant soft and hard hybrid control method for aero-engine sensors, which belongs to the field of aero-engine fault diagnosis.
  • Aeroengine sensors mainly measure and reflect various parameters of its working status, such as rotor speed, temperature and pressure of some working sections of the air path, etc. The accuracy of sensor measurement parameters directly affects the accuracy of control system operation and fault diagnosis results.
  • sensors work in high temperature and strong vibration environments, and are relatively low-reliability components in the system, making them prone to failure.
  • Sensor faults can be classified according to the degree of the fault into hard faults (generally referring to faults caused by structural damage, generally with large amplitudes and sudden changes) and soft faults (generally referring to variations in characteristics, generally with small amplitudes and sudden changes). slow).
  • Hard faults are generally caused by damage to sensor components, short circuits, open circuits, or interference from strong pulses in the electrical system.
  • Soft faults are generally caused by component aging, zero point drift and other reasons.
  • sensor failure affects the measurement output of the control system, it will also affect the work of the controller and the implementation of the control algorithm through erroneous feedback, ultimately having a negative impact on the stability and other performance of the entire system. Therefore, when a sensor fails, the control system needs to isolate the faulty sensor in time and reconstruct the sensor signal when the engine is working normally, so that the control system can maintain a fault-free working state and gain valuable time to eliminate engine faults. If the true state of the system can be correctly estimated when a fault occurs, the correct feedback control signal can be obtained to protect the system from the impact of the fault.
  • Sensor fault-tolerant control opens up a path to improve system reliability, maintainability and effectiveness, and has become a research focus in the aerospace field.
  • the fundamental characteristic of fault-tolerant control is that when the control system fails, the system can still maintain its operation in a safe state and meet certain performance index requirements as much as possible. It can enable a dynamic system to adapt to significant changes in its environment and avoid the impact on the stability and other performance of the entire system due to the failure of one or more critical components of the system. Therefore, it is necessary to study the fault-tolerant control method of aero-engine sensors.
  • the design methods of fault-tolerant control systems include "hardware redundancy” and "software redundancy”.
  • Hardware redundancy improves the fault tolerance of the system by providing backup for important components or components prone to failure.
  • Software redundancy improves the fault tolerance of the system by designing a controller to increase the redundancy of the entire system.
  • sensors in the aero-engine control system There are many sensors in the aero-engine control system. Therefore, using hardware redundancy to improve the reliability of components and high-reliability design methods are more costly, increase the complexity of the system, and affect system performance.
  • Fault diagnosis and fault-tolerant control technology using redundant signals generated by analytical mathematical models can greatly reduce costs.
  • a Kalman filter bank can be used to obtain the fault indication signal of the monitored system, and the purpose of fault diagnosis can be achieved by analyzing the trend of changes in each fault indication signal.
  • the present invention proposes a fault-tolerant soft and hard hybrid control method for aero-engine sensors.
  • This method uses a Kalman filter bank in a soft/hard fault diagnosis system to realize the detection of soft/hard faults of aero-engine sensors and the detection of system faults. An estimate of the true state.
  • the sensor is fault-free, the sensor measurement value is directly fed back to the fault-tolerant hybrid controller; when the sensor is faulty, the estimated output of the Kalman filter is fed back to realize signal reconstruction, so that the signal fed back to the fault-tolerant hybrid controller will not be distorted. If there is too much fluctuation, the engine is still in a stable operating state, thereby achieving fault-tolerant control of soft/hard sensor faults.
  • This invention was funded by the China Postdoctoral Science Foundation Project (2022TQ0179), the National Natural Science Foundation of China 61890920 and 61890921, and the National Key Research and Development Program Project 2018YFB1700102.
  • the present invention provides a fault-tolerant soft and hard hybrid control method for aircraft engine sensors.
  • a fault-tolerant soft and hard hybrid control method for aero-engine sensors is based on fault diagnosis based on the Kalman filter and realizes the detection of soft/hard faults of aero-engine sensors. When a sensor fault occurs, it can detect the true state of the system. The state is estimated and the feedback signal is reconstructed to achieve fault-tolerant hybrid control of soft/hard sensor faults. The steps are as follows:
  • S1 establishes a Kalman filter based on the aeroengine model.
  • S1.1 first uses the fitting method to establish a small deviation linearization model based on the aerodynamic and thermal nonlinear model at the aeroengine component level, and calculates each system matrix of its state space model based on the steady-state operating point.
  • the input quantity u of the small deviation linearization model is the fuel flow W fm ;
  • the state variable x is the high-pressure rotor speed XNLPC and the low-pressure rotor speed XNHPC;
  • the output quantity y is the high-pressure and low-pressure rotor speed, the high-pressure compressor outlet static pressure P and low
  • the total turbine inlet temperature T; and the corresponding parameter value at the steady-state operating point is used as the base value, that is, (x S , u S , y S ), where x S represents the state variable at the steady-state operating point, and u S represents The input quantity and y S at the steady-state operating point represent the output quantity at the steady-state operating point.
  • ⁇ represents the first-order derivative
  • x x S + ⁇ x
  • u u S + ⁇ u
  • y y S + ⁇ y
  • system matrices A, B, C, and D are calculated by program fitting.
  • w and v are system noise and measurement noise respectively, and it is assumed that w and v are Gaussian white noise with zero mean, and their covariance matrices are Q and R respectively.
  • a Kalman filter can be established as follows:
  • K represents the Kalman gain matrix
  • P is the solution to the Riccati Equation shown in formula (4):
  • S2 inputs the output signal of the aircraft engine into the sensor hard fault diagnosis system or soft fault diagnosis system according to the amplitude of the fault signal and the soft/hard switching rules.
  • S3 designs an aircraft engine sensor hard fault diagnosis system.
  • step S1 Use the Kalman filter established in step S1 to build a sensor hard fault diagnosis system, input the output values of the aeroengine sensor measurement parameters into the Kalman filter, and then add the estimated values of the measurement parameters after the Kalman filter to the sensor The difference between the output values of the measured parameters is the residual of the corresponding Kalman filter.
  • the absolute value of the Kalman filter residual corresponding to one or more sensor measurement values exceeds its own threshold (determined by the characteristics of the corresponding component of the sensor measurement parameter), it can be judged that the sensor has failed and the sensor can be repaired. Isolate and cut off the faulty sensor, and replace the measured value of the faulty sensor with the estimated value of the Kalman filter and feed it back to the controller to achieve signal reconstruction.
  • the absolute value of the Kalman filter residual value corresponding to one or more sensor measurement values is less than the threshold, it can be determined that the sensor is fault-free and the sensor output is fed back to the controller.
  • S4 designs an aircraft engine sensor soft fault diagnosis system.
  • n output parameters are shared in the fault diagnosis model, corresponding to n sensors respectively, and n+1 Kalman filters are used; where: the input y 0 of Kalman filter 0 is measured using all n sensors. value represents the normal state; the input yi of Kalman filter i uses all n-1 sensor measurement values except the corresponding own sensor. The input value yi of the Kalman filter and the estimated output value after filtering The difference is the residual.
  • SR i WSSR 0 -WSSR i .
  • S5 builds an aircraft engine sensor fault-tolerant soft and hard hybrid control system.
  • the output signal of the aeroengine is input into the sensor hard fault diagnosis system or soft fault diagnosis system.
  • the soft/hard fault diagnosis system when the sensor has no fault, the signal is directly fed back to the input terminal of the control system; when the sensor has a fault, the fault detection and diagnosis system diagnoses the fault, alarms in time, and reconstructs the feedback signal to make the engine It is still in a normal and stable working state; instead of when the engine is working normally, the feedback signal is distorted due to sensor failure, causing serious consequences to the entire system.
  • the fault-tolerant hybrid control system established for soft/hard faults of aero-engine sensors has fault-tolerant capabilities and can alleviate the negative impact of sensor faults on the overall performance of the system to a large extent.
  • the present invention designs a fault-tolerant hybrid control system based on Kalman filter for the problem of fault diagnosis of aero-engine sensors.
  • the designed soft fault diagnosis system performs residual processing on sensor measurement values and a set of Kalman filter estimated values and sums their weighted squares to compare with known thresholds to further detect whether there is a fault; the designed hard fault diagnosis system
  • the fault diagnosis system further detects the presence of a fault by comparing the absolute value of the residual of the sensor measurements and a Kalman filter estimate with a known threshold.
  • This aero-engine sensor fault-tolerant soft and hard hybrid control system realizes effective detection of soft/hard faults of aero-engine sensors, estimates the true state of the system when a sensor failure occurs, and reconstructs the sensor signal when the engine is operating normally, thereby Realizing fault-tolerant hybrid control of soft/hard sensor faults can effectively remove the negative impact of aero-engine sensor faults on the overall system performance, thus ensuring the stable operation of the aero-engine control system in the presence of sensor faults.
  • this method has a significantly lower cost; at the same time, this method can not only realize sensor fault-tolerant control, but also locate which sensor or sensors are faulty, and can also estimate the fault. size and severity.
  • Figure 1 is a schematic structural diagram of an aircraft engine sensor fault-tolerant soft and hard hybrid control system
  • Figure 2 is the W fm signal simulation diagram of the aero-engine sensor hard/soft fault system without fault-tolerant control;
  • Figure 2(a) is the W fm signal simulation diagram of the aero-engine sensor hard fault system without fault-tolerant control.
  • Figure 2(b) is the aviation engine sensor hard fault system without fault-tolerant control W fm signal simulation diagram.
  • Figure 3 is the W fm signal simulation diagram of the aero-engine sensor hard/soft fault system with fault-tolerant control
  • Figure 3(a) is the W fm signal simulation diagram of the aero-engine sensor hard fault system with fault-tolerant control
  • Figure 3(b) W fm signal simulation diagram with fault-tolerant control for aero-engine sensor soft fault system.
  • Figure 1 is a schematic structural diagram of an aircraft engine sensor fault-tolerant soft and hard hybrid control system.
  • the dotted line box on the left in Figure 1 is the aero-engine sensor hard fault diagnosis system, and the dotted line box on the right in Figure 1 is the aero-engine sensor soft fault diagnosis system.
  • the aircraft engine sensor fault-tolerant soft and hard hybrid control system inputs the output signal of the aircraft engine into the sensor hard fault diagnosis system or soft fault diagnosis system according to the amplitude of the fault signal and the soft/hard switching rules; after The soft/hard fault diagnosis system determines that when the sensor has no fault, the signal is directly fed back to the input terminal of the control system; when the sensor is faulty, the fault detection and diagnosis system diagnoses the fault, alarms in time, and reconstructs the feedback signal so that the engine still It is in a normal and stable working state; rather than when the engine is working normally, the feedback signal is distorted due to sensor failure, causing serious consequences to the entire system.
  • the difference between the output value y of the sensor measurement parameter and the estimated value of the measurement parameter after passing through the Kalman filter is the residual r.
  • the absolute value of the Kalman filter residual r corresponding to one or more sensor measurement values exceeds its own threshold, it can be judged that the sensor has failed, and it can be isolated and the faulty sensor can be cut off.
  • the estimated value of the Mann filter replaces the measured value of the faulty sensor and is fed back to the controller to realize signal reconstruction.
  • the absolute value of the Kalman filter residual value r is less than the threshold, it can be judged that the sensor is faultless and the sensor output is fed back to the controller.
  • filter 0 uses all sensor measurement values, representing the normal state.
  • the input yi of other filters is the output of all other sensors except the output of the corresponding own sensor.
  • the reason for this is that assuming a sensor fails, since only the Kalman filter corresponding to the sensor does not use the measurement value of the faulty sensor, only the estimation result it obtains is correct, and the rest of the Kalman filter Since the Kalman filter all uses the measured values of the faulty sensor, the estimation results of the remaining Kalman filters will deviate from the accurate value to varying degrees, so that the existence of the fault can be further determined.
  • the input value yi of the Kalman filter and the estimated output value after filtering The difference is the residual r. For Kalman filter i, there is:
  • the residual sequence r i can be obtained.
  • the residual vector r i obeys the multidimensional normal distribution. Therefore the sensor fault indicator variable can be constructed:
  • WSSR i Weight Sum of Squared Residuals
  • WSSR i Weighted sum of squared residuals
  • SR i WSSR 0 -WSSR i .
  • Implementation Case 1 and Implementation Case 2 of the present invention respectively add hard faults and soft faults of the low-pressure rotor speed
  • the fault-tolerant effect of the aero-engine sensor fault-tolerant soft and hard hybrid control system is verified.
  • Figure 2 is the W fm signal simulation diagram of the aero-engine sensor hard/soft fault system without fault-tolerant control;
  • Figure 2(a) is the W fm signal simulation diagram of the aero-engine sensor hard fault system without fault-tolerant control.
  • Figure 2(b) is the aviation engine sensor hard fault system without fault-tolerant control W fm signal simulation diagram.
  • Figure 3 is the W fm signal simulation diagram of the aero-engine sensor hard/soft fault system with fault-tolerant control;
  • Figure 3(a) is the W fm signal simulation diagram of the aero-engine sensor hard fault system with fault-tolerant control,
  • Figure 3(b) W fm signal simulation diagram with fault-tolerant control for aero-engine sensor soft fault system.
  • the sensor hard fault diagnosis system diagnoses the fault in time, feeds back the estimated output of the Kalman filter, and reconstructs the feedback signal so that the engine is still in a normal and stable operating state, eliminating fluctuations in the system's dynamic process and dynamic response time It is obviously significantly reduced and has a good fault-tolerant control effect.
  • a soft fault of the low-voltage rotor speed XNLPC sensor is added to simulate a fault that drifts over time to simulate a soft fault of the sensor.
  • the fault-tolerant soft and hard hybrid control method proposed by the present invention for aero-engine sensors is feasible.
  • This method is based on fault diagnosis based on the Kalman filter to realize the detection of soft/hard faults of aero-engine sensors and
  • the true state of the system is estimated to achieve fault-tolerant hybrid control of soft/hard sensor failures, which can effectively remove the negative impact of aero-engine sensor failure on the overall performance of the system, making the system more stable and improving various performance Indicators can better meet the requirements.

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Abstract

本发明属于航空发动机故障诊断领域，提出一种针对航空发动机传感器故障诊断的容错软硬混杂控制方法。该方法通过软/硬故障诊断系统中的卡尔曼滤波器组，实现对航空发动机传感器软/硬故障的检测和对系统真实状态的估计。其中所设计的软故障诊断系统通过对传感器测量值和一组卡尔曼滤波器估计值进行残差处理并对其加权平方求和，以比较已知阈值来进一步检测是否存在故障；所设计的硬故障诊断系统通过将传感器测量值和卡尔曼滤波器估计值的残差绝对值与已知阈值进行比较，以进一步检测是否存在故障。经验证，该方法能够有效去除航空发动机传感器故障对系统总体性能产生的负面影响，使得系统更加稳定，各项性能指标更能满足要求。

Description

一种针对航空发动机传感器故障诊断的容错软硬混杂控制方法 技术领域
本发明提出了一种针对航空发动机传感器的容错软硬混杂控制方法，属于航空发动机故障诊断领域。
背景技术
当下，高性能航空发动机正朝着高推重比、高速度、高可靠性等方面发展，航空发动机的控制系统实现的功能越来越复杂，而对控制系统可靠性的要求也越来越高。系统的故障会直接影响整个系统的可靠性，而故障诊断和容错控制系统，就是针对提高系统可靠性、可维护性和有效性提出的。航空发动机传感器主要测量与反映其工作状态的各个参数，如转子转速、部分气路工作截面的温度、压力等。传感器测量参数的精确程度直接影响控制系统工作与故障诊断结果的精度。随着航空发动机技术的发展，对航空发动机的控制系统中传感器的稳定性也提出了更高的要求。但传感器工作于高温及强振环境中，属于系统中可靠性比较低的元件，比较容易发生故障。传感器故障的分类，按照故障程度的大小可分为硬故障(泛指结构损坏导致的故障，一般幅值较大，变化突然)和软故障(泛指特性的变异，一般幅值较小，变化缓慢)。硬故障一般由于传感器元件损坏、电系统发生短路、断路或受较强脉冲干扰等原因引起。软故障一般是由部件老化、零点漂移等原因引起。典型的航空发动机传感器故障主要有三种：固有偏差故障、漂移故障、脉冲干扰故障。
由于传感器的故障影响控制系统的测量输出，进而也会通过错误的反馈影响控制器的工作和控制算法的实现，最终对整个系统的稳定性及其它性能带来负面影响。因此，当传感器发生故障时，控制系统需要及时隔离故障传感器，并重构发动机正常工作时的传感器信号，使得控制系统维持在无故障的工作状态，为排除发动机故障赢得宝贵的时间。如果能够在出现故障时正确地估计出系统的真实状态，就可以得到正确的反馈控制信号，使系统免受故障的影响。而传感器容错控制为提高系统的可靠性、可维护性和有效性开辟了一条路径，成为航空航天领域的一个研究重点。容错控制的根本特征是当控制系统发生故障时，系统依然能够维持其自身运行在安全状态，并尽可能的满足一定的性能指标要求。它可以使一个动态系统适应其环境的显著变化，避免因系统的一个或多个较关键部件的失效而对整个系统的稳定性及其它性能带来影响。因此，对航空发动机传感器容错控制方法的研究很有必要。
容错控制系统的设计方法有“硬件冗余”和“软件冗余”。硬件冗余是通过对重要部件或易发生故障部件提供备份，以提高系统的容错能力。软件冗余是通过设计控制器来提高整系统的冗余度，从而改善系统的容错性能。航空发动机控制系统中有许多传感器，因此采用硬 件冗余提高元器件的可靠性及高可靠性设计的方法成本较高，系统的复杂程度增加，且影响系统性能。而利用由解析数学模型产生的冗余信号的故障诊断及容错控制技术可以大大降低成本。在传感器故障中可以利用一个卡尔曼滤波器组，得到所监视系统的故障指示信号，通过对各个故障指示信号变化的趋势分析来实现故障诊断的目的。
本发明中提出了一种针对航空发动机传感器的容错软硬混杂控制方法，该方法通过软/硬故障诊断系统中的卡尔曼滤波器组，实现对航空发动机传感器软/硬故障的检测和对系统真实状态的估计。当传感器无故障时，传感器测量值直接反馈到容错混杂控制器；当传感器存在故障时，反馈卡尔曼滤波器的估计输出，实现信号的重构，使反馈到容错混杂控制器的信号不会有太大波动，发动机仍然处于稳定工作状态，从而实现对传感器软/硬故障的容错控制。本发明由中国博士后科学基金项目(2022TQ0179)，国家中国自然科学基金61890920、61890921，和国家重点研发计划项目2018YFB1700102资助。
发明内容
为了去除航空发动机传感器故障对系统总体性能产生的负面影响，使得系统更加稳定，各项性能指标更能满足要求，本发明提供了一种针对航空发动机传感器的容错软硬混杂控制方法。
本发明的技术方案为：
一种针对航空发动机传感器的容错软硬混杂控制方法，该方法以基于卡尔曼滤波器的故障诊断为基础，实现对航空发动机传感器软/硬故障的检测，并在发生传感器故障时对系统的真实状态加以估计，重构反馈信号，从而实现对传感器软/硬故障的容错混杂控制，步骤如下：
S1根据航空发动机模型建立卡尔曼滤波器。
S1.1首先基于航空发动机部件级气动热力非线性模型，利用拟合法建立小偏离线性化模型，针对稳态工作点计算出其状态空间模型的各个系统矩阵。
所述小偏离线性化模型的输入量u为燃油流量W fm；状态变量x为高压转子转速XNLPC、低压转子转速XNHPC；输出量y为高、低压转子转速，高压压气机出口静压P和低压涡轮进口总温T；并以稳态工作点上的相应参数值为基准值，即(x S，u S，y S)，其中，x S表示稳态工作点上的状态变量、u S表示稳态工作点上的输入量、y S表示稳态工作点上的输出量。
令Δx、Δu分别为状态变量和输入量的偏离量，Δy为输出量的偏离量，在稳态工作点(x S，u S，y S)处可得小偏离线性化模型的状态空间方程如下：
Figure PCTCN2022121216-appb-000001
其中，·表示一阶导数，x＝x S+Δx,u＝u S+Δu,y＝y S+Δy，然后由程序拟合计算出系统矩 阵A，B，C，D。
S1.2建立卡尔曼滤波器：在稳态工作点(x S，u S，y S)处建立的线性模型的基础上，对于输入不包含健康参数偏离量的发动机线性模型，形如下式：
Figure PCTCN2022121216-appb-000002
式中，w和v分别为系统噪声和测量噪声，并假设w和v为均值为零的高斯白噪声，其协方差矩阵分别为Q和R。接下来可以建立如下式的卡尔曼滤波器：
Figure PCTCN2022121216-appb-000003
式中，
Figure PCTCN2022121216-appb-000004
代表对变量的估计，K表示卡尔曼增益矩阵；P为公式(4)所示的黎卡提方程(Riccati Equation)的解:
AP+PA T-PC TR -1CP+Q＝0.(4)
S2根据故障信号的幅值大小和软/硬切换规则，将航空发动机的输出信号输入到传感器硬故障诊断系统或软故障诊断系统中。
S3设计航空发动机传感器硬故障诊断系统。
采用步骤S1中已建立的卡尔曼滤波器搭建传感器硬故障诊断系统，将航空发动机传感器测量参数的输出值输入到卡尔曼滤波器中，再将经过卡尔曼滤波器之后测量参数的估计值和传感器测量参数的输出值求差，二者之差即为对应卡尔曼滤波器的残差。
当某个或多个传感器测量值对应的卡尔曼滤波器残差的绝对值超出其自身的阈值(由传感器测量参数对应部件特性而定)时,就可以判断传感器发生了故障,并对其进行隔离，切断发生故障的传感器，由卡尔曼滤波器的估计值取代故障传感器的测量值反馈到控制器，实现信号的重构。
当某个或多个传感器测量值对应的卡尔曼滤波器残差值的绝对值小于阈值时，则可以判断传感器无故障，将传感器的输出反馈到控制器。
S4设计航空发动机传感器软故障诊断系统。
发生传感器软故障时，传感器测量参数的输出值偏离正常值的幅值较小，极容易被干扰噪声淹没。因而不能通过简单地比较残差是否超过阈值来判断，需要采用一组卡尔曼滤波器，并对残差序列进行加权平方和的处理。具体如下：
S4.1假设故障诊断模型中共用到n个输出参数，分别对应n个传感器，则采用n+1个卡 尔曼滤波器；其中：卡尔曼滤波器0的输入y 0，使用全部n个传感器测量值，代表正常状态；卡尔曼滤波器i的输入y i，使用除去对应自身传感器外的其余所有n-1个传感器测量值。卡尔曼滤波器的输入值y i和滤波之后的估计输出值
Figure PCTCN2022121216-appb-000005
之差即为残差。
S4.2对卡尔曼滤波器i的残差求取加权平均和WSSR i(i＝1,2,3,…)，当某一个传感器发生故障时，发动机测量参数与滤波器估计输出之间残差的特性相应发生改变，WSSR i就会发生较大的变化。再将每个滤波器的统计量WSSR i与正常模态的值WSSR 0相减，得到统计量SR i:
SR i＝WSSR 0-WSSR i.
S4.3检测传感器软故障时，求取各统计量SR i(i＝1,2,3,…)中的最大值MAX(SR i)，将其与既定的阈值进行比较，如果没有超出阈值，则可以认为系统没有发生传感器故障，直接反馈传感器测量值到控制器；如果超出阈值，则认为SR i最大值对应的传感器发生了故障；然后再依次判断其他SR i值中的最大值是否超过阈值，直到检测出所有传感器。如果有故障时，则用卡尔曼滤波器的估计值取代故障传感器的测量值反馈到控制器，重构反馈信号。
S5构建航空发动机传感器容错软硬混杂控制系统。
根据故障信号的幅值大小和软/硬切换规则，将航空发动机的输出信号输入到传感器硬故障诊断系统或软故障诊断系统中。经软/硬故障诊断系统判断，当传感器无故障时，信号直接反馈到控制系统输入端；当传感器存在故障时，故障检测与诊断系统诊断出故障，及时报警，同时重构反馈信号，使发动机仍然处于正常的稳定工作状态；而不至于当发动机正常工作时，由于传感器的故障导致反馈信号的失真，使得整个系统出现严重后果。此时，针对航空发动机传感器软/硬故障建立的容错混杂控制系统就具有了容错能力，能在很大程度上缓解传感器故障对系统总体性能的负面影响。
本发明的有益效果：
本发明针对航空发动机传感器的故障诊断问题，设计了基于卡尔曼滤波器的容错混杂控制系统。其中所设计的软故障诊断系统通过对传感器测量值和一组卡尔曼滤波器估计值进行残差处理并对其加权平方求和，以比较已知阈值来进一步检测是否存在故障；所设计的硬故障诊断系统通过将传感器测量值和一个卡尔曼滤波器估计值的残差绝对值与已知阈值进行比较，以进一步检测是否存在故障。此航空发动机传感器容错软硬混杂控制系统，实现了对航空发动机传感器软/硬故障的有效检测，并在发生传感器故障时对系统的真实状态加以估计，重构发动机正常工作时的传感器信号，从而实现对传感器软/硬故障的容错混杂控制，能够有效去除航空发动机传感器故障对系统总体性能产生的负面影响，从而保证了航空发动机控制系统在传感器故障存在情形下的稳定运行。该方法和采用硬件冗余提高传感器可靠性的设计方法相比，成本大大降低；同时该方法不仅能够实现传感器容错控制，还能够定位到是哪个 或哪几个传感器出现故障，还可以估计出现故障的大小及严重性。
附图说明
图1为航空发动机传感器容错软硬混杂控制系统结构示意图；
图2为航空发动机传感器硬/软故障系统无容错控制的W fm信号仿真图；图2(a)为航空发动机传感器硬故障系统无容错控制的W fm信号仿真图，图2(b)为航空发动机传感器软故障系统无容错控制的W fm信号仿真图。
图3为航空发动机传感器硬/软故障系统带有容错控制的W fm信号仿真图；图3(a)为航空发动机传感器硬故障系统带有容错控制的W fm信号仿真图，图3(b)为航空发动机传感器软故障系统带有容错控制的W fm信号仿真图。
具体实施方式
下面结合附图对本发明作进一步说明。
图1为航空发动机传感器容错软硬混杂控制系统的结构示意图。其中图1中左侧虚线框为航空发动机传感器硬故障诊断系统，图1中右侧虚线框为航空发动机传感器软故障诊断系统。
如图1所示，航空发动机传感器容错软硬混杂控制系统根据故障信号的幅值大小和软/硬切换规则，将航空发动机的输出信号输入到传感器硬故障诊断系统或软故障诊断系统中；经软/硬故障诊断系统判断，当传感器无故障时，信号直接反馈到控制系统输入端；当传感器存在故障时，故障检测与诊断系统诊断出故障，及时报警，同时重构反馈信号，使发动机仍然处于正常的稳定工作状态；而不至于当发动机正常工作时，由于传感器的故障导致反馈信号的失真，使得整个系统出现严重后果。
如图1左侧虚线框所示，所设计的航空发动机传感器硬故障诊断系统中，传感器测量参数的输出值y和经过卡尔曼滤波器之后测量参数估计值之差即为残差r，若用
Figure PCTCN2022121216-appb-000006
表示卡尔曼滤波器的估计值，则有：
Figure PCTCN2022121216-appb-000007
当某个或多个传感器测量值对应的卡尔曼滤波器残差r的绝对值超出其自身的阈值时,就可以判断传感器发生了故障,并对其进行隔离，切断发生故障的传感器，由卡尔曼滤波器的估计值取代故障传感器的测量值反馈到控制器，实现信号的重构。当卡尔曼滤波器残差值r的绝对值小于阈值时，则可以判断传感器无故障，将传感器的输出反馈到控制器。
如图1右侧虚线框所示，所设计的航空发动机传感器软故障诊断系统中，滤波器0使用了所有的传感器测量值，代表正常状态。其他滤波器的输入y i是除去相应自身传感器输出以外的其它所有传感器的输出。这么做的原因是，假设某个传感器发生了故障，由于只有和该 传感器对应的卡尔曼滤波器没有使用有故障的传感器的测量值，因此只有它得到的估计结果是正确的，而其余的卡尔曼滤波器由于都使用了有故障的传感器的测量值，所以其余的卡尔曼滤波器的估计结果都会不同程度的偏离准确值，这样就可以进一步判断出故障的存在。卡尔曼滤波器的输入值y i和滤波之后的估计输出值
Figure PCTCN2022121216-appb-000008
之差即为残差r，对于卡尔曼滤波器i，则有：
Figure PCTCN2022121216-appb-000009
由此可以得到残差序列r i，当传感器无故障且滤波过程趋于稳定时，残差向量r i服从多维正态分布
Figure PCTCN2022121216-appb-000010
因此可以构造传感器故障指示变量：
WSSR i＝(r i) T.(∑ i) -1.r i,
∑ i＝diag[σ i] 2,
WSSR i(Weight Sum of Squared Residuals)称为残差加权平方和，由于
Figure PCTCN2022121216-appb-000011
所以WSSR i服从χ 2分布。当某一个传感器发生故障时，发动机测量参数与滤波器估计输出之间残差的特性相应发生改变，WSSR i就会发生较大的变化。再将每个滤波器的统计量WSSR i(i＝1,2,3,…)与滤波器0的正常模态的值WSSR 0相减，得到统计量SR i:
SR i＝WSSR 0-WSSR i.
检测传感器软故障时，求取各统计量SR i(i＝1,2,3,…)中的最大值MAX(SR i)，将其与既定的阈值进行比较，如果没有超出阈值，则可以认为系统没有发生传感器故障，直接反馈传感器测量值到控制器；如果超出阈值，则认为SR i最大值对应的传感器发生了故障；然后再依次判断其他SR i值中的最大值是否超过阈值，直到检测出所有传感器。如果有故障时，则用卡尔曼滤波器的估计值取代故障传感器的测量值反馈到控制器，重构反馈信号。
本发明实施案例1和实施案例2分别加入低压转子转速XNLPC传感器硬故障和软故障，并将传感器硬故障/软故障系统加容错混杂控制措施前后的反馈到控制器的主燃油流量W fm信号作对比，通过仿真实例，验证航空发动机传感器容错软硬混杂控制系统的容错效果。
图2为航空发动机传感器硬/软故障系统无容错控制的W fm信号仿真图；图2(a)为航空发动机传感器硬故障系统无容错控制的W fm信号仿真图，图2(b)为航空发动机传感器软故障系统无容错控制的W fm信号仿真图。图3为航空发动机传感器硬/软故障系统带有容错控制的W fm信号仿真图；图3(a)为航空发动机传感器硬故障系统带有容错控制的W fm信号仿真图，图3(b)为航空发动机传感器软故障系统带有容错控制的W fm信号仿真图。
在实施案例1中，加入低压转子转速XNLPC传感器硬故障，模拟为：在t＝20s的时候，低压转子转速XNLPC传感器输出突然增加50转，持续时间为2秒。由图2(a)和图3(a) 可以看出，当传感器硬故障发生后，不加容错调整措施时，系统经过控制器的调整，虽然最终能达到稳定，但其动态过程的波动较大。加入容错控制措施后，传感器硬故障诊断系统及时诊断出故障，同时反馈卡尔曼滤波器的估计输出，重构反馈信号，使发动机仍然处于正常的稳定工作状态，系统动态过程的波动以及动态响应时间明显大幅度降低，起到了很好的容错控制效果。
在实施案例2中，加入低压转子转速XNLPC传感器软故障，模拟选择随时间漂移的故障，以此来模拟传感器软故障，故障信号出现在20秒到30秒之间。由图2(b)和图3(b)可以看出，当传感器软故障发生后，不加容错调整措施时，随时间的推移，故障系统输出信号逐渐偏离稳态值，直到t＝30s故障排除后才逐渐恢复到稳态值；加入容错控制措施后，漂移故障积累到一定程度，传感器软故障诊断系统中故障判断标志MAX(SR i)将会超过预先设置的阈值，从而触发信号重构开关，切换到卡尔曼滤波器的估计输出，使反馈到控制器的信号不会有太大波动，达到维持整个系统正常稳定的目的。
综上可见，本发明提出的针对航空发动机传感器的容错软硬混杂控制方法是可行的，该方法以基于卡尔曼滤波器的故障诊断为基础，实现对航空发动机传感器软/硬故障的检测，并在发生传感器故障时对系统的真实状态加以估计，从而实现对传感器软/硬故障的容错混杂控制，能够有效去除航空发动机传感器故障对系统总体性能产生的负面影响，使得系统更加稳定，各项性能指标更能满足要求。
以上所述实施例仅表达本发明的实施方式，但并不能因此而理解为对本发明专利的范围的限制，应当指出，对于本领域的技术人员来说，在不脱离本发明构思的前提下，还可以做出若干变形和改进，这些均属于本发明的保护范围。

Claims (2)

  1. 一种针对航空发动机传感器故障诊断的容错软硬混杂控制方法，其特征在于，所述的控制方法以基于卡尔曼滤波器的故障诊断为基础，实现对航空发动机传感器软/硬故障的检测，并在发生传感器故障时对系统的真实状态加以估计，重构反馈信号，从而实现对传感器软/硬故障的容错混杂控制，步骤如下：
    S1根据航空发动机模型建立卡尔曼滤波器；
    S2根据故障信号的幅值大小和软/硬切换规则，将航空发动机的输出信号输入到传感器硬故障诊断系统或软故障诊断系统中；
    S3设计航空发动机传感器硬故障诊断系统；
    采用步骤S1中已建立的卡尔曼滤波器搭建传感器硬故障诊断系统，将航空发动机传感器测量参数的输出值输入到卡尔曼滤波器中，再将经过卡尔曼滤波器之后测量参数的估计值和传感器测量参数的输出值求差，二者之差即为对应卡尔曼滤波器的残差；
    当某个或多个传感器测量值对应的卡尔曼滤波器残差的绝对值超出其自身的阈值时,则判断传感器发生故障,并对其进行隔离，切断发生故障的传感器，由卡尔曼滤波器的估计值取代故障传感器的测量值反馈到控制器，实现信号的重构；
    当某个或多个传感器测量值对应的卡尔曼滤波器残差值的绝对值小于阈值时，则判断传感器无故障，将传感器的输出反馈到控制器；
    S4设计航空发动机传感器软故障诊断系统；
    发生传感器软故障时，采用一组卡尔曼滤波器，并对残差序列进行加权平方和的处理：
    S4.1假设故障诊断模型中共用到n个输出参数，分别对应n个传感器，则采用n+1个卡尔曼滤波器；其中：卡尔曼滤波器0的输入y 0，使用全部n个传感器测量值，代表正常状态；卡尔曼滤波器i的输入y i，使用除去对应自身传感器外的其余所有n-1个传感器测量值；卡尔曼滤波器的输入值y i和滤波之后的估计输出值
    Figure PCTCN2022121216-appb-100001
    之差即为残差；
    S4.2对卡尔曼滤波器i的残差求取加权平均和WSSR i(i＝1,2,3,…)，当某一个传感器发生故障时，发动机测量参数与滤波器估计输出之间残差的特性相应发生改变，WSSR i发生变化；再将每个滤波器的统计量WSSR i与正常模态的值WSSR 0相减，得到统计量SR i:
    SR i＝WSSR 0-WSSR i.
    S4.3检测传感器软故障时，求取各统计量SR i(i＝1,2,3,…)中的最大值MAX(SR i)，将其与既定的阈值进行比较，如果没有超出阈值，则可以认为系统没有发生传感器故障，直接反馈传感器测量值到控制器；如果超出阈值，则认为SR i最大值对应的传感器发生了故障；然后再依次判断其他SR i值中的最大值是否超过阈值，直到检测出所有传感器；如果有故障时，则用卡尔曼滤波器的估计值取代故障传感器的测量值反馈到控制器，重构反馈信号；
    S5构建航空发动机传感器容错软硬混杂控制系统；
    根据故障信号的幅值大小和软/硬切换规则，将航空发动机的输出信号输入到传感器硬故障诊断系统或软故障诊断系统中；经软/硬故障诊断系统判断，当传感器无故障时，信号直接反馈到控制系统输入端；当传感器存在故障时，故障检测与诊断系统诊断出故障，及时报警，同时重构反馈信号，使发动机处于正常的稳定工作状态。
  2. 根据权利要求1所述的一种针对航空发动机传感器故障诊断的容错软硬混杂控制方法，其特征在于，所述的步骤S1建立卡尔曼滤波器具体步骤为：
    S1.1首先基于航空发动机部件级气动热力非线性模型，利用拟合法建立小偏离线性化模型，针对稳态工作点计算出其状态空间模型的各个系统矩阵；
    所述小偏离线性化模型的输入量u为燃油流量W fm；状态变量x为高压转子转速XNLPC、低压转子转速XNHPC；输出量y为高、低压转子转速，高压压气机出口静压P和低压涡轮进口总温T；以稳态工作点上的相应参数值(x S，u S，y S)为基准值，其中，x S表示稳态工作点上的状态变量、u S表示稳态工作点上的输入量、y S表示稳态工作点上的输出量；
    令Δx、Δu分别为状态变量和输入量的偏离量，Δy为输出量的偏离量，在稳态工作点(x S，u S，y S)处可得小偏离线性化模型的状态空间方程如下：
    Figure PCTCN2022121216-appb-100002
    其中，x＝x S+Δx,u＝u S+Δu,y＝y S+Δy，然后由程序拟合计算出系统矩阵A，B，C，D；
    S1.2建立卡尔曼滤波器：在稳态工作点(x S，u S，y S)处建立的线性模型的基础上，对于输入不包含健康参数偏离量的发动机线性模型，形如下式：
    Figure PCTCN2022121216-appb-100003
    式中，w和v分别为系统噪声和测量噪声，并假设w和v为均值为零的高斯白噪声，其协方差矩阵分别为Q和R；建立如下式的卡尔曼滤波器：
    Figure PCTCN2022121216-appb-100004
    式中，
    Figure PCTCN2022121216-appb-100005
    代表对变量的估计，K表示卡尔曼增益矩阵；P为公式(4)所示的黎卡提方程的解:
    AP+PA T-PC TR -1CP+Q＝0  (4)。
PCT/CN2022/121216 2022-09-08 2022-09-26 一种针对航空发动机传感器故障诊断的容错软硬混杂控制方法 Ceased WO2024050883A1 (zh)

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