旋翼系统典型故障的诊断与预测方法

在分析直升机旋翼系统典型故障的基础上,提出了旋翼系统典型故障的诊断与预测方法,包括:桨叶疲劳寿命预测方法,桨毂疲劳寿命预测方法,拉杆疲劳寿命预测方法,自动倾斜器轴承故障诊断方法等。同时,介绍了基于飞行状态识别,基于虚拟传感器,基于载荷实测的疲劳寿命预测方法和自动倾斜器轴承故障诊断方法的关键技术,说明了旋翼系统典型故障的可测试性以及实现旋翼系统基于状态维修(CBM)的可行性。     

引气系统建模及传感器集优化分析

MADe(维修感知设计环境)是基于功能流模型、系统失效知识和测试信息的系统测试性与健康感知综合仿真分析工具。给出了功能流模型定义及其建模流程,详细分析了飞机引气系统的工作模式、层次结构、功能流及典型部件的物理失效过程,并实现了引气系统典型工况的建模。基于引气系统功能流模型和功能流传播表对传感器集进行了优化,包括传感器集的选择和诊断规则分析。仿真结果表明,该方法能有效地实现引气系统的建模,实现引气系统故障诊断与健康监控方案的优化。     

A novel testability model for health management of heading attitude system

Prognostics and health management (PHM) is very important to guarantee the reliability and safety of aerospace systems, and sensing and test are the precondition of PHM. Integrating design for testability into early design stage of system early design stage is deemed as a fundamental way to improve PHM performance, and testability model is the base of testability analysis and design. This paper discusses a hierarchical model-based approach to testability modeling and analysis for heading attitude system health management. Quantified directed graph, of which the nodes represent components and tests and the directed edges represent fault propagation paths, is used to describe fault-test dependency, and quantitative testability information is assigned to nodes and directed edges. The fault dependencies between nodes can be obtained by functional fault analysis methodology that captures the physical architecture and material flows such as energy, heat, data, and so on. By incorporating physics of failure models into component, the dynamic process of a failing or degrading component can be projected onto system behavior, i.e., system symptoms. Then, the analysis of extended failure modes, mechanisms and effects is utilized to construct fault evolution-test dependency. Using this integrated model, the designers and system analysts can assess the test suite’s fault detectability, fault isolability and fault predictability. And heading attitude system application results show that the proposed model can support testability analysis and design for PHM very well.     

航空电子的故障预测与健康管理技术

分析了引发航空电子设备故障的各种因素;详细讨论了航空电子设备全寿命周期内的故障预测与健康管理（PHM）技术,包括故障检测与健康监测、健康信息处理、故障预测和余寿评估;还介绍了基于健康信息综合管理的航空电子PHM系统的设计。     

A novel approach of testability modeling and analysis for PHM systems based on failure evolution mechanism

Prognostics and health management (PHM) significantly improves system availability and reliability, and reduces the cost of system operations. Design for testability (DFT) developed concurrently with system design is an important way to improve PHM capability. Testability modeling and analysis are the foundation of DFT. This paper proposes a novel approach of testability modeling and analysis based on failure evolution mechanisms. At the component level, the fault progression-related information of each unit under test (UUT) in a system is obtained by means of failure modes, evolution mechanisms, effects and criticality analysis (FMEMECA), and then the failure-symptom dependency can be generated. At the system level, the dynamic attributes of UUTs are assigned by using the bond graph methodology, and then the symptom-test dependency can be obtained by means of the functional flow method. Based on the failure-symptom and symptom-test dependencies, testability analysis for PHM systems can be realized. A shunt motor is used to verify the application of the approach proposed in this paper. Experimental results show that this approach is able to be applied to testability modeling and analysis for PHM systems very well, and the analysis results can provide a guide for engineers to design for testability in order to improve PHM performance.     

Aircraft air conditioning system health state estimation and prediction for predictive maintenance

The vast potential of system health monitoring and condition based maintenance on modern commercial aircraft is being realized through the innovative use of Airplane Condition Monitoring System (ACMS) data. However there are few methods addressing the issues of failure prognostics and predictive maintenance for commercial aircraft Air Conditioning System (ACS). This study developed a Bayesian failure prognostics approach using ACMS data for predictive maintenance of ACS. First, a health index characterizing the ACS health state is inferred from a multiple sensor signals using a data driven method. Then a dynamic linear model is proposed to describe the degradation process for failure prognostics. Bayesian inference formulas are carried out for degradation estimation and prediction. The developed approach is applied on a passenger aircraft fleet with ACMS data recorded for one year. The analysis of the case study shows that the developed method can produce satisfactory prognostics results, where all the ACS failure precursors are identified in advance, and the relative errors for the failure time prediction made when just entering the degradation warning stage are less than 8%. This would allow operators to proactively plan future maintenance.     

A Fault Sample Simulation Approach for Virtual Testability Demonstration Test

Virtual testability demonstration test has many advantages,such as low cost,high efficiency,low risk and few restrictions.It brings new requirements to the fault sample generation.A fault sample simulation approach for virtual testability demonstration test based on stochastic process theory is proposed.First,the similarities and differences of fault sample generation between physical testability demonstration test and virtual testability demonstration test are discussed.Second,it is pointed out that the fault occurrence process subject to perfect repair is renewal process.Third,the interarrival time distribution function of the next fault event is given.Steps and flowcharts of fault sample generation are introduced.The number of faults and their occurrence time are obtained by statistical simulation.Finally,experiments are carried out on a stable tracking platform.Because a variety of types of life distributions and maintenance modes are considered and some assumptions are removed,the sample size and structure of fault sample simulation results are more similar to the actual results and more reasonable.The proposed method can effectively guide the fault injection in virtual testability demonstration test.     

A multi-rate sensor fusion approach using information filters for estimating aero-engine performance degradation

Gas-path performance estimation plays an important role in aero-engine health management, and Kalman Filter(KF) is a well-known technique to estimate performance degradation. In previous studies, it is assumed that different kinds of sensors are with the same sampling rate, and they are used for state estimation by the KF simultaneously. However, it is hard to achieve state estimation using various kinds of sensor measurements at the same sampling rate due to a complex network and physical characteristic differences between sensors, especially in an advanced multisensor architecture. For this purpose, a multi-rate sensor fusion using the information filtering approach is proposed based on the square-root cubature rule, which is called Multi-rate Squareroot Cubature Information Filter(MSCIF) to track engine performance degradation. Soft measurement synchronization of the MSCIF is designed to provide a sensor fusion condition for multiple sampling rates of measurement, and a fault sensor is isolated by maximum likelihood validation before state estimation. The contribution of this paper is to supply a novel multi-rate informationfilter approach for sensor fault tolerant health estimation of an aero-engine in a multi-sensor system. Tests are conducted for aero-engine performance degradation estimation with multiple sampling rates of sensor measurement on both digital simulation and semi-physical experiment.Experimental results illustrate the superiority of the proposed algorithm in terms of degradation estimation accuracy and robustness to sensor failure in a multi-sensor system.     

Robust fault detection and optimization for a network of unmanned vehicles with imperfect communication channels

The observer-based robust fault detection and optimization for a network of unmanned vehicles with imperfect communication channels and norm bounded modeling uncertainties are addressed. The network of unmanned vehicles is modeled as a discrete-time uncertain Markovian jump system. Based on the model, a residual generator is constructed and the sufficient condition for the existence of the desired fault detection filter is derived in terms of linear matrix inequality. Furthermore, a time domain optimization approach is proposed to improve the performance of the fault detection system. The problem of detecting small faults can be formulated as an optimization problem and its solution is given. For preventing false alarms, a new adaptive threshold function is established. The combined fault detection and optimization algorithm and the adaptive threshold are then applied to a network of highly maneuverable technology vehicles to illustrate the effective- ness of the orooosed aooroach.     

基于LMI和离散模型的航空发动机压气机传感器鲁棒故障诊断

针对航空发动机压气机健康监测提出了一种基于线性矩阵不等式(LMI)和H_∞优化理论的航空发动机压气机传感器鲁棒故障诊断的方法.在航空发动机具有模型不确定性和外界噪声的情况下,应用基于神经网络的线性拟合方法实现航空发动机压气机离散模型的建立;并通过LMI和H_∞优化问题的求解得到未知输入观测器的设计参数,实现具有强鲁棒性的传感器故障诊断.该方法比以前研究中未知输入观测器故障诊断方法的优点在于能够同时处理模型不确定性和外界噪声.应用ALSTOM公司提供的燃气涡轮压气机模型进行了仿真验证,在压气机具有白噪声模型误差和正弦外界干扰的情况下,实现对小于测量范围2%的传感器故障的检测和诊断.      

Adaptive modification of turbofan engine nonlinear model based on LSTM neural networks and hybrid optimization method

An accurate and reliable turbofan engine model which can describe its dynamic behavior within the full flight envelop and lifecycle plays a critical role in performance optimization, controller design and fault diagnosis. However, due to the performance differences caused by the tolerance of engine manufacturing and assembly, and performance degradation during continuously stringent environmental regulations, the model accuracy is severely reduced. In this paper, an adaptive modification method of turbofan engine nonlinear Component-Llevel Model (CLM) based on Long Short-Term Memory (LSTM) Neural Network (NN) and hybrid optimization algorithm is pro-posed. First, a dynamic compensator with a combined LSTM NN architecture is constructed to compensate for the initial error between the experimental data and CLM of a turbofan engine under health condition. Then, a sensitivity analysis approach based on the entropy coefficient and technique for order preference by similarity to an ideal solution integrated evaluation is developed to choose the unmeasurable health parameters to be adjusted. Finally, a parallel hybrid optimization algorithm is developed to complete the adaptive model modification when the performance degrades. The proposed method is verified on a military low-bypass twin-spool turbofan engine, and the experimental results show the effectiveness of the proposed method.     

机载系统测试性设计体系研究

军用飞机在外场表现出诊断能力不足的现象,严重影响飞机再次出动的效率。从军用飞机故障诊断 的实际使用需求出发,深入分析机载系统在设计、试验过程中存在的测试性设计问题;针对军用飞机整个研制 与使用过程,明确设计目标,提供设计、验证和评估方法,将原来相互孤立的设计工作进行整合,提出一套完整 的机载系统测试性设计体系,并对测试性设计过程中的诊断能力设计、测试性试验等关键环节进行深入剖析; 同时,在工作方法、专业定位和制度保障方面提出改进建议,能够有效促进测试性设计有机地融入机载系统的 设计过程,提升机载系统测试性水平,提高飞机综合诊断能力。     

电子设备故障预测与健康管理技术发展新动态

电子设备是各类航空、航天等高新技术装备必不可少的重要组成部分。与机械类设备存在明显退化状态征兆不同，电子设备退化状态无明显的外在表现，尚无有效征兆对其状态进行刻画，对其进行故障预测与健康管理存在一定的困难。针对该问题，梳理了电子设备故障预测与健康管理技术的基本概念和内涵，介绍了电子设备故障预测与健康管理技术的国内外研究现状，分析了当前复杂电子设备故障预测与健康管理技术面临的挑战和对策。在此基础上，结合未来复杂电子设备新特点及该领域最新研究进展，从基于间歇故障特征的健康状态表征、面向故障预测与健康管理的测试性设计和多源特征融合的健康状态评估等方面，提出了电子设备故障预测与健康管理技术发展的新方向。     

基于VMD的自适应复合多尺度模糊熵及其在滚动轴承故障诊断中的应用

提出了一种基于自适应多尺度模糊熵、ILS（迭代拉普拉斯得分）特征选择和粒子群优化支持向量机(PSO-SVM)的滚动轴承故障诊断方法。该方法采用变分模态分解对振动信号进行分解和重构,并计算重构信号的复合多尺度模糊熵;同时采用迭代拉普拉斯得分选择敏感故障特征,并将特征选择结果输入到基于粒子群优化支持向量机的多故障分类器进行识别。将提出的方法应用于滚动轴承试验数据分析。结果表明:该方法对试验数据的故障识别率为100%。并将基于ILS特征选择方法与基于SFS（sequential forward selection）特征选择进行了对比,表明基于SFS特征选择的最高识别率为92.86%,而基于ILS特征选择的故障识别率达到100%。      

Designing to MIL-STD-2165: Testability

The V-22 avionic hardware is the first to be designed under MIL-STD-2165 testability program requirements. This paper presents an overview of the avionics design-for-testability approach and lessons learned to date relative to the application of MIL-STD-2165. The paper will discuss incorporation of testability requirements up front in the avionics design which will drive the supportability philosophy at both the Organizational and Depot levels of maintenance. The paper will compare previous avionics hardware testability requirements versus those applied to the V-22 avionics and highlight areas of improvement. A discussion of testability design impacts on reduced level of testing (i.e. WRA/SRA/System) will be included. In addition, the paper discusses an innovative approach to meeting the user requirements for a man-portable forward deployed maintenance capability that forms the basis for a two level support scenario (Organizational and Depot). The innovation comes from the fact that the on-board Central Integrated Checkout system will provide data as well as fault isolation and will use this data as a mechanism to reduce the size and complexity of the stimulus and measurement hardware at either the Organizational or Depot level depending on the deployment requirements.     

基于SPSO-SVR的融合航空发动机传感器故障诊断

针对航空发动机常见的传感器故障问题, 提出了一种利用改进的粒子群算法训练支持向量回归机, 并利用融合机制将其应用于传感器故障诊断.论述了用一簇支持向量回归机(SVR)预测器对传感器进行实时检测, 通过逻辑判断机制隔离故障传感器, 并且依据剩余的无故障传感器信息实现信号重构.以某型航空发动机传感器在其整个工作范围内受到的冲击、偏置和漂移故障为例, 验证了基于自协调粒子群优化支持向量回归机(SPSO-SVR)算法的融合诊断机制对传感器单一故障和多重故障具有较高的精度和计算效率.      

深度自编码观测器飞机操纵面快速故障诊断

为了避免扩展多模型自适应估计故障诊断方法中的雅克比矩阵计算,解决飞机精确模型难以获得的问题,降低在线故障诊断的计算量,提出了一种基于深度自编码观测器的飞机操纵面快速故障诊断方法.基于离线训练、在线估计的思想,采用量测的飞行数据训练得到不同故障下的飞机模型,代替扩展多模型自适应估计方法的卡尔曼滤波器进行状态估计;基于基础自编码器的隐层节点数选取经验公式,推导了两种深度自编码器的隐层节点数选取的递推公式.仿真结果表明,该方法无需精确的飞机模型,故障诊断速度快、精度高.     

PHM技术在液压伺服系统中的应用

为了提高液压伺服系统的可靠性和可用性,将故障预测与健康管理（PHM）技术应用到该系统中,并对其核心技术进行研究。重点研究在液压伺服系统的性能发生退化时对该系统进行的故障检测、健康评估及预测。对液压伺服系统PHM技术研究可为系统故障检测、退化评估和寿命预测的工程实践提供有力的理论支撑,并且PHM技术研究是故障诊断技术新的发展方向。最终,实验结果证明了该PHM技术在液压伺服系统故障诊断上的有效性和适用性。     

基于深度森林的卫星ACS执行机构与传感器故障识别

针对卫星姿态控制系统（ACS）闭环回路的故障难以辨识的问题,引入深度森林算法,实现执行机构与传感器故障识别。首先针对可获取的少量卫星姿态控制系统遥测数据,结合系统动力学特性,研究合适的特征选择和特征提取方法,再结合深度森林算法进行故障信息学习与辨识,建立故障预测模型,实现执行机构故障与传感器故障的识别。半物理仿真结果表明：在存在气浮台干扰力矩、卫星转动惯量未知、飞轮非线性特性、闭环故障传播等多种不利因素情况下,深度森林算法对于执行机构和传感器故障具有高效的识别能力。     

综合模块化航电软件仿真测试环境研究

 伴随着综合模块化航空电子(IMA)软件在新一代飞机上的应用,其高复杂性、高度综合的特点以及分层的健康监控和故障管理模式给软件测试提出了挑战。传统的仿真测试环境在应对IMA软件测试中难以满足RTCA DO-178B中规定的对验证过程结果的验证的要求。本文在分析IMA软件特点的基础上,根据DO-178B的要求,综合国外的发展情况和国内的研究进展情况,研究综合模块化航电软件仿真测试环境需求,提出了基于软件故障注入的综合模块化航电软件灰盒仿真测试环境方案,并给出优势分析。该仿真测试环境方案以IMA软件为测试对象,应用软件故障注入技术和代码插装技术满足测试规范文件的要求。其具有通用灵活、适配性强、强实时性等特点,为中国新一代航电软件的系统验证和测试奠定了基础。