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.     

Sensor Optimization Selection Model Based on Testability Constraint

Sensor selection and optimization is one of the important parts in design for testability. To address the problems that the traditional sensor optimization selection model does not take the requirements of prognostics and health management especially fault prognostics for testability into account and does not consider the impacts of sensor actual attributes on fault detectability, a novel sensor optimization selection model is proposed. Firstly, a universal architecture for sensor selection and optimization is provided. Secondly, a new testability index named fault predictable rate is defined to describe fault prognostics requirements for testability. Thirdly, a sensor selection and optimization model for prognostics and health management is constructed, which takes sensor cost as objective function and the defined testability indexes as constraint conditions. Due to NP-hard property of the model, a generic algorithm is designed to obtain the optimal solution. At last, a case study is presented to demonstrate the sensor selection approach for a stable tracking servo platform. The application results and comparison analysis show the proposed model and algorithm are effective and feasible. This approach can be used to select sensors for prognostics and health management of any system.     

A discrete event systems approach to discriminating intermittent from permanent faults

Almost all work on model-based diagnosis （MBD） potentially presumes faults are per- sistent and does not take intermittent faults （IFs） into account. Therefore, it is common for diag- nosis systems to misjudge IFs as permanent faults （PFs）, which are the major cause of the problems of false alarms, cannot duplication and no fault found in aircraft avionics. To address this problem, a new fault model which includes PFs and IFs is presented based on discrete event systems （DESs）. Thereafter, an approach is given to discriminate between PFs and IFs by diagnosing the current fault. In this paper, the regulations of （PFs and IFs） fault evolution through fault and reset events along the traces of system are studied, and then label propagation function is modified to account for PFs and the dynamic behavior of IFs and diagnosability of PFs and IFs are defined. Finally, illustrative examples are presented to demonstrate the proposed approach, and the analysis results show the fault types can be discriminated within bounded delay if the system is diagnosable.     

Testability integrated evaluation method based on testability virtual test data

Testability virtual test is a new test method for testability verification, which has the advantages such as low cost, few restrictions and large sample of test data. It can be used to make up the deficiency of testability physical test. In order to take the advantage of testability virtual test data effectively and to improve the accuracy of testability evaluation, a testability integrated eval- uation method is proposed in this paper based on testability virtual test data. Considering the char- acteristic of testability virtual test data, the credibility analysis method for testability virtual test data is studied firstly. Then the integrated calculation method is proposed fusing the testability vir- tual and physical test data. Finally, certain helicopter heading and attitude system is presented to demonstrate the proposed method. The results show that the testability integrated evaluation method is feasible and effective.     

Remaining useful life prognostics for aeroengine based on superstatistics and information fusion

Remaining useful life（RUL） prognostics is a fundamental premise to perform conditionbased maintenance（CBM） for a system subject to performance degradation. Over the past decades,research has been conducted in RUL prognostics for aeroengine. However, most of the prognostics technologies and methods simply base on single parameter, making it hard to demonstrate the specific characteristics of its degradation. To solve such problems, this paper proposes a novel approach to predict RUL by means of superstatistics and information fusion. The performance degradation evolution of the engine is modeled by fusing multiple monitoring parameters, which manifest non-stationary characteristics while degrading. With the obtained degradation curve,prognostics model can be established by state-space method, and then RUL can be estimated when the time-varying parameters of the model are predicted and updated through Kalman filtering algorithm. By this method, the non-stationary degradation of each parameter is represented, and multiple monitoring parameters are incorporated, both contributing to the final prognostics. A case study shows that this approach enables satisfactory prediction evolution and achieves a markedly better prognosis of RUL.     

Flight dynamics modeling of a small ducted fan aerial vehicle based on parameter identifcation

This paper presents a simple and useful modeling method to acquire a dynamics model of an aerial vehicle containing unknown parameters using mechanism modeling,and then to design different identifcation experiments to identify the parameters based on the sources and features of its unknown parameters.Based on the mathematical model of the aerial vehicle acquired by modeling and identifcation,a design for the structural parameters of the attitude control system is carried out,and the results of the attitude control flaps are verifed by simulation experiments and flight tests of the aerial vehicle.Results of the mathematical simulation and flight tests show that the mathematical model acquired using parameter identifcation is comparatively accurate and of clear mechanics,and can be used as the reference and basis for the structural design.     

Investigation into Pilot Handling Qualities in Teleoperation Rendezvous and Docking with Time Delay

Teleoperation rendezvous and docking can be used as a backup for autonomous rendezvous and docking(RVD) for an unmanned spacecraft when the autonomous system is failure or for guiding the chaser docking with an uncooperative target.The theoretical model for analyzing the handling qualities in teleoperation RVD process is established based on the previous studies conducted by National Aeronautics and Space Administration(NASA).The predictive factor is introduced to describe the pilot’s predictive ability in the teleoperation tasks with time delay,which interrelates with the skills of a pilot and the predictive assist approach used in the tasks such as the predictive display method.Based on the semi-physical simulation system in our laboratory,900 experiments at two levels of time delay are carried out by 18 volunteers for validating the established model.The experimental results demonstrate the correctness of the theoretical model and indicate that a skilled pilot has a predictive ability of approximately 0.9 in teleoperation RVD tasks.The theoretical analysis shows that the handling qualities are greatly affected by the time delay and the predictive factor,and it is impossible to achieve a teleoperation RVD task for the skilled pilot when the time delay is larger than 9.0 s.     

一种新的气动弹性鲁棒稳定性分析方法(英文)

Air vehicles undergo variations in structural mass and stiffness because of fuel consumption and the failure of structural components, which might lead to serious influences on the aeroelastic characteristics. An approach for aeroelastic robust stability analysis taking into account the perturbations of structural mass and stiffness is developed. Applying the perturbation method and harmonic unsteady aerodynamic forces, the frequency-domain linear fractal transformation （LFT） representation of perturbed aeroelastic system is modeled. Then, the robust stability is analyzed by using the structured singular value ,u-method. The numerical results of a bi-spar wing show its effectiveness and low computational time in dealing with the robust problems with mass and stiffness perturbations. In engineering analysis for solving aeroelastic problems, the robust approach can be applied to flutter analysis for airplane with the fuel load variation and taking the damage conditions into consideration.     

渐变结构系统模糊可靠性灵敏度分析的矩方法(英文)

For a degradable structural system with fuzzy failure region, a moment method based on fuzzy reliability sensitivity algorithm is presented. According to the value assignment of performance function, the integral region for calculating the fuzzy failure probability is first split into a series of subregions in which the membership function values of the performance function within the fuzzy failure region can be approximated by a set of constants. The fuzzy failure probability is then transformed into a sum of products of the random failure probabilities and the approximate constants of the membership function in the subregions. Furthermore, the fuzzy reliability sensitivity analysis is transformed into a series of random reliability sensitivity analysis, and the random reliability sensitivity can be obtained by the constructed moment method. The primary advantages of the presented method include higher efficiency for implicit performance function with low and medium dimensionality and wide applicability to multiple failure modes and nonnormal basic random variables. The limitation is that the required computation effort grows exponentially with the increase of dimensionality of the basic random vari- able; hence, it is not suitable for high dimensionality problem. Compared with the available methods, the presented one is pretty competitive in the case that the dimensionality is lower than 10. The presented examples are used to verify the advantages and indicate the limitations.     

A GPA diagnostic system foraeroengine applications

In this research, a GPA(Gas Path Analysis) diagnostic system enhanced with GPA Index is described for gas path sensor and component fault diagnosis.A method of measurement correction is used in order that the measurement data obtained at un-standard ambient and operating conditions can be used for diagnostic analysis.The developed diagnostic system has been implemented into a Cranfield University gas turbine performance and diagnostic analysis software PYTHIA for gas turbine performance degradation analysis.The developed method and software have been applied to a model aero gas turbine engine to test the effectiveness of the system.The analysis shows that the developed diagnostic system can diagnose degraded sensor and components effectively using performance deviation measured at un-standard ambient and operational conditions.Theoretically, the idea of the diagnostic approach can be applied to different gas turbine engines.      

航空机电系统测试性建模与分析新方法

针对航空机电系统测试性设计(DFT)需求,提出基于面向对象的贝叶斯网络(OOBN)与状态-测试关联灵敏度指标的系统测试性建模与分析的新方法。该建模方法能清晰地刻画系统故障与测试间的关联程度,反映复杂系统的层次结构关系。基于信息论的交叉熵原理提出状态-测试关联灵敏度指标,并给出计算方法。该指标反映复杂机电系统测试中的不确定性影响,克服了基于香农熵的测点评判分析方法的缺点,结合测试性建模获得的模型信息进行推理计算,可用于测试性的定量分析。运用该综合分析方法对飞机燃油系统进行测试性建模与分析,结果表明所提出的方法与指标在航空机电系统DFT中具有实用性。     

基于贝叶斯网络的分层系统可靠性分析

由于分层系统的系统结构复杂,且系统不同层次可靠性信息具有不均衡性的特点,使得分层系统的可靠性建模较为困难.考虑二态情况,通过引入贝叶斯网络描述分层系统元素间的失效关系,并考虑了系统元素间级联失效问题,结合贝叶斯多源信息融合方法,以贝叶斯网络模型为基础整合了系统各层元素的可靠性信息,构成了一种较为通用的二态分层系统可靠性分析方法.算例结果表明:该方法在贝叶斯网络模型计算过程中能够融合系统各层元素可靠性信息并有效处理级联失效问题,提高了系统可靠性后验均值估计的准确性,减小了系统可靠性结论的后验方差.      

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。     

START: System Testability Analysis and Research Tool

START, a software package for automatic test sequencing and testability analysis of complex, hierarchically described modular systems, is described, and its use in modeling systems is examined. START uses algorithms based on information theory, heuristic search, and graph theory to solve various faces of the test sequencing and testability analysis problems. A system is modeled in the failure space as a hierarchical directed graph with nodes denoting modules and testpoints and with AND nodes denoting redundancy. Interconnections among the nodes denote their immediate functional dependencies. START supports hierarchical testing in accordance with the maintenance strategy; a failure source may be isolated to a component or a module at any level. Other practical features include options to integrate diagnosis with repair (after partial diagnosis) in order to optimize test time, test cost, or test and repair cost. An interactive menu-mouse graphical interface serves as a high-level front end to these algorithms and enables the user to graphically enter and modify hierarchical functional models of systems. START presents the gist of the outputs of the testability analysis algorithms as a concise testability report consisting of important figures of merit     

故障诊断策略的优化方法

 以相关性模型为基础,综合可靠性和测试费用等因素提出了一种系统级优选测试点方法和故障诊断策略。首先根据系统的相关性矩阵,将测试性度量、故障概率和测试费用统一考虑,推导出测试点的故障诊断信息量计算公式;然后根据信息量的大小依次选择测试点,并确定出优化的故障诊断策略。所得到的故障诊断策略确保了在较低的测试费用下,使用尽可能少的测试步骤快速完成系统的故障检测和隔离。     

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。     

军用飞机 PHM 系统一体化设计架构分析

军用飞机故障预测与健康管理（PHM）系统设计与工程应用存在设计标准缺失、复杂度高、准确率低等问题。通过对 PHM 系统数据、结构、功能维度的设计需求分析,提出三维多相迭代设计准则;基于三维多相设计准则对 PHM 系统设计流程进行梳理;提出基于软硬件协同加速的健康管理实时化、基于知识图谱的知识逻辑可视化、基于 MBSE ...     

面向测试性虚拟验证的功能-故障-行为-测试-环境一体化模型

 由于测试性预计和测试性试验验证客观存在的困难及问题,测试性虚拟验证将在测试性工程领域发挥重要作用.根据测试性虚拟验证对模型的需求,针对传统测试性模型存在的定量特征少、导致测试性评估结果不准确等问题,提出一种功能-故障-行为-测试-环境一体化模型.首先,分析并指出该一体化模型由功能、故障、行为、测试、环境5大类要素构成.然后,在数学上对该一体化模型及其子模型进行定义和描述,分析并建立各子模型之间的关联关系.最后以某导弹及其测试系统为案例进行实验和应用研究.结果表明,在建模信息量充足的情况下,基于该一体化模型得到的测试性虚拟试验验证与评估结果较准确,能较好地支持装备测试性摸底、鉴定与验收等.