基于Kolmogorov熵的转子-机匣系统故障诊断研究

提出了基于Kolmogorov熵的航空发动机转子—机匣系统状态识别和故障诊断新方法。应用关联积分算法,基于实测的航空发动机机匣振动时间序列求解了转子—机匣系统不同工作状态和故障状态的Kolmogorov熵;基于Kolmogorov熵,对航空发动机转子—机匣系统进行了状态识别和故障诊断。研究结果表明,该法具有较高的状态识别和故障诊断能力。     

SCADE 在航空发动机FADEC 软件开发中的应用

为了探索SCADE开发环境在基于模型设计（MBD）的软件开发中的优势,理解其建模和自动代码生成机制,研究其在基于模型的测试和覆盖率分析中的实现方法,基于某型航空发动机FADEC系统的健康管理软件开发,应用了SCADE开发环境的建模、仿真、测试及覆盖率分析、代码生成与集成的全流程的MBD开发方法,并进行了完整的系统测试,测试用例全部通过。系统测试的结果验证了基于SCADE开发环境进行FADEC软件开发的正确性和可靠性,为SCADE开发环境在航空发动机FADEC软件开发中的应用提供了技术指导和工程借鉴。     

系统测试性建模及验证方法

通过研究系统级测试性建模的基本原理,主要包括数学模型和图示模型,从结构、故障和测试三个方面给出了系统测试性建模的流程,最后给出了系统级测试性模型的验证方法,包括模型整体有效性验证、基于测试性分析报告的测试性模型验证等。     

基于时间应力分析的机内测试系统综合降虚警技术

 虚警率高是困扰机内测试（BIT）系统得到广泛应用的主要原因。针对该问题,从机电系统所承受时间应力的角度构建了机内测试系统综合降低虚警技术的总体模型。首先采用双支持向量机（SVM）的方法将实时应力信息与机内测试诊断结果相互关联。在此基础上,提出了基于核主元模糊聚类的虚警识别方法将机电系统多源信息进行综合分析,并通过优化决策实现多级降低机内测试系统虚警的目的。最后,针对某型直升机航空地平仪的机内测试系统进行了试验验证与分析。     

基于任务的微机电惯性系统规模化测试技术研究

针对传统惯性测试技术测试周期长、测试效率低、测试成本高等问题,提出了基于任务的微机电惯性系统规模化测试解决方案。研究分析了微机电惯性系统测试技术的研究现状和发展趋势,建立了基于任务的微机电惯性系统测试体系,详细论证了微机电惯性系统批量、并行、自动化测试解决方案的可行性,为微机电惯性系统规模化测试实践奠定了基础。     

基于交叉熵和空间分割的全局可靠性灵敏度分析

基于失效概率的全局灵敏度分析可以度量各个基本随机变量的不确定性对失效概率的影响程度,对如何降低结构的失效概率具有指导意义。基于交叉熵方法和空间分割提出一种新全局可靠性灵敏度分析方法。该方法采用交叉熵法自适应的确定重要抽样密度函数,有效地回避了传统重要抽样中设计点位置和个数求解的困难。基于评估失效概率所使用的样本,利用空间分割方法计算各个输入随机变量的全局可靠性灵敏度指标,能够提高样本的利用率和计算效率。文中利用一个数值算例和两个工程算例验证了所提方法的计算效率和精度。     

混合边界子结构的航空发动机机匣模型修正

提出了一种基于混合边界模态综合的复杂结构有限元模型修正方法。其主要步骤包括:①子结构划分,根据结构形式划分待修正区域,得到子结构和残余结构;②缩聚和装配,利用混合边界模态综合法,将子结构内部自由度集缩聚至混合边界自由度集,得到子结构缩聚矩阵,并与残余结构的系统矩阵进行装配;③修正,基于灵敏度分析方法,对装配后的残余结构进行参数修正。将该方法应用于航空发动机外机匣的精细化有限元建模及模型修正研究,针对局部连接结构参数修正,子结构模型修正方法的参数收敛后最大误差为064%,其计算效率提高了515倍。算例结果表明,该方法在保证修正精确度的同时,能提高大规模复杂结构有限元模型修正的计算效率。      

多因素耦合的舰机适配性评估方法

舰机适配性是航母与舰载机研制过程中的难点,充分协调舰艇系统和飞机航空系统之间的复杂关系是发挥航母战斗力的关键。文章提出了 1种基于改进的熵值法与突变级数法的多因素耦合的舰机适配性评估方法。首先,进行指标选取,建立多因素耦合的舰机适配性评估指标体系,并根据突变级数理论确定指标体系对应的突变模型;其次,利用改进的熵值法计算各个指标的贡献率,提高排序结果的准确性;再次,利用突变层次结构模型进行舰机适配性评估;最后,以国外某型航母与舰载机为评估对象,验证新的综合评估方法的有效性和可靠性。文章提出的方法不需要对指标赋权,减少评估过程中由于赋权带来的主观性,为舰机适配性评估提供了新的思路。     

基于PDCA循环的系统测试性设计方法

采用PDCA的核心思想，结合某型综合显示控制系统研发过程工程实际，提出了一套系统测试性设计的技术方法。通过将系统测试性设计过程按照“策划-实施-评估-改进”的戴明环循环思路，以测试性需求实现和验证为出发点，聚焦测试性设计技术状态风险管控，提出了测试性设计工作的关键技术路径，落实测试性设计过程闭环，可有效指导系统测试性设计。     

基于Simscape航空三级式起动/发电机建模仿真研究

基于现有航空三级式起动/发电机仿真建模方法，提出基于Simscape电力系统域的励磁机、旋转整流器和主电机统一域建模方法，给出建模过程，在所建立的子系统模型上搭建航空三级式起动/发电机从起动到发电过程的仿真系统，该系统可工作在起动状态和发电状态，实现起动到发电状态的转换。仿真结果表明，所给出的建模方法可行、建模结果正确，能够支持相关领域的仿真研究。     

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.     

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.     

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     

Testability evaluation using prior information of multiple sources

Testability plays an important role in improving the readiness and decreasing the lifecycle cost of equipment. Testability demonstration and evaluation is of significance in measuring such testability indexes as fault detection rate（FDR） and fault isolation rate（FIR）, which is useful to the producer in mastering the testability level and improving the testability design, and helpful to the consumer in making purchase decisions. Aiming at the problems with a small sample of testability demonstration test data（TDTD） such as low evaluation confidence and inaccurate result, a testability evaluation method is proposed based on the prior information of multiple sources and Bayes theory. Firstly, the types of prior information are analyzed. The maximum entropy method is applied to the prior information with the mean and interval estimate forms on the testability index to obtain the parameters of prior probability density function（PDF）, and the empirical Bayesian method is used to get the parameters for the prior information with a success-fail form. Then, a parametrical data consistency check method is used to check the compatibility between all the sources of prior information and TDTD. For the prior information to pass the check, the prior credibility is calculated. A mixed prior distribution is formed based on the prior PDFs and the corresponding credibility. The Bayesian posterior distribution model is acquired with the mixed prior distribution and TDTD, based on which the point and interval estimates are calculated.Finally, examples of a flying control system are used to verify the proposed method. The results show that the proposed method is feasible and effective.     

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.     

小子样复杂装备系统测试性评估中的验前参数值确定方法

针对当前小子样测试性评估中对验前信息处理不规范、结果可信度差的问题,研究了由专家经验信息、子系统试验信息和虚拟仿真信息确定系统测试性验前参数值的方法.根据不同来源的测试性验前信息,分别提出了模糊不确定性加权、分系统数据折合和基于相似度量的Dempster-Shafer(D-S)证据融合方法估计系统测试性验前参数值.实例分析表明:该方法计算测试性评估中的验前参数值比其他文献方法的计算结果低约0.8%.     

通用航空机队设备可靠性动态识别模型

通用航空机队设备可靠性是通航单位安全运行的前提条件,是影响运营单位经济效益的重要因素之一。根据通航单位机队设备可靠性数据统计、分析及实际使用情况,采用ATA100章节名称作为指标源。综合可变模糊识别方法和权重阶梯朴素贝叶斯分类器模型的优势,构建了通用航空机队设备可靠性动态识别模型。为了避免主观给定指标权重导致的不合理,应用熵权法客观获取指标权重。最后利用实例测试样本验证了权重阶梯朴素贝叶斯分类器的合理性,并基于该方法对待识别样本进行了可靠性状态识别。实例分析表明：基于权重阶梯朴素贝叶斯分类器的通用航空机队设备可靠性状态识别模型具有较强的可行性和合理性,为通用航空机队设备可靠性状态提供了一种科学的识别方法。     

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.