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.     

Applying fuzzy bi-dimensional scenario-based model to the assessment of Mars mission architecture scenarios

Sending man to Mars has been a long-held dream of humankind. NASA plans human planetary explorations using approaches that are technically feasible, have reasonable risks and have relatively low costs. This study presents a novel Multi-Attribute Decision Making (MADM) model for evaluating a range of potential mission scenarios for the human exploration of Mars. The three alternatives identified by the Mission Operations Directorate (MOD) at the Johnson Space Center (JSC) include split mission, combo lander and dual scenarios. The proposed framework subsumes the following key methods: first, the conjunction method is used to minimize the number of alternative mission scenarios; second, the Fuzzy Risk Failure Mode and Effects Analysis (RFMEA) is used to analyze the potential failure of the alternative scenarios; third, the fuzzy group Real Option Analysis (ROA) is used to estimate the expected costs and benefits of the alternative scenarios; and fourth, the fuzzy group permutation approach is used to select the optimal mission scenario. We present the results of a case study at NASA’s Johnson Space center to demonstrate: (1) the complexity of mission scenario selection involving subjective and objective judgments provided by multiple space exploration experts; and (2) a systematic and structured method for aggregating quantitative and qualitative data concerning a large number of competing and conflicting mission events.     

机载适航标准DO-178B/C软件开发过程研究

介绍了DO-178B/C标准的发展历程,结合对DO-178B/C标准机载软件生命周期过程的研究,分析了机载软件开发过程需求、设计、编码和集成子过程的关系及输入输出数据,在分析机载软件需求、设计、编码及集成子过程的目标、活动和转换准则的基础上,给出了各子过程的过程模型.展望了DO-178B/C标准的未来研究方向.     

空管灾害诱因分析及监测指标体系的构建

从操作人、空管设备、空管环境和管理的角度对空管的致灾诱因进行了详细分析。按照科学性、实用性、关联性和可操作性的原则,筛选出一组监测指标,在考虑空管行业数据来源的情况下,提出了现阶段和理想情况下的监测指标体系,目的在于通过对空管安全状态进行监测和诊断,及时采取预警和预控对策,促进空管系统处于安全状态,降低事故率。     

故障诊断的神经网络多重模型自适应方法

将神经网络与故障诊断的多重模型自适应方法相结合,提出了故障诊断的神经网络多重模型自适应方法,并对某型航空发动机控制系统传感器故障进行诊断仿真。仿真表明,该方法能够用来解决具有模型不确定性系统的故障诊断问题,同时,对未知的故障模态具有自学习能力     

用FTA-FMEA联合法做电子设备可靠性设计

介绍了一种以FTA和FMEA联合进行电路失效分析的方法,并在此基础上做可靠性设计,有效提高了星上电子设备的可靠性。文中有具体的步骤和实例,可供设计人员参照采用。     

砷化镓功率场效应管早期烧毁的失效分析

采用扫描俄歇微探针、扫描电子显微镜、电子探针微区分析仪和Ｘ射线透射仪等多种分析手段分析了一种Ｃ波段砷化镓功率场效应管早期烧毁失效的现象。初步建立了烧毁失效的模式,给出了相应的失效频数分布及其表面形貌状态。分析结果指出,烧毁失效模式中源一漏烧毁占较大的比例;其次是由于材料及器件制备工艺过程不完善而引起的空气桥烧毁,热斑烧毁,源或漏极条边缘毛刺、芯片表面缺陷、沾污和不可动多余物引起的烧毁失效。文章就烧毁失效的机理进行了分析和讨论。     

基于部件跟踪滤波器的解析余度技术

研究一种以发动机部件跟踪滤波器(CTF)为基础的解析余度技术, 它将CTF与故障检测、隔离和适应逻辑进行了有效的综合, 以改进发动机数控系统的可靠性。仿真表明, 本文所设计的解析余度技术, 在传感器无故障时, 机载模型能正确跟踪发动机的变化。当传感器发生故障时, 在不损坏机载模型的情况下, 又能及时、有效地进行硬、软故障的检测、隔离与适应。      

二维非定常分离准则探讨

利用奇点理论和连续方程证明,当坐标系与非定常分离点固结时,分离点应该是鞍型奇点。由此自然地导出了非定常分离准则。进而还讨论了在非定常流场中如何确定分离点的几种方法     

基于室内无线模型的穿墙损耗校正

K-M（Keenan-Motley）模型将单墙固定损耗值相加来计算室内无线信号穿透多墙的总损耗值,存在较大误差。针对该问题选取多种室内场景分别进行连续波（Continuous Wave,CW）测试,对无线信号穿墙损耗的影响因素和变化规律进行分析,提出了一种基于人工神经网络的室内无线模型穿墙损耗校正方法,对预处理后的测试数据进行训练并建立预测模型。经验证该预测模型符合校正判别准则,在实际场景下具有良好的预测准确度。