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.     

An assessment of semi-analytical models based on the absorption coefficient in retrieving the chlorophyll-a concentration from a reservoir

Monitoring chlorophyll-a (Chl-a) concentrations in inland waters is crucial for water quality management, since Chl-a is a proxy for phytoplankton biomass and, thus, for ecological health of a water environment. Chl-a concentration can be retrieved through the inherent optical properties (IOPs) of a water system, which, in turn, can be remotely sensed obtained. Quasi-analytical algorithm (QAA), originally developed for ocean waters, can also retrieve IOPs for inland waters after re-parameterizations. This study is aimed at assessing the performance of sixteen schemes composed by QAA original and re-parameterized versions followed by models that use absorption coefficients as inputs for estimating Chl-a concentration in Ibitinga reservoir, located at Tietê River cascading system, São Paulo State, Brazil. It was verified that only QAA_V5 based schemes were able to obtain reasonable estimates for image data and that by four models tested presented similar and acceptable results for QAA_V5 outputs. The best model were applied to a Ocean and Land Colour Instrument (OLCI) image. Light absorption in the reservoir showed to be dominated by colored dissolved organic matter (CDOM), and wide spatial and temporal variability of optical and water quality properties was observed.     

空客A330飞机CBMU数据库客户化及其工程管理的实现

CBMU是A330飞机电源系统中一个关键组件，负责监控电子、机械多个章节系统的跳开关状态。对于CBMU数据库软件构型的管理，通常是根据具体的硬件改装工作，由相关专业系统工程师自行控制。该方式容易导致CBMU数据库软件构型变更管理失控，严重时会影响到飞行安全。本文旨在分析梳理传统管理模式的基础上，结合实际经验，探讨构建一种新的管理模式，以达到有效管理CBMU数据库软件构型的目的。     

A data-driven health indicator extraction method for aircraft air conditioning system health monitoring

Prognostics and Health Management (PHM) has become a very important tool in modern commercial aircraft. Considering limited built-in sensing devices on the legacy aircraft model, one of the challenges for airborne system health monitoring is to find an appropriate health indicator that is highly related to the actual degradation state of the system. This paper proposed a novel health indicator extraction method based on the available sensor parameters for the health monitoring of Air Conditioning System (ACS) of a legacy commercial aircraft model. Firstly, a specific Airplane Condition Monitoring System (ACMS) report for ACS health monitoring is defined. Then a non-parametric modeling technique is adopted to calculate the health indicator based on the raw ACMS report data. The proposed method is validated on a single-aisle commercial aircraft widely used for short and medium-haul routes, using more than 6000 ACMS reports collected from a fleet of aircraft during one year. The case study result shows that the proposed health indicator can effectively characterize the degradation state of the ACS, which can provide valuable information for proactive maintenance plan in advance.     

直9涡轴8发动机的历程记录仪

本文简要介绍了适合直9涡轴8系列发动机使用的历程记录仪的硬件结构及软件设计原则。通过100多台的外场改装使用证明,记录仪改装简单、工作可靠,取得了满意的效果。     

复合材料钻削刀具状态集成学习分类技术

刀具状态监测关系到工件加工质量,因此实时掌握刀具的磨损状态具有重要的意义。针对单一模式识别分类器的局限性,提出了基于异态集成学习模型的刀具状态监测系统。在该系统中,根据集成学习基分类器选择原则,选取了支持向量机(SVM)、隐马尔科夫模型(HMM)以及径向基神经网络(RBF)这3个单分类器作为基分类器。为了验证监测系统的有效性,进行了复合材料钻削实验,提取了加工过程中的钻削力信号、振动信号的时域特征并利用局部保持法(LPP)进行了特征选择。通过与单分类器和集成学习分类效果的对比,表明了集成学习模型相比于单一基分类器具有更高的分类精度和更好的稳定性。     

基于数据挖掘的航空发动机状态参数离群点分析

利用机载数据采集系统和传感器获得的发动机状态参数往往存在粗大误差，影响故障诊断的准确性。采用数据挖掘算法中基于统计学的离群点分析，并与厂家系统的分析结果相比较，结果表明，该方法能够有效地剔除粗大误差且不会误删有用数据。     

地面站设备监控软件框架通用性研究

针对目前卫星地面应用系统中设备监控软件可重用性差的缺点,提出一种可扩展的通用设备监控软件框架设计方案.结合模块化和抽象接口设计思想,简化了组件开发,提高了软件的复用性.通过控制反转技术,降低了组件之间的耦合,使框架更加易于扩展,在实际应用中取得了良好的效果.     

全球导航完好性通道（GNIC）方案讨论

国际民航组织(ICAO)将采纳 GPS GLONASS INMARSAT 联合组成的 GNSS 系统供全球导航使用。对系统的增强和完好性监控是至关重要的问题。本文介绍近年来提出的各种地面完好性监控通道(GIC)的方案,包括地面监测台网、地面数据传输的通信链路和卫星转发。重点阐述 INMARSAT 提出的静止卫星加发导航信号的重叠(IGO)概念和全球性完好性监测台网(IMN)。然后进行比较和讨论,并提出需进一步跟踪研究。     

固体发动机试车燃气的扩散及对环境的影响

对地处复杂山区的固体火箭发动机试车站的燃气组成及燃气在大气中的扩散和对环境的影响,采用理论预测、数学模式和监测相结合的方法进行了系统研究,配合进行了同步的气象特征研究.评估了不同大气稳定度下污染浓度的分布,提出了有效的防污染措施.