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.     

Physical implications of discrepancy between summer and winter PC indices observed in the course of magnetospheric substorms

The PC index based on a statistically justified relationship between the polar cap magnetic activity and the interplanetary electric field E_KL has been derived as a value standardized for the E_KL intensity regardless of season, UT and hemisphere. As a result, the summer and winter PC indices are consistent with one another under ordinary conditions. Discrepancies between the summer and winter PC indices arising in the course of magnetospheric substorms are analyzed in this paper. It is argued that the channel of enhanced conductivity, formed in the auroral oval owing to intense auroral particle precipitation, strongly improves the conditions for closure of the Region 1 field-aligned currents in the winter dark polar region but only trivially affects the conditions of the Region 1 FAC closure in the summer sunlit ionosphere. Since the coefficients describing the relationship between E_KL and the polar cap magnetic activity were derived for statistically justified (i.e., mean) conditions, their application to such abnormal situation, as intense field-aligned currents in the winter dark polar region, leads to overestimation of the winter PC index. The summer and winter PC indices level off as soon as the intense auroral particle precipitation terminates and the auroral ionosphere in the winter and summer polar caps returns to the ordinary (statistically justified) state.     

Using vegetation indices and texture measures to estimate vegetation fractional coverage (VFC) of planted and natural forests in Nanjing city,China

Vegetation fractional coverage (VFC) is an important vegetation parameter affecting exchanges of carbon, water, energy between the atmosphere and surface. In this study, the applicability of tonal and texture measures calculated using an IKONOS_2 image in retrieving VFC of forests was investigated in the urban area of Nanjing city, China. Four spectral vegetation indices (VI) and six texture measures (TEX) were related to VFCs acquired from in situ measurements. Models for estimating VFC based on VIs or/and TEXs were established and validated for planted low broad-leaf forest plots (PLB), planted mature forest plots (PMF), natural broad-leaf forest plots (NBF), and all forest plots (ALLv), respectively. The results show that high spatial resolution remote sensing data is applicable to estimate VFC in urban areas, and TEXs may act as effective supplements of vegetation indices (VIs) for the retrieval of VFC. VIs are suitable for VFC estimation of mature forests (such as NBF and PMF) with high vegetation density, and TEXs can yield a more accurate estimate for planted forests (such as PLB and PMF) with regular spatial distribution if they are calculated with proper parameters, such as window size. The combination of VIs and TEXs improve the estimation of VFC if forest types are not previously differentiated. The results can be used as a reference for determining effective spectral or texture parameters in VFC estimation under similar environmental conditions according to vegetation maturity and regularity.     

外围投篮命中率的概率分析

利用几何概率的方法，解决投篮命中率问题，为将概率论应用到体育中去提供了新的思路。     

径向磁力轴承系统的时间响应特性分析

本文介绍了径向磁力轴承控制系统的二次型性能指标加仅矩阵的选择，径向磁力轴承的跟踪特性和抗干扰特性分析，并给出了仿真曲线。     

基于CCIP攻击模式的载机平台参数敏感性分析

以指标敏感性分析相关理论为指导,构建CCIP火控攻击模型、弹丸三维弹道方程模型;分析了CCIP攻击模式下载机平台传感器数据更新周期对命中精度以及网络负载的影响,设计单指标敏感性分析和综合指标敏感性分析。通过软件仿真,筛选出满足命中精度要求的载机平台传感器数据更新周期的优化指标组合。仿真结果可为载机平台传感器数据更新周期的设计提供参考。     

鱼雷过靶距离测试方法

通过分析模拟被鱼雷攻击目标的状态,提出了四点布阵测距和二点布阵测距的水听器配置方案,利用坐标转换、中心点法、最小距离法进行距离解算,可有效地对鱼雷过靶距离进行测试,同时,可利用深度信息对二点布阵测距的微量误差进行修正.     

基于引文索引的航空发动机咨询通告分析方法

引入了引文索引方法对整部航空规章中所有咨询通告进行整体上的分析,提出了一种基于引文索引的航空发动机咨询通告分析方法.首先,确定某部航空规章的咨询通告为研究对象,根据咨询通告间的相互引用关系画出引文网络图;然后,在引文网络图的基础上结合评价指标的定性分析和定量分析对咨询通告进行全面的研究分析;最后,应用此方法对美国某部航空规章的咨询通告进行实例分析,同时也说明了基于引文索引的航空发动机咨询通告分析方法的可行性.     

基于混沌遗传算法优化神经网络方法的救护直升机效能评估与指标优化研究

采用遗传算法与神经网络相结合的方法研究救护直升机的效能评估,建立了评估指标与效能评估值的非线性关系,克服了传统线性评估方法的不足,结果表明GA-BP算法能够很好地评估救护直升机效能,并保持了很高的精确度;同时,采用BP-GA算法对救护直升机的指标进行了优化,结果同样表明该方法可以有效地优化救护直升机的指标.     

情报研究成果的AHP-F评价

本文采用层次分析法与模糊数学综合译判法相结合的方法——AHP-F法,提出了综合情报评判研究成果指标体系和方法,旨在实现对情报研究成果的科学有效的管理。