Domain decomposition for an asymptotic geological fault modeling

We present in this paper a domain decomposition method to treat faults in geological basin modeling. The particularity of this model is that the faults whose widths are very small in comparison with the basin size, are not characterized as subdomains any more but as interfaces between sedimentary blocks. The originality of this work lies in the formulation of this new fault model and in the definition and the computation of the interface conditions between the subdomains. To cite this article: E. Flauraud et al., C. R. Mecanique 331 (2003).     

Railway wheel fault diagnosis using a fuzzy-logic method

A simple method for diagnosis of railway wheel defects using fuzzy-logic is proposed. The method is based on vibration measurements at different train speeds on healthy wheels and wheels with defects known a priori. The measured vibrations are statistically analyzed and confidence intervals for healthy and defective wheels depending on train speed and frequency of analysis are established. To facilitate the implementation of the method a fuzzy-logic is adopted. The fuzzy-logic model stores the obtained experience in a database and performs the decision making on damage extent and consequently the need of preventive maintenance. In view of the obtained results, the advantages and the limitations of the proposed approach as well as suggestions for further improvements are presented and discussed.     

Defect clusters and precipitation/oxidation of MgO-Co1−xO solid solution

MgO and Co_1−xO powders in 9:1 and 1:9 molar ratio (denoted as M₉C₁ and M₁C₉, respectively) were sintered and homogenized at 1600°C followed by annealing at 850°C and 800°C, respectively to form defect clusters and precipitates. Analytical electron microscopic observations indicated the protoxide remained as rock salt structure with complicated planar diffraction contrast for M₉C₁ sample, however with spinel paracrystal precipitated from the M₁C₉ sample due to the assembly of charge- and volume-compensating defects of the 4:1 type, i.e., four octahedral vacant sites surrounding one Co³⁺-filled tetrahedral interstitial site. The spacing of such defect clusters is 4.5 times the lattice spacing of the average spinel structure of Mg-doped Co_3−δO₄, indicating a higher defect cluster concentration than undoped Co_3−δO₄. The {111} faulting of Mg-doped Co_3−δO₄/Co_1−xO in the annealed M₁C₉ sample implies the possible presence of zinc blend-type defect clusters with cation vacancies assembled along oxygen close packed (111) plane.     

Novel CD-MOF NIR-II fluorophores for gastric ulcer imaging

Gastric ulcers are one of the most common stomach diseases that often accompanied by inflammation, congestion, edema, scar tissue formation, and pyloric obstruction. Fiberoptic endoscopy and X-ray analysis of the upper GI tract have become the diagnostic procedure of choice for patients. However, conventional diagnosis technology is either invasive or radioactive. Herein, a novel CD-MOF NIR-II fluorophore (GPs-CH1055) was developed. The relative fluorophore intensity was largely consistent at various media and pH buffers, and it can swell into gel particles in solvents and be completely expelled from the gastrointestinal tract without being assimilated. GPs-CH1055 has been further evaluated in vivo, and exhibited strong retention effect on the gastric ulcer sites, bright NIR-II signals with high spatial and temporal resolution. Therefore, GPs-CH1055 shows great promise for realizing real-time gastric ulcer imaging and diagnosis.     

一种嵌入式软件逻辑覆盖测试方法研究

针对嵌入式软件测试覆盖率低的问题,本文提出了基于软件故障注入的逻辑覆盖测试方法,首先就嵌入式系统常用传感器建立故障模式库,设计了嵌入式软件故障注入系统;其次选取中间层作为故障注入点,研究基于VxWorks653嵌入式操作系统的故障注入实现方式,并通过分析故障信号在软件系统中的传播,提出优化测试用例的方法;最后通过实验验证了该方法可有效提高容错设计功能、冗余设计功能、故障检测功能测试的逻辑覆盖率;有助于提高嵌入式软件的可靠性。     

Covariate model-based fault tree analysis for risk assessment in chemical process industries: A case study of a chlorine manufacturing facility

Fault tree analysis (FTA) is a promising quantitative technique for risk analysis in chemical process industries (CPIs). In FTA, a certain sequence of basic events (causes) leads to one specific Top event (critical event of interest). However, the conventional fault tree analysis has the limitations of staticity and uncertainty. The staticity in conventional FTA arises due to its inability to accommodate time-dependent characteristics of the process system. Whereas uncertainty primarily lies in the failure probability data of basic events. This paper proposes an innovative methodology that uses a time-dependent covariate model to update the failure probability values of major contributing basic events in FTA. A novel subclass of the family of phase-type distributions is used to model the covariates corresponding to the basic events. The newly developed methodology is applied for a case study in a chlorine manufacturing facility to estimate the chlorine release probability. The blockage in the pipeline was identified as the significant reason for chlorine release from expert opinion and sensitivity analysis. The results of the proposed model of FTA are compared with that of conventional FTA.     

石墨烯嵌锂的拉曼成像

锂离子电池由于具有高能量密度,高循环寿命,低自放电率的优势,成为当前使用最为广泛的储能器件。层状材料是极为常用的负极材料,其微观嵌锂行为的研究对提高电池的能量密度和循环寿命有重要意义。本工作发展了一种新的平板微电池结构,可用于研究锂离子在各类二维层状纳米材料中的嵌锂行为。我们用机械剥离的单片少层石墨烯为正极,热蒸镀的锂金属为负极,构成石墨烯电池,用恒电压放电的方法进行嵌锂测试。采用拉曼成像技术收集石墨烯G峰信号的空间分布,实现对锂的嵌入过程的显微观测。发现了锂在石墨烯中沿层间扩散迁移,以及石墨烯断层对锂扩散的阻碍作用。这些结果有助于理解放电时锂在石墨烯电极中扩散过程,并且这项研究开发的平板微电池结构可用于多种材料的电化学过程中的微观过程表征,同时可实现与光学、电学、电子显微学等多种表征手段的兼容,具有较好的应用前景。     

Machine learning as an online diagnostic tool for proton exchange membrane fuel cells

Proton exchange membrane fuel cells are considered a promising power supply system with high efficiency and zero emissions. They typically work within a relatively narrow range of temperature and humidity to achieve optimal performance; however, this makes the system difficult to control, leading to faults and accelerated degradation. Two main approaches can be used for diagnosis, limited data input which provides an unintrusive, rapid but limited analysis, or advanced characterisation that provides a more accurate diagnosis but often requires invasive or slow measurements. To provide an accurate diagnosis with rapid data acquisition, machine learning methods have shown great potential. However, there is a broad approach to the diagnostic algorithms and signals used in the field. This article provides a critical view of the current approaches and suggests recommendations for future methodologies of machine learning in fuel cell diagnostic applications.     

Wavelet subspace decomposition of thermal infrared images for defect detection in artworks

Health of ancient artworks must be routinely monitored for their adequate preservation. Faults in these artworks may develop over time and must be identified as precisely as possible. The classical acoustic testing techniques, being invasive, risk causing permanent damage during periodic inspections. Infrared thermometry offers a promising solution to map faults in artworks. It involves heating the artwork and recording its thermal response using infrared camera. A novel strategy based on pseudo-random binary excitation principle is used in this work to suppress the risks associated with prolonged heating. The objective of this work is to develop an automatic scheme for detecting faults in the captured images. An efficient scheme based on wavelet based subspace decomposition is developed which favors identification of, the otherwise invisible, weaker faults. Two major problems addressed in this work are the selection of the optimal wavelet basis and the subspace level selection. A novel criterion based on regional mutual information is proposed for the latter. The approach is successfully tested on a laboratory based sample as well as real artworks. A new contrast enhancement metric is developed to demonstrate the quantitative efficiency of the algorithm. The algorithm is successfully deployed for both laboratory based and real artworks.     

基于功率谱包络能量和SVM的舰用发动机故障诊断

发动机是军舰上的重要部件之一,其稳定性对军舰的正常航行具有重要影响。以舰用发动机关键部件(主泵轴承)为具体研究对象,提出了基于功率谱包络能量和支持向量机相结合的故障诊断方法。首先获取了大量可表征舰用发动机主泵轴承健康状态的振动加速度信息,对其进行功率谱分析,获得其功率谱的包络能量;以获取的舰用发动机主泵轴承功率谱的包络能量构建特征向量,并设计基于SVM的舰用发动机主泵轴承故障诊断模型,对主泵轴承的故障进行诊断研究。研究结果表明,采用基于功率谱包络能量和SVM相结合的舰用发动机关键部件故障诊断方法,可以很好实现主泵轴承的故障诊断效能,为舰用发动机主泵轴承故障诊断的工程应用奠定了基础。