[可靠性建模及试验技术]重型机床液压元件油液污染退化量分布剖面可靠性建模

针对重型机床液压系统故障频繁且多与油液中的固态颗粒污染物相关的问题，进行了油液污染趋势变化试验。通过时域分析获得了油样颗粒数的有量纲和量纲一参数，通过Q-Q图和K-S检验分析有量纲参数，污染颗粒数是退化量服从正态分布的退化数据。进行了油液污染与环境相关性分析试验，采用相关系数法分析得到，颗粒数变化量与一定范围内的温度、流量、压力的相关性小；将液压元件分为管路、阀、过滤器三类，用直径5 μm左右的颗粒和直径大于15 μm的颗粒分别研究管路及阀件的堵塞和磨损情况，以过滤器过滤精度大小的颗粒研究过滤器的堵塞情况，设定ISO4406标准20/17级对应的颗粒数为阈值，利用退化量分布建立了液压元件单一故障模式的可靠性模型；利用竞争失效模型将上述模型融合为多故障模式下的可靠性模型。     

Grape skin extracts as a sustainable source of antioxidants in an oil-in-water emulsion: an alternate natural approach to synthetic antioxidants using principal component analysis

Grape (Kyoho) skin, used to retard lipid oxidation in edible oil foods, was investigated to reduce lipid oxidation in an oil-in-water (O/W) emulsion during 20 day of storage. The antioxidant efficacy of Kyoho skin extracts in O/W emulsions was determined by the measurement of secondary oxidation products. Moreover, principal component analysis (PCA) was conducted to determine similarities between emulsions treated with or without Kyoho skin extracts and standards. The data revealed that Kyoho skin extracts exhibited >93% inhibition and reported a similar p-anisidine (4.30–20.71) and TBARS (6.08–11.15 mg MDA L⁻¹) values over the standards during 20 day of storage. PCA (PCs 1 (51.83%) and 2 (18.85%)) demonstrated a similarity in the contribution of Kyoho skin extracts over the synthetic antioxidants in O/W emulsion. Overall, these findings highlighted the possibility of using Kyoho skin extracts as natural antioxidants to decrease oxidative rancidity in foods.     

A dimensional reduction approach to modulate the core ruminal microbiome associated with methane emissions via selective breeding

The rumen is a complex microbial system of substantial importance in terms of greenhouse gas emissions and feed efficiency. This study proposes combining metagenomic and host genomic data for selective breeding of the cow hologenome toward reduced methane emissions. We analyzed nanopore long reads from the rumen metagenome of 437 Holstein cows from 14 commercial herds in 4 northern regions in Spain. After filtering, data were treated as compositional. The large complexity of the rumen microbiota was aggregated, through principal component analysis (PCA), into few principal components (PC) that were used as proxies of the core metagenome. The PCA allowed us to condense the huge and fuzzy taxonomical and functional information from the metagenome into a few PC. Bivariate animal models were applied using these PC and methane production as phenotypes. The variability condensed in these PC is controlled by the cow genome, with heritability estimates for the first PC of ~0.30 at all taxonomic levels, with a large probability (>83%) of the posterior distribution being >0.20 and with the 95% highest posterior density interval (95%HPD) not containing zero. Most genetic correlation estimates between PC1 and methane were large (≥0.70), with most of the posterior distribution (>82%) being >0.50 and with its 95%HPD not containing zero. Enteric methane production was positively associated with relative abundance of eukaryotes (protozoa and fungi) through the first component of the PCA at phylum, class, order, family, and genus. Nanopore long reads allowed the characterization of the core rumen metagenome using whole-metagenome sequencing, and the purposed aggregated variables could be used in animal breeding programs to reduce methane emissions in future generations.     

Novel preparation approach with a 2-step process for spherical particles with high drug loading and controlled size distribution using melt granulation: MALCORE®

A novel approach for preparing drug-containing particles (DCPs) with controlled size distribution and high drug loading was developed using melt granulation. This approach comprises two steps. First, melting component adsorbed particles (MAs) were prepared by mixing and heating the melting components with a porous carrier using a high shear granulator. Second, DCPs were prepared by layering the drug on MAs using a fluidized bed rotor granulator. The time taken for both steps was within 30 min. Adding the polymer in the second step remarkably increased the viscosity of the mixture of melting components and the polymer. Therefore, DCPs could be successfully loaded with a high amount of drug (70% w/w). The particle size distribution of the DCPs was narrow, and it depended on that of the MAs. The flowability of the DCPs was excellent, and the sphericity was close to 1. A unique particle formulation mechanism was suggested based on the observation of DCPs using scanning electron microscopy. The manufacturing time and DCP characteristics were not affected by the manufacturing scale. In conclusion, we have successfully developed a highly efficient novel approach for preparing optimal DCPs through melt granulation, named “Melt Adsorption and Layering with Porosity Core” (MALCORE®).     

Design of data feature-driven 1D/2D convolutional neural networks classifier for recycling black plastic wastes through laser spectroscopy

In this study, two types of convolutional neural network (CNN) classifiers are designed to handle the problem of classifying black plastic wastes. In particular, the black plastic wastes have the property of absorbing laser light coming from spectrometer. Therefore, the classification of black plastic wastes remains still a challenging problem compared to classifying other colored plastic wastes using existing spectroscopy (i.e., NIR). When it comes the classification problem of black plastic wastes, effective classification techniques by the laser spectroscopy of Fourier Transform-Infrared Radiation (FT-IR) with Attenuated Total Reflectance (ATR) and Raman to analyze the classification problem of black plastic wastes are introduced. Due to the strong ability of extracting spatial features and remarkable performance in image classification, 1D and 2D CNN through data features are designed as classifiers. The technique of chemical peak points selection is considered to reduce data redundancy. Furthermore, through the selection of data features based on the extracted 1D data with peak points is introduced. Experimental results demonstrate that 2DCNN classifier designed with the help of 2D data feature selection as well as 1DCNN classifier shows the best performance compared with other reported methods for classifying black plastic wastes.     

嵌套式复合型材构件制造技术及其专用机床

嵌套式冷轧复合型材构件制造技术包括平整、加强筋成型、第一辊压模具组成型、冲孔、主截面成型、第二辊压模具组的横截面成型、嵌套合抱、第一构件与第二构件嵌套抱合后成为复合型材构件与第三辊压模具组紧贴等工序组成,在冲孔工序之前先完成加强筋成型工序,保证冲孔加工后的孔距尺寸精度,显著提高产品质量;上述所有工序都是设置在同一条生产线上的专用机床连续自动完成,实现了嵌套式冷轧复合型材构件的自动化生产,节约工时,降低产品成本。     

零能耗太阳能住宅的工业化建造

当前建筑业正处于向工业化发展转型的重要时期,以中国国际太阳能十项全能竞赛(SD 2013)中的北京交通大学"i-Yard"参赛作品为例,重点从建筑部品系统的划分和工业化建造等方面进行解析,为零能耗、可持续建筑的模数化设计和快速工业化生产提供了有益的参考。     

一种基于可预测偏最小二乘法的故障检测方法

将可预测元分析(Fore CA)与偏最小二乘法(PLS)结合用于故障检测,在选取合适的可预测元的基础上,运用偏最小二乘回归,进一步提高模型对系统的预测能力,克服了偏最小二乘回归方法无法反映系统动态时序特性的缺陷,并构造CUSUM统计量和SPE统计量以检测故障是否发生。最后通过TE模型上的仿真实验结果表明:Fore PLS方法能有效检测慢漂移等故障。