Application of higher order spectral features and support vector machines for bearing faults classification

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.     

PMU based voltage security assessment of power systems exploiting principal component analysis and decision trees

The need for feature selection and dimension reduction is felt as a fundamental step in security assessment of large power systems in which the number of features representing the state of power grids dramatically increases. These large amounts of attributes are not proper to be used for computational intelligence (CI) techniques as inputs, because it may lead to a time consuming procedure with insufficient results and they are not suitable for on-line purposes and updates.This paper proposes a combined method for an online voltage security assessment in which the dimension of the token data from phasor measurement units (PMUs) is reduced by principal component analysis (PCA). Then, the features with different stability indices are put into several categories and feature selection is done by correlation analysis in each category. These selected features are then given to decision trees (DTs) for classification and security assessment of power systems.The method is applied to 39-bus test system and a part of Iran power grid. It is seen from the results that the DTs with reduced data have simpler splitting rules, better performance in saving time, reasonable DT error and they are more suitable for constant updates.     

基于静态顶空-气质联用法分析酱香型白酒空杯香气成分

采用静态顶空与气质联用（GC-MC）技术测定酱香型白酒空杯留香中挥发性香气成分。模拟品酒师感官品评过程中空杯留香方法，通过单因素试验分别考察平衡温度、平衡时间对酱香型白酒空杯留香香气成分的影响。结果表明，经过样品预处理的空杯在80 ℃平衡15 min的静态顶空条件下，检测出酱香型白酒中空杯留香香气成分最多。在此优化条件下，共检测出酱香型白酒中空杯留香的香气成分34种，通过香气活力值（OAV）评价香气贡献情况，确定出具有香气贡献的关键化合物（OAV＞1）共11种，以酯类、醛类、酸类香气为主。     

Laplacian regularized robust principal component analysis for process monitoring

Principal component analysis (PCA) is one of the most widely used techniques for process monitoring. However, it is highly sensitive to sparse errors because of the assumption that data only contains an underlying low-rank structure. To improve classical PCA in this regard, a novel Laplacian regularized robust principal component analysis (LRPCA) framework is proposed, where the “robust” comes from the introduction of a sparse term. By taking advantage of the hypergraph Laplacian, LRPCA not only can represent the global low-dimensional structures, but also capture the intrinsic non-linear geometric information. An efficient alternating direction method of multipliers is designed with convergence guarantee. The resulting subproblems either have closed-form solutions or can be solved by fast solvers. Numerical experiments, including a simulation example and the Tennessee Eastman process, are conducted to illustrate the improved process monitoring performance of the proposed LRPCA.     

快干羟基丙烯酸酯乳液的合成及性能研究

采用核壳乳液聚合方法制备了羟值为 1.5%的低羟丙烯酸酯乳液。研究了聚合物理论 T_g、壳中羟基质量分数、核聚合物 Hansch参数对双组分漆膜干燥速度的影响。结果表明：漆膜干燥速度随理论 T_g、壳中羟基质量分数及核聚合物 Hansch参数的增加而加快，当 T_g为 50 ℃、壳中羟基质量分数为 1.1%、核聚合物 Hansch参数为 3.43时，漆膜于 25 ℃、60%相对湿度下的实干时间可缩短至 95 min，在大巴、工程机械涂装等需要快干的领域具备潜在应用价值。     

一种基于双重ddPCR的肉制品中牛源性成分的定量分析方法

将低价肉类原料掺入高价肉制品是一种典型的肉类掺假方式，为准确鉴别牛肉及牛肉制品的真伪，建立一种基于双重微滴式数字聚合酶链式反应（droplet digital polymerase chain reaction，ddPCR）的牛源性成分定量分析方法。通过对14 种动物的Beta-actin单拷贝基因序列进行分析，设计牛源性特异性引物、探针与动物源性成分通用引物、探针并建立双重ddPCR体系，推导出牛源性成分质量与其特异性扩增拷贝数之间的计算公式，对牛源性成分进行定量检测该方法得到牛源性成分质量M牛（mg）与其特异性扩增拷贝数浓度C牛（copies/μL）之间的计算公式为M牛=0.033C牛＋2.37，对已知牛肉质量的混合肉样品与不同部位的牛肉样品进行检测，结果显示牛肉定量检测值与实际质量基本一致。同时拷贝数相对含量可辅助判断肉制品中是否存在非牛源性的其他动物源性成分掺假。对市售样品的检测发现，存在目标肉样含量不达标以及不同程度的动物源性成分等掺假现象，说明该检测方法可为牛肉制品掺假量化判定和混合源性产品分级鉴定提供技术支撑。     

基于XML的信息物理融合系统组件建模与仿真

信息物理融合系统（CPS）涉及多种计算模型的集成和协同工作，针对CPS设计方法不统一、重塑性差、复杂度高、难以协同建模验证等问题，提出一种结构化、可描述行为的异元组件模型。首先，用统一组件建模方法进行建模，解决模型不开放问题；然后，用可扩展标记语言（XML）规范描述各类组件，解决不同计算模型描述语言不一致和不可扩展问题；最后，用多级开放组件模型的协同仿真验证方式进行仿真验证，解决验证的不可协同问题。通过通用组件建模方法、XML组件规范描述语言以及验证工具平台XModel对医用恒温箱进行了建模、描述和仿真。医用恒温箱的案例表明，这种模型驱动建立可重塑异元组件并确认其设计正确性的过程，支持信息物理协同设计和边构建边纠正，可避免在系统实现过程中发现问题时再进行反复修改。     

Semiconductor‐ionic materials could play an important role in advanced fuel‐to‐electricity conversion

Functional semiconductor‐ionic materials can be used to realize a single component or so‐called “three‐in‐one” fuel cell design. Such materials integrate the functionalities of fuel cell's anode, electrolyte, and cathode into one component. The underlying principle of a single‐component fuel cell design combines material band structures with ionic species/transport. The performance values of such devices could exceed that of traditional fuel cells. This could represent a major progress in fuel cell science and technology and lies grounds for a new direction of fuel cell R&D and commercialization.     

Preparation and properties of fluorinated silicon two‐component polyurethane hydrophobic coatings

A polyurethane (PU) hydrophobic coating was prepared by the two‐component method, polycarbonate diol and isophorone diisocyanate becoming a two‐phase composition. The PU films with hydrophobic surface were prepared by establishing a rough structure on the surface of silica (SiO₂) modified with silane coupling agents (γ‐(2,3‐epoxypropoxy)propytrimethoxysilane (KH560) and (heptadecafluoro‐1,1,2,2‐tetradecyl)trimethoxysilane (FAS)). First, the surface of SiO₂ was covered by a layer of hydrophobic methyl and fluorocarbon (C–F) groups. Then, the SiO₂ and modified SiO₂ were obtained by the introduction of KH560 and FAS with the silanol reaction by ultrasonic stirring. The effect of SiO₂ and modified SiO₂ on the structure and hydrophobic properties of PU was investigated by a series of test instruments. The results showed that the introduction of SiO₂ and modified SiO₂ was beneficial for increasing the roughness of the PU coating surface; the roughness of FAS/SiO₂‐PU could reach up to 14.790 nm, four times better than pure PU. A hydrophobic modified PU coating with water contact angle 123° was fabricated by using the hydrophobic C–F group FAS as a low surface energy material and establishing a micro rough structure on the surface of PU. Moreover, PU modified with KH560 and FAS can reduce the glass transition temperature (T_g) of soft segments, resulting in improvement of micro‐phase separation. © 2020 Society of Chemical Industry     

Isogeometric topology optimization based on energy penalization for symmetric structure

We present an energy penalization method for isogeometric topology optimization using moving morphable components (ITO–MMC), propose an ITO–MMC with an additional bilateral or periodic symmetric constraint for symmetric structures, and then extend the proposed energy penalization method to an ITO–MMC with a symmetric constraint. The energy penalization method can solve the problems of numerical instability and convergence for the ITO–MMC and the ITO–MMC subjected to the structural symmetric constraint with asymmetric loads. Topology optimization problems of asymmetric, bilateral symmetric, and periodic symmetric structures are discussed to validate the effectiveness of the proposed energy penalization approach. Compared with the conventional ITO–MMC, the energy penalization method for the ITO–MMC can improve the convergence rate from 18.6% to 44.5% for the optimization of the asymmetric structure. For the ITO–MMC under a bilateral symmetric constraint, the proposed method can reduce the objective value by 5.6% and obtain a final optimized topology that has a clear boundary with decreased iterations. For the ITO–MMC under a periodic symmetric constraint, the proposed energy penalization method can dramatically reduce the number of iterations and obtain a speedup of more than 2.