Chemical process fault diagnosis based on enchanted machine‐learning approach

In the chemical industry, fault diagnosis is a challenging task due to the complexity of chemical equipment. This paper proposes a machine learning‐based approach to achieve the goal of fault diagnosis. First, in order to reduce the impact of redundant features, support vector machine recursive feature elimination (SVMRFE) is used to select important features. The trained probabilistic neural network (PNN) is then used for fault diagnosis. Considering that the diagnostic performance is affected by its hidden layer element smoothing factor (σ), the modified bat algorithm (MBA) is used to optimize the PNN to obtain optimal global parameter values. The MBA adopts a better optimization mechanism than the basic algorithm and achieves excellent global convergence. It can globally optimize the smoothing factor, which effectively improves the fault diagnosis ability of the PNN. During the testing of the Tennessee Eastman (TE) process data set, we evaluate the performance of the proposed model by comparing the F₁‐score and accuracy of the different methods. The charts provided describe the fault diagnostic results and classification for the different models. The results indicate that the MBA has a better optimization ability than other traditional optimization algorithms. At the same time, the combination method proposed in this paper is also superior to others and can significantly improve the accuracy of TE process fault diagnosis.     

轧机垂直系统动力学参数辨识

 针对轧机垂直系统动力学参数可信度不足等问题，提出一种基于实测数据的改进粒子群算法辨识轧机垂直系统动力学参数的方法。首先,将轧机垂直系统刚度和阻尼考虑成达芬振子和范德波尔振子，构建轧机垂直系统非线性动力学模型，并对粒子群算法进行改进；然后，通过数值仿真算例辨识得到系统在感染噪声和不含噪声时的动力学参数，验证了该算法的有效性；最后，以现场某轧机垂直系统为研究对象，基于现场实测数据，应用该算法进行辨识，得到了轧机垂直系统动力学参数估计，通过实测位移、速度和加速度信号分别与辨识后的位移、速度和加速度信号进行对比，证明该方法辨识轧机垂直系统动力学参数结果可靠，具有一定的工程应用价值。     

不同类型断层控制油气垂向富集的差异——以渤海湾盆地歧口凹陷歧南斜坡区为例

渤海湾盆地历经多期构造变动，断层圈闭发育普遍。断层作为断层圈闭的边界条件，在油气成藏过程中起着至关重要的作用。以断层圈闭较发育的歧南斜坡区为例分析了同向断层和反向断层控制油气垂向富集的差异，基于三维地震资料，从断层形成过程和断层圈闭分布出发，剖析了断层遮挡圈闭的成因及分布规律。结合油水分布规律，基于油藏解剖并应用储层定量荧光技术明确了不同类型断层控藏的差异及其形成机理。研究表明：①断层分段生长作用和断层上盘与下盘的差异活动是同向断层控制下断层遮挡圈闭形成的主要原因，斜坡区反向断层控制形成的断层圈闭则是断块掀斜翘倾作用所致；②同向断层圈闭发育在断层上盘分段点位置，只有当同向断层进入"硬连接"阶段方可形成断层圈闭，反向断层圈闭形成在断层下盘，在反向断层活动初期便可形成；③同向断层既可以控制油气在多套含油气系统中聚集成藏，也可在一套含油气系统中富集，而反向断层往往控制油气在一套含油气系统中富集；④不同类型断层控制油气垂向聚集的差异与圈闭发育位置以及断-盖配置有关。采用泥岩涂抹系数对沙河街组一段中部盖层控制的含油气系统的垂向调整进行定量评价，泥岩涂抹系数低于3.5时油气保存，泥岩涂抹系数高于3.5时油气垂向渗漏。     

Simultaneous identification of structural stiffness and mass parameters based on Bare-bones Gaussian Tree Seeds Algorithm using time-domain data

This paper mainly illustrates the Tree Seeds Algorithm (TSA) to tackle structural damage identification problem. The damage model is simulated by the alterations of both stiffness and mass parameters. The objective function is introduced by minimizing the differences between the measured and calculated acceleration data. To enhance the performance of the standard TSA, two modifications including the bare-bones Gaussian updated mechanism and the withering process are introduced. The modified algorithm is named after the BGTSA. In the numerical simulation part, the BGTSA is firstly used to make comparisons with several state-of-the-art algorithms on the CEC05. Secondly, the BGTSA is utilized to deal with the structural damage identification problem by optimizing the acceleration-based nonlinear objective function. Numerical experiments involving a simply supported beam and a truss are carried out to verify the effectiveness of the proposed algorithm. The final results show that with low amount of acceleration data, the BGTSA can acquire better identification results compared with other evolutionary algorithms. Therefore the proposed algorithm could be viewed as a potential tool to solve the structural damage identification problem.     

渤海湾盆地沙垒田地区新近纪走滑断裂发育特征及其对油气富集的控制作用

沙垒田凸起地区是渤海海域西部新近系油气最为富集的区域，为了明确新近系走滑断裂发育特征及对油气富集的控制作用，依托连片高精度三维地震、钻井取心等资料，对走滑断裂类型及发育特征进行了详细刻画，并对走滑断裂与成藏要素之间的关系进行了探讨。综合研究表明：研究区发育断穿基底走滑断裂、盖层走滑断裂及伸展-走滑断裂三种不同类型走滑断裂；走滑断裂对圈闭的控制作用表现为在不同类型走滑断裂控制下，断裂带内及断裂带间形成了多种类型圈闭，其中大型披覆背斜圈闭、断鼻、断背斜圈闭为有利圈闭，小型断块圈闭为不利圈闭；走滑断裂对输导体系的控制作用体现在断裂活动控制了骨架砂体输导体系及断裂输导体系的形成发育，有利于周边凹陷成熟烃源岩排烃及运移输导；走滑断裂对储层的控制作用表现为走滑作用形成的高角度裂缝提高了储层渗透率，改善了储层物性。新近纪普遍发育的走滑断裂是研究区油气富集的主控因素。     

Elastic Properties and Stacking Fault Energies of Borides,Carbides and Nitrides from First-Principles Calculations

Owing to the excellent elastic properties and chemical stability, binary metal or light element borides, carbides and nitrides have been extensively applied as hard and low-compressible materials. Researchers are searching for harder materials all the time. Recently, the successful fabrication of nano-twinned cubic BN(Tian et al. Nature 493:385–388, 2013) and diamond(Huang et al. Nature 510:250–253, 2014) exhibiting superior properties than their twin-free counterparts allows an efficient way to be harder. From this point of view, the borides, carbides and nitrides may be stronger by introducing twins, whose formation tendency can be measured using stacking fault energies(SFEs). The lower the SFEs, the easier the formation of twins. In the present study, by means of first-principles calculations, we first calculated the fundamental elastic constants of forty-two borides, seventeen carbides and thirty-one nitrides, and their moduli, elastic anisotropy factors and bonding characters were accordingly derived. Then, the SFEs of the {111} 112 glide system of twenty-seven compounds with the space group F43 m or Fm3m were calculated. Based on the obtained elastic properties and SFEs, we find that(1) light element compounds usually exhibit superior elastic properties over the metal borides, carbides or nitrides;(2) the 5 d transitionmetal compounds(ReB_2, WB, OsC, RuC, WC, OsN_2, TaN and WN) possess comparable bulk modulus( B) with that of cBN( B = 363 GPa);(3) twins may form in ZrB, HfN, PtN, VN and ZrN, since their SFEs are lower or slightly higher than that of diamond(SFE = 277 mJ/m~2). Our work can be used as a valuable database to compare these compounds.     

基于线状特性的大数据地铁用户识别与定位方法

从MR原始数据特性出发研究数据结构与字段,采用基于线状特性分析MR大数据的地铁用户识别过滤方法和位置定位方法,该方法经过验证能够有效输出地铁用户数据并定位在地图上,解决人工地铁测试效率低且测试片面的问题,实现基于大数据的自动化地铁网络评估,提供海量的有效定位数据用于地铁评估与分析。     

Reliability-based fault analysis models with industrial applications: A systematic literature review

Effective and early fault detection and diagnosis techniques have tremendously enhanced over the years to ensure continuous operations of contemporary complex systems, control cost, and enhance safety in assets-intensive industries, including oil and gas, process, and power generation. The objective of this work is to understand the development of different fault detection and diagnosis methods, their applications, and benefits to the industry. This paper presents a contemporary state-of-the-art systematic literature survey focusing on a comprehensive review of the models for fault detection and their industrial applications. This study uses advanced tools from bibliometric analysis to systematically analyze over 500 peer-reviewed articles on focus areas published since 2010. We first present an exploratory analysis and identify the influential contributions to the field, authors, and countries, among other key indicators.  A network analysis is presented to unveil and visualize the clusters of the distinguishable areas using a co-citation network analysis. Later, a detailed content analysis of the top-100 most-cited papers is carried out to understand the progression of fault detection and artificial intelligence–based algorithms in different industrial applications. The findings of this paper allow us to comprehend the development of reliability-based fault analysis techniques over time, and the use of smart algorithms and their success. This work helps to make a unique contribution toward revealing the future avenues and setting up a prospective research road map for asset-intensive industry, researchers, and policymakers.     

深圳抽水蓄能电站监控新技术应用研究与实践

通过对安防、工业电视和门禁等新技术的研究,设计了一套先进的监控系统,包括硬件平台、系统管理、应用软件,利用深圳抽水蓄能电站已形成的光纤网络通道把分布在方圆10 km范围内多个站端设备以环路的方式连接起来,把可靠成熟的新技术融合到整个平台。自首台机组成功投运,监控系统为电站正常运行保驾护航,高清摄像机、视觉识别、行为分析、边界防护等新技术的应用为工程项目增添了亮点,使电站现场管理更加智能化、规范化。     

基于变量决策层的启发式变量选择策略

启发式分支策略是SAT求解器中不可或缺的一部分，直接影响求解器的效率。早期的启发式分支决策需要遍历整个子句数据库，效率比较低。随着独立变量状态衰减和（Variable State Independent Decaying Sum, VSIDS）分支策略的出现，SAT求解器的效率有所提高，但VSIDS策略以及它的延伸策略中变量的增量都只是与变量的冲突次数有关，没有考虑变量的决策层在分支策略中的影响。因此当发生冲突时，如果与冲突有关的变量的得分相同而决策层不同时，对于变量的选择就具有随机性。基于此，本文在阐述变量的决策层的重要性之后在VSIDS策略的基础上，提出一种基于变量决策层的启发式变量选择策略--HSVDL策略。然后通过实例显示HSVDL策略在变量决策阶段选择决策层低的变量的可能性比选择决策层高的变量的可能性要大，而且得分比较小，减少了内存的占用。最后通过实验表明HSVDL策略能够求解出更多的实例，求解器的效率也有所提高，说明该策略有一定的优势。