Actuator fault tolerant control using adaptive RBFNN fuzzy sliding mode controller for coaxial octorotor UAV

In this paper, a robust controller for a Six Degrees of Freedom (6 DOF) coaxial octorotor helicopter control is proposed in presence of actuator faults. Radial Base Function Neural Network (RBFNN), Fuzzy Logic Control approach (FLC) and Sliding Mode Control (SMC) technique are used to design a controller, named Fault Tolerant Control (FTC), for each subsystem of the octorotor helicopter. The proposed FTC scheme allows avoiding difficult modeling, attenuating the chattering effect of the SMC, reducing the rules number of the fuzzy controller, and guaranteeing the stability and the robustness of the system. The simulation results show that the proposed FTC can greatly alleviate the chattering effect, good tracking in presence of actuator faults.     

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.     

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。     

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.     

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.     

A novel adaptive convolutional neural network for fault diagnosis of hydraulic piston pump with acoustic images

As an essential part of hydraulic transmission systems, hydraulic piston pumps have a significant role in many state-of-the-art industries. Thus, it is important to implement accurate and effective fault diagnosis of hydraulic piston pumps. Owing to the heavy reliance of shallow machine learning models on the expertise and experience of engineers, fault diagnosis based on deep models has attracted significant attention from academia and industry. To construct a deep model with good performance, it is necessary and challenging to tune the hyperparameters (HPs). Since many existing methods focus on manual tuning and use common search algorithms, it is meaningful to explore more intelligent algorithms that can automatically optimize the HPs. In this paper, Bayesian optimization (BO) is employed for adaptive HP learning, and an improved convolutional neural network (CNN) is established for fault feature extraction and classification in a hydraulic piston pump. First, acoustic signals are transformed into time–frequency distributions by a continuous wavelet transform. Second, a preliminary CNN model is built by setting initial HPs. The range of each HP to be optimized is identified. Third, BO is employed to select the optimal combination of HPs. An improved model called CNN-BO is constructed. Finally, the diagnostic efficiency of CNN-BO is analyzed using a confusion matrix and t-distributed stochastic neighbor embedding. The classification performance of different models is compared. It is found that CNN-BO has a higher accuracy and better robustness in fault diagnosis for a hydraulic piston pump. This research will provide a basis for ensuring the reliability and safety of the hydraulic pump.     

FPGA互连测试中的反馈桥接故障覆盖问题

现场可编程门阵列(FPGA)内部资源众多,其中互连资源出现故障的概率远远高于片内其他资源,而在以往许多互连测试研究中,所生成的测试配置存在无法覆盖反馈桥接故障的难题,所以较难有测试配置实现故障列表的100%覆盖。因此通过约束桥接故障只发生在单个查找表(LUT)内的信号线上,并结合单项函数,对反馈桥接故障模型进行优化改进,从根本上解决难题；然后对优化后的反馈桥接故障设置相应的约束条件,再使用布尔可满足性理论(SAT)生成满足约束条件的测试配置。采用优化后的故障模型对ISCAS"89基准电路进行了测试配置生成实验,结果表明生成的测试向量解决了反馈桥接故障的覆盖难题,并且在实现故障列表的100%覆盖下,优化后的故障模型所需要的测试配置数最少。     

Efficient fault diagnosis method of PEMFC thermal management system for various current densities

The temperature of a fuel cell has a considerable impact on the saturation of a membrane, electrochemical reaction speed, and durability. So thermal management is considered one of the critical issues in polymer electrolyte membrane fuel cells. Therefore, the reliability of the thermal management system is also crucial for the performance and durability of a fuel cell system. In this work, a methodology for component-level fault diagnosis of polymer electrolyte membrane fuel cell thermal management system for various current densities is proposed. Specifically, this study suggests fault diagnosis using limited data, based on an experimental approach. Normal and five component-level fault states are diagnosed with a support vector machine model using temperature, pressure, and fan control signal data. The effects of training data at different operating current densities on fault diagnosis are analyzed. The effects of data preprocessing method are investigated, and the cause of misdiagnosis is analyzed. On this basis, diagnosis results show that the proposed methodology can realize efficient component-level fault diagnosis using limited data. The diagnosis accuracy is over 92% when the residual basis scaling method is used, and data at the highest operating current density is used to train the support vector machine.     

招平断裂带中段金矿床控矿条件与成矿规律研究

招平断裂带中段为胶东地区重要的金成矿带，目前已查明的金矿床主要包括夏甸大型金矿床和大尹格庄特大型金矿床，姜家窑和曹家洼中型金矿床，以及焦格庄小型金矿床。研究人员对招平断裂带中段各类金矿床的构造控矿条件、分布规律和矿体赋存规律仍缺乏较系统的认识，亟待开展进一步研究。通过对金矿区地质特征、金矿床地质特征、构造控矿特征和金矿成矿规律进行分析和总结，得出大尹格庄特大型金矿主要受招平主干断裂控制；夏甸大型金矿床主要受控于主干控矿断裂下盘伴生、派生的低级和低序次断裂，夏甸北耩矿体主要受控于主断裂下盘岩体局部张性显微裂隙带；招平断裂带中段大尹格庄特大型金矿床、夏甸特大型金矿床和焦格庄中、小型金矿床大致呈等间距分布，近EW向断裂与招平断裂的交会部位是有利成矿部位；断裂的拐弯或交会部位为主要赋矿部位。