Study on the influence of segmented fuel cell by grooving method and its application in oxygen starvation diagnosis

The oxygen starvation in fuel cells is an important reason for the deterioration of durability. The segmented fuel cell is a method to study the gas distribution inside the fuel cell. In order to study the influence of the grooving method on segmented fuel cell and its application in oxygen starvation diagnosis, a five-serpentine-channel three-dimensional two-phase simulation model is established by FLUENT. Through steady-state simulation, the effect of grooving method on fuel cell performance is studied. The overall performance (polarization curve) of the fuel cell drops slightly, but the current density distribution on the anode graphite plate changes greatly due to the grooves. The “current concentration” phenomenon is proposed based on the current density distribution. Through dynamic simulation, the oxygen starvation under current load mode and voltage load mode is simulated, and the “starvation coefficient” is defined as an oxygen starvation diagnostic index. In the current load mode, the “starvation coefficient” never exceed 15%, because when the oxygen starvation is severe, the simulation cannot converge or even cannot maintain, which corresponds to the voltage reversal in reality. However, in the voltage load mode, the “starvation coefficient” can reach up to 100%. The conclusions have important guiding significance for the judgment of the internal reaction uniformity of the segmented fuel cell by grooving method and provide a theoretical basis for judging whether a fuel cell is out of oxygen by segmented fuel cell.     

Remaining useful life prediction for degrading systems with random shocks considering measurement uncertainty

Degradation data have been widely used for the remaining useful life (RUL) prediction of systems. Most existing works apply a preset model to capture the degradation process and focus on the degradation process without shocks or constant shock effects. More generally, the actual degradation path is unobservable due to the existence of measurement uncertainty, which interferes with the determination of the degradation model. Besides, the effect of random shocks is usually fluctuating. Given these problems, a general degradation model with the random shock fluctuant effects considering the measurement uncertainty is first developed to describe the degradation process, and a two-step approach combining the arithmetic average filter and the Bayesian information criterion is adopted to identify the degradation path. Subsequently, the transfer processes of the actual degradation state and the abrupt change caused by shocks are depicted using a two-dimensional state-space model, and an expectation-maximization algorithm combined with the particle filtering is developed for parameter estimation. Furthermore, the explicit solution of RUL distribution is obtained when only considering harmful shocks, while a simulation method of RUL distribution is provided when both harmful and beneficial shocks exist. Finally, the effectiveness of the proposed method is verified by a numerical example and two practical case studies.     

Deep multi-scale adversarial network with attention: A novel domain adaptation method for intelligent fault diagnosis

Data driven-based intelligent fault diagnosis methods, as a promising approach, have been widely employed in the health management and maintenance decision of rotating machinery. However, the domain shift phenomenon caused by internal and external interference inevitably exists in practical application scenarios, which significantly deteriorates the performances of the intelligent diagnosis model. And the preparation of label information in real complex scenes is usually time-consuming and expensive. To overcome these challenges, a novel unsupervised domain adaptation framework named deep multi-scale adversarial network with attention (MSANA) is introduced for machinery fault diagnosis. It is established based on two main components, one is the shared feature generator, which is constructed by two novel multi-scale modules with attention mechanism, and the other part is a fault pattern recognition module composed of two differentiated discriminators. While the multi-scale module is used to obtain rich features through different internal perceptual scales, the attention mechanism determines the weights of different scales, which promotes the dynamic adjustment performance and adaptive ability of the model. Then, decision boundary assisted adversarial learning strategy is employed to eliminate domain distribution differences and obtain domain-invariant features. A total of ten rolling bearing-based transfer scenarios and six gearbox-based transfer scenarios are adopted to evaluate the transferability of the proposed MSANA model, and the cross-domain transfer results show that it has superior transferability and stability.     

A precision CNC turn-mill machining center with gear hobbing capability

With ever increasing demand for small parts with complex shapes and high dimensional accuracy, many traditional machine tools have become ineffective for machining these miniature components. Typical examples include dental implants, parts used in mechanical watch movements, and parts used in medical endoscopes. This paper introduces our PC-controlled CNC turn-mill machining center. It has 5 axes, an automatic bar feeder, an automatic part collection tray, and a tool changer. It features a special control algorithm for the synchronization of its axes that produces not only higher accuracy but also makes the machine easier to use. In addition, a volumetric error compensation algorithm is implemented to improve accuracy. Based on experiments, the machining error is ±3 μm for turning, ±7 μm for milling and the maximum profile error is less than ±7.5 μm for gear hobbing.     

40SiO2–40P2O5–20ZrO2 sol-gel infiltrated sSEBS membranes with improved methanol crossover and cell performance for direct methanol fuel cell applications

Membranes commonly used in direct methanol fuel cell (DMFC) are expensive and show a great permeability to methanol which reduces fuel utilization and leads to mixed potential at the cathode. In this work, sulfonated styrene-ethylene-butylene-styrene (sSEBS) modified membranes with zirconia silica phosphate sol-gel phase are developed and studied in order to evaluate their potential use in DMFC applications. The synthesized hybrid membranes and sSEBS are subjected to an exhaustive physicochemical characterization by liquid uptake, ion exchange capacity, atomic force microscopy, X-ray photoelectron spectroscopy and dynamic mechanical and thermogravimetric analyses. Likewise, the potential use of the prepared membranes in DMFC is evaluated by means of electrochemical characterizations in single cell, determining the limiting methanol crossover current densities, proton conductivities and DMFC performances. The hybrid membranes show lower water and methanol uptakes, higher stiffness, water retention capability, upper power density and lower methanol crossover than sSEBS and Nafion 112.     

Fault tolerant steer-by-wire systems: An overview

Steer-by-Wire (SbW) is considered to be the most significant innovation among X-by-Wire technologies that will revolutionise the automotive industry. A SbW system comprises of electronic control units, steering assist motors, and sensors that can potentially replace mechanical steering column linkages in a car. Among many issues that should be resolved before the commercialisation of SbW systems, maintaining reliability and fault-tolerance in such systems are two of the most pressing issues. There are many studies reported in the literature on fault detection and isolation (FDI), but fault diagnosis and fault tolerance in SbW systems has not received extensive attention. In this paper, an overview of fault diagnosis approaches with various forms of redundancy for SbW is presented. The existing approaches to fault tolerant control are considered and classified according to different criteria, such as design methodologies and applications. In addition, a comparison of different approaches is briefly carried out to provide an overall picture of historical, current, and future developments in this fault tolerant control for SbW systems.     

Robotic grinding of complex components: A step towards efficient and intelligent machining – challenges,solutions, and applications

Robotic grinding is considered as an alternative towards the efficient and intelligent machining of complex components by virtue of its flexibility, intelligence and cost efficiency, particularly in comparison with the current mainstream manufacturing modes. The advances in robotic grinding during the past one to two decades present two extremes: one aims to solve the problem of precision machining of small-scale complex surfaces, the other emphasizes on the efficient machining of large-scale complex structures. To achieve efficient and intelligent grinding of these two different types of complex components, researchers have attempted to conquer key technologies and develop relevant machining system. The aim of this paper is to present a systematic, critical, and comprehensively review of all aspects of robotic grinding of complex components, especially focusing on three research objectives.For the first research objective, the problems and challenges arising out of robotic grinding of complex components are identified from three aspects of accuracy control, compliance control and cooperative control, and their impact on the machined workpiece geometrical accuracy, surface integrity and machining efficiency are also identified. For the second aim of this review, the relevant research work in the field of robotic grinding till the date are organized, and the various strategies and alternative solutions to overcome the challenges are provided. The research perspectives are concentrated primarily on the high-precision online measurement, grinding allowance control, constant contact force control, and surface integrity from robotic grinding, thereby potentially constructing the integration of “measurement – manipulation – machining” for the robotic grinding system. For the third objective, typical applications of this research work to implement successful robotic grinding of turbine blades and large-scale complex structures are discussed. Some research interests for future work to promote robotic grinding of complex components towards more intelligent and efficient in practical applications are also suggested.     

Industrial Internet Platform (IIP) enabled Smart Product Lifecycle-Service System (SPLSS) for manufacturing model transformation: From an industrial practice survey

Nowadays, the focus of enterprises is gradually expanding from product manufacturing system to Product service system (PSS). In the existing research, scholars mainly focus on the in-depth study of a certain method or technology, and the research on traditional PSS is relatively mature. In addition, it is a tendency that transform towards SPLSS with the characteristics of industrial internet platform (IIP) and sustainable operation theory. Based on this, this paper explores the research path from traditional product service system (PSS) to smart product service system (SPSS), and then to smart product lifecycle service system (SPLSS), which can endow the products with intelligent perception, intelligent analysis and intelligent decision-making capabilities based on Industrial Internet Platform (IIP). A reference model of IIP enabled SPLSS for industrial services based on system engineering method and theory from a very detailed practical investigation is proposed. Moreover, on the basis of this model, its three dimensional subsystems are expanded, including: supporting environment subsystem, evolution path subsystem, and added value subsystem. The study can provide a reference for the enterprise's manufacturing strategy and manufacturing path. It will promote the transformation and upgrading of enterprise manufacturing model to improve service efficiency and reduce operating costs, and then realize the value-added of products and services.     

单目相机非合作目标提取及位姿检测

针对单一方法难以兼顾图像全局和局部信息准确提取现场环境下的非合作目标边缘的难点,融合聚类等多种算法,提出一种边缘提取新方法。首先,根据图像像素值聚类,每2类间通过阈值进行分割得到1张二值图像;接着将二值图像进行或操作合并。将图像取反后保留最大面积的连通域得到目标分割图片,并提取目标边缘。最后,根据Zernike矩进行亚像素边缘计算。该边缘提取新方法具有较强的适应性,在实际的环境下均可快速有效提取出目标边缘。实验中的非合作目标为设备的三个内孔,用上述方法提取亚像素边缘后拟合出圆心,并用圆心进行相对位姿测量。实验结果表明,该方法鲁棒性强、精度高,最大的位置偏差为0.12 mm,垂直光轴方向姿态角的测量精度可达0.02°,其他两个姿态角的测量精度可达0.07°和0.08°。     

增材制造复杂流道水冷电机壳体对驱动电机持续功率影响的研究

目的 提高量产铸造电机壳体的换热效率,确保电机在高功率持续工作状态下不会过热,从而提高电机的持续功率。方法 基于增材思维对电机水冷壳体的流道进行优化,改变流道形状以增大流道表面积、消除流道涡流并减小流道与内壁的间距。通过仿真分析,不断优化迭代得到最佳的流道设计方案。运用选区激光熔化（SLM）增材技术及相应的后处理工艺,制造出复杂流道结构的电机壳体。结果 采用SLM增材技术制造的AlSi10Mg铝合金壳体在x、xz、z 3个方向上的屈服强度均大于230 MPa,即使在较小壁厚的条件下,壳体强度仍满足设计要求。采用该壳体后,电机的持续功率从原量产电机的45 kW提升到50.7 kW,且仍能连续稳定运行45 min,同时电机温度未超过130℃。微观组织检测和工业CT测试结果显示,SLM电机壳体结构致密,未见气孔夹杂。该增材制造壳体的质量为6.95 kg,与量产电机壳体相比,减重约19%。结论 通过增材制造技术设计制造的电机壳体整体性能良好,可以有效提高换热效率以及电机的持续功率,并实现了电机的减重。