陈四楼煤矿岩巷综合机械化掘进探索

为提高岩巷单进水平，减轻职工劳动强度，在“多上设备少上人”的理念指导下，陈四楼煤矿一直在探索着岩巷快速掘进技术，先后在不同地点、不同条件下实践了6条不同设备配套形式的岩巷作业线，通过对不同岩巷作业线优缺点的对比，在探索中不断改进，使得在不同条件下的岩巷作业线形式逐渐清晰、成熟，最终总结出了适合陈四楼煤矿较为成熟的岩巷作业线，车场、联络巷等较短或拐弯较多的巷道采用耙装机+胶带输送机+采区矸石仓岩巷作业线形式，主要大巷采用液压钻车+挖掘装载机+胶带输送机+采区矸石仓岩巷作业线，与传统的人工打眼、耙矸机+矿车出矸系统比较，采用岩巷作业线平均单进水平由传统工艺的60 m/月提高到90 m/月，施工人员由12人减少到8人，极大地提升了岩巷单进水平、提高工效，降低工人劳动强度。     

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.     

A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks

This work proposes a novel fault diagnostic technique for photovoltaic systems based on Artificial Neural Networks (ANN). For a given set of working conditions - solar irradiance and photovoltaic (PV) module's temperature - a number of attributes such as current, voltage, and number of peaks in the current–voltage (I–V) characteristics of the PV strings are calculated using a simulation model. The simulated attributes are then compared with the ones obtained from the field measurements, leading to the identification of possible faulty operating conditions. Two different algorithms are then developed in order to isolate and identify eight different types of faults. The method has been validated using an experimental database of climatic and electrical parameters from a PV string installed at the Renewable Energy Laboratory (REL) of the University of Jijel (Algeria). The obtained results show that the proposed technique can accurately detect and classify the different faults occurring in a PV array. This work also shows the implementation of the developed method into a Field Programmable Gate Array (FPGA) using a Xilinx System Generator (XSG) and an Integrated Software Environment (ISE).     

Diagnosability Study of Multistage Manufacturing Processes Based on Linear Mixed-Effects Models

Automatic in-process data collection techniques have been widely used in complicated manufacturing processes in recent years. The huge amounts of product measurement data have created great opportunities for process monitoring and diagnosis. Given such product quality measurements, this article examines the diagnosability of the process faults in a multistage manufacturing process using a linear mixed-effects model. Fault diagnosability is defined in a general way that does not depend on specific diagnosis algorithms. The concept of a minimal diagnosable class is proposed to expose the “aliasing” structure among process faults in a partially diagnosable system. The algorithms and procedures needed to obtain the minimal diagnosable class and to evaluate the system-level diagnosability are presented. The methodology, which can be used for any general linear input–output system, is illustrated using a panel assembly process and an engine head machining process.     

家庭建筑综合节能的理念与实践

通过对家庭建筑综合节能认识和实践应用进行阐述,说明了只要竖立了正确的家庭建筑节能观念和进行系统的家庭建筑节能规划,寻常百姓家也可以开展大量卓有成效的节能活动。     

Practical relevance of faults,diagnosis methods,and tolerance measures in elastically actuated robots

Elastically actuated robots promise safe human–robot interaction and energy-efficient motions. Yet, increased complexity and critical operation states might increase the practical fault risk. This paper explores faults in such robots using expert data from the field and identifies components that show increased fault occurrence: the highest fault sensitivity occurs in kinematics, electronics, sensors, and software. Since elastic actuators are an active field of research, few cases of industrial application exist and thus most experts in this study have academic background. Beyond assessing fault sensitivity, countermeasures such as redundant design are compiled. A brief literature review discusses fault diagnosis and fault-tolerant design with respect to these insights. Despite the availability of a few promising methods in robotics, neither diagnosis nor tolerance do receive sufficient recognition leaving potential for practical application.     

网格圈目数与纱线性能的关系

网格圈是紧密纺纱技术的关键部件,其空隙的大小及织造密度的高低都影响纱线的性能。分析了不同目数的网格圈对不同线密度纱线性能的影响,讨论了纺制不同线密度纱线对网格圈的选择。     

在线传感器网络智能预知维修诊断技术在空压机组上的运用

为解决企业传统设备管理中离线点巡检存在的问题,结合在线传感器网络的优点,以空压机组为实例,提出了一种在线智能预知维修诊断控制策略,进一步做好企业设备的运行管理工作。     

Effects of mental process integrated nursing training using mobile device on students’ cognitive load,learning attitudes,acceptance, and achievements

Clinical nursing training is important to nursing educators and student nurses in nursing education since safe and competent care depends on good clinical problem solving skills. Therefore, developing better cognitive problem-solving strategies or tools are essential for clinical nursing practices. Moreover, learning diagnosis is also a critical determinant in the acquisition, processing, and application of clinical skills in nursing practices. Bearing this in mind, this study aims to develop a mobile interactive learning and diagnosis (MILD) system to support problem-based learning (PBL) in a clinical nursing course based on the testing-based approach. Using mobile devices as a learning tool to integrate both real-world and digital-world resources for students and adopting PBL as a learning strategy to facilitate the development of the clinical problem solving skills. To show the effectiveness of the proposed approach, an experiment was conducted in a foundations of nursing course at a nursing college in Taiwan. The experimental results show that the proposed approach is helpful to students in improving learning performance and reducing cognitive loads. Moreover, it was also found that most students showed positive perceptions toward the usage of the proposed system.     

克拉玛依油田石炭系火山岩区断层封闭性研究

为填补克拉玛依油田火山岩断裂封闭机理及定量评价研究的空白,在解剖典型火山岩油藏的基础上,应用综合因子法,综合考虑断面正压力(σ)、断裂带泥质充填物含量(Rm)、断层紧闭指数(I),结合了流体指数F对克拉玛依地区4条主要断层的封闭性进行评价,结果表明,综合因子Q与断裂封闭性有正相关关系,克拉玛依地区由东南向西北,断裂级别依次减小,断层封闭性逐渐变差。综合因子法的评价结果与实际地质情况较相符,可作为火山岩区断层封闭性的评价方法进行推广。