神华乌海能源公司安全监控系统联网建设解读

神华乌海能源公司(以下简称"公司")在安全监控联网系统的开发过程中,针对可能出现的各种问题,提出了综合性的联网解决方案。同时又通过强有力的管理手段,保障了系统的稳定可靠运行,使系统达到了预期的应用效果,提升了公司的安全管理水平。     

智能建筑弱电技术的研究

通过对智能建筑弱电技术中的电线/电缆导管的敷设、电气照明安装、工程中的冲突以及弱电系统的施工计划等问题的研究,总结了智能建筑弱电技术中的综合布线系统、计算机网络系统、子系统间接口方面、母线的安装、弱电设备功能以及配电装置等技术问题的解决要点。所述内容对智能建筑整体运行和安全管理以及电气施工安装的质量监控等具有较强的参考价值。     

Temporal dynamics and drivers of lake ecosystem metabolism using high resolution observations in a shallow,tropical, eutrophic lake (Laguna Lake,Philippines)

The physical, chemical, and biological dynamics under changing atmospheric conditions of Laguna Lake, Philippines were analyzed from intensive observations. Diel measurements were conducted for 48 continuous hours for both dry and wet tropical seasons in addition to fine resolution long-term monitoring. Results revealed significant vertical and diel variations of lake variables in spite of a shallow water depth (2.5 m) caused by the intense surface heating from solar irradiance (~800 W m^?2) and accentuated by the lake's high turbidity (16–32 ftu). Late afternoon land-lake breeze (~5–7 m s^?1) regularly breaks daytime thermal stratification, and convective cooling at night maintains isothermal condition until dawn of the next day. The stratified condition demonstrated a wind-driven, density-induced 2-layer current pattern with a windward moving epilimnion (~4 folds faster) and a compensating hypolimnetic flow in the general lake circulation direction. Laguna Lake was observed to have a dominating diel cycle but also undergoes significant seasonal limnological variations brought primarily by climate, hydrology, and its interaction with the adjacent sea. Significantly correlated variations of pH, chlorophyll-a and DO in the dry season were indicative of the higher biological activity associated with the intrusion of polluted waters from Metro Manila. The non-occurrence of thermal over-turn was observed to be regularly followed by bottom hypoxic conditions (2–4 mg L^?1), indicative of the eutrophic condition of the lake and the importance of diel wind-induced mixing in the bottom supply of DO. Laguna Lake was found to be predominantly net heterotrophic (GPP:R < 1, NEP < 0).     

Toward in-situ flaw detection in laser powder bed fusion additive manufacturing through layerwise imagery and machine learning

Process monitoring in additive manufacturing may allow components to be certified cheaply and rapidly and opens the possibility of healing defects, if detected. Here, neural networks (NNs) and convolutional neural networks (CNNs) are trained to detect flaws in layerwise images of a build, using labeled XCT data as a ground truth. Multiple images were recorded after each layer before and after recoating with various lighting conditions. Classifying networks were given a single image or multiple images of various lighting conditions for training and testing. CNNs demonstrated significantly better performance than NNs across all tasks. Furthermore, CNNs demonstrated improved generalizability, i.e., the ability to generalize to more diverse data than either the training or validation data sets. Specifically, CNNs trained on high-resolution layerwise images from one build showed minimal loss in performance when applied to data from an independent build, whereas the performance of the NNs degraded significantly. CNN accuracy was also demonstrated to be a function of flaw size, suggesting that smaller flaws may be produced by mechanisms that do not alter the surface morphology of the build plate. CNNs demonstrated accuracies of 93.5 % on large (>200 μm) flaws when testing and training on components from the same build and accuracies of 87.3 % when testing on a previously unseen build. Finally, evidence linking the formation of large lack-of-fusion defects to the presence of process ejecta is presented.     

Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network

Effective tool wear monitoring (TWM) is essential for accurately assessing the degree of tool wear and for timely preventive maintenance. Existing data-driven monitoring methods mainly rely on complex feature engineering, which reduces the monitoring efficiency. This paper proposes a novel TWM model based on a parallel residual and stacked bidirectional long short-term memory (PRes–SBiLSTM) network. First, a parallel residual network (PResNet) is used to extract the multi-scale local features of sensor signals adaptively. Subsequently, a stacked bidirectional long short-term memory (SBiLSTM) network is used to obtain the time-series features related to the tool wear characteristics. Finally, the predicted tool wear value is outputted through a fully connected network. A smoothing correction method is applied to improve the prediction accuracy. The proposed model is experimentally verified to have a high prediction accuracy without sacrificing its generalization ability. A TWM system framework based on the PRes–SBiLSTM network is proposed, which has a certain reference value for TWM in actual industrial environments.     

煤矿带式输送机监控软件的设计

采用VB6.0设计了一套煤矿带式输送机监控软件,介绍了软件总体设计及实时监控主画面设计方案,阐述了采用MSComm控件、Winsock控件分别实现上下位机的串口通信、以太网通信的方法。该监控软件画面直观,控制灵活,较为实用。     

液压支架工作状态模糊识别系统研究

针对目前综采工作面液压支架压力监测系统只能监测综采工作面压力,不能对液压支架工作状态进行识别的问题,设计了一种液压支架工作状态模糊识别系统。该系统可对现有的综采工作面液压支架压力监测系统监测到的压力数据进行模糊识别,根据模糊识别输出值即可判断液压支架的5种工作状态,即降架、移架、升架、增阻、卸压保持;当液压支架处于增阻工作状态且增阻时间过长时,该系统可及时通知液压支架操作工增大液压支架压力,辅助实现顶煤破碎。测试结果验证了该系统的可行性。     

煤矿监控图像增强算法的分析与实现

针对煤矿井下粉尘多、光照差的恶劣环境使得矿井监控图像偏暗、对比度低、视觉效果差的特点,提出了一种基于小波变换和模糊理论的图像增强算法。该算法选择小波变换为工具分解图像,应用新的模糊隶属度和增强算子对高频信息进行模糊处理,利用直方图均衡化对低频信息进行处理,最后对图像进行重构。处理结果较好地增强了图像细节信息,从整体上改善了图像效果。     

煤矿井下高压降尘供水系统监控装置的研制

根据煤矿井下高压降尘系统对供水的要求,研制了一种基于PIC18F4580单片机的井下高压降尘供水系统监控装置,介绍了该装置的硬件和软件设计。该装置可实时监测水箱水位、压力、运行状态,并可将这些保护状态信息通过CAN总线接口和转换单元并入煤矿监测监控网,实现远程监测。实验结果表明,该监控装置达到了设计要求,能保证高压降尘供水系统的实时监测与安全运行。     

矿用救生舱环境监控系统软件的研究

给出了矿用救生舱环境监控系统的组成,主要介绍了该系统软件的功能设计、总体架构及界面设计方案。该系统软件可实时显示各传感器的运行状态和检测数据、舱内气体余量、电源电量、照明灯状态、空气净化状态、自动控制状态、舱门状态等,人机交互界面友好,操作简便。该软件经长期连续运行实验,工作正常,达到了设计要求。