石化设备“漆膜”形成机制与防范

"漆膜"问题近年来成为困扰石化关键动设备润滑维保的重要难题之一。结合石化关键机组的不同特点,探讨"漆膜"的形成机制,分析漆膜的危害和防范方法,提出既要做好日常在用油漆膜的去除,同时也要做好日常润滑管理,才能有效防范机组漆膜故障。     

电力供电大楼机房能耗监测与分析系统研究

针对提倡节能减排的社会背景，设计实现了面向电力供电大楼机房能耗监测与分析系统，基于现有的机房能耗数据资源，通过对机房能耗数据多方面的监测以及科学化的分析，为管理者更好地制定节能措施提供决策支持。系统功能分为能耗环境监测、能耗行为分析、节能改造建议、节能效果评估4个子模块；利用GESD算法设计了能耗异常检测方法，利用SSH框架实现了B/S架构；在数据展示方面利用了Extjs、BIRT报表2种技术，利用POI实现了导出Excel格式的能耗数据文件。实际应用表明，该系统为管理者制定节能减排措施提供了帮助。     

高应力区沿空留巷围岩现状监测研究与分析

陈四楼矿计划实施沿空留巷的2802运输巷，断层较多，直接顶厚度较大，巷道矿压显现较为明显。针对顶板下沉、两帮外鼓、顶板淋水较大、巷道顶板受压破碎、金属网锈蚀严重、压脱锚杆较多、两帮位移较大等现象，提出在高应力区采用矿压监测的方法，通过对巷道变形现状进行的监测分析，对巷道破坏变形程度有了清晰的认识，对造成巷道破坏变形原因和影响因素有了详细的分析，根据注浆加固可改善围岩结构，提高围岩承载能力及锚杆、锚索锚固性能，是一种较为有效的围岩控制措施，确定了需要采用注浆加固的方式对进行巷道围岩破坏变形治理，并根据现场实际情况对注浆材料的性能提出了要求。     

用于矿山等极端环境中的高精度定位算法

高精度室内定位技术在矿山、建筑、施工等多种极端环境下都有需求。基于超宽带通信(Ultra Wideband,UWB)的室内定位技术能够提供较高的定位精度,但超宽带信号易受到障碍物的干扰,并且在类似矿山等极端环境中,障碍物不可避免。因此,超宽带非视线传输(Non-Line-of-Sight,NLoS)测距成为当前的一个研究热点。当前研究大多集中在识别和消除非视线传输对超宽带测距的影响上,实际应用中,大多数情形下超宽带信号根本无法穿透障碍物,会导致定位标签只能够与2个或1个信标节点通信,但完成三边定位至少需要3个信标节点。为此,提出了一种基于目标行为分析的定位算法,该算法能够在短时间内仅剩余一个信标节点与标签通信时,也可实现高精度定位。该算法以超宽带通信为基础,使用信号飞行时间(Time of Flight,TOF)算法测距并定位。通过分析信号正常时的目标运动轨迹来判断其速度和方向,当信号不正常时(可通信的信标节点减少),可利用预测的速度和方向参数来实现辅助定位。将该算法部署在一个基于超宽带定位的系统中进行了测试,结果表明:当能够与标签通信并完成TOF算法的信标节点数量少于3个时,基于NLoS传输识别和消除的算法无法完成定位,而基于目标行为分析的定位算法依然能够实现定位,在单信标节点区域的平均定位误差为0.93 m,最大误差为1.41 m,在2个信标节点测距的区域,平均定位误差为0.64 m,最大误差为1.2 m。     

基于小波分析的谐波在线监测系统

针对传统谐波在线监测系统存的结果不准确，无法描述谐波动态变化特点，设计了基于小波分析的谐波在线监测系统。首先设计了谐波在线监测系统的硬件模块，然后谐波在线监测系统的软件模块，依据小波变换分析谐波信号特征，得到的谐波变化率，从而谐波在线监测系统的监测结果，最后采用具体实验进行了谐波在线监测系统性能的仿真实验，由实验结果可知，该系统的谐波监测精度高，可以有效检测各种谐波分量，保证电力系统稳定运行。     

煤矿运输安全智能监控信息系统的设计

以李雅庄煤矿为研究对象,进行了矿井运输安全智能监控信息系统的开发。该系统采用自动化技术、信息技术与煤矿运输系统深度融合,集先进的计算机、网络通信、图像处理、现代检测和自动化技术于一体,可以实现矿井运输的智能监测与自动控制。实践应用表明,该系统有效地保障了运输系统的自动调度和智能控制,保证了安全生产。     

Consideration of Stretchable Dry Electrodes for Smart Shirts Aimed for Biological Signal Monitoring

Dry electrodes of a smart shirt for measuring biological signals were prepared by the additive printing method using a stretchable conductive paste with Ag fillers loading. Interfacial impedance between the electrode and skin depended on the contact pressure of electrode and the moisture of stratum corneum. When moisture of stratum corneum was more than 30%, ECG could be measured using the stretchable electrodes printed on a compression shirt. However, noise signal in the ECG signal increased when the moisture of stratum corneum was less than 30%. To solve this problem, we propose to use additional electrodes made of an electrically conductive kneaded rubber.     

Back-analysis of geotechnical parameters on PVD-improved ground in the Mekong Delta

This study presents a back-analysis of geotechnical parameters on prefabricated vertical drain improved ground at a site in the Mekong Delta. Various time?settlement behaviors that reflected different clay thicknesses and loading patterns were observed. The total surface settlement behavior at several monitoring locations was simulated using an updated exponential method that considered staged construction. The analyzed results were validated by substituting the values into a theoretical solution for radial consolidation. The estimated theoretical behaviors were comparable with the monitored behaviors. The geotechnical parameters were back-analyzed by applying the previously analyzed results to various theoretical and empirical formulas. However, the use of extensometer data that were installed at large intervals produced different values of the geotechnical properties. Furthermore, finite element analysis supported the back-analyzed total settlement behaviors and nearly disregarded the application of the geotechnical properties that were obtained using either surface or subsurface settlement data. However, settlements and excess pore pressures in the sublayers were not successfully predicted even when the geotechnical properties were adjusted. Thus, subsurface instruments that can be installed closely in thick clay deposits are required to reliably reevaluate the variations in geotechnical properties along a certain depth.     

Classification and regression models of audio and vibration signals for machine state monitoring in precision machining systems

We present a data-driven method for monitoring machine status in manufacturing processes. Audio and vibration data from precision machining are used for inference in two operating scenarios: (a) variable machine health states (anomaly detection); and (b) settings of machine operation (state estimation). Audio and vibration signals are first processed through Fast Fourier Transform and Principal Component Analysis to extract transformed and informative features. These features are then used in the training of classification and regression models for machine state monitoring. Specifically, three classifiers (K-nearest neighbors, convolutional neural networks and support vector machines) and two regressors (support vector regression and neural network regression) were explored, in terms of their accuracy in machine state prediction. It is shown that the audio and vibration signals are sufficiently rich in information about the machine that 100% state classification accuracy could be accomplished. Data fusion was also explored, showing overall superior accuracy of data-driven regression models.