首页> 外文期刊>Journal of Tribology >Detailed State of the Art Review for the Different Online/Inline Oil Analysis Techniques in Context of Wind Turbine Gearboxes
【24h】

Detailed State of the Art Review for the Different Online/Inline Oil Analysis Techniques in Context of Wind Turbine Gearboxes

机译:风力涡轮机变速箱中不同在线/在线油分析技术的详细技术综述

获取原文
获取原文并翻译 | 示例
       

摘要

The main driver behind developing advanced condition monitoring (CM) systems for the wind energy industry is the delivery of improved asset management regarding the operation and maintenance of the gearbox and other wind turbine components and systems. Current gearbox CM systems mainly detect faults by identifying ferrous materials, water, and air within oil by changes in certain properties such as electrical fields. In order to detect oil degradation and identify particles, more advanced devices are required to allow a better maintenance regime to be established. Current technologies available specifically for this purpose include Fourier transform infrared (FTIR) spectroscopy and ferrography. There are also several technologies that have not yet been or have been recently applied to CM problems. After reviewing the current state of the art, it is recommended that a combination of sensors would be used that analyze different characteristics of the oil. The information individually would not be highly accurate but combined it is fully expected that greater accuracy can be obtained. The technologies that are suitable in terms of cost, size, accuracy, and development are online ferrography, selective fluorescence spectroscopy, scattering measurements, FTIR, photoacoustic spectroscopy, and solid state viscometers.
机译:为风能行业开发高级状态监测(CM)系统的主要推动力是提供有关齿轮箱以及其他风力发电机组件和系统的运行和维护的改进资产管理。当前的齿轮箱CM系统主要通过识别某些属性(例如电场)的变化来识别油中的铁质材料,水和空气,从而检测故障。为了检测油的降解并识别颗粒,需要更先进的设备以建立更好的维护制度。专门用于此目的的当前技术包括傅里叶变换红外(FTIR)光谱学和铁素体学。还有一些尚未或最近已应用于CM问题的技术。在回顾了当前的技术水平之后,建议使用传感器的组合来分析油的不同特性。该信息将不会是非常准确的,但是结合起来可以完全预期可以获得更高的准确性。就成本,尺寸,准确性和发展而言,合适的技术是在线铁成像,选择性荧光光谱,散射测量,FTIR,光声光谱和固态粘度计。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号