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Development of in-service condition monitoring system using combined vibrational and acoustic emission signature for wind turbine gearbox and generator shaft

机译:利用振动和发电机轴的组合振动和声发射特征开发在役状态监测系统

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A review of current progress in Condition Monitoring (CM) of wind turbine gearboxes and generators is presented, as an input to the design of a new continuous CM system with automated warnings based on a combination of vibrational and Acoustic Emission (AE) analysis. For wind turbines, existing reportage on vibrational monitoring is restricted to a few case histories whilst data on AE is even scarcer. In contrast, this paper presents combined vibration and AE monitoring performed over a continuous period of 5 days on a wind turbine. The vibrational and AE signatures for a healthy wind turbine gearbox and generator were obtained as a function of wind speed and turbine power, for the full normal range of these operational variables. i.e. 5-25m/s and 0-300kW respectively. The signatures have been determined as a vital pre-requisite for the identification of abnormal signatures attributable to shaft and gearbox defects. Worst-case standard deviations have been calculated for the sensor data. These standard deviations determine the minimum defect signal that could be detected within the defined time interval without false alarms in an automated warning system.
机译:提出了一种综述风力涡轮机齿轮箱和发电机的现状监测(CM)的进展,作为基于振动和声发射(AE)分析的组合的自动警告的新连续CM系统的输入。对于风力涡轮机,关于振动监测的现有报告限制为几个案例历史,而AE的数据甚至是稀缺。相比之下,本文介绍了在风力涡轮机上连续5天的连续时间进行的振动和AE监测。一个健康的风力涡轮机齿轮箱和发电机的振动和AE签名得到作为风速和涡轮机功率的函数,对于这些操作变量的全部正常范围内。即分别为5-25m / s和0-300kw。签名已被确定为识别轴和齿轮箱缺陷的异常签名的重要前提条件。已经计算了传感器数据的最坏情况标准偏差。这些标准偏差确定可以在定义的时间间隔内检测到的最小缺陷信号,而自动警告系统中没有误报。

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