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Detection of gearbox failures by combined acoustic emission and vibration sensing in rotating machinery

机译:通过组合声发射和旋转机械振动感测的齿轮箱故障检测

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The wind energy industry continuously demands significant improvements in wind turbine maintenance strategies through the use of condition monitoring systems (CMS). Regardless of their design, wind turbines have rotating parts which need to be monitored during operation in order to avoid unpredicted failures. In this document an experiment conducted in the University of Newcastle is described and initial data analysis is presented. The experiment was performed in a gear test rig of the Design Unit department, where gearbox contact fatigue tests are frequently performed. The testing procedure involves the running of the gears to destruction, starting with 'fresh' gears and operating for a period of time until they fail. In this experiment, the gear monitored is periodically checked to give an indication of the tooth's cross section loss. A combination of vibration analysis and Acoustic Emission (AE) analysis is utilised in this experiment as it is believed that for reliable diagnosis of rotating machinery, multi-sensing technology should be used. Accelerometers and acoustic emission sensors were deployed throughout the tests, conducted in 5 stages over 7 weeks. For the vibration data processing calculation of the data, the Root Mean Square (RMS) and Crest Factor values are calculated and frequency spectrum analysis is performed. For the acoustic emission data processing RMS and energy parameters are calculated. These parameters are shown giving information about the potential gear deterioration over time. The results of the experiment are finally presented and show minimal changes in the vibration and acoustic emission parameters in the first 2 stages of the tests, indicating a reliable baseline reading. However, in the later stages of the tests, data showed very clear indication of possible gear deterioration, with an increase in the values of RMS for both acoustic emission and vibration data and a modal shift in the vibration spectrum. Visual inspection performed afterwards confirmed the onset of severe macro-pitting failures in the gears.
机译:风能产业通过使用条件监测系统(CMS)不断提高风力涡轮机维护策略的显着改善。无论其设计如何,风力涡轮机都具有在操作期间需要监测的旋转部件,以避免未预测的故障。在本文件中,描述了在纽卡斯尔大学进行的实验,并提出了初始数据分析。该实验在设计单元部门的齿轮试验台上进行,通常进行齿轮箱接触疲劳试验。测试程序涉及齿轮的运行来破坏,从“新鲜”档位开始,并在失败之​​前运行一段时间。在该实验中,定期检查监测的齿轮以指示齿的横截面损失。在该实验中使用振动分析和声学发射(AE)分析的组合,因为它被认为是为了可靠的旋转机械诊断,应使用多感测技术。在整个测试中部署加速度计和声发射传感器,在7周内在5阶段进行。对于数据的振动数据处理计算,计算均匀平方(RMS)和CREST因子值,并执行频谱分析。对于声学发射数据处理,计算RMS和能量参数。显示这些参数,其提供有关潜在齿轮劣化的信息随着时间的推移。最终呈现实验结果,并在测试的前2个阶段中显示振动和声发射参数的最小变化,表明可靠的基线读数。然而,在测试的后期阶段,数据显示了可能的齿轮劣化的非常清晰的指示,并且用于声发射和振动数据的RMS值和振动频谱中的模态移位增加。之后进行的目视检查证实了齿轮中的严重宏观点故障的发作。

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