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首页> 外文期刊>Journal of Physics: Conference Series >Stochastic simulation assessment of an automated vibration-based condition monitoring framework for wind turbine gearbox faults
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Stochastic simulation assessment of an automated vibration-based condition monitoring framework for wind turbine gearbox faults

机译：用于风力涡轮机齿轮箱故障自动振动条件监测框架的随机仿真评估

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摘要

Effectively monitoring the health of a wind turbine gearbox is a complex and often multidisciplinary endeavor. Recently, condition monitoring practices increasingly combine knowledge from fields like signal processing, machine learning, and mechanics. Such a diverse approach becomes necessary when dealing with the vast amount of data that is generated by the multitude of sensors that are typically placed on a wind turbine gearbox. Ideally, this approach needs to be automated and scalable as well, since it is unfeasible to perform all the necessary processing work manually in a continuous manner. This paper focuses on assessing the performance of such an automated processing framework for the case of gearbox fault detection using vibration measurements. A year of vibration measurements on a gearbox is simulated by stochastic variation of the operating conditions and the system behavior. A bearing fault is progressively introduced as to track the detection capabilities of the framework in such stochastic circumstances. The used signal model is based on previously obtained experience with experimental data sets originating from wind turbine gearboxes. The framework itself consists of multiple pre-processing steps where each step tries to deal with compensating for the external or unwanted influences such as speed variation or noise. Finally, multiple features are calculated on the pre-processed signals and trended as to see whether the processing scheme can provide any benefit compared to basic traditional statistical indicators. It is shown that the multi-step pre-processing approach is beneficial and robust for the advanced feature calculation and thus the early fault detection.
机译：有效地监测风力涡轮机齿轮箱的健康是一种复杂和经常多学科的努力。最近，条件监测实践越来越多地将知识与信号处理，机器学习和力学等领域相结合。在处理由通常放置在风力涡轮机齿轮箱上的大量传感器产生的大量数据时，这种多种方法变得必要。理想情况下，这种方法也需要自动化和可扩展，因为以连续方式手动执行所有必要的处理是不可行的。本文重点介绍使用振动测量来评估这种自动化处理框架的性能。通过运行条件的随机变化和系统行为模拟齿轮箱上的一年的振动测量。逐步引入轴承故障以跟踪这种随机环境中框架的检测能力。使用的信号模型基于先前获得的经验，其具有源自风力涡轮机齿轮箱的实验数据集。该框架本身包括多个预处理步骤，其中每个步骤试图处理补偿外部或不需要的影响，例如速度变化或噪声。最后，在预处理的信号上计算多个特征，并促使处理方案与基本传统统计指标相比可以提供任何好处。结果表明，对于高级特征计算，多步预处理方法对高级特征计算以及早期故障检测是有益的和鲁棒。

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