论文标题

基于自动化功能学习的风力涡轮机的振动故障诊断

Vibration Fault Diagnosis in Wind Turbines based on Automated Feature Learning

论文作者

Meyer, Angela

论文摘要

越来越多的风力涡轮机配备了振动测量系统,以仔细监测和早期检测发生故障条件。分析了振动测量值,以连续评估组件健康状况并防止可能导致下降的失败。这项研究的重点是变速箱监视,但也适用于其他子系统。当前最新的变速箱故障诊断算法依赖于基于人类分析师定义的故障签名的统计或机器学习方法。这有多个缺点。人类分析师定义故障签名是一个耗时的过程,需要对变速箱组合物进行高度详细的了解。每台新涡轮机需要重复这项工作,因此随着受监控涡轮机的增加,它不能很好地扩展,尤其是在快速增长的投资组合中。此外,人类分析师定义的断层签名可能会导致偏见和不精确的决策界限,从而导致不精确和不确定的故障诊断决策。我们提出了一种新型的准确故障诊断方法,用于克服这些缺点的振动监控风力涡轮机组件。我们的方法结合了基于卷积神经网络和隔离森林的自主数据驱动的故障签名学习和健康状态分类。我们通过两个风力涡轮机变速箱的振动测量来证明其性能。与最先进的方法不同,我们的方法不需要变速箱类型的特定诊断专业知识,并且不仅限于预定义的频率或光谱范围,但可以立即监视完整的光谱。

A growing number of wind turbines are equipped with vibration measurement systems to enable a close monitoring and early detection of developing fault conditions. The vibration measurements are analyzed to continuously assess the component health and prevent failures that can result in downtimes. This study focuses on gearbox monitoring but is applicable also to other subsystems. The current state-of-the-art gearbox fault diagnosis algorithms rely on statistical or machine learning methods based on fault signatures that have been defined by human analysts. This has multiple disadvantages. Defining the fault signatures by human analysts is a time-intensive process that requires highly detailed knowledge of the gearbox composition. This effort needs to be repeated for every new turbine, so it does not scale well with the increasing number of monitored turbines, especially in fast growing portfolios. Moreover, fault signatures defined by human analysts can result in biased and imprecise decision boundaries that lead to imprecise and uncertain fault diagnosis decisions. We present a novel accurate fault diagnosis method for vibration-monitored wind turbine components that overcomes these disadvantages. Our approach combines autonomous data-driven learning of fault signatures and health state classification based on convolutional neural networks and isolation forests. We demonstrate its performance with vibration measurements from two wind turbine gearboxes. Unlike the state-of-the-art methods, our approach does not require gearbox-type specific diagnosis expertise and is not restricted to predefined frequencies or spectral ranges but can monitor the full spectrum at once.

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