Time-series data is widely adopted in condition monitoring and fault diagnosis of wind turbines as well as other energy systems, where long-term dependency is essential to form the classifiable features. To address the issues that the traditional approaches either rely on expert knowledge and handcrafted features or do not fully model long-term dependencies hidden in time-domain signals, this work presents a novel fault diagnosis framework based on an end-to-end Long Short-term Memory (LSTM) model, to learn features directly from multivariate time-series data and capture long-term dependencies through recurrent behaviour and gates mechanism of LSTM. Experimental results on two wind turbine datasets show that our method is able to do fault classification effectively from raw time-series signals collected by single or multiple sensors and outperforms state-of-art approaches. Furthermore, the robustness of the proposed framework is validated through the experiments on small dataset with limited data.

中文翻译：

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基于长短期记忆网络的风力发电机组故障诊断

时间序列数据被广泛用于风力涡轮机以及其他能源系统的状态监测和故障诊断，其中长期依赖性对于形成可分类特征至关重要。为了解决传统方法要么依赖专家知识和手工特征，要么不能完全模拟隐藏在时域信号中的长期依赖关系的问题，这项工作提出了一种基于端到端长短的新型故障诊断框架-term Memory (LSTM) 模型，直接从多元时间序列数据中学习特征，并通过 LSTM 的循环行为和门机制捕获长期依赖关系。在两个风力涡轮机数据集上的实验结果表明，我们的方法能够从单个或多个传感器收集的原始时间序列信号中有效地进行故障分类，并且优于最先进的方法。此外，通过对数据有限的小数据集的实验验证了所提出框架的稳健性。