This paper presents a new deep learning-based model for fault detection in offshore wind turbines. To design a generalizable model for fault detection, we use 5 sensors and a sliding window to exploit the inherent temporal information contained in the raw time-series data obtained from sensors. The proposed model uses the nonlinear relationships among multiple sensor variables and the temporal dependency of each sensor on others that considerably increases the performance of fault detection model. A 10-fold cross-validation is used to verify the generalization of the model and evaluate the classification metrics. To evaluate the performance of the model, simulated data from a benchmark floating offshore wind turbine (FOWT) with supervisory control and data acquisition (SCADA) are used. The results illustrate that the proposed model would accurately disclose and classify more than 99% of the faults. Moreover, it is generalizable and can be used to detect faults for different types of systems.

中文翻译：

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基于DeepLearning的海上风机故障检测通用模型

本文提出了一种新的基于深度学习的海上风机故障检测模型。为了设计用于故障检测的通用模型，我们使用5个传感器和一个滑动窗口来利用从传感器获取的原始时间序列数据中包含的固有时间信息。提出的模型利用了多个传感器变量之间的非线性关系以及每个传感器对其他传感器的时间依赖性，这大大提高了故障检测模型的性能。10倍交叉验证用于验证模型的一般性并评估分类指标。为了评估模型的性能，使用了来自带有监控和数据采集（SCADA）的基准浮式海上风力涡轮机（FOWT）的模拟数据。结果表明，所提出的模型将准确地揭示和分类超过99％的故障。而且，它是通用的，可用于检测不同类型系统的故障。