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首页> 外文期刊>IEEE transactions on industrial informatics >A Small-Sample Wind Turbine Fault Detection Method With Synthetic Fault Data Using Generative Adversarial Nets
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A Small-Sample Wind Turbine Fault Detection Method With Synthetic Fault Data Using Generative Adversarial Nets

机译：基于生成对抗网络的具有综合故障数据的小样本风力发电机组故障检测方法

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摘要

The limited fault information caused by small fault data samples is a major problem in wind turbine (WT) fault detection. This paper proposes a small-sample WT fault detection method with the synthetic fault data using generative adversarial nets (GANs). First, based on prior knowledge, a rough fault data generation process is developed to transform the normal data to the rough fault data. Second, a rough fault data refiner is developed by GANs to make the rough fault data more similar with the real fault data. Moreover, to make the generated data better suited to the WT conditions, GANs are improved in both the generative model and the discriminative model. Third, artificial intelligence (AI)-based WT fault detection models can be well trained by using only the generated data in the condition of small fault data sample. Finally, three groups of generated data evaluation experiments and four groups of WT fault detection comparative experiments are conducted using real WT data collected from a wind farm in northern China. The results indicate that the method proposed in this paper is effective.
机译：由少量故障数据样本引起的有限故障信息是风力涡轮机（WT）故障检测中的主要问题。提出了一种利用生成对抗网络（GANs）综合故障数据的小样本WT故障检测方法。首先，基于先验知识，开发了粗糙故障数据生成过程，以将正常数据转换为粗糙故障数据。其次，GANs开发了一个粗故障数据精简器，以使粗故障数据与真实故障数据更加相似。此外，为了使生成的数据更适合WT条件，GAN在生成模型和判别模型上均得到了改进。第三，在小故障数据样本的情况下，仅使用生成的数据就可以很好地训练基于人工智能的WT故障检测模型。最后，使用从中国北方风电场收集的真实WT数据进行了三组生成的数据评估实验和四组WT故障检测比较实验。结果表明本文提出的方法是有效的。

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