The Health Status Prediction of the Wind Turbine Based on the Anomaly Analysis and the LSTM Prediction

Yiqing Zhou, Jian Wang, Hanfeng Zheng
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Abstract

The traditional data driven faulty prediction model aims to build a nonlinear mapping between the reference signal input and the target degradation index. This kind of faulty detection model has achieved a certain degree of detection accuracy, However, most of these prediction models execute prediction based on the current input data [1]. With the development of the modern industrial process, the modern industrial manufacturing equipment is becoming high complexity and large scale. The health situation of the manufacturing equipment is usually associated with not only the current input data, but also the historical data. In this article, the hybrid approach combined with the anomaly clustering and the LSTM is proposed for the faulty classification of the wind turbine. The anomaly analysis is first used to choose the input sensing signal which can well represent the situation of the health situation of the wind turbine, afterwards, the chosen signal input is used as the input of the LSTM data. The result shows that the proposed model performs quite well in the faulty prediction of the wind turbine.
基于异常分析和LSTM预测的风力机健康状态预测
传统的数据驱动故障预测模型旨在建立参考信号输入与目标退化指标之间的非线性映射关系。这种故障检测模型已经达到了一定的检测精度,但是这些预测模型大多是基于当前输入数据进行预测的[1]。随着现代工业进程的发展,现代工业制造设备向高复杂性、大型化方向发展。制造设备的健康状况通常不仅与当前输入数据相关联,还与历史数据相关联。本文提出了将异常聚类与LSTM相结合的混合方法用于风力发电机组故障分类。首先通过异常分析选择能很好地代表风力机健康状况的感知信号输入,然后将选择的信号输入作为LSTM数据的输入。结果表明,该模型对风力机的故障预测具有较好的效果。
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