基于深度自编码器和支持向量数据描述的燃气轮机高温部件异常检测

Anomaly Detection of Gas Turbine Hot Components Based on Deep Autoencoder and Support Vector Data Description

开展燃气轮机高温部件的异常检测能有效提高其运行安全性和可靠性。随着人工智能技术的兴起,数据驱动的故障诊断方法已经越来越流行。然而,在实际应用中,燃气轮机故障数据很少甚至几乎没有。针对仅有正常数据场景下的燃气轮机高温部件异常检测问题,提出了一种基于深度自编码器(deep autoencoder,DAE)和支持向量数据描述(supportvectordatadescription,SVDD)融合的DAE-SVDD异常检测方法。该方法利用正常数据训练深度自编码器,并利用深度自编码器的重构误差来训练支持向量数据描述。与传统异常检测方法相比,该方法显著提高了异常检测精度,能实现更灵敏鲁棒的燃气轮机高温部件异常检测。

Anomaly detection of gas turbine hot components can ensure its operational safety and reliability.With the boom of artificial intelligence,data-driven fault diagnosis is becoming increasingly popular.However,in actual applications,fault data of gas turbines are rare or even unavailable.Aiming to solve the anomaly detection problem of gas turbine hot components in the case of only normal data available,this paper proposed an anomaly detection method based on the fusion of deep autoencoder and support vector data description.This method uses normal data to train deep autoencoder and then uses the reconstruction errors of deep autoencoder to train support vector data description.Experiments show that,compared with conventional anomaly detection methods,the proposed method can significantly improve the anomaly detection accuracy and realize more sensitive and robust anomaly detection of gas turbine hot components.