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

白明亮; 张冬雪; 刘金福; 刘娇; 于达仁

doi:10.12096/j.2096-4528.pgt.21021

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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

智能涡轮发电技术 | 更新时间：2025-07-04

- 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
- 发电技术 2021年42卷第4期页码：422-430
- 作者机构：
  
  1.哈尔滨工业大学控制科学与工程系, 黑龙江省哈尔滨市 150001
  2.哈尔滨工业大学能源科学与工程学院, 黑龙江省哈尔滨市 150001
  3.中国航空工业集团公司沈阳飞机设计研究所, 辽宁省沈阳市 110035
- 作者简介：
  
  [ "白明亮(1996), 男, 博士研究生, 研究方向为智能故障诊断、时间序列数据预测与机器学习, mingliangbai@outlook.com" ]
  [ "张冬雪(1996), 女, 硕士研究生, 研究方向为动力装置与能源系统的建模及系统运行优化调度, 19S102132@stu.hit.edu.cn" ]
  刘金福(1977), 男, 教授, 博士生导师, 研究方向为动力装置建模仿真、控制与故障诊断, 本文通信作者, jinfuliu@hit.edu.cn
  [ "刘娇(1990), 女, 博士, 研究方向为发动机健康管理, liujiaohit@hotmail.com" ]
  [ "于达仁(1966), 男, 教授, 博士生导师, 研究方向为动力装置建模仿真、控制与故障诊断, yudaren@hit.edu.cn" ]
- 基金信息：
  
  国家自然科学基金项目(51976042);国家重大科技专项(2017-I-0007-0008)
- DOI：10.12096/j.2096-4528.pgt.21021
  中图分类号： TK05
- 收稿：2021-03-23，
  
  纸质出版：2021-08-31
- 稿件说明：
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白明亮, 张冬雪, 刘金福, 等. 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[J]. 发电技术, 2021,42(4):422-430.

Mingliang BAI, Dongxue ZHANG, Jinfu LIU, et al. Anomaly Detection of Gas Turbine Hot Components Based on Deep Autoencoder and Support Vector Data Description[J]. Power Generation Technology, 2021, 42(4): 422-430.
白明亮, 张冬雪, 刘金福, 等. 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[J]. 发电技术, 2021,42(4):422-430. DOI： 10.12096/j.2096-4528.pgt.21021.

Mingliang BAI, Dongxue ZHANG, Jinfu LIU, et al. Anomaly Detection of Gas Turbine Hot Components Based on Deep Autoencoder and Support Vector Data Description[J]. Power Generation Technology, 2021, 42(4): 422-430. DOI： 10.12096/j.2096-4528.pgt.21021.

摘要

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(support vector data description，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。

Abstract

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.

关键词

Keywords

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