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

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

Abstract

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.

Key words: gas turbine, hot components, deep autoencoder (DAE), support vector data description (SVDD), anomaly detection, fault diagnosis

CLC Number:

TK05

Mingliang BAI, Dongxue ZHANG, Jinfu LIU, Jiao LIU, Daren YU. 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.

Figures/Tables 14

Fig.1 Thermocouples at turbine outlet

Fig.2 Layout of thermocouples

Fig.3 Gas rotation

Fig.4 Effect of gas rotation on turbine exhaust gas temperature

Fig.5 Principle of deep autoencoder

Fig.6 Hypersphere in high-dimensional space

Fig.7 Anomaly detection procedure of DAE-SVDD

Tab. 1 Dataset description

参数	正常数据			异常数据
参数	训练集	验证集	测试集	异常数据
样本数目	2 100	450	450	500

Fig.8 The variation of working condition parameters of the first 500 samples in the training set

Tab. 2 training result of deep autoencoder

参数	正常数据			故障数据
参数	训练集	验证集	测试集	故障数据
E_RMSE	0.030 3	0.029 9	0.029 5	0.067 3

Fig.9 Relationship between distance and radius of hypersphere in different data

Tab. 3 Anomaly detection accuracy of DAE-SVDD

参数	正常数据			故障数据
参数	训练集	验证集	测试集	故障数据
检测精度	0.969 0	0.968 9	0.982 2	0.946 0

Fig.10 Relationship between the distance from the center of the hypersphere and the radius of the hypersphere in the training set, validation set and test set of normal data as well as fault data

Tab. 4 Anomaly detection accuracy comparison between of DAE-SVDD and SVDD

算法	正常数据			故障数据
算法	训练集	验证集	测试集	故障数据
SVDD	0.948 1	0.946 7	0.953 3	0.926 0
DAE-SVDD	0.969 0	0.968 9	0.982 2	0.946 0

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