An anomaly detection method for gas turbines in power plants using conditional variational autoencoder optimized with self-attention

An innovative anomaly detection (AD) framework designed for gas turbines (GTs) in power plants working under variable operating conditions (OCs) was proposed based on conditional variational autoencoder (CVAE) optimized with self-attention (SA) in this paper. Three key factors contribute to the mode...

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Vydané v:	Reliability engineering & system safety Ročník 267; s. 111894
Hlavní autori:	Chen, Dongchao, Li, Xiuxia, Xu, Jingquan, Wang, Zhong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.03.2026
Predmet:	Anomaly detection Conditional variational autoencoder Gas turbine Self-attention Variable operating conditions COAT HI RCAT GTP TET RDCT CIAT Self-attention LS DL LV LRP FNR AMFormer KDE OCSVM EGDP HI-FM PCC DS Variable operating conditions MD LSD ML Pr FWT AD EH AE DCD IS EP LOP Anomaly detection LPT_flow_mod PCA ET RA LOT MAE AS LPT_eff_mod FGMMP FGTFCVP FGHOT ANI SIS HPT_eff_mod IGVCSO CBVP BPT FM CVAE AUROC FPR SA SVR Conditional variational autoencoder R Re OC PHM MFPLCSO SA-CVAE GD SP MSE Acc CC CSP OS COP GL RUL CPFM CP GT LFCPF TE EGT CPD CPF IGV IGVP Gas turbine GCV VAE TEP TIT HRSG
ISSN:	0951-8320
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