An anomaly detection method for gas turbines in power plants using conditional variational autoencoder optimized with self-attention - Chen, Dongchao; Li, Xiuxia; Xu, Jingquan; Wang, Zhong (Reliability engineering & system safety, 2026)
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 model's superior performance in AD. First, the marginal distributions (MDs) of parameters were transformed into conditional probability distributions (CPDs) by incorporating OC variables as conditional inputs in the variational autoencoder (VAE) model, and decoupled modeling of equipment states and external OCs is achieved. Second, by embedding SA modules, the method dynamically adjusts weight allocation of the parameters to capture complex dependencies among them, which can enhance the capability of the model to represent intricate coupling relationships in multi-dimensional parameters. Third, the logarithmic reconstruction probability (LRP), derived from the probabilistic output of SA-CVAE, serves as a unified health indicator (HI) to evaluate the health condition of the GT. The indicator not only accounts for the discrepancy between the reconstructed and original inputs but also incorporates the influence of latent variable (LV) variance on the reconstruction results. Experiments were carried out on one synthetic, one public, and two real-world datasets, with SA-CVAE compared against six leading AD methods. The results demonstrate that under varying OCs, SA-CVAE exhibits superior performance in AD tasks, achieving leading or competitive results across key evaluation metrics.