汽轮机异常检测：一种使用增强型长短期记忆变分自编码器的无监督学习方法
Steam Turbine Anomaly Detection: An Unsupervised Learning Approach Using Enhanced Long Short-Term Memory Variational Autoencoder
AI总结：
针对汽轮机异常检测中固有异常、时间信息缺失和高维复杂性等挑战，提出ELSTMVAE-DAF-GMM无监督学习方法，通过融合潜在嵌入与重建差异的深度高级特征结合GMM，实现高准确率、低误报的异常检测。
摘要：
作为核心火力发电设备，汽轮机在面临停机、维护和损坏等中断时会产生巨大的费用并对运行造成不利影响。准确的异常检测是确保汽轮机安全稳定运行的前提。然而，汽轮机异常检测面临的挑战，包括固有异常、缺乏时间信息分析以及高维数据复杂性，限制了现有方法的有效性。为了应对这些挑战，我们提出了一种使用深度高级特征和混合高斯模型的增强型长短期记忆变分自编码器（ELSTMVAE-DAF-GMM），用于在无标签数据集中进行精确的无监督异常检测。具体而言，将LSTM与VAE结合的LSTMVAE被用于将高维时间序列数据投影到低维相空间。深度自编码器-局部异常因子（DAE-LOF）样本选择机制被用于消除训练过程中的固有异常，进一步提高了模型的精度和可靠性。新型深度高级特征（DAF）融合了来自LSTMVAE模型的潜在嵌入和重建差异，并在连续且结构化的相空间内提供了更全面的数据表示，通过协同时间动态与数据模式变化显著增强了异常检测。这些DAF被纳入GMM中，以确保稳健有效的无监督异常检测。我们利用工业汽轮机的实际运行数据并进行了对比和消融实验，结果表明，与现有方法相比，该方法具有高准确率和极低误报率的优异异常检测结果。
英文摘要：
As core thermal power generation equipment, steam turbines incur significant expenses and adverse effects on operation when facing interruptions like downtime, maintenance, and damage. Accurate anomaly detection is the prerequisite for ensuring the safe and stable operation of steam turbines. However, challenges in steam turbine anomaly detection, including inherent anomalies, lack of temporal information analysis, and high-dimensional data complexity, limit the effectiveness of existing methods. To address these challenges, we proposed an Enhanced Long Short-Term Memory Variational Autoencoder using Deep Advanced Features and Gaussian Mixture Model (ELSTMVAE-DAF-GMM) for precise unsupervised anomaly detection in unlabeled datasets. Specifically, LSTMVAE, integrating LSTM with VAE, was used to project high-dimensional time-series data to a low-dimensional phase space. The Deep Autoencoder-Local Outlier Factor (DAE-LOF) sample selection mechanism was used to eliminate inherent anomalies during training, further improving the model's precision and reliability. The novel deep advanced features (DAF) hybridize latent embeddings and reconstruction discrepancies from the LSTMVAE model and provide a more comprehensive data representation within a continuous and structured phase space, significantly enhancing anomaly detection by synergizing temporal dynamics with data pattern variations. These DAF were incorporated into GMM to ensure robust and effective unsupervised anomaly detection. We utilized real operating data from industry steam turbines and conducted both comparison and ablation experiments, demonstrating superior anomaly detection outcomes characterized by high accuracy and minimal false alarm rates compared with existing methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
