通过燃气轮机排气温度对燃烧室及涡轮前几级叶片等高温部件开展异常检测，早期可靠的检测异常对确保燃气轮机高效运行至关重要。随着机器学习的广泛应用，数据驱动的状态监测方法已经越来越流行。针对故障数据缺失场景下的的燃气轮机排气温度分布异常检测问题，使用深度自编码器（Deep Autoencoder，DAE）学习特征，并采用隔离森林（isolated Forset，iForset）学习特征数据的正常信息，从而实现异常检测。与其他单分类的异常检测方法对比，该方法具有最佳的检测性能指标，能实现有效灵敏的燃气轮机排气温度异常检测。
Abnormal detection is carried out on high-temperature components such as the combustion chamber and the blades of the first several stages of the turbine through the exhaust temperature of the gas turbine. Early and reliable abnormal detection is crucial to ensure the efficient operation of the gas turbine. With the wide application of machine learning, data-driven condition monitoring methods have become more and more popular. To solve the problem of gas turbine exhaust temperature distribution anomaly detection in the case of missing fault data, deep autoencoder (DAE) was used to learn characteristics, and isolated forset (iForset) was used to learn normal information of characteristic data, so as to achieve abnormal detection. Compared with other single classification anomaly detection methods, this method has the best detection performance index and can realize effective and sensitive gas turbine exhaust temperature anomaly detection.
2023,45(24): 132-136 收稿日期：2022-11-23
DOI：10.3404/j.issn.1672-7649.2023.24.024
分类号：TK478
作者简介：李坤泰(1998-),男,硕士研究生,研究方向为燃气轮机性能预测与故障诊断
参考文献：
[1] 余又红, 贺星. 燃气轮机性能退化的动态特性[J]. 海军工程大学学报, 2012, 24(5): 39-42
[2] GAO F T, HUANG J, et al. Nonlinear Kalman filters for aircraft engine gas path health estimation with measurement uncertainty[J]. Aerospace Science and Technology, 2018, 76: 126-140.
[3] LEE H, LI G, RAI A, et al. Real-time anomaly detection framework using a support vector regression for the safety monitoring of commercial aircraft[J]. Advanced Engineering Informatics, 2020, 44: 101071
[4] WONG P K, YANG Z, VONG C M, et al. Real-time fault diagnosis for gas turbine generator systems using extreme learning machine[J]. Neurocomputing, 2014, 128: 249-257
[5] LIU J. Gas path fault diagnosis of aircraft engine using HELM and transfer learning[J]. Engineering Applications of Artificial Intelligence, 2022, 114: 105149
[6] MONTAZERI-Gh M, NEKOONAM A. Gas path component fault diagnosis of an industrial gas turbine under different load condition using online sequential extreme learning machine[J]. Engineering Failure Analysis, 2022, 135: 106115
[7] ZHOU D, YAO Q, WU H, et al. Fault diagnosis of gas turbine based on partly interpretable convolutional neural networks[J]. Energy, 2020, 200: 117467
[8] TAN Y, NIU C, TIAN H, et al. Decay detection of a marine gas turbine with contaminated data based on isolation forest approach[J]. Ships and Offshore Structures, 2021, 16(5): 546-556
[9] YAN W. Detecting gas turbine combustor anomalies using semi-supervised anomaly detection with deep representation learning[J]. Cognitive Computation, 2020, 12(2): 398-411
[10] 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
[11] FU S, ZHONG S, LIN L, et al. A re-optimized deep auto-encoder for gas turbine unsupervised anomaly detection[J]. Engineering Applications of Artificial Intelligence, 2021, 101: 104199
[12] 刘娇. 燃气轮机高温部件故障早期预警研究[D]. 哈尔滨: 哈尔滨工业大学, 2019.
[13] 房友龙, 刘东风, 余又红, 等. 一种基于经验的燃气轮机参数折合方法[J]. 航空动力学报, 2018, 33(11): 2802-2808
[14] LIU F T, TING K M, ZHOU Z H. Isolation forest[C]//2008 Eighth IEEE International Conference on Data Mining. IEEE, 2008: 413-422.
