摘要
在未标记且极不平衡的监测数据中进行异常检测是能源行业目前急需解决且最具挑战性的问题之一。由于自动编码器具有强大的高维数据分析能力,使用自动编码器进行异常检测变得越来越流行。基于O-DAE(优化的深度自编码器)和SVDD(支持向量数据描述),提出一种新的异常检测方法。首先,建立了一种样本筛选机制,用于去除未标记训练集中的异常样本,使得训练模型几乎不学习异常样本的特征。其次,以自编码器的隐藏特征和重构误差作为最终特征数据进行异常检测。最后,对不同结构的深度学习方法进行研究与比较,并对某汽轮机实际运行数据进行了实验,结合支持向量数据描述检测异常。与传统异常检测方法相比,该方法的异常检测精度提高了50%,能实现更灵敏鲁棒的汽轮机设备性能无监督异常检测。
Anomaly detection in unlabeled and highly imbalanced monitoring data is one of the most urgent to be solved and challenging industry problems.The use of autoencoders for anomaly detection is becoming more and more popular due to the powerful high-dimensional data analysis capabilities of autoencoders.A new anomaly detection method is developed base on O-DAE(optimized deep autoencoder)and SVDD(support vector data description).Firstly,to make the training model hardly learns the features of abnormal samples,a sample screening mechanism is established to remove abnormal samples in the unlabeled training set.Secondly,the hidden features and recon-struction errors of the autoencoder are used as the final feature data for anomaly detection.Finally,the deep learning methods with different architectures are studied and compared,the experiments on the actual operation data of a steam turbine are conducted,and the detection abnormity is described combining with the support vector data.Com-pared with the traditional anomaly detection method,the anomaly detection accuracy of this method is improved by 50%,which can realize more sensitive and robust unsupervised anomaly detection of equipment performance for steam turbines.
作者
许伟明
李学敏
张祎
Maulidi Barasa
张培泽
易佑中
XU Weimin;LI Xuemin;ZHANG Yi;Maulidi Barasa;ZHANG Peize;YI Youzhong(School of Energy and Power Engineering,Huazhong University of Science and Technology,Wuhan 430074,China)
出处
《浙江电力》
2023年第7期102-109,共8页
Zhejiang Electric Power
基金
国家重点研发计划资助项目(2018YFB606101)。
关键词
深度自动编码器
支持向量数据描述
汽轮机
异常检测
deep autoencoder
support vector data description
steam turbine
anomaly detection
作者简介
许伟明(1998),男,硕士研究生,主要从事故障诊断的研究工作。
