使用卷积自编码器的异常检测和根本原因分析:一个真实案例研究

IF 3.7 3区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Piero Danti , Alessandro Innocenti , Sascha Sandomier
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引用次数: 0

摘要

异常检测是识别数据中可能表明偏离预期规范的异常模式的过程。本文提出了一种半监督深度学习解决方案,用于检测利用天然气内燃机产生热量和电力的洋马能源设备的异常情况。分析的主要设备是安装在某设施学校能源厂的一台20千瓦的微型热电联产机组。更详细地说,这项工作中考虑的数据集由每15分钟临时获取的12个特征组成。作者利用深度学习架构,一个具有1-D卷积层的自动编码器来保持时间相关性,训练以学习共同生成器的正常行为并报告未见的操作。考虑到自编码器容易产生假阳性的事实,采用基于快速傅立叶变换的技术来过滤假检测,提高算法的鲁棒性。作为最后的贡献,我们解释了一种幼稚的方法来解决异常的根本原因,并在一次实际的CHP故障中证明了其有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Anomaly detection and root cause analysis using convolutional autoencoders: A real case study
Anomaly detection is the process of identifying unusual patterns in data that may indicate a deviation from the expected norm. This paper proposes a semi-supervised deep learning solution to detect anomalies of a YANMAR energy device that produces heat and power utilizing an internal combustion engine supplied with natural gas. The main equipment of the analysis is a 20 kWe micro-cogeneration unit installed in the energy plant of a facility school. More in detail, the dataset considered in this work consists of 12 features temporally acquired every 15 min. The authors exploit a deep learning architecture, an autoencoder with 1-D convolutional layers to retain temporal correlations, trained to learn the normal behavior of the cogenerator and report unseen operations. In consideration of the fact that autoencoders tend to yield false positives, a Fast-Fourier-Transform-based technique has been applied to filter spurious detections and improve the algorithm’s robustness. As the last contribution, a naive methodology to address the root cause of the anomalies has been explained and its effectiveness has been proved in a real malfunctioning of the CHP.
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来源期刊
Journal of Computational Science
Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
5.50
自引率
3.00%
发文量
227
审稿时长
41 days
期刊介绍: Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).
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