| Author Name | Affiliation | | Yidan Xin | School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China | | Shaolin Hu | School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China
Guangdong Provincial Key Lab.of Petrochemical Equipment and Fault Diagnosis, School of Automation,Guangdong University of Petrochemical Technology,Maoming 525000, Guangdong, China | | Wenzhuo Chen | School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China | | He Song | School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China |
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| Abstract: |
| The flue temperature is one of the important indicators to characterize the combustion state of an ethylene cracker furnace, the outliers of temperature data can lead to the false alarm. Conventional outlier detection algorithms such as the Isolation Forest algorithm and 3-sigma principle cannot detect the outliers accurately. In order to improve the detection accuracy and reduce the computational complexity, an outlier detection algorithm for flue temperature data based on the CLOF (Clipping Local Outlier Factor, CLOF) algorithm is proposed. The algorithm preprocesses the normalized data using the cluster pruning algorithm, and realizes the high accuracy and high efficiency outlier detection in the outliers candidate set. Using the flue temperature data of an ethylene cracking furnace in a petrochemical plant, the main parameters of the CLOF algorithm are selected according to the experimental results, and the outlier detection effect of the Isolation Forest algorithm, the 3-sigma principle, the conventional LOF algorithm and the CLOF algorithm are compared and analyzed. The results show that the appropriate clipping coefficient in the CLOF algorithm can significantly improve the detection efficiency and detection accuracy. Compared with the outlier detection results of the Isolation Forest algorithm and 3-sigma principle, the accuracy of the CLOF detection results is increased, and the amount of data calculation is significantly reduced. |
| Key words: temperature data outlier detection ethylene cracker furnace clustering data clipping LOF |
| DOI：10.11916/j.issn.1005-9113.22021 |
| Clc Number:TP18 |
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| Descriptions in Chinese: |
| 基于CLOF的乙烯裂解炉温度测量数据异常值检测 辛一丹1，胡绍林1,2，陈文卓1，宋鹤1 （1. 西安理工大学 自动化与信息工程学院，西安710048；2. 广东石油化工学院 自动化学院石油化工设备与故障诊断省级重点实验室，广东 茂名525000） 摘要：烟道温度是表征乙烯裂解炉燃烧状态的重要指标之一，温度采样数据异常值会直接影响裂解炉炉管结焦诊断。使用常规异常检测算法如孤立森林算法对烟道温度测量数据进行异常值检测无法检测出所有的异常值。为提高检测准确率同时降低运算量，本文提出一种基于CLOF(Clipping Local Outlier Factor, CLOF)算法的烟道测量数据异常值检测算法，该算法将聚类剪枝与离群因子检测算法相结合，在原始数据中筛选出异常值候选集，对异常值候选集中的数据点进行离群因子检测，实现了对裂解炉烟道温度测量数据高准确率、高效率异常值检测。采用某石化工厂乙烯裂解炉烟道温度实测数据，根据实验效果对CLOF算法中主要参数进行选定，对比分析孤立森林算法、常规LOF算法与CLOF算法异常值检测效果。结果显示，CLOF算法中合适的裁剪系数能显著提高检测效率与检测准确度，相比孤立森林算法的异常值检测结果，CLOF检测结果的准确率大幅提高，数据计算量也显著减少。 关键词：温度数据；异常检测；乙烯裂解炉；聚类；数据减枝；局部离群因子 |
