铝电解槽寿命相关数据分析与可视化研究

Study on Data Analysis and Visualization of Lifespan of Aluminum Electrolysis Cells

随着工业信息化的深入发展,铝电解生产过程中产生的数据量日益庞大,传统的数据分析方法在应对复杂的工业环境时显得不足。提出了一套基于边缘端-云端实时感知系统的铝电解大数据分析方案,集成了在线监测、人工离线检测及记录数据。首先使用经典统计分析方法对铝电解槽寿命及工艺参数进行了初步研究,发现平均寿命为2064 d,运行时长超过2000 d的槽占58%。接着,应用探索性数据分析(EDA)方法对数据进行了缺失、噪声和特征缩放等预处理,并通过可视化方法揭示了工艺参数的分布特征及其相关性。最终,提取出关键变量,为铝电解槽寿命预测模型的建立提供了基础数据。

With the deepening development of industrial informatization,the volume of data generated during aluminum electrolysis production has increased significantly,rendering traditional data analysis methods insufficient for addressing complex industrial environments.In this paper,a big data analysis framework for aluminum electrolysis based on an edge-cloud real-time sensing system was proposed,which integrates online monitoring,manual offline detection and recorded data.Firstly,the classical statistical analysis methods were employed to conduct an initial study on the lifespan and process parameters of aluminum electrolysis cells,revealing an average lifespan of 2064days,with 58%of the cells operating for over 2000days.Then,the Exploratory Data Analysis(EDA)method was applied to perform data preprocessing,including handling missing values,noise reduction,and feature scaling.The distribution characteristics and correlations of process parameters are visualized.Finally,the key variables were extracted,which provides foundational data for the establishment of the lifespan prediction model for aluminum electrolysis cells.