This paper describes the development of “heats” and input variables selection models that are incorporated into a water detection framework for an industrial steelmaking electric arc furnace (EAF). The selection models in this work are developed based on latent variable methods. The latent variable methods used in this work are multiway principal component analysis (MPCA) and multiway projection to latent structures (MPLS). The particular problems related to latent variable methods discussed in this paper include data preprocessing, including alignment, unfolding method, centering, and scaling. The outcome of the heats selection model is heats with normal operation and the outcome of the input variables selection model is variables that are highly correlated with the off‐gas water vapour. The water detection framework and developed models are useful in practical settings for the prediction of water leakage and the design of appropriate fault detection and diagnosis strategies.

中文翻译：

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用于设计适用于工业电弧炉的水检测框架的热量和输入变量选择

本文描述了“热”和输入变量选择模型的开发，这些模型已被并入工业炼钢电弧炉（EAF）的水检测框架中。本工作中的选择模型是基于潜在变量方法开发的。在这项工作中使用的潜在变量方法是多路主成分分析（MPCA）和多路投影到潜在结构（MPLS）。本文讨论的与潜在变量方法有关的特定问题包括数据预处理，包括对齐，展开方法，居中和缩放。热量选择模型的结果是正常运行的热量，输入变量选择模型的结果是与废气水蒸气高度相关的变量。