Abstract Soft-sensors usually assist in dealing with the unavailability of hardware sensors in process industries, thus allowing for less fault occurrence and better control performance. However, nonlinear, non-stationary, ill-data, auto-correlated and co-correlated behaviors in industrial data always make general data-driven methods inadequate, thus resorting to kernel-based methods provide a necessary alternative. This paper gives a systematic review of various state-of-the-art kernel-based methods with applications for data pre-processing, sample selection, variable selection, model construction and reliability analysis of soft-sensors. An integrated review of various kernel-based soft-sensor modeling methods is attempted, including on-line, multi-output, small-data-driven, multi-step-ahead and semi-supervised applications. The discussion is further to provide an overview of achieving hard-to-measure variable prediction, fault detection and advanced control of process industries. Finally, data-driven soft-sensors with kernel methods perspectives on potential challenges and opportunities have been highlighted for future explorations in the process industrial communities.

中文翻译：

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在过程工业中重启数据驱动的软传感器：核方法综述

摘要 软传感器通常有助于处理过程工业中硬件传感器不可用的问题，从而减少故障发生并提高控制性能。然而，工业数据中的非线性、非平稳、不良数据、自相关和共相关行为总是使一般的数据驱动方法不足，因此求助于基于核的方法提供了一种必要的替代方法。本文系统地回顾了各种最先进的基于内核的方法，并应用于软传感器的数据预处理、样本选择、变量选择、模型构建和可靠性分析。尝试对各种基于内核的软传感器建模方法进行综合审查，包括在线、多输出、小数据驱动、多步超前和半监督应用。讨论将进一步概述实现过程工业的难以测量的变量预测、故障检测和高级控制。最后，在过程工业社区的未来探索中，数据驱动的软传感器具有潜在挑战和机遇的核心方法观点。