Soft sensors are widely constructed in process industry to realize process monitoring, quality prediction, and many other important applications. With the development of hardware and software, industrial processes have embraced new characteristics, which lead to the poor performance of traditional soft sensor modeling methods. Deep learning, as a kind of data-driven approach, shows its great potential in many fields, as well as in soft sensing scenarios. After a period of development, especially in the last five years, many new issues have emerged that need to be investigated. Therefore, in this article, the necessity and significance of deep learning for soft sensor applications are demonstrated first by analyzing the merits of deep learning and the trends of industrial processes. Next, mainstream deep learning models, tricks, and frameworks/toolkits are summarized and discussed to help designers propel the developing progress of soft sensors. Then, existing works are reviewed and analyzed to discuss the demands and problems occurred in practical applications. Finally, outlook and conclusions are given.

中文翻译：

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数据驱动软传感器深度学习综述


软传感器在过程工业中被广泛构建，以实现过程监控、质量预测和许多其他重要应用。随着硬件和软件的发展，工业过程呈现出新的特点，导致传统的软测量建模方法性能不佳。深度学习作为一种数据驱动的方法，在许多领域以及软传感场景中展现出巨大的潜力。经过一段时间的发展，特别是近五年的发展，出现了许多新的问题需要研究。因此，本文首先通过分析深度学习的优点和工业流程的趋势，论证深度学习对于软测量应用的必要性和意义。接下来，对主流深度学习模型、技巧和框架/工具包进行总结和讨论，以帮助设计人员推动软传感器的发展进步。然后，对现有工作进行回顾和分析，讨论实际应用中出现的需求和问题。最后给出展望和结论。