As IoT-enabled manufacturing is still in its infancy, there are several key research gaps that need to be addressed. These gaps include the understanding of the characteristics of the big data generated from industrial IoT sensors, the challenges they present to process data analytics, as well as the specific opportunities that the IoT big data could bring to advance manufacturing. In this paper, we use an inhouse-developed IoT-enabled manufacturing testbed to study the characteristics of the big data generated from the testbed. Since the quality of the data usually has the most impact on process modeling, data veracity is often the most challenging characteristic of big data. To address that, we explore the role of feature engineering in developing effective machine learning models for predicting key process variables. We compare complex deep learning approaches to a simple statistical learning approach, with different level or extent of feature engineering, to explore their pros and cons for potential industrial IoT-enabled manufacturing applications.

由于支持物联网的制造业仍处于起步阶段，因此需要解决几个关键的研究空白。这些差距包括对从工业物联网传感器生成的大数据的特性的理解，它们在处理数据分析方面所面临的挑战以及物联网大数据可能为推进制造业带来的特定机会。在本文中，我们使用内部开发的支持物联网的制造测试平台来研究测试平台生成的大数据的特征。由于数据质量通常对流程建模影响最大，因此数据准确性通常是大数据最具挑战性的特征。为了解决这个问题，我们探索了特征工程在开发有效的机器学习模型以预测关键过程变量中的作用。