Bang Seung Hwan, Ak Ronay, Narayanan Anantha, Lee Y Tina, Cho Hyunbo
Systems Integration Division, Engineering Laboratory, National Institute of Standards and Technology (NIST), Gaithersburg, MD, 20899, USA.
Department of Industrial and Management Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Gyeongbuk, 37673, Republic of Korea.
Comput Ind. 2019 Jan;104. doi: 10.1016/j.compind.2018.07.001.
Data analytics techniques have been used for numerous manufacturing applications in various areas. A common assumption of data analytics models is that the environment that generates data is stationary, that is, the feature (or label) space or distribution of the data does not change over time. However, in the real world, this assumption is not valid especially for manufacturing. In non-stationary environments, the accuracy of the model decreases over time, so the model must be retrained periodically and adapted to the corresponding environment(s). Knowledge transfer for data analytics is an approach that trains a model with knowledge extracted from data or model(s). Knowledge transfer can be used when adapting to a new environment, while reducing or eliminating degradation in the accuracy of the model. This paper surveys knowledge transfer methods that have been widely used in various applications, and investigates the applicability of these methods for manufacturing problems. The surveyed knowledge transfer methods are analyzed from three viewpoints: types of non-stationary environments, availability of labeled data, and sources of knowledge. In addition, we categorize events that cause non-stationary environments in manufacturing, and present a mechanism to enable practitioners to select the appropriate methods for their manufacturing data analytics applications among the surveyed knowledge transfer methods. The mechanism includes the steps 1) to detect changes in data properties, 2) to define source and target, and 3) to select available knowledge transfer methods. By providing comprehensive information, this paper will support researchers to adopt knowledge transfer in manufacturing.
数据分析技术已被应用于各个领域的众多制造应用中。数据分析模型的一个常见假设是，生成数据的环境是稳定的，也就是说，数据的特征（或标签）空间或分布不会随时间变化。然而，在现实世界中，尤其是对于制造业来说，这个假设并不成立。在非平稳环境中，模型的准确性会随着时间的推移而降低，因此必须定期对模型进行重新训练，并使其适应相应的环境。数据分析的知识转移是一种利用从数据或模型中提取的知识来训练模型的方法。在适应新环境时可以使用知识转移，同时减少或消除模型准确性的下降。本文调查了在各种应用中广泛使用的知识转移方法，并研究了这些方法在制造问题中的适用性。从非平稳环境的类型、标记数据的可用性和知识来源三个角度对所调查的知识转移方法进行了分析。此外，我们对制造中导致非平稳环境的事件进行了分类，并提出了一种机制，使从业者能够在所调查的知识转移方法中为其制造数据分析应用选择合适的方法。该机制包括以下步骤：1）检测数据属性的变化；2）定义源和目标；3）选择可用的知识转移方法。通过提供全面的信息，本文将支持研究人员在制造业中采用知识转移。
