Published: November 05, 2018
Seung Hwan Bang, Ronay Ak, Anantha Narayanan Narayanan, Yung-Tsun T. Lee, Hyunbo Cho
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 always valid. In nonstationary environments, the accuracy of the model decreases over time, so the model must be retrained periodically and adapt to the corresponding environment (s). Knowledge transfer for data analytics model is an approach that can be used when adapting to a new environment while reducing or eliminating degradation in the accuracy of the model. This paper surveys the knowledge-transfer methods that are widely used in various applications, and investigates the applicability of these methods for manufacturing problems. Our knowledge-transfer survey focuses on transferring knowledge extracted from data or model. The paper analyzes the surveyed knowledge-transfer methods from three viewpoints: sources of knowledge, types of nonstationary environments, and availability of labeled data. By providing comprehensive information, this paper will support researchers to adopt knowledge transfer in manufacturing.
Citation: Computers in Industry
Pub Type: Journals
Data analytics, Knowledge transfer, Manufacturing, Nonstationary environments
Created November 05, 2018, Updated March 06, 2019