Abstract
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.