Decision-Level Data Fusion in Quality Control and Predictive Maintenance
Yupeng Wei, Dazhong Wu, Janis Terpenny
Abstract
Data fusion integrates data from multiple sources to improve prediction performance. While significant research has been conducted to develop data-level and feature-level fusion methods, very few studies are performed to develop more effective decision-level data fusion methods. This research aims at developing a decision-level data fusion approach that transforms low-dimensional decisions (i.e., predictions) made based on individual sensor data such as temperature and vibration to high-dimensional decisions. Integration of these high-dimensional decisions is formulated as a convex optimization problem rather than a traditional multivariate linear regression problem. The proposed decision-level data fusion approach is demonstrated in two cases: 1) quality control in additive manufacturing and 2) predictive maintenance in aircraft engines. Experimental results have shown that the proposed decision-level fusion method can reduce prediction variance by at least 30% as well as increase prediction accuracy by 45%.