Decision-Level Data Fusion in Quality Control and Predictive Maintenance

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%.