Prognostics and health management methods can provide advance warning of failure; reduce the life cycle cost of a product by decreasing inspection costs, downtime, and inventory; and assist in the design and logistical support of fielded and future electronic products. Traditional prognostic methods, such as data-driven methods and physics of failure methods have some limitations. This paper presents a fusion prognostics method, which fuses data-driven methods and physics of failure methods to predict the remaining useful life of electronic products. This method integrates the advantage and overcome the limitations of the data-driven methods and the physics of failure methods to provide better predictions.

Index Terms:Prognostics and health management; Data-driven method; Physics of Failure analysis; Remaining useful life; Fusion prognostics

Complete article is available to CALCE Consortium Members.

© IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.