Performance degradation assessment of rolling element bearings is vital for the reliable and cost-efficient operation and maintenance of rotating machines, especially for the implementation of condition-based maintenance (CBM). For robust degradation assessment of rolling element bearings, uncertainties such as those induced from usage variations or sensor errors must be taken into account. This paper presents an information exergy index for bearing performance degradation assessment that combines singular value decomposition (SVD) and the information exergy method. Information exergy integrates condition monitoring information of multiple instants and multiple sensors, and thus performance degradation assessment uncertainties are reduced and robust degradation assessment results can be obtained using the proposed index. The effectiveness and robustness of the proposed information exergy index are validated through experimental case studies.