A novel deep learning architecture for multi-source data fusion
A team of researchers proposes a deep learning architecture called CCDNN to learn correlated representations for multi-source data fusion. The method demonstrates promising performance, surpassing existing methods in reconstruction tasks and achieving better results in industrial fault diagnosis and remaining useful life cases.
SourceIEEE Chinese Association of Automation·JournalIEEE/CAA Journal of Automatica Sinica·TypeComputational simulation/modeling·DateMay 15, 2026