TY  - JOUR
AU  - Shao, Haidong
AU  - Lin, Jing
AU  - Zhang, Liangwei
AU  - Galar, Diego
AU  - Kumar, Uday
PY  - 2021
DO  - 10.1016/j.inffus.2021.03.008
SN  - 1566-2535
UR  - https://hdl.handle.net/11556/4144
AB  - Collaborative fault diagnosis can be facilitated by multisensory fusion technologies, as these can give more reliable results with a more complete data set. Although deep learning approaches have been developed to overcome the problem of relying on...
LA  - eng
TI  - A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance
TY  - journal article
ER  -