RT Journal Article
T1 A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance
A1 Shao, Haidong
A1 Lin, Jing
A1 Zhang, Liangwei
A1 Galar, Diego
A1 Kumar, Uday
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 subjective experience in conventional fault diagnosis, there are two remaining obstacles to collaborative efficiency: integration of multisensory data and fusion of maintenance strategies. To overcome these obstacles, we propose a novel two-part approach: a stacked wavelet auto-encoder structure with a Morlet wavelet function for multisensory data fusion and a flexible weighted assignment of fusion strategies. Taking a planetary gearbox as an example, we use noisy vibration signals from multisensors to test the diagnosis performance of the proposed approach. The results demonstrate that it can provide more accurate and reliable fault diagnosis results than other approaches.
SN 1566-2535
YR 2021
FD 2021-10
LK https://hdl.handle.net/11556/4144
UL https://hdl.handle.net/11556/4144
LA eng
NO Shao , H , Lin , J , Zhang , L , Galar , D & Kumar , U 2021 , ' A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance ' , Information Fusion , vol. 74 , pp. 65-76 . https://doi.org/10.1016/j.inffus.2021.03.008
NO Publisher Copyright: © 2021
DS TECNALIA Publications
RD 31 jul 2024