A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance

Shao, Haidong; Lin, Jing; Zhang, Liangwei; Galar, Diego; Kumar, Uday

A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance

Identifiers

URI: https://hdl.handle.net/11556/4144

ISSN: 1566-2535

DOI: 10.1016/j.inffus.2021.03.008

Publication date

2021-10

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Abstract

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.

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Keywords

Collaborative maintenance , Fault diagnosis , Multi-sensor information fusion , Planetary gearbox , Prognostics and health management , Stacked wavelet auto-encoder , Software , Signal Processing , Information Systems , Hardware and Architecture

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journal article

Citation

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

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