2MNet: Multi-sensor and multi-scale model toward accurate fault diagnosis of rolling bearing

Yang Guan, Zong Meng, Dengyun Sun, Jingbo Liu and Fengjie Fan

Reliability Engineering and System Safety, 2021, vol. 216, issue C

Abstract: Rolling bearing is an indispensable element of rotating machinery, timely and accurate fault diagnosis of rolling bearing plays an important role in the safe and reliable operation of modern industrial systems. Considering the bottleneck that the information collected by a single sensor and single scale features extracted by conventional networks are not comprehensive, a multi-sensor and multi-scale model (2MNet) is proposed to bring a new perspective to accurate fault diagnosis. Most notably, multi-sensor vibration signals in three directions can be fused by defining a novel correlation kurtosis weighted fusion rule. Furthermore, the implication of multi-scale is twofold: one is the multi-scale feature extraction by optimizing the conventional deep residual network and adding dilated convolution, and the other is to achieve multi-scale feature fusion by combining the pyramid principle which can connect deep and shallow features. The superiority and applicability of the model are confirmed by numerical simulation and rolling bearing data.

Keywords: Fault diagnosis; Rolling bearing; Multi-sensor information fusion; Deep multi-scale feature extraction and fusion (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:216:y:2021:i:c:s0951832021005263

DOI: 10.1016/j.ress.2021.108017

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