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

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.

Automated summary

A 2MNet model is proposed for accurate fault diagnosis of rolling bearings, which combines multiple sensors and multiple scales for feature fusion. The model incorporates a novel fusion rule and the pyramid principle, demonstrating superior performance and applicability.