An intelligent fault diagnosis for machine maintenance using weighted soft-voting rule based multi-attention module with multi-scale information fusion

Abstract

The ability of engineering systems to process multi-scale information is a crucial requirement in the development of an intelligent fault diagnosis model. This study develops a hybrid multi-scale convolutional neural network model coupled with multi-attention capability (HMS-MACNN) to solve both the inefficient and insufficient extrapolation problems of multi-scale models in fault diagnosis of a system operating in complex environments. The model's capabilities are demonstrated by its ability to capture the rich multi-scale characteristics of a gearbox including time and frequency multi-scale information. The capabilities of the Multi-Attention Module, which consists of an adaptive weighted rule and a novel weighted soft-voting rule, are respectively integrated to efficiently consider the contribution of each characteristic with different scales-to-faults at both feature- and decision-levels. The model is validated against experimental gearbox fault results and offers robustness and generalization capability with F1 value that is 27% higher than other existing multi-scale CNN-based models operating in a similar environment. Furthermore, the proposed model offers higher accuracy than other generic models and can accurately assign attention to features with different scales. This offers an excellent generalization performance due to its superior capability in capturing multi-scale information and in fusing advanced features following different fusion strategies by using Multi-Attention Module and the hybrid MS block compared to conventional CNN-based models.