Machine fault diagnosis method using Ghost-AdderNet and WSNs with sensor computing

Abstract

This paper proposes a machine fault diagnosis method using AdderNet with Ghost modules (Ghost-AdderNet) and wireless sensor networks (WSNs) with sensor computing. The proposed Ghost-AdderNet is a specially designed lightweight convolutional neural network (CNN) for machine fault diagnosis on resource-constrained WSN sensor nodes. It reduces the model size and computational cost by replacing the multiplication operations in the CNN with additions or subtractions while decreasing the model parameters by using Ghost modules. The proposed fault diagnosis method is verified by embedding and evaluating the designed Ghost-AdderNet on a commercial WSN node, JN5169 from NXP. The results show that, compared with raw data transmission mode, the proposed method can significantly reduce model size and the payload transmission data of WSNs, and save 25.1 mJ (29.1 %) node energy while maintaining acceptable diagnosis accuracy (above 99.6 %).