To address the problems such as difficult data transmission and storage due to the large amount of operational status monitoring low-value density data, poor real-time performance of fault identification due to bandwidth impact, and the difficulty of deploying effectively large and deep learning models to edge-side hardware, this study proposes a gearbox edge intelligent fault diagnosis method based on multiplicative-convolutional network (MCN). Firstly, motivated by the merits of feature representation in signal filtering and feature extraction in deep learning, a lightweight MCN model is formulated. Secondly, a set of end-side edge intelligent processing unit prototype is made by using the embedded microcontroller unit. The system can be deployed directly at the edge of the gearbox, where the parameters of the MCN-based edge model can be trained and updated on the cloud side and deployed to the edge. The edge-side completes data acquisition, processing,and fault status identification, which can consume a large amount of sensor data directly. The experimental results show that MCN has an average recognition accuracy of 99. 75% , and the gearbox edge intelligent diagnosis system deployed with MCN can accurately identify the fault state at 0. 696 s.