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.
gear fault diagnosisedge computingmultiplication-convolution networkdeep learningembedded system
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