Deep Learning-Based Cloud–Edge Collaboration Framework for Remaining Useful Life Prediction of Machinery

Abstract

In the context of Industry 4.0, intelligent manufacturing services put forward the requirement of rapid response in the remaining useful life (RUL) of machinery. To achieve fast-responding and highly accurate RUL prediction services while mining the intra-kernel correlations of the sensor monitoring data, a deep learning-based cloud–edge collaboration framework was proposed in this article. We encapsulated a cloud prediction engine (Cloud-PE) with a deep prediction model in the cloud service layer and an edge prediction engine (Edge-PE) with a shallow prediction model in the edge service layer. The Cloud-PE assisted the Edge-PE in achieving fast and highly accurate RUL prediction by sharing depth model parameters. Both prediction models were constructed on the basis of a novel blueprint separable convolution neural network. To continuously improve the performance of Edge-PE in a context-aware manner, we adopted an update method for the Edge-PE with the assistance of the Cloud-PE. The experimental results demonstrated that the proposed framework can provide more accurate RUL prediction than existing data-driven prediction methods, and the training time of the prediction model is also significantly reduced.