LAUSR

In the context of the Industrial Internet of things (IIoT), large-scale IIoT data is generated, which can be effectively mined to provide valuable information for condition monitoring (CM). However, traditional CM methods cannot meet unprecedented challenges concerning large-scale IIoT data transmission, storage and analysis. Therefore, manufacturers have begun to shift from the traditional manufacturing paradigm to smart manufacturing, which integrates the encapsulated manufacturing services and the enabling cloud-edge computing technology to handle large-scale IIoT data. To enhance the agility, scalability and portability of traditional manufacturing services, a microservices-based cloud-edge collaborative CM platform for smart manufacturing systems is proposed. First, leveraging the microservices management system, the lightweight edge and cloud services are constructed from the microservices level, which enables flexible deployment and upgrade of services. Next, the proposed platform architecture effectively integrates the computing and storage capabilities of the cloud layer and the real-time nature of the edge layer, where the cloud-edge collaborative mechanism is introduced to achieve real-time diagnosis and enhance prognosis accuracy. Finally, based on the proposed system, the diagnosis and prognosis tasks are implemented on a manufacturing line, and the results show that the diagnostic accuracy is 90% and the prediction error is 50%.