Microservices-based cloud-edge collaborative condition monitoring platform for smart manufacturing systems

Abstract

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%.

KEYWORDS:

• Condition monitoring
• Industrial Internet of things
• large-scale data
• cloud-edge computing
• microservices

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.

Additional information

Funding

This work is supported in part by the National Key Research and Development Program of China [grant number 2018YFB1701200], National University of Singapore, and China Scholarship Council [grant number 202006280396].

Notes on contributors

Hanbo Yang

Hanbo Yang received the B.Eng. degree in mechanical engineering from University of Electronic Science and Technology of China, Chengdu, China, in 2016. He is currently working toward the Ph.D. degree at the School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China. He is also currently a visiting student with the Department of Mechanical Engineering, National University of Singapore. His research interests include cloud manufacturing, prognostics, and health assessment.

S. K. Ong

S. K. Ong is currently an associate professor at National University of Singapore (NUS). She received a B.Eng. and Ph.D. degrees in mechanical engineering from NUS, Singapore. She is a Fellow of CIRP International Academy for Production Engineering. Her research interests include digital twin, virtual reality, and augmented reality.

A. Y. C. Nee

A. Y. C. Nee, is currently an emeritus professor at National University of Singapore (NUS). He received a Ph.D. degree from the Institute of Science and Technology, Victoria University of Manchester (UMIST) in 1973. He was formerly Director, Division of Research Administration, Office of Deputy President (Research & Technology) of NUS, and was also Co-Director of Singapore-MIT Alliance. He was elected as a president of CIRP International Academy for Production Engineering in 2012. He is also a Fellow of the Singapore Academy of Engineering, and a Fellow of the Society of Manufacturing Engineers. His research interests include digital twin, virtual reality, and augmented reality.

Gedong Jiang

Gedong Jiang received the B.E. degree in mechanical engineering and the Ph.D. degree in mechanical engineering from the Xi’an Jiaotong University, Xi’an, China, in 1992 and 1998 respectively. She is currently a Professor with the School of Mechanical Engineering, Xi’an Jiaotong University. She was a visiting scholar with the University of Manitoba, Manitoba, Canada, in 2010. Her research includes the intelligent manufacturing, condition monitoring, and robotics.

Xuesong Mei

Xuesong Mei received the Ph.D. degree in mechanical engineering from Xi’an Jiaotong University, Xi’an, China, in 1991. Since 1991, he has been with the School of Mechanical Engineering, Xi’an Jiaotong University. He is currently the director of the Shaanxi Key Laboratory of Intelligent Robots, Xi’an Jiaotong University, Xi’an, China. His research interests include high-speed high precision numerical control systems, and advanced laser machining technology.