Machine Learning based Real Time Predictive Maintenance at the Edge for Manufacturing Systems: A Practical Example

Abstract

Edge Computing is becoming more and more essential for the Industrial Internet of Things (industrial IoT) for predictive maintenance in the industry 4.0 framework. The transition from central to distributed approaches (edge nodes), will enhance the capabilities of handling real-time big data from IoT by ensuring low latency and high bandwidth. Moreover, with the developed architecture the possibility is given to have distributed Machine Learning (ML) by enabling edge devices to learn local ML models. Therefore, the performances of the global diagnostic models can be improved. In this paper, the developed setup is composed of PC's, NVIDIA Jetson Nano Developers kits (for edge computing), and a smartphone for real-time displaying. The implemented real-time supervised machine learning approaches are applied on an industrial oilinjection screw compressor instrumented with vibration sensors. Time domain features are calculated online with the help of sliding windows and the features are automatically classified. Embedded in the equipment, the used algorithms obtained very good real-time diagnostic performances.