LAUSR

Large and complex machines are the backbone of manufacturing and will remain key to industrialization for the foreseeable future as will leveraging essential technologies, such as the Internet of Things. However, efforts to make this machinery as smart as it could be are falling behind the curve. Data quality is often too low or too heterogeneous for useful analytics making maintenance troublesome. Additionally, many machines are still dumb with no uniform way to extract data or monitor their operation. In part, the success of future concepts like intelligent manufacturing, cyber physical systems, and industry 4.0 depends on solving these problems. Hence, this article presents FADA the groundwork for an ontology and 3-layer cloud-fog-edge architecture for large and complex machines that places data acquisition and IoT at the forefront. The ontology provides a flexible framework for standardizing data. The fog nodes acquire data directly from smart machines, while the edge nodes harvest data from dumb equipment through a recognition model. The fog nodes are flexible and multi-threaded to provide faster higher-performance computing power. To evaluate the proposed architecture and concepts, we implemented FADA in two factory-based testbeds: one with IoT-enabled equipment, the other with mostly dumb machines. The response times and influence rates recorded are promising and indicate that the system is highly adaptable to many different scenarios. We also conducted comparative experiments between FADA and a conventional data acquisition system to compare the occupied disk space, processing time, and data uploading time, which show that the FADA can save 2.9 TB of disk space per day, and reduce the server’s processing time by 184.8 ms per time over the conventional data acquisition system(CDAS), when 20000 fog nodes simultaneously access the server. The results show improvements by FADA in all metrics.