Non invasive software architecture for data pipelines with legacy support in smart manufacturing

Smart manufacturing relies on the digitization of all the industrial processes, from production to business operations. It uses Industrial Internet of Things (IIoT) principles to equip devices with smart sensors and actuators, integrating machines and software through data collection, advanced computational methods, and remote control. Our research is motivated by a real digital transition application in the luxury fashion in Italy. The customers wish to update legacy systems, to comply with new Industry 4.0 standards. Due to industrial property requirements, as well as brand secrets, they require the whole architecture to run on-premises. A further requirement is that the system installation must be non-invasive, potentially running on systems with frugal setups in terms of hardware and software. Adhering to such requirements and principles, this paper proposes an architecture for data pipeline in smart manufacturing that runs on-premises, offering support to legacy machines. It is capable of identifying unknown hardware, in terms of semantics of its sensors. The core component of such a concrete architecture is an innovative Extract-Transform-Load (ETL) connector, called sEmantic eXtended ETL (exETL), that manages numerous heterogeneous data sources, and recognizes and configures automatically new machinery sensors. It employs a dedicated Machine Learning (ML) pipeline. The flexibility of the proposed architecture is compared to alternative solutions that exploit existing technologies. Its computational effectiveness is assessed by building an emulated environment, and running extensive experiments on real data. Our results show that our data pipeline is lightweight, more flexible than competitors, and capable of integrating legacy or new machinery seamlessly.