Technical Components Integration Using APIs for Predictive Maintenance in the Context of Industry 4.0 Digital Transformation

Digital Transformation (DT) is essential to support approaches that contribute to sustainability and circularity in different Industry 4.0 application contexts. DT presents several challenges and opportunities that go beyond data acquisition processes and technologies, including Edge and Cloud implementations, Analytics and Artificial Intelligence (AI) algorithms, and the Internet of Things (IoT) and Application Programming Interface (API), as well as the need to define and use interoperability standards for circular manufacturing in a cyber-physical system (CPS). Nowadays, predictive maintenance plays a key role in sustainable manufacturing and production systems, offering intelligent and optimized machine maintenance supported by AI algorithms for condition-based maintenance. Considering that CPSs are usually designed with a modular architecture, it is of great importance to properly define the interfaces between different technical components or modules, providing an adequate flow of data from the data sources to the user. In this context, the development of APIs must follow a standardized way to allow the flow of data and information between machines, systems, and users. This article presents the development of APIs that promote the integration of different technical components in a CPS, building a pipeline to collect data from machine sensors and process them, applying preprocessing techniques and AI algorithms for predictive maintenance of industrial machines and systems and providing decision support regarding maintenance planning.