Application of a Visual and Data Analytics Platform for Industry 4.0 Enabled by the Interoperable Data Spine: A Real-World Paradigm for Anomaly Detection in the Furniture Domain

Nowadays, the rise of cloud and edge computing, alongside the rapid growth of Industrial Internet of Things (IIoT), has led to the creation of many services and platforms related to the manufacturing domain. Due to this wide availability of various solutions related to Industry 4.0, there is a high demand for interoperability and platforms that will enable the collection of different solutions and their access through a common entry point. Furthermore, the quick and effortless application of the established solutions to the other organizations or domains is of utmost importance. In this paper, we are introducing the application of a Visual and Data Analytics platform that has been previously applied in other use cases to a furniture manufacturing industry for providing machine faults detection toward predictive maintenance. The applied solution utilizes unsupervised machine learning algorithms, boosting techniques, and web-based visual analytics. Furthermore, this paper presents how this platform was applied to the furniture pilot using the Data Spine as interoperability enabler that provides platform integration and interoperability in a standardized way and application in various domains and scenarios.