Rolling bearing operation is affected by friction, wear and lubrication mechanisms, fluid dynamics and lubricant rheology, material properties, and contact mechanics. Changes in rolling surfaces occur due to plastic deformation, rolling contact wear, and rolling contact fatigue. Wear particles can be formed and mixed into the lubricant. Increased levels of vibrations due to surface degradation can be monitored by sensors. Rolling contact wear and rolling contact fatigue during rolling bearing operation can be diagnosed by combining measured and interpreted condition monitoring data with theory, and conclusions drawn thereof can support a continuous prognosis for the remaining bearing life. In the present work, connections between bearing diagnostics and tribological mechanisms are outlined.

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Industrial cyber-physical systems rely increasingly on data from IoT devices and other systems as continuously emerging use cases implement new intelligent features. Edge computing can be seen as an extension of the cloud in close physical proximity, in which some of the typical cloud computing loads are beneficial to run. This paper studies data analytics application development for integration of industrial IoT data and composition of application services executed on edge and cloud. A solution is designed to support heterogeneous hardware and run-time platforms, and focuses on the service layer that enables flexible orchestration of data flows and dynamic service compositions. The unified model and system architecture implemented, using the open Arrowhead Framework model, is verified through two representative industrial use cases.