Enabling Cognitive Manufacturing in Heterogeneous Industrial Automation Systems

With the increased digitization in the manufacturing sector, cognitive computing entails great potential to improve services and production. This is also referred to as cognitive manufacturing. The general idea is to simulate human cognitive processes - with the aim to improve decision making - by using machine learning (ML) to leverage the increased amount of data. However, the seamless adoption of cognitive computing and ML techniques to industrial automation systems on all abstraction levels is currently impeded by different challenges. Prominent blocking points are the heterogeneity of the systems, which impedes uniform data access and ML integration, and the lack of support for managing various ML life cycle phases. In this work, we propose a framework to manage data and ML life cycles in industrial automation systems. The framework comprises an architecture for the flexible integration of ML components (from component to cloud level) and their adaptive management (including retraining and updates). We address three phases of ML explicitly: pre-deployment, deployment, and post-deployment. We present first results and experiences of applying the framework to an industrial use case and discuss its future potential towards enabling cognitive manufacturing.