Abstract
Machine learning algorithms rely on a broad database for high quality results. However, studies show that many companies are not willing to share their data with other companies, for exam-ple in the form of a shared data cloud. Therefore, the goal should be to make efficient machine learning possible with decentralized data storage that allows confidential data to remain in the respective company of origin. This article presents a new concept in this respect and analyses its potential for intelligent automation systems taking predictive maintenance as an example. The feasibility of the concept using various existing approaches will be discussed, before po-tential benefits for plant operators and manufacturers, with particular consideration of the per-spective of small and medium-sized companies, will be discussed.
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