Due to the rise of new information and communication technologies manufacturing companies have access
to huge amounts of power consumption data which are measured by sensors and processed by information
systems. One of the most promising applications of extracting value out of the collected data is the detection
of anomalies in process data from industrial machines and equipment. Many research and industry use cases
apply machine learning (ML) techniques for anomaly detection. These techniques enable manufacturing
companies to optimize their manufacturing processes but also to be more energy efficient and therefore have
an impact for sustainable manufacturing. Most of the ML applications use central server infrastructures for
data collection from different sources to process and analyse it for further usage. Nevertheless, privacy
concerns and security risks motivate manufacturers to store the collected sensitive data from the production
line locally. Therefore, suppliers of industrial machines (e.g. robots, machine tools) do not have the
possibility, to store and analyse the data in the cloud, where data from all the machines of the supplier in
different companies could be analysed and used for ML applications. One of the new paradigm shifts in ML
is the concept of federated learning (FL) which enables local devices to use ML without sending data to a
central server. This paper introduces an architecture for using the concepts of FL in manufacturing processes
enabling machine suppliers to use ML for optimizing machine processes in a collaborative manner.
Therefore, the more general federated learning concept is extended for industrial machinery and equipment
using the industrial communication framework OPC-UA. Our architecture is tested and validated by using
an industrial dataset of different compressors’ power consumption.
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