Reducio: Data Aggregation and Stability Detection for Industrial Processes Using In-Network Computing
Abstract
Modern manufacturing environments handle increasing numbers of raw data streams that carry large volumes of data. Searching this raw data for anomalous events, such as faults, failures, or degrading product quality, across multiple data sources can help engineers optimize the underlying manufacturing processes. Yet, facilitating corresponding analyses on the massive data streams is challenging: it requires powerful processing platforms, but strict latency requirements or limited bandwidths often make sending the full raw data to suitable, centralized locations (in the cloud or locally) impossible. As a middle ground, the processing logic for finding relevant events can also be placed on the edge, close to the data source, but this still requires high-speed compute capabilities. In this paper, we show that in-network computing provides an effective solution to this dilemma. In particular, we design Reducio, which detects relevant events directly on the data path and dynamically adapts where and in which resolution data is forwarded for further analysis. Underneath, Reducio leverages the process semantics of clocked manufacturing processes to first aggregate raw data streams across multiple sensors and independent machines on a shop floor. It then uses the aggregates to detect anomalous events and assess the stability of the underlying processes to switch between data resolutions and identify machine or sensor malfunctions. We demonstrate the practicality of Reducio by applying a Tofino prototype to the clocked process of fineblanking in several experiments, which reveal that Reducio can detect instabilities in a timely manner while reducing the data volumes by up to 90 % without losing important process information.
Liam Tirpitz
Ike Kunze, M.Sc.
Researcher
Philipp Niemietz
Kathrin Gerhardus
Thomas Bergs
Klaus Wehrle
Head of Group