Leveraging Knowledge Graph Embedding Techniques for Industry 4.0 Use Cases

TL;DR

This paper explores how knowledge graph embedding techniques can be applied to Industry 4.0 data, enabling scalable machine learning for predictive maintenance and automation in complex sensor-driven manufacturing environments.

Contribution

It introduces methods for converting Industry 4.0 sensor data graphs into vector embeddings to facilitate machine learning applications.

Findings

Graph embedding enables scalable machine learning on sensor data.

Embedding techniques improve predictive maintenance accuracy.

The approach reduces feature engineering effort.

Abstract

Industry is evolving towards Industry 4.0, which holds the promise of increased flexibility in manufacturing, better quality and improved productivity. A core actor of this growth is using sensors, which must capture data that can used in unforeseen ways to achieve a performance not achievable without them. However, the complexity of this improved setting is much greater than what is currently used in practice. Hence, it is imperative that the management cannot only be performed by human labor force, but part of that will be done by automated algorithms instead. A natural way to represent the data generated by this large amount of sensors, which are not acting measuring independent variables, and the interaction of the different devices is by using a graph data model. Then, machine learning could be used to aid the Industry 4.0 system to, for example, perform predictive maintenance. However, machine learning directly on graphs, needs feature engineering and has scalability issues. In this paper we discuss methods to convert (embed) the graph in a vector space, such that it becomes feasible to use traditional machine learning methods for Industry 4.0 settings.