  The fourth industrial revolution creates ubiquitous sensor data in production
plants. To generate maximum value out of these data, reliable and precise time
series-based machine learning methods like temporal neural networks are needed.
This paper proposes a novel sequence-to-sequence deep learning architecture for
time series segmentation called PrecTime which tries to combine the concepts
and advantages of sliding window and dense labeling approaches. The
general-purpose architecture is evaluated on a real-world industry dataset
containing the End-of-Line testing sensor data of hydraulic pumps. We are able
to show that PrecTime outperforms five implemented state-of-the-art baseline
networks based on multiple metrics. The achieved segmentation accuracy of
around 96% shows that PrecTime can achieve results close to human intelligence
in operational state segmentation within a testing cycle.
