PrecTime: A Deep Learning Architecture for Precise Time Series Segmentation in Industrial Manufacturing Operations
This addresses the need for reliable time series segmentation in industrial settings to maximize value from sensor data, though it appears incremental as it builds on existing methods.
The paper tackles the problem of precise time series segmentation in industrial manufacturing by proposing PrecTime, a novel deep learning architecture that combines sliding window and dense labeling approaches, achieving a segmentation accuracy of around 96% and outperforming five state-of-the-art baseline networks.
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.