PrecTime: A Deep Learning Architecture for Precise Time Series Segmentation in Industrial Manufacturing Operations

Abstract

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.

arXiv commentPreprint

References in corpus (7)

• DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
• Robust Real-World Image Super-Resolution against Adversarial Attacks
• SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging
• Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification
• SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification
• Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning
• Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant Representation

Cited by in corpus (1)

• PromptTSS: A Prompting-Based Approach for Interactive Multi-Granularity Time Series Segmentation