Modular framework for scalable analysis and modeling of time-dependent industrial sensor data using the example of the Bosch Production Line Performance Dataset
Publication date
2026-06-09
Document type
Konferenzbeitrag
Organisational unit
Scopus ID
Conference
36th CIRP Design Conference (CIRP Design 2026) ; Tokyo, Japan ; March 16–18, 2026
Publisher
Elsevier BV
Series or journal
Procedia CIRP
ISSN
Periodical volume
142
First page
19
Last page
24
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
Keyword
Deep learning
Industrial sensor data
Scalable data processing
Time series
Abstract
The processing of large multivariate sensor data from industrial manufacturing systems requires scalable and robust Artificial Intelligence methods, which focus on the temporal dependencies within the data. This paper presents a modular framework for data preliminary processing, feature engineering and modeling of time series, specially adapted to the Bosch Production Line Performance Dataset. In addition to classic time series baselines, machine learning-based models with manually extracted time series features as well as deep learning architectures such as long short-term memory and Transformer are evaluated. The implementation takes place in a distributed Apache Spark environment to efficiently address data volume and high dimensionality. Our results show that Deep Learning models better capture long-term dependencies, while classic Machine Learning baselines achieve predictions quickly, scalable due to effective feature engineering. The presented pipeline is a practical and scalable approach in terms of analyzing industrial sensor data which contributes to a data-driven transformation in smart manufacturing and Industry 4.0.
Description
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)
Version
Published version
Access right on openHSU
Metadata only access