A hybrid generative and transformer-based framework for anomaly detection in industrial sensor time-series for predictive maintenance.
- DOI
- 10.1038/s41598-026-62330-8
- Published
- 2026-07-28
- Container
- Sci Rep
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-026-62330-8,
title = {A hybrid generative and transformer-based framework for anomaly detection in industrial sensor time-series for predictive maintenance.},
author = {Asim N and Matloob I and Khan Z and Rukaiya R and Khan S and Alfraihi H.},
year = {2026},
journal = {Sci Rep},
doi = {10.1038/s41598-026-62330-8},
url = {https://doi.org/10.1038/s41598-026-62330-8}
}RIS
TY - JOUR TI - A hybrid generative and transformer-based framework for anomaly detection in industrial sensor time-series for predictive maintenance. AU - Asim N AU - Matloob I AU - Khan Z AU - Rukaiya R AU - Khan S AU - Alfraihi H. PY - 2026 JO - Sci Rep DO - 10.1038/s41598-026-62330-8 UR - https://doi.org/10.1038/s41598-026-62330-8 ER -
APA
N, A., I, M., Z, K., R, R., S, K., & H., A. (2026). A hybrid generative and transformer-based framework for anomaly detection in industrial sensor time-series for predictive maintenance.. Sci Rep. https://doi.org/10.1038/s41598-026-62330-8
Source records
- europe-pmc · retrieved 2026-09-25T12:32:52.289Z
- doaj · retrieved 2026-09-25T12:32:52.290Z