From Detection to Diagnosis: A Layered Hybrid Framework for Anomaly Characterization in Maritime Sensor Streams

Nadeem Iftikhar, Cosmin-Stefan Raita, Aziz Kadem, David Buncek, Matthew Haze Trinh, Yi-Chen Lin, Anders Vestergaard, Gianna Belle

2025

Abstract

Effective knowledge discovery from industrial sensor data depends on a deep understanding of data quality issues. In the maritime domain, sensor streams often suffer from a diverse set of problems, from simple signal freezes to complex, context-dependent behavioral shifts. Merely detecting these events as a monolithic “anomaly” class provides limited actionable insight. This paper argues for a shift from anomaly detection to anomaly characterization. We propose a novel, layered hybrid framework that systematically identifies and classifies data issues into distinct types. Our pipeline effectively combines the reliability of statistical methods with the advanced pattern-finding ability of machine/deep learning. Each layer acts as a specialized filter that identifies a specific type of anomaly and cleans the data for the next, more advanced analysis. We demonstrate on real-world vessel data that this layered characterization not only achieves high detection accuracy but, more importantly, transforms raw detection flags into actionable knowledge for operational decision-making.

Paper Citation

in Harvard Style

Iftikhar N., Raita C., Kadem A., Buncek D., Trinh M., Lin Y., Vestergaard A. and Belle G. (2025). From Detection to Diagnosis: A Layered Hybrid Framework for Anomaly Characterization in Maritime Sensor Streams. In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN , SciTePress, pages 380-387. DOI: 10.5220/0013741100004000

in Bibtex Style

@conference{kdir25,
author={Nadeem Iftikhar and Cosmin-Stefan Raita and Aziz Kadem and David Buncek and Matthew Trinh and Yi-Chen Lin and Anders Vestergaard and Gianna Belle},
title={From Detection to Diagnosis: A Layered Hybrid Framework for Anomaly Characterization in Maritime Sensor Streams},
booktitle={Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2025},
pages={380-387},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013741100004000},
isbn={},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - From Detection to Diagnosis: A Layered Hybrid Framework for Anomaly Characterization in Maritime Sensor Streams
SN -
AU - Iftikhar N.
AU - Raita C.
AU - Kadem A.
AU - Buncek D.
AU - Trinh M.
AU - Lin Y.
AU - Vestergaard A.
AU - Belle G.
PY - 2025
SP - 380
EP - 387
DO - 10.5220/0013741100004000
PB - SciTePress