Publications
2024
Schultheis, Alexander
Exploring a Hybrid Case-Based Reasoning Approach for Time Series Adaptation in Predictive Maintenance Proceedings Article
In: Proceedings of the Workshops at the 32nd International Conference on Case-Based Reasoning (ICCBR-WS 2024) co-located with the 32nd International Conference on Case-Based Reasoning (ICCBR 2024), Merida, Mexico, July 1, 2024, pp. 230–235, CEUR-WS.org., 2024.
Abstract | Links | BibTeX | Tags: {Temporal Case-Based Reasoning, Explainable Case-Based Reasoning, Hybrid Case-Based Reasoning, Internet of Things, Predictive Maintenance}, Time Series Data
@inproceedings{Schultheis.2024,
title = {Exploring a Hybrid Case-Based Reasoning Approach for Time Series Adaptation in Predictive Maintenance},
author = {Alexander Schultheis},
url = {https://www.wi2.uni-trier.de/shared/publications/2024_ICCBR_DC_Schultheis.pdf},
year = {2024},
date = {2024-01-01},
booktitle = {Proceedings of the Workshops at the 32nd International Conference
on Case-Based Reasoning (ICCBR-WS 2024) co-located with the 32nd
International Conference on Case-Based Reasoning (ICCBR 2024), Merida,
Mexico, July 1, 2024},
volume = {3708},
pages = {230–235},
publisher = {CEUR-WS.org.},
series = {CEUR Workshop Proceedings},
abstract = {Predictive Maintenance (PredM) is a vital concept within Industry 4.0, focusing on proactive machine maintenance through analysis of sensor data to uphold quality standards and prevent downtime. PredM traditionally employs data analysis methods or Machine Learning (ML) algorithms for anomaly detection in time series data from sensors. Despite ample error-free data, the occurrence of errors is rare. Case-Based Reasoning (CBR) offers an adaptive artificial intelligence approach effective in domains with limited fault data. The sub-research area of Temporal Case-Based Reasoning (TCBR) explores the processing of time series data based on CBR methods. Integrating TCBR methods into PredM leverages human involvement, addressing data privacy concerns and facilitating knowledge transfer.
While the retrieval in TCBR has already been investigated, the adaptation of the time series contained in the retrieval results has not yet been considered. On this basis, however, it is possible to determine the further course of the time series as an alternative to ML prediction approaches. For the PredM use case with rare fault data, it is important to determine the further course of the time series and how much time remains before a possible fault case occurs. This research summary therefore investigates a hybrid CBR approach that uses deep learning methods like transformers for adaptation. The aim is to predict the course of a time series as accurately as possible, which is evaluated for the PredM use case. Such a hybrid CBR model should also extend an explanatory component for the predicted time series.},
keywords = {{Temporal Case-Based Reasoning, Explainable Case-Based Reasoning, Hybrid Case-Based Reasoning, Internet of Things, Predictive Maintenance}, Time Series Data},
pubstate = {published},
tppubtype = {inproceedings}
}
Predictive Maintenance (PredM) is a vital concept within Industry 4.0, focusing on proactive machine maintenance through analysis of sensor data to uphold quality standards and prevent downtime. PredM traditionally employs data analysis methods or Machine Learning (ML) algorithms for anomaly detection in time series data from sensors. Despite ample error-free data, the occurrence of errors is rare. Case-Based Reasoning (CBR) offers an adaptive artificial intelligence approach effective in domains with limited fault data. The sub-research area of Temporal Case-Based Reasoning (TCBR) explores the processing of time series data based on CBR methods. Integrating TCBR methods into PredM leverages human involvement, addressing data privacy concerns and facilitating knowledge transfer.
While the retrieval in TCBR has already been investigated, the adaptation of the time series contained in the retrieval results has not yet been considered. On this basis, however, it is possible to determine the further course of the time series as an alternative to ML prediction approaches. For the PredM use case with rare fault data, it is important to determine the further course of the time series and how much time remains before a possible fault case occurs. This research summary therefore investigates a hybrid CBR approach that uses deep learning methods like transformers for adaptation. The aim is to predict the course of a time series as accurately as possible, which is evaluated for the PredM use case. Such a hybrid CBR model should also extend an explanatory component for the predicted time series.
While the retrieval in TCBR has already been investigated, the adaptation of the time series contained in the retrieval results has not yet been considered. On this basis, however, it is possible to determine the further course of the time series as an alternative to ML prediction approaches. For the PredM use case with rare fault data, it is important to determine the further course of the time series and how much time remains before a possible fault case occurs. This research summary therefore investigates a hybrid CBR approach that uses deep learning methods like transformers for adaptation. The aim is to predict the course of a time series as accurately as possible, which is evaluated for the PredM use case. Such a hybrid CBR model should also extend an explanatory component for the predicted time series.