Physics-Informed Machine Learning for Predictive Maintenance: Applied Use-Cases. Huber, L. G., Palmé, T., & Chao, M. A. In 2023 10th IEEE Swiss Conference on Data Science (SDS), pages 66–72, June, 2023. ISSN: 2835-3420doi abstract bibtex The combination of physics and engineering information with data-driven methods like machine learning (ML) and deep learning is gaining attention in various research fields. One of the promising practical applications of such hybrid methods is for supporting maintenance decision making in the form of condition-based and predictive maintenance. In this paper we focus on the potential of physics-informed data augmentation for ML algorithms. We demonstrate possible implementations of the concept using three use cases, differing in their technical systems, their algorithms and their tasks ranging from anomaly detection, through fault diagnostics up to prognostics of the remaining useful life. We elaborate on the benefits and prerequisites of each technique and provide guidelines for future practical implementations in other systems.
@inproceedings{huber_physics-informed_2023,
title = {Physics-{Informed} {Machine} {Learning} for {Predictive} {Maintenance}: {Applied} {Use}-{Cases}},
shorttitle = {Physics-{Informed} {Machine} {Learning} for {Predictive} {Maintenance}},
doi = {10.1109/SDS57534.2023.00016},
abstract = {The combination of physics and engineering information with data-driven methods like machine learning (ML) and deep learning is gaining attention in various research fields. One of the promising practical applications of such hybrid methods is for supporting maintenance decision making in the form of condition-based and predictive maintenance. In this paper we focus on the potential of physics-informed data augmentation for ML algorithms. We demonstrate possible implementations of the concept using three use cases, differing in their technical systems, their algorithms and their tasks ranging from anomaly detection, through fault diagnostics up to prognostics of the remaining useful life. We elaborate on the benefits and prerequisites of each technique and provide guidelines for future practical implementations in other systems.},
booktitle = {2023 10th {IEEE} {Swiss} {Conference} on {Data} {Science} ({SDS})},
author = {Huber, Lilach Goren and Palmé, Thomas and Chao, Manuel Arias},
month = jun,
year = {2023},
note = {ISSN: 2835-3420},
keywords = {Anomaly Detection, ConditionBased Maintenance, Data science, Decision making, Deep Learning., Deep learning, Distance measurement, Fault Diagnostics, Fault Prognostics, Machine learning algorithms, Prediction algorithms, Predictive Maintenance, Task analysis, physics-informed Machine Learning},
pages = {66--72},
}
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We demonstrate possible implementations of the concept using three use cases, differing in their technical systems, their algorithms and their tasks ranging from anomaly detection, through fault diagnostics up to prognostics of the remaining useful life. 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