Combining Knowledge and Deep Learning for Prognostics and Health Management

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Published Jun 29, 2022
Maximilian-Peter Radtke Jürgen Bock

Abstract

In the recent past deep learning approaches have achieved remarkable results in the area of Prognostics and Health Management (PHM). These algorithms rely on large amounts of data, which is often not available, and produce outputs, which are hard to interpret. Before the broad success of deep learning machine faults were often classified using domain expert knowledge based on experience and physical models. In comparison, these approaches only require small amounts of data and produce highly interpretable results. On the downside, however, they struggle to predict unexpected patterns hidden in data. This research aims to combine knowledge and deep learning to increase accuracy, robustness and interpretability of current models.

How to Cite

Radtke, M.-P., & Bock, J. (2022). Combining Knowledge and Deep Learning for Prognostics and Health Management. PHM Society European Conference, 7(1), 594–597. https://doi.org/10.36001/phme.2022.v7i1.3302
Abstract 193 | PDF Downloads 100

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Keywords

PHM, Deep Learning, Knowledge, Hybrid AI

Section
Doctoral Symposium