Model-Based Diagnosis for Cyber-Physical Production Systems Based on Machine Learning and Residual-Based Diagnosis Models
Abstract
This paper introduces a novel approach to Model-Based Diagnosis (MBD) for hybrid technical systems. Unlike existing approaches which normally rely on qualitative diagnosis models expressed in logic, our approach applies a learned quantitative model that is used to derive residuals. Based on these residuals a diagnosis model is generated and used for a root cause identification. The new solution has several advantages such as the easy integration of new machine learning algorithms into MBD, a seamless integration of qualitative models, and a significant speed-up of the diagnosis runtime. The paper at hand formally defines the new approach, outlines its advantages and drawbacks, and presents an evaluation with real-world use cases.
Cite
Text
Bunte et al. "Model-Based Diagnosis for Cyber-Physical Production Systems Based on Machine Learning and Residual-Based Diagnosis Models." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.33012727Markdown
[Bunte et al. "Model-Based Diagnosis for Cyber-Physical Production Systems Based on Machine Learning and Residual-Based Diagnosis Models." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/bunte2019aaai-model/) doi:10.1609/AAAI.V33I01.33012727BibTeX
@inproceedings{bunte2019aaai-model,
title = {{Model-Based Diagnosis for Cyber-Physical Production Systems Based on Machine Learning and Residual-Based Diagnosis Models}},
author = {Bunte, Andreas and Stein, Benno and Niggemann, Oliver},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2019},
pages = {2727-2735},
doi = {10.1609/AAAI.V33I01.33012727},
url = {https://mlanthology.org/aaai/2019/bunte2019aaai-model/}
}