Probabilistic Machine Learning for Uncertainty-Aware Diagnosis of Industrial Systems
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866918146379612160 |
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| author | Mohammadi, Arman Krysander, Mattias Jung, Daniel Frisk, Erik |
| author_facet | Mohammadi, Arman Krysander, Mattias Jung, Daniel Frisk, Erik |
| contents | Deep neural networks has been increasingly applied in fault diagnostics, where it uses historical data
to capture systems behavior, bypassing the need for high-fidelity physical models.
However, despite their competence in prediction tasks, these models often struggle with
the evaluation of their confidence. This matter is particularly
important in consistency-based diagnosis where decision logic is highly sensitive to false alarms.
To address this challenge, this work presents a diagnostic framework that uses
ensemble probabilistic machine learning to
improve diagnostic characteristics of data driven consistency based diagnosis
by quantifying and automating the prediction uncertainty.
The proposed method is evaluated across several case studies using both ablation
and comparative analyses, showing consistent improvements across a range of diagnostic metrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18810 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Probabilistic Machine Learning for Uncertainty-Aware Diagnosis of Industrial Systems Mohammadi, Arman Krysander, Mattias Jung, Daniel Frisk, Erik Machine Learning Deep neural networks has been increasingly applied in fault diagnostics, where it uses historical data to capture systems behavior, bypassing the need for high-fidelity physical models. However, despite their competence in prediction tasks, these models often struggle with the evaluation of their confidence. This matter is particularly important in consistency-based diagnosis where decision logic is highly sensitive to false alarms. To address this challenge, this work presents a diagnostic framework that uses ensemble probabilistic machine learning to improve diagnostic characteristics of data driven consistency based diagnosis by quantifying and automating the prediction uncertainty. The proposed method is evaluated across several case studies using both ablation and comparative analyses, showing consistent improvements across a range of diagnostic metrics. |
| title | Probabilistic Machine Learning for Uncertainty-Aware Diagnosis of Industrial Systems |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.18810 |