Concept of a causality-driven fault diagnosis system for cyber-physical production systems
Author:
Affiliation:
1. Fraunhofer IWU,Digitalization in production,Dresden,Germany
2. Fraunhofer IWU,Production systems and factory automation
Funder
Ministry of Education
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10217218/10217836/10218199.pdf?arnumber=10218199
Reference41 articles.
1. cause2e: A Python package for end-to-end causal analysis;gmenbaum;Github,2023
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3. DYNOTEARS: Structure Learning from Time-Series Data;pamfil;ArXiv,2020
4. causalgraph: A Python Package for Modeling, Persisting and Visualizing Causal Graphs Embedded in Knowledge Graphs;pieper;ArXiv,2023
5. First Approaches to Automatically Diagnose and Reconfigure Hybrid Cyber-Physical Systems
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1. Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process;Sensors;2024-06-08
2. Dynamic Analysis of Evolving Industrial Alarm Floods Using an Adaptive Causal Directed Graph;2023 IEEE 2nd Industrial Electronics Society Annual On-Line Conference (ONCON);2023-12-08
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