eddardd/CrossDomainFaultDiagnosis
Repository containing the code for the experiments and examples of my Bachelor Thesis: Cross Domain Fault Detection through Optimal Transport
This project helps operations engineers and process control specialists diagnose equipment faults in dynamic systems, like chemical reactors or tank systems, without needing to collect dangerous real-world fault data. It takes simulated operational data and, using advanced optimal transport methods, adapts it to predict faults accurately in real processes. This allows for safer and more efficient development of automated fault diagnosis systems.
No commits in the last 6 months.
Use this if you need to train fault diagnosis systems for industrial processes, but collecting real fault data is costly, risky, or impractical.
Not ideal if your system's operational data distribution doesn't significantly differ between simulation and reality, or if you already have ample real-world fault data.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Aug 03, 2023
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