mohyunho/MOO_ELM
Multi-Objective Optimization of ELM for RUL Prediction
This project helps predict the Remaining Useful Life (RUL) of critical machinery like turbofan engines. By analyzing operational sensor data, it identifies the best neural network configurations to forecast when equipment will need maintenance or fail. Maintenance engineers, reliability analysts, and operations managers can use this to get faster, optimized predictions for asset management.
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Use this if you need to quickly and efficiently predict the remaining operational time of industrial equipment based on sensor data, prioritizing both prediction accuracy and computational speed.
Not ideal if your primary concern is achieving the absolute highest prediction accuracy regardless of the computational time or the complexity of the model.
Stars
15
Forks
2
Language
Python
License
Apache-2.0
Category
Last pushed
Jun 01, 2022
Commits (30d)
0
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