mohyunho/NAS_transformer
Evolutionary Neural Architecture Search on Transformers for RUL Prediction
This project helps operations engineers and predictive maintenance specialists automatically design the most effective Transformer-based neural networks for Remaining Useful Life (RUL) prediction. By taking your equipment's multivariate time series data as input, it finds the best network architecture and outputs the optimized model ready for deployment, helping you anticipate equipment failures more accurately. This is designed for engineers and data scientists focused on industrial asset health.
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Use this if you need to optimize the accuracy of your RUL predictions on industrial machinery using advanced deep learning models, but want to avoid manual trial-and-error in model architecture design.
Not ideal if you are looking for a simple, off-the-shelf RUL prediction model without needing to customize or optimize its underlying deep learning architecture.
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50
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7
Language
Python
License
Apache-2.0
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Last pushed
Apr 18, 2023
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