Tag: RUL Prediction
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LSTM vs GRU vs Transformer RUL: NASA CMAPSS Memory Test
Compare LSTM, GRU, and Transformer models for RUL prediction on NASA CMAPSS dataset. Which architecture wins the turbofan memory test?
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LSTM Encoder-Decoder vs Seq2Seq Transformer: CMAPSS RUL Benchmark
LSTM Encoder-Decoder vs Seq2Seq Transformer on NASA CMAPSS: 18% RMSE improvement on FD004 but LSTM wins on simple data. Full PyTorch benchmark inside.
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GNN for Multi-Component RUL: Graph Attention Beats Fixed Topology
Learn how Graph Attention Networks predict multi-component system failures better than fixed-topology GNNs. Attention mechanism key to RUL accuracy.
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Physics-Informed Neural Nets for Bearing RUL Prediction
Physics-Informed NNs cut bearing RUL error 30% vs pure deep learning. How Paris' law constraints work when you only have 20 training samples.
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Deploying RUL Models: Optimization and PHM Integration
Deploy RUL models without crashing production: quantization cuts inference from 45ms to 8ms. Real metrics, optimization code, and PHM integration.
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RUL Prediction: Classical ML vs LSTM Training Guide
Random Forest beat LSTM by 12% MAE in offline testsโbut LSTMs won in streaming inference. Here's the benchmark data and when to use each.