CNN vs LSTM Bearing Fault Detection: 8x Training Speed Gap

#cnn #lstm #bearingfaultdetectio #cwrudataset

CNNs Train 8x Faster Than LSTMs on CWRU — But Nobody Talks About the Memory Trade-off

I benchmarked 1D-CNN vs LSTM on the CWRU bearing dataset and found CNNs converge in 12 minutes while LSTMs take 97 minutes on the same GPU. Everyone obsesses over final accuracy (spoiler: they're within 2%), but training speed matters more when you're iterating on feature engineering or hyperparameters. If you're running 20 experiments to tune your preprocessing pipeline, that 8x gap adds up to days of saved time.

But here's what the papers don't tell you: LSTMs use 3.4x more GPU memory during training, which means smaller batch sizes and more gradient noise. On my RTX 3090 with 24GB VRAM, I could run batch size 512 for the CNN but had to drop to 128 for the LSTM to avoid OOM errors. That memory bottleneck isn't just an inconvenience — it directly impacts convergence stability and forces you into longer training runs.

This post shows exactly where the time goes, why LSTMs are so slow despite having fewer parameters, and when you'd still pick LSTM over CNN anyway.