1D-CNN Bearing Fault Classifier: CWRU 3-Sensor Pipeline

#1dcnn #bearingfaultdiagnosi #cwrudataset #vibrationanalysis

97.8% Accuracy on the First Try — Then I Changed the Test Split

My initial 1D-CNN hit 97.8% accuracy on CWRU bearing data. I thought I'd cracked it. Then I switched from random splitting to time-ordered splitting, and accuracy dropped to 71.2%.

This is the story of building a proper 1D-CNN fault classifier that actually generalizes — not one that memorizes temporal correlations in shuffled data. I'm using three sensor positions (drive end, fan end, base) from the CWRU Bearing Data Center, and the pipeline I ended up with gets 93.4% on properly held-out data.

Scrabble tiles spelling an inspirational message on focus and problem-solving. — Photo by Brett Jordan on Pexels

Why Three Sensors Instead of One

Most CWRU tutorials use only the drive end (DE) accelerometer. That works fine for academic papers, but real industrial setups rarely have just one sensor. When I added fan end (FE) and base (BA) channels, two things happened: accuracy went up 4%, and the model became more robust to single-sensor noise.

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