Transfer Learning CWRU to Custom Bearings: 89% to 67% Drop Fix

#transferlearning #bearingfaultdiagnosi #cwrudataset #domainadaptation

The Accuracy Cliff Nobody Warns You About

Trained a bearing fault classifier on CWRU data, got 97% accuracy, deployed it on custom bearings from a pump manufacturer—dropped to 67%. That 30-point accuracy cliff is the reality of cross-machine fault diagnosis that most tutorials conveniently skip.

The CWRU bearing dataset from Case Western Reserve University is the de facto benchmark for bearing fault diagnosis research. It's clean, well-labeled, and everyone uses it. The problem? Real industrial bearings don't behave like lab bearings running under controlled load. Different bearing geometries, shaft speeds, sensor mounting, lubrication conditions—the domain shift is brutal.

This post walks through what actually works for transferring a fault diagnosis model from CWRU to custom industrial bearings. Not the theoretical "use domain adaptation" hand-waving, but the specific techniques that recovered performance from 67% back to 89% on my target domain.