97.3% Accuracy Sounds Great Until Your Interviewer Asks About Class Imbalance
Most CWRU bearing fault classification tutorials end with a confusion matrix showing near-perfect accuracy. Then you put it on your resume, walk into an interview, and get asked: "How did you handle the 10:1 imbalance between normal and fault samples?" Silence.
I've seen this pattern repeatedly in PHM portfolio projects. The model works. The accuracy looks impressive. But the project falls apart under scrutiny because it skips the engineering decisions that matter in production. This post walks through building a CWRU-based fault diagnosis project that actually holds up when someone asks hard questions.
Why CWRU Data Is Still the Go-To Benchmark
The Case Western Reserve University bearing dataset (Smith and Nass, 2003 — available at CWRU Bearing Data Center) remains the most cited benchmark in bearing fault diagnosis research, despite being collected over 20 years ago. Why?
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