FFT vs CWT vs Wavelet Packet: CWRU Bearing Speed Test

#fft #cwt #wavelet #cwrudataset

The 15-Second Bottleneck That Killed My Real-Time PHM Pipeline

I built a bearing fault classifier that hit 96% accuracy on the CWRU dataset — then watched it fail catastrophically when I tried to run it on actual shop floor hardware. The culprit? Continuous Wavelet Transform (CWT) chewing through 15 seconds per signal while production bearings spun at 1797 RPM, generating new data every 0.1 seconds. By the time my feature extraction finished, I'd missed 150 new samples.

This isn't a CWT vs FFT tutorial. This is a speed benchmark that answers one question: which time-frequency method can actually run in real-time on edge hardware?

I tested FFT, CWT, and Wavelet Packet Decomposition (WPD) on the same 12k CWRU bearing dataset — same fault types (inner race, outer race, ball), same load conditions, same 12 kHz sampling rate. The results flipped my assumptions about "advanced" methods being worth the computational cost.