Predictive Maintenance Python Tutorial: FFT Pipeline

#fft #predictivemaintenanc #python #vibrationanalysis

The 128-Point FFT Trap That Kills Bearing Detection

Most first FFT pipelines fail because they blindly copy tutorial code using 128 or 256-point FFTs. Here's what actually happens: you miss the bearing fault frequencies entirely because your frequency resolution is too coarse.

Last month I ran a basic FFT setup on CWRU bearing data. The inner race defect frequency (BPFI) should've shown up around 162 Hz. Got nothing. Tried different preprocessing. Still nothing. The problem wasn't the sensor or the data — it was the FFT window size giving me only 6.25 Hz resolution bins. When your target frequency sits between bins, it gets smeared across neighbors and disappears into noise.

This post walks through building a working FFT pipeline from scratch using real bearing vibration data. The goal: detect a known inner race fault at 162 Hz. Along the way I'll show the parameter choices that matter, the ones that don't, and where beginners typically derail.