Why Most Vibration Analysis Tutorials Skip the Hard Part
Most FFT tutorials show you perfect sine waves and clean frequency peaks. Then you load real bearing data and get a noisy mess with peaks everywhere.
The gap between "here's numpy.fft.fft()" and "here's a working predictive maintenance system" is where most starter projects die. You need the right sampling rate, the right window size, the right preprocessing, and the right interpretation of what those peaks actually mean. Miss any of these and you'll either detect phantom faults or miss real ones.
This post walks through a complete vibration analysis pipeline on the CWRU bearing dataset — the same data used in academic bearing fault detection research. We'll cover the sensor setup parameters that matter, the FFT configuration that produces interpretable spectra, and the frequency ranges where bearing faults actually show up. Not theory. Working code that processes 12,000 samples per second and outputs actionable fault indicators.
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