test

The standard tool for this is a spectrogram. The idea is simple once you see it. You chop the ten-second clip into tiny overlapping slices — about 128 milliseconds each, the length of a blink. For each slice you ask: which frequencies are present, and how loud is each? You stack those answers side by side and you get a picture: time on the horizontal axis, frequency on the vertical axis, brightness representing energy.

Think of it as sheet music for sound. Where a musician would write high C, loud, for half a second, the spectrogram shows a bright spot near the top of the page, half a second wide.

Image prompt: Two side-by-side panels. Left: a raw audio waveform (the squiggly line everyone recognises). Right: the corresponding spectrogram — time on the x-axis, frequency on the y-axis, with brighter colours where there's more energy. A subtle arrow between them. Annotate the spectrogram with a small label "frequency over time". Flat, modern editorial style. Aspect ratio 16:9.

Once you have the spectrogram, you can ask interesting questions of it. Where does the energy live? Pumps have a motor that hums at a low frequency, around 50 to 100 Hz, with harmonics extending up to maybe 500 Hz. That energy is always there, healthy or not — it's the equivalent of the engine note in a car. It tells you nothing about whether the pump is failing.

The interesting stuff lives higher up, between 1,000 and 8,000 Hz. A bearing with a tiny crack in its raceway produces short, sharp clicks every time the crack passes the rolling element — like a fingernail tapping on glass, but at frequencies your ears barely notice. Cavitation, where dissolved gases boil out of the fluid and collapse violently, produces a hissy, broadband rush of noise across the same range. Impeller damage shifts the balance of energy between specific sub-bands. These are the signatures I wanted the model to learn.