Vibration Analysis With a Sound Card and FFT

Your computer already contains a two-channel, 16-or-24-bit ADC that samples at 48,000 times per second or better. It is called the sound card, and for questions like "is this motor vibrating more than it did last month," it is a legitimate measurement front end, as a long tradition of sound-card oscilloscope projects attests. Paired with a piezo disc that costs less than a coffee, it will show you a vibration spectrum. This post covers the electronics you need between the disc and the jack, what the spectrum means once you see it, and, just as important, what this rig cannot tell you.

The sensor and the circuit it needs

A piezo disc is a thin ceramic layer on a brass plate. Strain it and it generates charge; electrically it behaves like a small capacitor in series with a voltage source, which has two consequences. First, it is a very high impedance source. Plugged straight into a typical line input (10 to 47 kΩ), the disc's own capacitance forms a high-pass filter with a corner in the hundreds of hertz, so exactly the low-frequency content you care about gets thrown away. A 1 MΩ load resistor across the disc moves that corner down to where it belongs. Second, a sharp mechanical impact can generate transients of tens of volts, far beyond the roughly 1 Vrms a consumer line input expects.

So the minimum sensible conditioning is: a 1 MΩ resistor across the piezo, a series resistor into the port, a pair of back-to-back diodes across the input to clamp transients, and a series capacitor to block DC. The nicer version adds an op-amp or JFET follower at the sensor so the high-impedance side stays short and the cable is driven at low impedance; the classic sound-card oscilloscope front ends are exactly this circuit. Keep the working signal around half a volt and let the clamp catch the accidents.

What a sound card can and cannot measure

The honest datasheet of this instrument matters more than usual. Line inputs are AC-coupled, so the response rolls off somewhere around 10 to 20 Hz: a shaft turning at 600 RPM has its fundamental at 10 Hz, right at the edge, and anything slower is invisible. On the high end, sample rates of 96 or 192 kHz promise wide bandwidth, but cheap cards keep their anti-aliasing filters tuned for audio, so treat anything above 20 kHz as unverified until you test it. And the amplitude axis is uncalibrated: gain varies by card, by driver, and by the volume slider. Every number you read is in arbitrary units. That rules out judging severity against standards like ISO 20816, which is defined in calibrated millimetres per second. What survives is exactly what a prototype bench needs: relative and comparative measurement, on the same rig, over time.

Reading the spectrum

Machine faults have well-known spectral signatures, all referenced to the shaft's running speed (called 1×). Imbalance shows up as a dominant peak at 1×. Misalignment tends to raise 2×, and a 2× peak rivaling the 1× is a classic tell. Mechanical looseness sprays harmonics across the spectrum. Rolling-element bearings are the interesting case: their defect frequencies are non-integer multiples of shaft speed, computable from the bearing geometry (the datasheet-derived BPFO and BPFI figures), so a peak at, say, 3.57× that grows week over week is a bearing race telling you its story. Gearboxes add tooth-mesh frequencies (tooth count times shaft speed) with sidebands spaced at 1×.

A single FFT gives you that snapshot. A waterfall, which stacks spectra over time as a colored map, gives you the dynamics, and it earns its keep during a run-up or coast-down: components tied to shaft speed slide diagonally as the machine accelerates, while structural resonances stand still as vertical ridges. Where a sliding order crosses a standing ridge, the amplitude blooms, and you have found a critical speed without any calibration at all.

Wiring it into a dashboard

Serial Studio treats audio capture as just another data source: the audio input driver (part of Pro) opens the microphone or line-in, and each channel becomes a dataset, one sample per frame, so a 48 kHz capture is a 48,000-frame-per-second stream. In Quick Plot mode the app builds an audio group automatically with the FFT already configured to match the device's sample rate. In a project you assemble it yourself, and there is one setting worth underlining: set the dataset's FFT sampling rate to the actual audio rate, because the default of 100 Hz will scale the frequency axis nonsense. The FFT plot itself (a free-tier widget, using a Blackman-Harris window) draws the spectrum; the waterfall widget (Pro) draws the spectrogram, reusing the same FFT settings.

From there the workflow is the one described above: mount the disc firmly on the bearing housing (a dab of wax or a magnet base beats tape), capture a baseline spectrum while the machine is known-good, and compare against it later. For before-and-after checks, resonance hunting, and teaching yourself what imbalance looks like, this rig is genuinely good.

Where the cheap rig stops

It is worth saying plainly: this is a qualitative instrument. A real condition-monitoring channel uses an IEPE accelerometer with a calibration certificate stating its sensitivity in millivolts per g, a characterized flat response, and a DAQ with known gain, and that chain is what standards, warranties, and acceptance tests require. The sound-card rig cannot make absolute severity judgments, cannot compare one machine against another, and cannot see slow shafts at all. What it can do is tell you which frequencies are present and whether they are growing, for the price of a piezo disc and an afternoon. For a surprising number of practical questions, that is the whole job.