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Sensors & Data Acquisition — question 8 of 10

Write a real answer. It is graded against the question's rubric, and the mastery movement is computed by the same engine the product uses. Scoring here is deterministic rather than model-written, so this page costs nothing to run and nothing is saved. In a track built from your own posting, the same rubric is graded by the model, which is what produces feedback about your reasoning rather than about coverage.

This is a diagnostic question about recognizing and confirming aliasing in real spectral data, not designing the anti-alias filter itself.

You're monitoring vibration on a rotating shaft with an accelerometer sampled at 2 kHz, so your Nyquist frequency is 1 kHz. You expect meaningful vibration content up to about 900 Hz from known mechanical modes. During a test, the FFT of your data shows a strong, narrow peak at exactly 300 Hz that doesn't correspond to any known shaft speed, gear mesh frequency, or bearing defect frequency in your system. What's your leading hypothesis, and how would you confirm it on the bench?

How sure are you?

Being confidently wrong costs more mastery than admitting a guess — that is deliberate, and it is how misconceptions get found.

Neither is graded, and nothing here is recorded.