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how to understand the gun-fire vibration spectrum

how to understand the gun-fire vibration spectrum

how to understand the gun-fire vibration spectrum

why at the same frequency, there are two different G^2/Hz?
what is the G^2/Hz between two adjacent Hz numbers?

Frequency (Hz) PSD
14.949 1.00E-07
14.949 0.002403
18.333 0.007607
21.718 0.002403
21.718 1.00E-07
31.881 1.00E-07
31.881 0.002403
36.667 0.007607
41.453 0.002403
41.453 0.002922
49.138 0.003356
49.138 0.004555
55.000 0.008511
60.862 0.004255
60.862 0.003295
66.565 0.004297
66.565 0.004991
73.333 0.009763
80.102 0.004381
80.102 0.004996
300.000 0.01464
600.000 0.04347
700.000 0.04347
2000.000 0.002246

RE: how to understand the gun-fire vibration spectrum

if I was to guess I'd say these spikes represent direct input loads.

I'm more curious about the "1e-7" values ?? do these frequencies match up with isolators ??

another day in paradise, or is paradise one day closer ?

RE: how to understand the gun-fire vibration spectrum

Graph the data using log scale on frequency and much will become apparent. Your PSD is not real data; it's "envelope" data, which is created by taking real data and drawing limits around the data to account for untested conditions and margin against the environment. The two-value frequencies show up as vertical lines on a semilog plot, as do all segments of the graph, which answers your question about what are the correct values between the given points. Your vibration testing guys do this sort of thing every day; you given them the data points and they program them into the shaker table controller.

Your semilog plot will show 4 little "houses" starting at 18.333 Hz, which represent the fundamental and its next 3 harmonics. They have a non-zero width, possibly because the customer wants to account for variations in the frequencies from the gunfire. However, by putting wide envelope around these frequencies, they may have added a substantial amount of energy that might not be present in the real scenario. You should ask your customer for the the raw data and graph them together. If the spectral lines in the raw data exceed the tops of the houses, then they did a reasonable job converting the energies. Likewise if the spectral lines are actually distributions.

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