bearing op• 資料種類:signal → table
• 呼叫:import acoustics; acoustics.envelope_spectrum(x, rate, low, high, order=4, n_peaks=5)(或 opsacoustics.get("envelope_spectrum"))
帶通 → 解調 → 變換。軸承缺陷真正顯現的地方。
> 以下的詳細說明為原文 —— 摘要與標題已翻譯。
The three steps are each already available (`dsp.bandpass`,
`dsp.envelope, numpy.fft`); what is not available anywhere in
:mod:dsp is the *composition*, and the composition is the diagnostic. The
raw spectrum of a defective bearing shows a resonance at some kHz and
nothing at the defect rate; the envelope of that resonance band, transformed,
shows the defect rate as a clean line.
`low / high` are the demodulation band in Hz and are required,
not optional. Choosing the band is the analysis; a default would hide the
one decision that has to be made. :func:spectral_kurtosis finds a
candidate band when there is no prior knowledge of the resonance.
The envelope's mean is removed before the transform (otherwise a large DC
line dominates every plot), amplitudes are single-sided (`2/N`), and DC is
excluded from peak picking.
Returns a dict: `freqs, magnitude, peak_freq, peak_amplitude`,
`peak_freqs / peak_amplitudes (the n_peaks` largest, descending),
`band, envelope_mean, resolution_hz`, plus two numbers that exist
because **this operator always returns a peak frequency, including when
there is nothing there**:
`peak_prominence`
the peak divided by the median of the magnitude spectrum.
`band_fraction`
the RMS of the band-passed signal divided by the RMS of the input — how
much of the record actually lives in the demodulation band.
Found by adversarial audit and not repaired by an exception, because there
is nothing invalid to refuse: a constant signal band-passed over
100-2000 Hz has an envelope made of rounding error, and this operator dutifully
reported `peak_freq = 8.0000 Hz`. Nothing raised, nothing was NaN, and
`8 Hz` is a perfectly plausible number to write down. Measured, the four
cases separate on the returned numbers rather than on any invented
threshold:
================ ======== ========= =========== =============
input peak Hz peak amp prominence band_fraction
================ ======== ========= =========== =============
AM, defect 107 107.0000 4.997e-01 10018.6 9.999e-01
impulse + noise 107.0000 1.968e-01 9384.7 9.201e-01
white noise 128.0000 2.785e-02 365.2 3.745e-01
constant signal 8.0000 1.691e-12 173.0 1.995e-12
================ ======== ========= =========== =============
No cut-off is imposed here: a defect that is genuinely 20 dB into the noise
is a real finding and refusing it would be worse than reporting it. The
numbers are returned so the caller can see the difference between row 1 and
row 4, which `peak_freq` alone does not show.
Measured on :func:synthesize_bearing_signal (25600 Hz, 1 s, 3 kHz carrier,
107 Hz defect, `m = 0.5) demodulated over 2000-4000 Hz: `peak_freq =
107.000000` Hz, peak_amplitude = 0.499677` — the modulation depth
itself, because the analytic envelope of that signal is exactly
`1 + 0.5 cos(2 pi 107 t)`. The ordinary spectrum of the same signal has a
one-sided amplitude of 4.291662e-16 at 107 Hz: the defect rate is not
present as a frequency component at all, which is the entire point of the
operator.
**That number needs a scaling step that used to be missing from this
sentence.** `dsp.spectrum returns the raw |rfft|`, not an amplitude —
the raw value in that bin is 5.493328e-12, and the one-sided amplitude
above is `mag * (2.0 / len(x)), here 2/25600 = 7.8125e-05`. This
operator and :func:order_spectrum apply that `2/N` *internally* and so
return amplitudes directly (carrier 3000 Hz: raw 12800, amplitude
1.000000; sidebands 2893 / 3107 Hz: raw 3200, amplitude 0.250000 = m/2).
The two conventions coexist in the library, so do not apply `2/N` twice
when comparing a `dsp` spectrum against one of these.
Raises `ValueError: everything :func:_as_signal and dsp.bandpass`
refuse (non-finite, complex, masked, non-1-D, a band edge outside
`(0, rate/2)`, a signal too short for zero-phase filtering), plus a
non-positive `n_peaks`.
• acoustic_condition_monitoring 族使用指南
• 範例資料目錄(下載 URL / 授權) —— 2-D 用 skimage.data(BSD/公有領域)加合成圖,3-D 給出真實資料源(Stanford/PDS 等)的下載 URL。
• 運算子來歷與參考文獻 —— 該運算子族所依據的研究/方法出處。
• 演算法的正典(作者・年份)與用途見上面的族使用指南。
• acoustic_condition_monitoring — py -3.11 examples/acoustic_condition_monitoring.py
table 作為輸入)bearing)bearing_defect_frequencies · spectral_kurtosis · cepstrum
*Provenance: acoustics.py — ACOUSTICS 運算子登記表。本條目由 tools/opdocs.py md 自動產生(請勿手動編輯)。*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.