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Communication Dans Un Congrès Année : 2022

Nonstationarity of deterministic signals: when to use frequency estimation algorithms under real-time constraints

Résumé

Signals are ubiquitous in engineering fields and have a common classification by their statistical properties with respect to time. In practice, engineers are faced with a choice to use a frequency estimation algorithm (one that assumes fixed-time representations, e.g. the FFT) or a time-frequency estimation algorithm (one that does not assume a fixed time representation e.g., empirical mode decomposition) depending on the nonstationarity of the signal. In this study, we quantize the trade off between accuracies and computational burden for real-time sinusoidal parameter extraction of frequency estimators for deterministic signals with varying degrees of nonstationarity. Additionally, we demonstrate the implications for different nonstationary signals of different statistical kinds: stationarity about the mean (or linear trend); stationarity about the variance; time-invariance of the autocovariance; and wide-sense stationarity. Lastly, we benchmark on synthetic and experimental signals frequently seen around material characterization in metals: one where a very low-stress solicitation is performed in very high cycle fatigue tests, and two, in acoustic material characterization seen in nonlinear ultrasound, non-destructive testing.
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Dates et versions

hal-03848168 , version 1 (10-11-2022)

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  • HAL Id : hal-03848168 , version 1

Citer

Shawn Kiser, Marc Rebillat, Mikhail Guskov, Nicolas Ranc. Nonstationarity of deterministic signals: when to use frequency estimation algorithms under real-time constraints. 16ème Congrès Français d'Acoustique, CFA2022, Société Française d'Acoustique; Laboratoire de Mécanique et d'Acoustique, Apr 2022, Marseille, France. ⟨hal-03848168⟩
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