Frequency-domain characterization of Singular Spectrum Analysis eigenvectors

Leles, Michel C. R.; Cardoso, Adriano S. V.; Moreira, Mariana Geny; Guimarães, Homero Nogueira; Silva, Cristiano M.; Pitsillides, Andreas

doi:10.1109/isspit.2016.7886003

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…The resolution of the signal decomposition performed by SSA, expressed by the number of components obtained, was governed by the so-called Window Length. Leles at al expressed the effect of modification of this parameter as separability [40]. The study presented in this manuscript is a continuation of work carried out by Garcia et al, where SSA was used to study static pressure measurements from a centrifugal compressor experiment [41,42].…”

Section: Introductionmentioning

confidence: 93%

Singular spectrum analysis as a tool for early detection of centrifugal compressor flow instability

2021

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Centrifugal compressor machinery is subject to a potentially damaging phenomenon called surge at low mass flow rates. This effect may be preceded by a phenomena known as inlet recirculation -a flow reversal upstream of the impeller. A methodology to isolate inlet recirculation as a characteristic feature for monitoring of centrifugal compressor instability is presented in this study. The methodology is based on a nonparametric time series analysis technique called as singular spectrum analysis (SSA). SSA decomposes a signal into a number of Reconstructive Components (RCs), from which data trends and oscillatory components may be extracted. The frequency spectra of each RC and their relative contributions to the reconstruction of the original signal were examined and comparisons were made with spectral maps in existing literature. Individual and independent RCs were chosen to construct a compressor's instability monitoring system. Additionally, the performance of SSA was determined by the Window length parameter. The effect of modification of this parameter was also studied, and the various viable choices of component for the basis of inlet recirculation diagnosis were considered. The methodology was implemented in pressure dynamical signals measured in an experimental centrifugal compressor rig. High frequency pressure measurements were taken at a number of flow conditions and locations within the compressor. The results demonstrated the potential of a methodology based on SSA to identify and extract oscillatory components with information about the local effect of inlet recirculation and eventually successfully monitor centrifugal compressor's instability.

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Section: Introductionmentioning

confidence: 93%

Singular spectrum analysis as a tool for early detection of centrifugal compressor flow instability

2021

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“…where f s is the sampling rate [44,45]. Harris and Yuan showed in [42] for the univariate case that a periodic oscillation contained in the data lead to an even and odd filter.…”

Section: Dimensionality Reduction Methodsmentioning

confidence: 99%

Cardiac and Respiratory Self-Gating in Radial MRI Using an Adapted Singular Spectrum Analysis (SSA-FARY)

Rosenzweig

Scholand

Holme

et al. 2020

IEEE Trans. Med. Imaging

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Cardiac Magnetic Resonance Imaging (MRI) is time-consuming and error-prone. To ease the patient's burden and to increase the efficiency and robustness of cardiac exams, interest in methods based on continuous steady-state acquisition and self-gating has been growing in recent years. Self-gating methods extract the cardiac and respiratory signals from the measurement data and then retrospectively sort the data into cardiac and respiratory phases. Repeated breathholds and synchronization with the heart beat using some external device as required in conventional MRI are then not necessary. In this work, we introduce a novel self-gating method for radially acquired data based on a dimensionality reduction technique for time-series analysis (SSA-FARY). Building on Singular Spectrum Analysis, a zero-padded, time-delayed embedding of the auto-calibration data is analyzed using Principle Component Analysis. We demonstrate the basic functionality of SSA-FARY using numerical simulations and apply it to in-vivo cardiac radial single-slice bSSFP and Simultaneous Multi-slice radiofrequency-spoiled gradient echo measurements, as well as to Stack-of-Stars bSSFP measurements. SSA-FARY reliably detects the cardiac and respiratory motion and separates it from noise. We utilize the generated signals for high-dimensional image reconstruction using parallel imaging and compressed sensing with in-plane wavelet and (spatio-)temporal total-variation regularization.

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“…where f s is the sampling rate [252,253]. Harris and Yuan showed in [250] for the univariate case that a periodic oscillation contained in the data lead to an even and odd filter.…”

Section: Dimensionality Reduction Methodsmentioning

confidence: 99%

Fast and Robust Multi-Dimensional Cardiac Magnetic Resonance Imaging

Rosenzweig¹

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Frequency-domain characterization of Singular Spectrum Analysis eigenvectors

Cited by 6 publications

References 12 publications

Singular spectrum analysis as a tool for early detection of centrifugal compressor flow instability

Singular spectrum analysis as a tool for early detection of centrifugal compressor flow instability

Cardiac and Respiratory Self-Gating in Radial MRI Using an Adapted Singular Spectrum Analysis (SSA-FARY)

Fast and Robust Multi-Dimensional Cardiac Magnetic Resonance Imaging

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