New theoretical insights in the decomposition and time-frequency representation of nonstationary signals: the IMFogram algorithm

Cicone, Antonio; Li, Wing Suet; Zhou, Haijun

doi:10.48550/arxiv.2205.15702

“…However, the FIF method has some properties that makes it preferable. In particular, it is more robust to noise (Cicone et al, 2016), it is not prone to mode mixing (Cicone et al, 2024b), and it generates no unwanted oscillations as defined by Cicone et al (2022). The FIF shares some of these good properties with the Ensemble Empirical Mode Decomposition (EEMD, Wu and Huang (2009)), but for EEMD this comes with a severe increase in computational cost.…”

Section: Fif-based Tsunami Detectionmentioning

confidence: 99%

Tsunami detection methods for Ocean-Bottom Pressure Gauges

Angeli,

Armigliato,

Zanetti

et al. 2024

Preprint

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Abstract. Real-time detection of tsunami waves is a fundamental part of tsunami early warning and alert systems. Several algorithms have been proposed in the literature for that. Three of them and a newly developed one, based on the Fast Iterative Filtering technique, are applied here to a large number of records from the DART monitoring network in the Pacific Ocean. The techniques are compared in terms of earthquake and tsunami event-detection capabilities and statistical properties of the detection curves. The classical Mofjeld's algorithm is very efficient in detecting seismic waves and tsunamis, but it does not always characterize the tsunami waveform correctly. Other techniques, based on Empirical Orthogonal Functions and cascade of filters respectively, show better results in wave characterization but they usually have larger residual than Mofjeld's. The FIF-based detection method shows promising results in terms of detection rates of tsunami events, filtering of seismic waves and characterization of wave amplitude and period. The technique is a good candidate for monitoring networks and in data assimilation applications for realtime tsunami forecasts.

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“…IMFogram is an analogue of a spectrogram which allows fast calculations based on the decomposition results and identification for both local frequency and amplitude information of the signal. Antonio decomposes the signal in the literature 22 using FIF, which is an IF based on a fast implementation of the FFT. The basic idea is to decompose a non-linear and non-smooth signal into IMFs by means of a double convolution filter.…”

Section: Imfogrammentioning

confidence: 99%

“…20,21 Recently, Antonio et al proposed a TFA method called IMFogram. 22,23 The method first calculates the decomposition of the signal by fast iterative filtering (FIF), and then analyses each decomposed IMF using IMFogram. It identifies both local frequency and local amplitude information for each IMF at the same time and ultimately produces a time-frequency representation (TFR) with accurate resolution.…”

Section: Introductionmentioning

confidence: 99%

Wind turbine gearbox fault diagnosis via adaptive IMFogram

Yi,

Zhang,

Huang

et al. 2023

Structural Health Monitoring

1

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Wind turbines usually operate in mountainous, offshore and other field environments. As the wind speed is constantly changing, the gearboxes of wind turbines operate under variable operating conditions, resulting in a non-stationery and time-varying signal characteristic. For existing time–frequency analysis (TFA) methods, they undergo low time–frequency energy concentration as well as noise interference during the fault feature extraction. IMFogram, a recently proposed time–frequency representation (TFR) method, calculates local frequency and amplitude information of signals based on fast iterative filtering (FIF). Moreover, it is able to remove uncertainty on the time–frequency plane, allowing a long enough time window to reduce the local boundary effects associated with the signal decomposition methods. Therefore, it has potential advantages for TFA. However, the actual performance of IMFogram calculation largely depends on the decomposition effect provided by FIF and the selection of model parameters. With the purpose of obtaining a more distinct and focused TFR of the wind turbine vibration signal, a novel TFA method, namely Adaptive IMFogram (AIMFogram), is proposed in this paper. Firstly, a range of intrinsic mode functions (IMFs) are gained after the decomposition of vibration signals using an improved variational nonlinear chirp mode decomposition, which is treated as the input to AIMFogram to solve the unsatisfied result by traditional FIF under strong noise. Secondly, Renyi entropy-based particle swarm optimization is conducted to perform adaptive parameter optimization of the AIMFogram. Later, using the optimal parameter, the proposed AIMFogram achieves a high-resolution TFR of complex multi-component vibration signals. To verify whether the proposed AIMFogram is effective or not, the method was successfully employed to the fault diagnosis of the rolling bearing in an experimental bench and the gearbox in a 850-kW wind turbine.

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“…

Non-linear couplings of the Earth's ionosphere with the geospace environment occur in a largely varying range of spatial and temporal scales. As some radio remote sensing techniques like GNSS measurements suffer from artificial radio frequency interference (RFI) at the smallest scales representing ionospheric scintillation, is it advantageous to have ionospheric scales observations based on in-situ measurements.We investigated the variability of the in-situ plasma density and magnetic field measured by Swarm satellites [1] now creating the climatology of their scales by leveraging the Fast Iterative Filtering (FIF) technique [2].FIF is able to provide a very fine time-frequency representation decomposing any non-stationary, nonlinear signals, into oscillating modes, called intrinsic mode components or functions (IMCs or IMFs), characterized by their specific frequency.The results are obtained by time-integrating the instantaneous time-frequency representations, provided through the so-called "IMFogram" [3]. These IMFograms have the potential to show the greater details of the scale sizes and their variations, illustrating the time development of the multi-scale processes during various disturbances of geospace.

…”

mentioning

confidence: 99%

“…The results are obtained by time-integrating the instantaneous time-frequency representations, provided through the so-called "IMFogram" [3]. These IMFograms have the potential to show the greater details of the scale sizes and their variations, illustrating the time development of the multi-scale processes during various disturbances of geospace.…”

mentioning

confidence: 99%

Spatio-Temporal Scales of Plasma Density in Topside Ionosphere

Urbar,

Spogli,

Alfonsi

et al. 2023

Proceedings of the XXXVth URSI General Assembly and Scientific Symposium – GASS 2023

0

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Non-linear couplings of the Earth's ionosphere with the geospace environment occur in a largely varying range of spatial and temporal scales. As some radio remote sensing techniques like GNSS measurements suffer from artificial radio frequency interference (RFI) at the smallest scales representing ionospheric scintillation, is it advantageous to have ionospheric scales observations based on in-situ measurements.We investigated the variability of the in-situ plasma density and magnetic field measured by Swarm satellites [1] now creating the climatology of their scales by leveraging the Fast Iterative Filtering (FIF) technique [2].FIF is able to provide a very fine time-frequency representation decomposing any non-stationary, nonlinear signals, into oscillating modes, called intrinsic mode components or functions (IMCs or IMFs), characterized by their specific frequency.The results are obtained by time-integrating the instantaneous time-frequency representations, provided through the so-called "IMFogram" [3]. These IMFograms have the potential to show the greater details of the scale sizes and their variations, illustrating the time development of the multi-scale processes during various disturbances of geospace.

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New theoretical insights in the decomposition and time-frequency representation of nonstationary signals: the IMFogram algorithm

Cited by 3 publications

References 56 publications

Tsunami detection methods for Ocean-Bottom Pressure Gauges

Tsunami detection methods for Ocean-Bottom Pressure Gauges

Wind turbine gearbox fault diagnosis via adaptive IMFogram

Spatio-Temporal Scales of Plasma Density in Topside Ionosphere

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