Real time generator coherency evaluation via Hilbert transform and signals morphological similarity

Author(s):  
Davide Lauria ◽  
Cosimo Pisani
2020 ◽  
Vol 2020 (48) ◽  
pp. 17-24
Author(s):  
I.M. Javorskyj ◽  
◽  
R.M. Yuzefovych ◽  
P.R. Kurapov ◽  
◽  
...  

The correlation and spectral properties of a multicomponent narrowband periodical non-stationary random signal (PNRS) and its Hilbert transformation are considered. It is shown that multicomponent narrowband PNRS differ from the monocomponent signal. This difference is caused by correlation of the quadratures for the different carrier harmonics. Such features of the analytic signal must be taken into account when we use the Hilbert transform for the analysis of real time series.


2016 ◽  
Vol 37 (11) ◽  
pp. 1885-1909 ◽  
Author(s):  
Mojtaba Jafari Tadi ◽  
Eero Lehtonen ◽  
Tero Hurnanen ◽  
Juho Koskinen ◽  
Jonas Eriksson ◽  
...  

2018 ◽  
Vol 175 ◽  
pp. 02008 ◽  
Author(s):  
Daizong Yang ◽  
Yue Zhang

Electrocardiogram(ECG) is an important physiological signal of the human body. It is widely used in identification and arrhythmia detection. The first step of ECG application is signal segmentation, that is, the QRS detection. An effective and real-time QRS detection algorithm is proposed in this paper. A differentiator with adjustable center frequency is used to capture the first derivative information of the frequency band of the electrocardiogram. Then Hilbert transform is used to generate the envelope of the first derivative. After that, a dual threshold method is introduced to decrease FP and FN. Finally, a more precise R wave position is determined based on derivative method. The detector is validated on MIT-BIH arrhythmia database. The result show that the proposed algorithm has a high Sensitivity of 99.87%, Specificity of 99.84%, and the detection error rate is 0.28%. The average execution time of a 30 minutes record is 2.45s.


2021 ◽  
Author(s):  
Danhui Dan ◽  
Houjin Li

Vortex-induced vibration(VIV) is a serious problem of suspension bridges and other long-span bridges during the service period. It can cause the excessive amplitude of the structure under low wind speed, which not only affects the driving comfortableness and safety but also makes the structure face the risk of fatigue failure. The previous research on the identification and evaluation of bridge VIV events during the service period is based on the offline batch processing and analysis of monitoring data, which can not realize real-time perception, calculation, and early warning online. In this paper, according to the vibration characteristics of single-mode sinusoidal-like vibration of engineering structure during VIV, an intelligent monitoring and early warning method for VIV of suspension bridge based on recursive Hilbert transform is proposed. Firstly, the real-time acceleration integral algorithm is used to realize the real-time calculation from the acceleration monitoring data to the dynamic displacement of the stiffening beam, and then the recursive Hilbert transform is used to obtain the real-time analytical signal of the structural displacement during VIV; based on its single-mode near-circular trajectory characteristic, the VIV index and the real-time analysis method are proposed to characterize the development trend of VIV events. This online extraction algorithm can realize the first time warning and the whole process tracking and perception of VIV events. Furthermore, this article also provides a real-time online identification method of key motion parameters such as the instantaneous frequency, phase and amplitude of the structure during VIV, which lays a foundation for real-time monitoring of the whole process of VIV and further evaluation and management decision-making. The accuracy, reliability and engineering feasibility of the proposed method are verified by numerical simulation and VIV monitoring data of a real bridge.


2013 ◽  
Author(s):  
Jiping Guo ◽  
Xiang Peng ◽  
Jiping Yu ◽  
Xiaoli Liu ◽  
Ameng Li ◽  
...  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Michael Rosenblum ◽  
Arkady Pikovsky ◽  
Andrea A. Kühn ◽  
Johannes L. Busch

AbstractComputation of the instantaneous phase and amplitude via the Hilbert Transform is a powerful tool of data analysis. This approach finds many applications in various science and engineering branches but is not proper for causal estimation because it requires knowledge of the signal’s past and future. However, several problems require real-time estimation of phase and amplitude; an illustrative example is phase-locked or amplitude-dependent stimulation in neuroscience. In this paper, we discuss and compare three causal algorithms that do not rely on the Hilbert Transform but exploit well-known physical phenomena, the synchronization and the resonance. After testing the algorithms on a synthetic data set, we illustrate their performance computing phase and amplitude for the accelerometer tremor measurements and a Parkinsonian patient’s beta-band brain activity.


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