scholarly journals A Comparison between Piezoelectric Sensors Applied to Multiple Partial Discharge Detection by Advanced Signal Processing Analysis

2020 ◽  
Vol 2 (1) ◽  
pp. 55 ◽  
Author(s):  
Amanda Binotto ◽  
Bruno Albuquerque de Castro ◽  
Vitor Vecina dos Santos ◽  
Jorge Alfredo Ardila Rey ◽  
André Luiz Andreoli
Keyword(s):  
Signal Processing ◽  
Frequency Analysis ◽  
Lead Zirconate Titanate ◽  
Failure Detection ◽  
Lead Zirconate ◽  
Electric Power System ◽  
Piezoelectric Sensors ◽  
Time Frequency ◽  
Total Failure ◽  
Short Time

The development of sensors applied to failure detection systems for power transformers is a critical concern since this device stands out as a strategic component of the electric power system. Among the most common issues is the presence of partial discharges (PDs) in the insulation system of the transformer, which can lead the device to total failure. Aiming to prevent unexpected damages, several PD monitoring approaches have been developed. One of the most promising is the Acoustic Emission (AE) technique, which captures the acoustic signals generated by PDs using piezoelectric sensors. Although many studies have proved the effectiveness of AE, most signal processing approaches are strictly related to the frequency analysis of PD signals, which can hide important information such as the repetition rate of the failure. This article presents a comparison between two types of piezoelectric transducers: the microfiber composite (MFC) and the lead zirconate titanate (PZT). To ensure the detection of multiple PDs, time–frequency analysis was carried out by short-time Fourier transform (STFT). Intending to compare the sensibility of the transducers, the AE signals were windowed, and the root mean square (RMS) value was extracted for each part of the signal. The results indicate that spectrogram and RMS analysis have great potential to detect multiple PD activity. Although MFC was two times more sensitive to PD detection than the PZT sensor, PZT presents a higher frequency response band (0–100 kHz) than MFC (80 kHz).

scholarly journals Controllable multichannel acousto-optic modulator and frequency synthesizer enabled by nonlinear MEMS resonator

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gayathri Pillai ◽  
Sheng-Shian Li
Keyword(s):  
Signal Processing ◽  
Lead Zirconate Titanate ◽  
Frequency Synthesizer ◽  
Lead Zirconate ◽  
Oxide Semiconductor ◽  
Processing Unit ◽  
Potential Candidate ◽  
Optical Modulators ◽  
Critical Signal ◽  
Processing Technologies

AbstractNonlinear physics-based harmonic generators and modulators are critical signal processing technologies for optical and electrical communication. However, most optical modulators lack multi-channel functionality while frequency synthesizers have deficient control of output tones, and they additionally require vacuum, complicated setup, and high-power configurations. Here, we report a piezoelectrically actuated nonlinear Microelectromechanical System (MEMS) based Single-Input-Multiple-Output multi-domain signal processing unit that can simultaneously generate programmable parallel information channels (> 100) in both frequency and spatial domain. This significant number is achieved through the combined electromechanical and material nonlinearity of the Lead Zirconate Titanate thin film while still operating the device in an ambient environment at Complementary-Metal–Oxide–Semiconductor compatible voltages. By electrically detuning the operation point along the nonlinear regime of the resonator, the number of electrical and light-matter interaction signals generated based on higher-order non-Eigen modes can be controlled meticulously. This tunable multichannel generation enabled microdevice is a potential candidate for a wide variety of applications ranging from Radio Frequency communication to quantum photonics with an attractive MEMS-photonics monolithic integration ability.

scholarly journals Molecular Design Using Signal Processing and Machine Learning: Time-Frequency-like Representation and Forward Design

2021 ◽  
Author(s):  
Alain Beaudelaire Tchagang ◽  
Ahmed H. Tewfik ◽  
Julio J. Valdés
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
High Speed ◽  
Density Functional ◽  
Molecular Design ◽  
Absolute Error ◽  
Molecular Data ◽  
Deep Convolutional Neural Networks ◽  
Time Frequency ◽  
Short Time

Abstract Accumulation of molecular data obtained from quantum mechanics (QM) theories such as density functional theory (DFTQM) make it possible for machine learning (ML) to accelerate the discovery of new molecules, drugs, and materials. Models that combine QM with ML (QM↔ML) have been very effective in delivering the precision of QM at the high speed of ML. In this study, we show that by integrating well-known signal processing (SP) techniques (i.e. short time Fourier transform, continuous wavelet analysis and Wigner-Ville distribution) in the QM↔ML pipeline, we obtain a powerful machinery (QM↔SP↔ML) that can be used for representation, visualization and forward design of molecules. More precisely, in this study, we show that the time-frequency-like representation of molecules encodes their structural, geometric, energetic, electronic and thermodynamic properties. This is demonstrated by using the new representation in the forward design loop as input to a deep convolutional neural networks trained on DFTQM calculations, which outputs the properties of the molecules. Tested on the QM9 dataset (composed of 133,855 molecules and 16 properties), the new QM↔SP↔ML model is able to predict the properties of molecules with a mean absolute error (MAE) below acceptable chemical accuracy (i.e. MAE < 1 Kcal/mol for total energies and MAE < 0.1 ev for orbital energies). Furthermore, the new approach performs similarly or better compared to other ML state-of-the-art techniques described in the literature. In all, in this study, we show that the new QM↔SP↔ML model represents a powerful technique for molecular forward design. All the codes and data generated and used in this study are available as supporting materials. The QM↔SP↔ML is also housed at the following website: https://github.com/TABeau/QM-SP-ML.

The Applicated Comparation of Three Signal Analytical Methods for Ocean CSEM Signature

2013 ◽  
Vol 798-799 ◽  
pp. 561-564
Author(s):  
Ji Yu Zhou ◽  
Feng Dao Zhou
Keyword(s):  
Frequency Analysis ◽  
Oil And Gas ◽  
Time Frequency Analysis ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Analysis Technique ◽  
Oil And Gas Resources ◽  
Concentration Degree ◽  
Short Time ◽  
Better Than

Sea is rich in oil and gas resources, the marine controlled source electromagnetic method (CSEM) is a kind of method seabed oil gas geophysical technology rising in recent years. Because of the problem of CSEM about the air wave in the shallow water, the research of time-frequnecy analysis technique is used to suppress the air wave in this paper. The basic idea is: because of the CSEM signals speed are different in the air and submarine, so the time which received by the receiving points are also different through these two kinds of ways. Using the time-frequency analysis technique and theoretical calculation, we can determine which part of the signal is spread over the ocean, so as to suppress the air wave effectively. This paper lists several methods of time-frequency analysis, such as Short-time Fourier transform, W-V distribution, Wavelet transform, Hilbert Huang transform. Through the time-frequency graph,we get the conclusion that HHT is better than others in concentration degree,and W-V distribution is better than STFT.Compared with the original signal, the time-frequency graph is the best in using Smooth Puseudo W-V Distribution.I have a detailed analysis about real case in using SPWVD at last.

Time-frequency analysis of heart rate variability using short-time Fourier analysis

2000 ◽  
Vol 21 (2) ◽  
pp. 229-240 ◽  
Author(s):  
Sigrid Elsenbruch ◽  
Zhishun Wang ◽  
William C Orr ◽  
J D Z Chen
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Fourier Analysis ◽  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Short Time

Stationarity test of vibration signals with surrogate data and time–frequency analysis

2016 ◽  
Vol 20 (8) ◽  
pp. 1143-1154
Author(s):  
Zuo-Cai Wang ◽  
Feng Wu ◽  
Wei-Xin Ren
Keyword(s):  
Frequency Analysis ◽  
Surrogate Data ◽  
Transform Method ◽  
Vibration Signals ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Analysis Methods ◽  
Stationarity Test ◽  
Modulated Signals ◽  
Short Time

The stationarity test of vibration signals is critical for the extraction of the signal features. In this article, the surrogate data with various time–frequency analysis methods are proposed for stationary test of vibration signals. The surrogate data are first generated from the Fourier spectrum of the original signal with keeping the magnitude of the spectrum unchanged and replacing its phase by a random sequence. The local and global spectra of the original signal and the surrogate data are then estimated by four time–frequency analysis methods, which are short-time Fourier transform, multitaper spectrograms, wavelet transform, and S-transform methods. The index of nonstationarity is then defined based on the distances between the local and global spectra. Three kinds of synthetic signals, which are stationary signals, frequency-modulated signals, and amplitude-modulated signals, are tested to compare the efficiency of the four time–frequency analysis methods as mentioned. The results show that with a certain observation scale value, the index of nonstationarity based on the short-time Fourier transform or wavelet transform method may fail to test the stationarity of the signal. The parametric studies and sensitivity analysis of the observation scale and noise-level effect are also extensively conducted. The results show that the index of nonstationarity calculated using the multitaper spectrograms’ method is more suitable for stationarity test of frequency-modulated signals, while the index of nonstationarity calculated using the S-transform method is more suitable for stationarity test of amplitude-modulated signals. The results also show that the noise has a significant effect on the stationarity test results. Finally, the stationarity of a real vibration signal measured from a cable is tested, and the results show that the proposed index of nonstationarity can effectively test the stationarity of real vibration signals.

Overview of Time-Frequency Analysis Techniques in Vibration Signals of Rotating Machinery

2014 ◽  
Vol 684 ◽  
pp. 124-130
Author(s):  
Hong Li ◽  
Qing He ◽  
Zhao Zhang
Keyword(s):  
Frequency Analysis ◽  
Local Characteristic ◽  
Vibration Signals ◽  
Time Frequency Analysis ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Analysis Techniques ◽  
Advantages And Disadvantages ◽  
Analysis Methods ◽  
Short Time

There is very rich fault information in vibration signals of rotating machineries. The real vibration signals are nonlinear, non-stationary and time-varying signals mixed with many other factors. It is very useful for fault diagnosis to extract fault features by using time-frequency analysis techniques. Recent researches of time-frequency analysis methods including Short Time Fourier Transform, Wavelet Transform, Wigner-Ville Distribution, Hilbert-Huang Transform, Local Mean Decomposition, and Local Characteristic-scale Decomposition are introduced. The theories, properties, physical significance and applications, advantages and disadvantages of these methods are analyzed and compared. It is pointed that algorithms improvement and combined applications of time-frequency analysis methods should be researched in the future.

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