Analysis Of Electrical Signals Research Articles

Monitoring of Three-Phase Signals Based on Singular-Value Decomposition

In this paper, a new approach for the analysis of three-phase electrical signals is considered. While most of the existing techniques are based on fixed transforms such as the Clarke transform, this paper investigates the use of a data-driven approach called the singular value decomposition (SVD). As compared to other transforms, this paper shows that the SVD has the distinct advantage of clearly separating the contributions of the phasor configuration and signal instantaneous parameters. Under additive white Gaussian noise, this paper also describes several algorithms based on the SVD for signal monitoring. The first algorithm can detect unbalanced systems and classify them into two categories: 1) system with an off-nominal subspace and 2) systems with ellipticity. The second algorithm can estimate the angular frequency based on the periodogram of the right singular vectors. As compared to other existing approaches, simulations show that the proposed techniques give a good compromise between computational complexity and statistical performance.

Anatomy of electrical signals and dc-voltage line shape in spin-torque ferromagnetic resonance

The electrical detection of spin torque ferromagnetic resonance (st-FMR) is becoming a popular method for measuring the spin-Hall angle of heavy metals (HM). However, various sensible analysis on the same material with either the same or different experimental setups yielded different spin-Hall angles with large discrepancy, indicating some missing ingredients in our current understanding of st-FMR. Here we carry out a careful analysis of electrical signals of the st-FMR in a HM/ferromagnet (HM/FM) bilayer with an arbitrary magnetic anisotropy. The FM magnetization is driven by two radio-frequency (rf) forces: the rf Oersted field generated by an applied rf electric current and the so called rf spin-orbit torque from the spin current flowing perpendicularly from the HM to the FM due to the spin-Hall effect. By using the universal form of the dynamic susceptibility matrix of magnetic materials at the st-FMR, the electrical signals originated from the anisotropic magnetoresistance, anomalous Hall effect and inverse spin-Hall effect are analysed and dc-voltage lineshape near the st-FMR are obtained. Angle-dependence of dc-voltage is given for two setups. A way of experimentally extracting the spin-Hall angle of a HM is proposed.

MRD-Nets: Multi-Scale Residual Networks With Dilated Convolutions for Classification and Clustering Analysis of Spacecraft Electrical Signal

The fault detection of spacecraft electronic load systems is a crucial part of the spacecraft prognostics and health management system. To detect the abnormal state of spacecraft electronic load systems, complex electrical signals should be processed rapidly and accurately. For the fault detection of spacecraft electronic load systems, a robust unsupervised clustering analysis method and an accurate supervised classification method are of great importance. However, the traditional machine learning methods have poor performance when processing high-dimensional signal data because of the lack of ability to extract complex features from the signals. Therefore, neural-network-based deep learning (DL) models which can extract features from signals automatically are more suitable in this situation. In this paper, a novel convolutional neural network (CNN) module, the multi-branch residual module with dilated convolutions (MRD module), is proposed to extract multi-scale features from the electrical signal. Then, a well-designed CNN model named MRD-CNN is presented for the supervised classification task of signal. Furthermore, for the unsupervised clustering task, the clustering variational autoencoder with MRD modules (MRD-CluVAE) is proposed. The MRD-CluVAE can extract high-quality features from signal data and output the clustering results directly. To evaluate the performance of the proposed models, comparisons among the proposed models and other baseline algorithms are carried out. The experimental results show that the MRD-CNN model achieves higher classification performance and stability, while the MRD-CluVAE has a higher clustering accuracy than other algorithms. These methods can be utilized to resolve the classification and recognition problems of spacecraft electronic load signals.

Discriminating cloud to ground lightning flashes based on wavelet analysis of electric field signals

Lightning discharges produce electromagnetic radiation in a wide frequency range, but its propagation in a certain frequency range are usually used by lightning detection networks. Investigation of lightning activities in time-frequency domain can be obtained by using the wavelet transform. This study proposes a new approach using the discrete wavelet transform (DWT) algorithm to classify the detected lightning strikes. The measuring station would capture lightning electric field in 500 ms time scale and then utilizes a wavelet based recognizer algorithm to duly differentiate the cloud to ground flash from other cloud activities. Wavelet transform allows the expansion of transient events into a small number of coefficients. A total of 200 lightning flashes were randomly selected among the captured lightning discharges in South of Malaysia in one year. Initially, the cloud to ground and other cloud activities were manually analysed and discriminated. Then, these lightning flashes were analysed using different mother wavelets such as Haar, symmlet, Coiflet, and Daubechies by means of MATLAB program. Haar mother wavelet gives the best result for CG decomposition analysis. A total of 24 decomposition layers were chosen and the energy level of each layer was calculated to obtain the correlation between energy fluctuation and type of signal. The investigations reveal that the CG discharges have higher energy in 17th to 20th layers compared to the rest. However, the opposite results were obtained for the case of other cloud activities. To increase the accuracy of the wavelet transform approach algorithm, another filter was added to the algorithm flowchart. The proposed CG discrimination algorithm successfully classified 92% of the randomly selected flashes.

Enhanced Instantaneous Power Theory Decomposition for Power Quality Smart Converter Applications

Power theories are advancing signal decomposition approaches used for electrical power signal analysis. Currently, they are key tools for designing reliable and efficient power electronic interface controllers. Due to the inherent uncertainty in the power generation within new technology of distributed generation units (such as renewable-based microgrids), most of these theories are not directly applicable in the era of new power engineering systems. Furthermore, the traditional formulations are not appropriate once we face the nonlinearity in the consumption along a weak grid that may not maintain steady voltage or frequency. Therefore, a new signal decomposition method with a higher level of selectivity must be developed to modify the traditional power theories and improve the control strategies from a design viewpoint. This paper describes a novel formulation of an instantaneous power theory, enhanced instantaneous power theory (EIPT), for unbalanced and nonlinear three-phase power systems. EIPT establishes a proper decomposition of current components for cases of balanced, unbalanced, and distorted voltage sources. In addition to mathematical analysis, this research provides comprehensive simulations under different loads and source conditions, which supports the performance of the EIPT in the active filtering methods. Moreover, the performance of the proposed method is compared with the state-of-the-art approach widely known as the conservative power theory. Our results indicate that this new comprehensive approach is helpful in optimizing control strategies for power electronic interfaces and power quality compensators.

Analysis of impedance data from bubble flow in a glass/SU8 microfluidic device with on-channel sensors

This paper presents the design and original fabrication of a fully transparent microfluidic reactor with integrated electrical sensors over the microchannel. The transparent chip design allows for microscale photochemistry, and permits direct, real-time visual and electrical observation. The microchip uses optically transparent indium tin oxide (ITO) electrodes as sensing elements for in-channel phase tracking. High-speed videography was used to validate the electrical measurement data. The device permits visual observation of microchannel bubble flow while simultaneously acquiring electrical data. The aim of this work is to develop and optically verify electrical data acquisition and signal analysis methodologies, which in future applications may be used for inexpensive non-destructive monitoring of two-phase microchannel flows, in systems where detailed visual observation may be infeasible.

An Algorithm for Induction Motor Monitoring System Based on Electrical Signals Analysis

An Algorithm for Induction Motor Monitoring System Based on Electrical Signals Analysis

Determination of Potassium Anomalous Rectifying Currents by Analysis of Electrical Cardiac Signals

This article describes the determination of potassium anomalous rectifying currents by analysis of electrical cardiac signals in reverse electrocardiography. Contemporary cardiodiagnostic systems include algorithms and methods for noninvasive investigation of the electrophysiological properties of the heart. Comparative data are presented on the determination of potassium anomalous rectifying currents by an electrophysiological method; we propose a noninvasive method for analysis of the electrical cardiac signal.

Wind power generation fault diagnosis based on deep learning model in internet of things (IoT) with clusters

With the rapid development of wind power capacity and sustained growth in total operation time, the maintenance of wind turbines is becoming increasingly prominent, so we urgently need to develop effective wind turbine fault diagnosis and prediction system. The main fault characteristics of wind turbines are summarized from two aspects of fault diagnosis and fault prediction. Aiming at the difficult problems of fault diagnosis, we analyze and summarize the research status of fault diagnosis methods based on vibration, electrical signal analysis and pattern recognition algorithm. At the same time, we point out the technical characteristics, limitations and future trends of various methods. Based on the characteristics of mechanical structure and electronic system degradation in wind turbines, we summarize the current research progress and propose a fault prediction method based on physical failure model and data driven model fusion. In this paper, we use the deep learning model in the framework of the internet of things to predict and diagnose the faults of wind power generation. The experimental results show that the algorithm proposed in this paper can predict the fault types and make reasonable diagnosis.

DWT-based methodology for detection of seismic precursors on electric field signals in Mexico

ABSTRACTThis paper presents an analysis of atmospheric electric field signals which were taken on an important seismic activity period from 2012 to 2015 to study its relationship with seismic events. For this purpose, several measurements were acquired every second by using a triaxial electric field monitoring system. Furthermore, the discrete wavelet transform (DWT) was applied to electric field signals with seismic events of magnitudes greater than Mw > 5.5, which occurred in Mexico with different focal mechanisms. The analysed epochs consist of 24 h of observations for a data-set corresponding to 55 different earthquakes (EQs). The time series were processed 12 h before and 12 h after each seismic event. The proposed methodology proves to be an efficient tool to detect signals with relations between electric field and seismic activity. The methodology presented herein shows important anomalies on different time instants according to the focal mechanism. Finally, a statistical post-processing algorithm was performed in order to quantify the data dispersion as a measure of seismic activity. It is found that the variance increases before, during, and after the seismic event about the coefficients D1 to D7 obtained using the DWT.

Electrical Signal Analysis for Bearing Fault of Wind Turbine Based on EMD

In this paper, according to the bearing current signal is nonstationary and nonlinear characteristics, establish of doubly fed wind turbine simulation model and test bench, respectively under the normal and fault condition of bearing of motor current signal by EMD processing and analysis. Through the simulation analysis and experimental verification, it shows that after the EMD decomposition of the IMF component can effectively isolate the fault information in the current signal.EMD is an effective method of current signal processing

Application of Welding Electrical Signal Monitoring and Analysis System Based on Virtual Instrument

In this paper, a welding electrical signal monitoring and analysis has been established basing on virtual instrument. The system contains two parts that are hardware and software. Hardware of the VI system using the data acquisition card, shielding box, control module is made by national instrument. Software including data collection and analysis system are programming using LabVIEW. Two groups of welding electrical signal getting from the two welders by same process have been acquired and analyzed using the system. The results of each analysis module have a high consistency. The system can accurately evaluate welding spatter size, welding stability and melt transfer in the welding process. Additionally, the system has the characters of friendly interface, simple operation, stable performance and high-reliability. Therefore, the system established in this paper can satisfy the requirements of monitoring and analysis during the quality of welding process.

Fourier, Wavelet, and Hilbert-Huang Transforms for Studying Electrical Users in the Time and Frequency Domain

The analysis of electrical signals is a pressing requirement for the optimal design of power distribution. In this context, this paper illustrates how to use a variety of numerical tools, such as the Fourier, wavelet, and Hilbert-Huang transforms, to obtain information relating to the active and reactive power absorbed by different types of users. In particular, the Fourier spectrum gives the most important frequency components of the electrical signals, and the wavelet analysis highlights the non-stationarity of those frequency contributions, whereas the Hilbert-Huang transform, by means of the Empirical Mode Decomposition, provides a more complete spectrum of frequencies.

Three-phase electrical signals analysis for mechanical faults monitoring in rotating machine systems

The current paper proposes a method to detect mechanical faults in rotating machines using three-phase electrical currents analysis. The proposed fault indicator relies on the use of instantaneous symmetrical components (ISCs), followed by a demodulation step enhancing the small modulations generated in electrical signals by mechanical faults. The limitations due to the multi-component nature of electrical signals, as well as to the noise naturally present in the measured signals are studied and taken into account in order to elaborate a proper and efficient algorithm to compute a mechanical fault indicator. It is theoretically shown that the ISCs based approach results in an increase of the signal-to-noise ratio compared to a single-phase approach, finally leading to an improvement of early fault detection capabilities. This result is validated using both synthetic and experimental signals where the proposed method is used to detect bearing faults and the obtained results are compared to single-phase results.

Modulation signal bispectrum analysis of electric signals for the detection and diagnosis of compound faults in induction motors with sensorless drives

ABSTRACTAs a prime driver, induction motor is the most electric energy consuming component in industry. The exposure of the motor to stator winding asymmetry, combined with broken rotor bar fault significantly increases the temperature and reduces the efficiency and life of the motor. Accurate and timely diagnosis of these faults will help to maintain motors operating under optimal status and avoid excessive energy consumption and severe damages to systems. This paper examines the performance of diagnosing the effect of asymmetry stator winding on broken rotor bar (BRB) faults under closed loop operation modes. It examines the effectiveness of conventional diagnostic features in both motor current and voltage signals using spectrum and modulation signal bispectrum analysis (MSBA). Evaluation results show that the combined faults cause an additional increase in the sideband amplitude and this increase in sideband can be observed in both the current and voltage signals under the sensorless control mode. MSB analysis has a good noise reduction capability and produces a more accurate and reliable diagnosis in that it gives a more correct indication of the fault severity and its location for all operating conditions.

Nerve Electrical Signal Analysis Based on Matlab

Action potential is one of the main ways to transmit information in the nervous system. In fact, a single electrode tends to record the activities of more than one neuron, therefore, the recorded signals need to be analyzed to get the single neuron discharge sequences. This paper separates and analyzes the nerve signals of rat dorsal root ganglion by the program written in Matlab and commercial data analysis system LabChart respectively. The results showed that both of them can achieve to separate the different amplitudes signal and conduct statistical analysis, but Matlab program is simple and can directly isolate different neural signals which is easy to understand and accept, it can also process the data according to their specific characteristics and get more consistent results to the reality, while LabChart can only get the results close to the reality. In addition, LabChart can be modified to get more practical analysis through the analysis of Matlab. In a conclusion, Matlab program can satisfy the need to deal with the data independently, and it can also work as helper to improve the results of a commercial software processing.

Heart rate measurement and electrical pulse signal analysis for subjects span of 20–80 years

The preliminary research constructs the heart beat or pulse measurement for medical science. The electrical pulse signal analysis that supports one significant clinical study. The research prototype focuses the pulse rate and analysis system which consist of hardware and software parts. The hardware uses the ATMEGA 2560 ADK R3 for processing the input optical sensors, output result to LCD, and record data into SD memory card. The software as embedded algorithm is designed for controlling the input/output parts. Research techniques are applied in term of analog to digital converter, I2C, and data grouping.The 40 subjects are informed consent and measured with ethical research. Experimental result explains the subject behaviors with maximum/minimum pulse values, patterns, and similarity three groups. The distinctive point of medical and health science prototype is accuracy, durability, low power consumption, and cheaper price.

Online condition monitoring of wind turbines through three-phase electrical signature analysis

In the context of the KAStrion European project, a complete solution was proposed in order to monitor wind turbines. The developed solution comprises both hardware and software parts of the condition monitoring system. In terms of software, modules for vibration analysis and electrical signal analysis have been developed. The current paper presents the electrical analysis solution proposed in the context of this project. The electrical module is able to detect both mechanical and electrical faults in a wind turbine system. The goal of the mechanical fault detection using electrical signals is to confirm the faults also detected by vibration analysis, while the main focus of the module remains the detection of electrical faults. Results showing the performance of mechanical fault detection are presented using electrical signals acquired on the test-bench developed for testing the KAStrion system. Moreover, results regarding the electrical unbalance are presented using signals acquired on a three-phase transformer. The final solution has been implemented on two onshore wind turbines since the end of 2014, and online condition monitoring results are presented at the end of the paper.

The monitoring of transient regimes on machine tools based on speed, acceleration and active electric power absorbed by motors

This paper intend to propose some new results related with computer aided monitoring of transient regimes on machine-tools based on the evolution of active electrical power absorbed by the electric motor used to drive the main kinematic chains and the evolution of rotational speed and acceleration of the main shaft. The active power is calculated in numerical format using the evolution of instantaneous voltage and current delivered by electrical power system to the electric motor. The rotational speed and acceleration of the main shaft are calculated based on the signal delivered by a sensor. Three real-time analogic signals are acquired with a very simple computer assisted setup which contains a voltage transformer, a current transformer, an AC generator as rotational speed sensor, a data acquisition system and a personal computer. The data processing and analysis was done using Matlab software. Some different transient regimes were investigated; several important conclusions related with the advantages of this monitoring technique were formulated. Many others features of the experimental setup are also available: to supervise the mechanical loading of machine-tools during cutting processes or for diagnosis of machine-tools condition by active electrical power signal analysis in frequency domain.

Analytical and numerical solution to the nonlinear cubic Duffing equation: An application to electrical signal analysis of distribution lines

The Duffing oscillator represents an important model to describe mathematically the nonlinear behaviour of several phenomena occurring in physics and engineering. In this paper, analytical and numerical solutions to the nonlinear cubic Duffing equation governing the time behaviour of an electrical signal are found as a function of the magnitude and of the sign of the nonlinear parameter, of the damping parameter and for different values of the forcing term. A stability analysis of the Duffing equation in the absence of the forcing term is also performed as a function of the sign and magnitude of the nonlinear parameter. A fitting procedure of the Duffing solution to the current signal flowing in different distribution lines allows us to determine the degree of nonlinearity of the electrical signal suggesting a potential way to quantify the nonlinear behaviour of current electrical signals.