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Corrosion degrades the performance of any civil structure. This work proposes a methodology based on the autocorrelation of vibration signals, a data treatment stage, and a processing stage based on one-dimension convolutional neural networks (1D-CNNs) to detect, locate, and quantify the corrosion damage. The autocorrelation method generally highlights or broadens relevant features into the vibration signals. The treatment stage splits, shuffles, and normalizes the autocorrelated data, then 1D-CNNs analyze data to compute a set of damage indicators. These indices represent the probability of damage in the structure for a particular location and a specific severity level. A truss-type bridge model is studied to test the proposed method, a truss-type bridge model located at the Autonomous University of Queretaro, Mexico, is studied. There are three levels of damage, i.e., incipient, moderate, severe, and healthy conditions. Obtained results demonstrate that the proposed method is a valuable tool since 100% of effectiveness is obtained. [ABSTRACT FROM AUTHOR]

Epilepsy, a neurological disorder, affects millions of persons worldwide. It is distinguished by causing recurrent seizures in patients, which can conduct to severe health problems. Consequently, it is essential to offer a method capable of timely predicting a seizure before its appearance, so patients can avoid possible injuries by taking preventive action. In this sense, a method based on the integration of discrete wavelet transform (DWT), fractal dimension, and support vector machine (SVM) is presented for the prediction of an epileptic seizure up to 30 min before its onset through the analysis of electroencephalogram (EEG) signals. DWT is initially applied to the EEG signals to obtain their main neurological bands; then, five fractal dimension indices (e.g. Sevcik, Petrosian, Box, Higuchi, and Katz) are explored as potential seizure indicators. Finally, an SVM is developed to predict the epileptic seizure automatically. The effectiveness of the proposal to predict an epileptic crisis is validated by employing a database of 14 subjects with 42 epileptic seizures provided by the Massachusetts Institute of Technology and the Children's Hospital Boston. The results demonstrate that the proposal can predict an epileptic seizure up to 30 min before its onset with a high accuracy of 93.33%. [ABSTRACT FROM AUTHOR]

Sudden cardiac death (SCD) is deemed as one of the main causes of death in humans. Therefore, the prediction of an SCD event will help people to receive timely treatment, allowing saving their life. In this sense, this paper introduces a new methodology based on the adroit fusion of fractal dimension (FD) algorithms and a fuzzy logic system for predicting an SCD event automatically. Five FD implementations are investigated in this work: Katz's FD, Sevcik's FD, Box's FD, Higuchi's FD, and Petrosian's FD, in order to evaluate the geometrical complexity in electrocardiogram signals of 20 patients with SCD and 18 patients with a normal cardiac rhythm offered by Boston's Beth Israel Hospital. The results indicate that the FD-based methodology can predict an SCD event up to 60 min before the onset, reaching an accuracy of 91.54%. [ABSTRACT FROM AUTHOR]

The application of signal processing techniques is a fundamental step for fault diagnostic methodologies. The application of empirical mode decomposition (EMD)-based methods such as classic EMD, ensemble EMD (EEMD), and complete EEMD (CEEMD) is presented in this work for the analysis of inrush current signals. This analysis leads to the detection of short-circuited turns in transformers. Results show that CEEMD provides the best performance, as it readily extracts the information related to the fault, requiring of acceptable computational resources. Actual inrush current signals of a transformer with short-circuited turns are also considered. The number of short-circuited turns ranges from 5 to 40. Useful indices, such as the Shannon Entropy, Energy, and root-mean-square value, are obtained from the information provided by the CEEMD approach. These indices are analyzed for both the transient state and the steady state of the current signals, providing the proper quantification of the fault severity. [ABSTRACT FROM AUTHOR]

Inrush current has been well-studied in transformers using different methods to diagnose internal faults. In this paper, a new methodology is proposed using experimental data for obtaining a time-model based on inrush currents. The main contribution of this paper consists of providing a model that accurately reproduces the inrush and steady state currents of single-phase transformers, offering the unique ability to analyze several severity levels of turn to turn faults. The obtained model considers eleven cases of inter-turn fault (2, 3, 4, 5, 10, 15, 20, 25, 30, 35, and 40 short-circuited turns) and allows the study of indices to detect inter-turn faults. The proposed model is based on two functions: a steady-state function based on Fourier series and a residual transient function based on a sum of Gaussian functions. The model allows the study of harmonic content because of its direct time-frequency representation. The model is developed using experimental signals measured from a single-phase transformer, which is validated by means of error criteria that assesses experimental and modeled signals. Its capability to reproduce non-modeled signals with different inter-turn fault cases is also evaluated. The modeling of these signals can be a useful tool for simulation-based applications where a reliable waveform reproduction is needed. On the other hand, indices such as total harmonic distortion, energy spectral density and second harmonic ratio are obtained from the proposed model to study its behavior, depending on the severity of the inter-turn faults. Results demonstrate its usefulness to characterize the level of fault severity. [ABSTRACT FROM AUTHOR]

Induction motors, key elements for industry, are susceptible to one or more faults at the same time; yet, they can keep working without affecting the process, but increasing the production costs. For this reason, a monitoring system that can efficiently diagnose the induction motor condition, even under multiple combined faults, is a demanding task. In this work, a methodology and its implementation into a field programmable gate array for an online and real-time monitoring system of multiple combined faults are presented. First, the fractal dimension approach, using the Katz algorithm, is introduced as a measure of variation of 3-axis startup vibration signals for the induction motor condition, considering that these signals describe changes on its dynamic characteristics due to the different faults. Then, an artificial neural network determines in an automatic way the induction motor condition according to the fractal dimension values. The obtained results show a higher overall efficiency than previous works for detecting broken rotor bars, outer-race bearing defects, unbalance, and their combinations, as well as a healthy condition. [ABSTRACT FROM AUTHOR]

For industry, the induction motors are essential elements in production chains. Despite the robustness of induction motors, they are susceptible to failures. The broken rotor bar (BRB) fault in induction motors has received special attention since one of its characteristics is that the motor can continue operating with apparent normality; however, at certain point the fault may cause severe damage to the motor. In this work, a methodology to detect BRBs using vibration signals is proposed. The methodology uses the Shannon entropy to quantify the amount of information provided by the vibration signals, which changes due to the presence of new frequency components associated with the fault. For automatic diagnosis, the K-means cluster algorithm and a decision-making unit that looks for the nearest cluster through the Euclidian distance are applied. Unlike other reported works, the proposal can diagnose the BRB condition during startup transient and steady state regimes of operation. Additionally, the proposal is also implemented into a field programmable gate array in order to offer a low-cost and low-complex online monitoring system. The obtained results demonstrate the proposal effectiveness to diagnose half, one, and two BRBs. [ABSTRACT FROM AUTHOR]

Accurate and fast characterization of voltage variations helps to evaluate their severity on equipment and activate protections. In this paper, a methodology for tracking and characterization of voltage variations, sample to sample, is presented. It consists of a Hilbert transform to estimate the voltage of the signal’s envelope, a fuzzy logic system to track down the type of voltage variation, and a rule-based method for the final identification and decision making according to IEEE Std 1159-2009. Unlike some techniques presented in the literature for tracking voltage variations such as the Kalman filter and adaptive linear network techniques, the proposed methodology requires neither a harmonic model nor an algorithm to adjust the model parameters, which in many cases increases the computational burden and time tracking. It is worth mentioning that the proposed classification stage does not need a training stage; therefore, its development is easier and its efficiency does not depend on a data training set. The performance of the proposed methodology is validated and tested using synthetic signals as well as real measurements of voltage variations. In addition, an implementation of our methodology into an field-programmable gate array based system is performed in an effort to offer a low-cost and portable system-on-a-chip solution for online and real-time monitoring of voltage variations. [ABSTRACT FROM PUBLISHER]

The development and application of techniques and methodologies for the analysis of power quality (PQ) signals that offer a more efficient and reliable analysis in terms of processing and performance are still issues for industrial and academic fields, mainly considering the quick growing of the PQ data in modern power systems. In this regard, an iterative downsampling stage fused to the empirical mode decomposition (EMD) method is proposed. It is validated and tested using synthetic and real signals. In general, the aim of the proposed method is to extract the fundamental component as the first intrinsic mode function (IMF) to simplify the remaining decomposition. The proposed method is compared to the classical EMD and the ensemble EMD (EEMD). Advantages of the proposed method include a more adequate IMF extraction than the EMD technique, and a noticeable reduction of computational burden compared to the EEMD. The results obtained from the synthetic and real signals demonstrate the reliability and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

The detection and classification of power quality (PQ) disturbances have become a pressing concern due to the increasing number of disturbing loads connected to the power line and the susceptibility of certain loads to the presence of these disturbances; moreover, they can appear simultaneously since, in any real power system, there are multiple sources of different disturbances. In this paper, a new dual neural-network-based methodology to detect and classify single and combined PQ disturbances is proposed, consisting, on the one hand, of an adaptive linear network for harmonic and interharmonic estimation that allows computing the root-mean-square voltage and total harmonic distortion indices. With these indices, it is possible to detect and classify sags, swells, outages, and harmonics–interharmonics. On the other hand, a feedforward neural network for pattern recognition using the horizontal and vertical histograms of a specific voltage waveform can classify spikes, notching, flicker, and oscillatory transients. The combination of the aforementioned neural networks allows the detection and classification of all the aforementioned disturbances even when they appear simultaneously. An experiment under real operating conditions is carried out in order to test the proposed methodology. [ABSTRACT FROM PUBLISHER]

From voltage and current signals it is possible to obtain relevant information for solving some problems in several industrial and scientific applications as power quality (PQ) monitoring, monitoring and diagnosis of electrical machines, electric systems protection and control. At present, the PQ monitoring, measure through a set of PQ indices (PQI), is an important topic for the industrial sector since a poor PQ, characterised by the presence of harmonics in the power line, produces irregular or wrong operation of protection systems, excessive neutral currents in 3‐phase four‐wire systems, overheating of motors, transformers, capacitor banks and wiring in general. The PQI calculation is performed by many techniques proposed in the literature; however, they do not have either good performance for transient signals or the requirements for satisfying the power standards. This work proposes the assessment of the PQI‐based in neural networks for transient or stationary signals in 3‐phase power systems without losing the power standard requirements. Besides, this work contributes to the industrial application field by allowing the continuous and online monitoring of the PQI thanks to the field programmable gate array implementation of the proposed methodology. [ABSTRACT FROM AUTHOR]

Abstract: Manufacturing cells are present in almost all the industrial sector. Unfortunately, all machine tools into the manufacturing cell are connected to the same power line, implying that their operation adds nonlinear loads such as harmonics and interharmonics that affect the general machine-tool condition. A novel NN-based methodology for harmonic monitoring through time in transient and stationary signals that satisfies the IEC61000-4-7 standard is presented, as well as its implementation into a field programmable gate array (FPGA) for continuous and online monitoring. The proposed method and the developed instrument have been tested in a real manufacturing cell. [Copyright &y& Elsevier]

Power quality disturbance (PQD) monitoring has become an important issue due to the growing number of disturbing loads connected to the power line and to the susceptibility of certain loads to their presence. In any real power system, there are multiple sources of several disturbances which can have different magnitudes and appear at different times. In order to avoid equipment damage and estimate the damage severity, they have to be detected, classified, and quantified. In this work, a smart sensor for detection, classification, and quantification of PQD is proposed. First, the Hilbert transform (HT) is used as detection technique; then, the classification of the envelope of a PQD obtained through HT is carried out by a feed forward neural network (FFNN). Finally, the root mean square voltage (Vrms), peak voltage (Vpeak), crest factor (CF), and total harmonic distortion (THD) indices calculated through HT and Parseval's theorem as well as an instantaneous exponential time constant quantify the PQD according to the disturbance presented. The aforementioned methodology is processed online using digital hardware signal processing based on field programmable gate array (FPGA). Besides, the proposed smart sensor performance is validated and tested through synthetic signals and under real operating conditions, respectively. [ABSTRACT FROM AUTHOR]

• Different statistical algorithms for monitoring a truss bridge are evaluated. • SMR and RMS can be used for detecting incipient damages in a bridge. • They can deal with noisy signals. Truss-type structures are commonly used for configuring civil structures such as bridges, roof supports, cranes, among others. However, they are susceptible to suffer different types of damage such as cracks, loosened bolts, and corrosion, being the latter one of the most common and aggressive ones. In this paper, it is explored the application of statistical time features (STFs) extracted from raw vibration signals of truss-type bridges under dynamic excitation for assessing their health condition. Then, the Kruskal-Wallis method (KWM) is used for determining the most discriminating STFs and a feature reduction criterion is applied to select the most useful ones for assessing the structure's condition. The selected STF is employed for configuring a decision tree and thus determining the condition of the structure automatically. The effectiveness of the proposal is verified under three levels of corrosion damage (i.e., incipient, moderate and severe), which are artificially generated. For the analysis, the tri-axial vibrations are explored. Results show that the proposal can identify damage from an incipient damage condition. An accuracy of 100% is reached using only a sensor placed on the structure. [ABSTRACT FROM AUTHOR]

Although induction motors (IMs) are robust and reliable electrical machines, they can suffer different faults due to usual operating conditions such as abrupt changes in the mechanical load, voltage, and current power quality problems, as well as due to extended operating conditions. In the literature, different faults have been investigated; however, the broken rotor bar has become one of the most studied faults since the IM can operate with apparent normality but the consequences can be catastrophic if the fault is not detected in low-severity stages. In this work, a methodology based on convolutional neural networks (CNNs) for automatic detection of broken rotor bars by considering different severity levels is proposed. To exploit the capabilities of CNNs to carry out automatic image classification, the short-time Fourier transform-based time–frequency plane and the motor current signature analysis (MCSA) approach for current signals in the transient state are first used. In the experimentation, four IM conditions were considered: half-broken rotor bar, one broken rotor bar, two broken rotor bars, and a healthy rotor. The results demonstrate the effectiveness of the proposal, achieving 100% of accuracy in the diagnosis task for all the study cases. [ABSTRACT FROM AUTHOR]

• FPGA implementations are suitable for online real-time power quality monitoring. • Hilbert Filter can be used to obtain the envelope of the voltage signal. • Power Quality Disturbances affect most of electrical equipment. • Power quality monitoring is necessary to early detection of disturbances. Power quality (PQ) related studies have become more relevant in recent years since the advent of modern industry related processes and technologies. As a result, PQ strategies have been formulated, facilitating a more efficient control in the generation and distribution of energy resources, therefore reducing both the operational cost for the provider, as well as the service fee for the consumer. This project develops a Hilbert Filter based methodology to monitor power quality indicators, through the analysis of voltage signals in order to detect disturbances that cause instability, quality drops or interruptions. The proposed methodology is implemented online, using digital signal processing based on field programmable gate arrays (FPGAs). As a result, a novel and simple methodology that detects the most common and important voltage disturbances for monitoring PQ is presented, as well as a discussion on its performance when compared to other current software-based alternatives available on the field. [ABSTRACT FROM AUTHOR]

Empirical mode decomposition (EMD)-based methods are powerful digital signal processing techniques because they do not need a priori information of the target signal due to their intrinsic adaptive behavior. Moreover, they can deal with non-linear and non-stationary signals. This paper presents the field programmable gate array (FPGA) implementation for the complete ensemble empirical mode decomposition (CEEMD) method, which is applied to the condition monitoring of an induction motor. The CEEMD method is chosen since it overcomes the performance of EMD and EEMD (ensemble empirical mode decomposition) methods. As a first application of the proposed FPGA-based system, the proposal is used as a processing technique for feature extraction in order to detect and classify broken rotor bar faults in induction motors. In order to obtain a complete online monitoring system, the feature extraction and classification modules are also implemented on the FPGA. Results show that an average effectiveness of 96% is obtained during the fault detection. [ABSTRACT FROM AUTHOR]

Sudden cardiac death (SCD) is one of the main causes of death among people. A new methodology is presented for predicting the SCD based on ECG signals employing the wavelet packet transform (WPT), a signal processing technique, homogeneity index (HI), a nonlinear measurement for time series signals, and the Enhanced Probabilistic Neural Network classification algorithm. The effectiveness and usefulness of the proposed method is evaluated using a database of measured ECG data acquired from 20 SCD and 18 normal patients. The proposed methodology presents the following significant advantages: (1) compared with previous works, the proposed methodology achieves a higher accuracy using a single nonlinear feature, HI, thus requiring low computational resource for predicting an SCD onset in real-time, unlike other methodologies proposed in the literature where a large number of nonlinear features are used to predict an SCD event; (2) it is capable of predicting the risk of developing an SCD event up to 20 min prior to the onset with a high accuracy of 95.8%, superseding the prior 12 min prediction time reported recently, and (3) it uses the ECG signal directly without the need for transforming the signal to a heart rate variability signal, thus saving time in the processing. [ABSTRACT FROM AUTHOR]

Highlights • A new methodology for accurate response prediction of large structures is proposed. • It uses EMD, MI index, and a probabilistic Bayesian-based training algorithm. • An MI index is proposed to determine the optimum number of neurons in the NN model. • Bayesian regularization is proposed to train the optimized NN model. • It is applied to a 1:20-scaled 38-story highrise building and a 5-story steel frame. Abstract An accurate response prediction model is of great importance in various applications such as damage detection, structural health monitoring, and vibration control. Development of such a methodology for large civil structures is challenging because of their size and complicated behavior and noise-contaminated, nonlinear, and nonstationary nature of the signals. In addition, the prediction model must have a low computational burden for real-time applications. In this article, a new methodology and a nonlinear autoregressive exogenous model (NARX)-based recurrent neural network (NN) model is presented for accurate response prediction of large structures. The methodology is based on adroit integration of three concepts: a recent signal processing concept, empirical mode decomposition (EMD), mutual information (MI) index from the information theory, and a probabilistic Bayesian-based training algorithm. The EMD method is used to remove the noise in the measured signals. An MI index is proposed to determine the optimum number of neurons in the hidden layer of the NN model with the goal of reducing the computational requirements without affecting its performance. Finally, Bayesian regularization (BR) is proposed to train the optimized NN model. The effectiveness of the proposed methodology is assessed by predicting the structural response of a 1:20-scaled 38-story highrise building structure subjected to seismic excitations and ambient vibrations, and a five-story steel frame subjected to different levels of the Kobe earthquake. [ABSTRACT FROM AUTHOR]

Currently, renewable energies, including wind energy, have been experiencing significant growth. Wind energy is transformed into electric energy through the use of wind turbines (WTs), which are located outdoors, making them susceptible to harsh weather conditions. These conditions can cause different types of damage to WTs, degrading their lifetime and efficiency, and, consequently, raising their operating costs. Therefore, condition monitoring and the detection of early damages are crucial. One of the failures that can occur in WTs is the occurrence of cracks in their blades. These cracks can lead to the further deterioration of the blade if they are not detected in time, resulting in increased repair costs. To effectively schedule maintenance, it is necessary not only to detect the presence of a crack, but also to assess its level of severity. This work studies the vibration signals caused by cracks in a WT blade, for which four conditions (healthy, light, intermediate, and severe cracks) are analyzed under three wind velocities. In general, as the proposed method is based on machine learning, the vibration signal analysis consists of three stages. Firstly, for feature extraction, statistical and harmonic indices are obtained; then, the one-way analysis of variance (ANOVA) is used for the feature selection stage; and, finally, the k-nearest neighbors algorithm is used for automatic classification. Neural networks, decision trees, and support vector machines are also used for comparison purposes. Promising results are obtained with an accuracy higher than 99.5%. [ABSTRACT FROM AUTHOR]

Concrete beams are elements used in many civil structures; unfortunately, they can contain cracks that lead to the collapse of the structures if those defects are not detected early enough. In this article, a new method to determine the structural condition of concrete beams subjected to bending is proposed. In general, it is based on the processing of the acoustic emissions (AE) signals, which are generated during the application of a load, by using the mathematical tool called wavelet transform (WT). The sound of the internal energy/crack is recorded as a hit or AE signal event; then, those signals acquired as waveforms are post-processed with the continuous WT (CWT); then, the wavelet energy (WE) is calculated for each hit by using an adequate scale range and the most convenient mother wavelet. Thus, with this method, it is possible to determine the structural condition (healthy or damaged) of concrete beams subjected to bending just by calculating the WE of any hit at any time and, even more, it is possible to define more precisely the stage of the structural condition as a healthy condition, micro-cracks appearance, the manifestation of a principal crack (hit with the highest WE), propagation of the principal crack, and final rupture. This method is experimentally validated in the laboratory, and additionally, ultrasonic pulse velocity tests (UPVT) are performed for some specimens to confirm the change between healthy and damaged conditions. The results are promising in order to apply this effective method in concrete beams of real-life structures. [ABSTRACT FROM AUTHOR]

The transformer is one of the most important electrical machines in electrical systems. Its proper operation is fundamental for the distribution and transmission of electrical energy. During its service life, it is under continuous electrical and mechanical stresses that can produce diverse types of damage. Among them, short-circuited turns (SCTs) in the windings are one of the main causes of the transformer fault; therefore, their detection in an early stage can help to increase the transformer life and reduce the maintenance costs. In this regard, this paper proposes a signal processing-based methodology to detect early SCTs (i.e., damage of low severity) through the analysis of vibroacoustic signals in steady state under different load conditions, i.e., no load, linear load, nonlinear load, and both linear and nonlinear loads, where the transformer is adapted to emulate different conditions, i.e., healthy (0 SCTs) and with damage of low severity (1 and 2 SCTs). In the signal processing stage, the contrast index is analyzed as a fault indicator, where the Unser and Tamura definitions are tested. For the automatic classification of the obtained indices, an artificial neural network is used. It showed better results than the ones provided by a support vector machine. Results demonstrate that the contrast estimation is suitable as a fault indicator for all the load conditions since 89.78% of accuracy is obtained if the Unser definition is used. [ABSTRACT FROM AUTHOR]

Due to the relevance and severity of damages caused in induction motors by broken rotor bars (BRBs), the development and application of new detection methods that offer an efficient and reliable diagnosis in terms of processing and performance are still demanding tasks. This paper presents a methodology based on the Synchrosqueezing transform for detection of BRBs during the startup transient. In order to validate the proposal, a synthetic signal with different noise levels and real current signals of an induction motor are analyzed. Three severities of damage are considered: half broken, one broken, and two broken rotor bars. For automatic diagnosis, a threshold-based stage using the Pearson product-moment correlation coefficient is presented. Both synthetic and real experiment results demonstrate the proposal effectiveness. [ABSTRACT FROM AUTHOR]

Recently, the analysis of ultra-low-frequency (ULF) geomagnetic signals in order to detect seismic anomalies has been reported in several works. Yet, they, although having promising results, present problems for their detection since these anomalies are generally too much weak and embedded in high noise levels. In this work, a short-time multiple signal classification (ST-MUSIC), which is a technique with high-frequency resolution and noise immunity, is proposed for the detection of seismic anomalies in the ULF geomagnetic signals. Besides, the energy (E) of geomagnetic signals processed by ST-MUSIC is also presented as a complementary parameter to measure the fluctuations between seismic activity and seismic calm period. The usefulness and effectiveness of the proposal are demonstrated through the analysis of a synthetic signal and five real signals with earthquakes. The analysed ULF geomagnetic signals have been obtained using a tri-axial fluxgate magnetometer at the Juriquilla station, which is localized in Queretaro, Mexico (geographic coordinates: longitude 100.45° E and latitude 20.70° N). The results obtained show the detection of seismic perturbations before, during, and after the main shock, making the proposal a suitable tool for detecting seismic precursors. [ABSTRACT FROM PUBLISHER]

• A method to predict sudden cardiac death (SCD) from ECG signals is presented. • The complete ensemble empirical mode decomposition is used to decompose ECG signals. • The method consists of statistical indices, Kruskal-Wallis test, and a support vector machine. • SCD event can be predicted 30 min before it occurs with 97.28% of accuracy. Cardiovascular diseases are a significant global health problem, often culminating in sudden cardiac death (SCD). Approximately 4 million annual deaths worldwide are attributed to SCD. Although the causes are varied, the heart's electrical activity is always affected prior to an SCD event. This work explores the capabilities of the complete ensemble empirical mode decomposition (CEEMD) method for extracting relevant time-domain features from electrocardiogram (ECG) signals. CEEMD method is compared with EMD and EEMD methods. Additionally, a machine learning approach incorporating statistical indices, the Kruskal-Wallis test, and a support vector machine (SVM) is utilized for automatic analysis and prediction. SVM results are benchmarked against k-nearest neighbors, decision trees, and neural networks. Results demonstrate that the proposed methodology can predict the SCD event 30 min before it occurs with an accuracy of 97.28%, positively impacting on the improvement of timely interventions and, consequently, the survival rates and quality of life of people. [ABSTRACT FROM AUTHOR]

• This work proposes a multi-objective voltage control for multiple Electric Springs (ESs) in active unbalanced distribution systems. • This multi-objective voltage control relies on a robust ES model, a modified backward/forward solution method (BFSM), and a continuous genetic algorithm (CGA). • The proposed ES model is suitable for unbalanced conditions and can be implemented at any network location in single-, two-, and three-phase connections. • It considers the self and mutual effects of the distribution line and the power flow of the elements connected at the point of common coupling (PCC) to determine the ES output voltage. This work proposes a multi-objective voltage control for multiple Electric Springs (ESs) in active unbalanced distribution systems, where the goal is to provide a suitable simulation scheme to control the ESs in real-time applications. This multi-objective voltage control relies on a robust ES model, a modified backward/forward solution method (BFSM), and a continuous genetic algorithm (CGA). The proposed ES model is suitable for unbalanced conditions and can be implemented at any network location in single-, two-, and three-phase connections. Also, it considers the self and mutual effects of the distribution line (DL) and the power flow of the elements connected at the point of common coupling (PCC) to determine the ES output voltage. The ES model is adapted to the BFSM to quickly and reliably solve the power flow in the distribution network. Besides, the CGA simultaneously solves the mode of operation and the amount of reactive power exchange of the ESs to minimize power losses, voltage unbalance, and voltage deviation. The proposed voltage control is evaluated on an active distribution network based on a modified IEEE 13-bus for considering both load variations and power intermittences. [ABSTRACT FROM AUTHOR]

Cable-stayed bridges are widely used all around the world. Unfortunately, during their service life, they are exposed to adverse conditions that may cause their deterioration and, consequently, their collapse. Vibration-based structural health monitoring techniques have become the most promising alternatives for efficiently detecting and locating damage into civil structures. In this regard, this paper presents a new methodology based on statistical features, Principal component analysis (PCA), and Mahalanobis distance (MD) for detecting and locating a cable loss in the Río Papaloapan bridge (RPB) using vibration signals. It is based on the extraction of a set of statistical time features (STFs) from vibration signals, which are analyzed using the autocorrelation function (ACF) to denoise and strengthen the features found in them. Then PCA-based models are computed by using the STFs to enhance the damage location process. Then a new damage index based on MD is proposed to indicate if a damage exists and its location. [ABSTRACT FROM AUTHOR]

The economic and personal consequences that a car accident generates for society have been increasing in recent years. One of the causes that can generate a car accident is the stress level the driver has; consequently, the detection of stress events is a highly desirable task. In this article, the efficacy that statistical time features (STFs), such as root mean square, mean, variance, and standard deviation, among others, can reach in detecting stress events using electromyographical signals in drivers is investigated, since they can measure subtle changes that a signal can have. The obtained results show that the variance and standard deviation coupled with a support vector machine classifier with a cubic kernel are effective for detecting stress events where an AUC of 0.97 is reached. In this sense, since SVM has different kernels that can be trained, they are used to find out which one has the best efficacy using the STFs as feature inputs and a training strategy; thus, information about model explain ability can be determined. The explainability of the machine learning algorithm allows generating a deeper comprehension about the model efficacy and what model should be selected depending on the features used to its development. [ABSTRACT FROM AUTHOR]

Large civil structures such as bridges must be permanently monitored to ensure integrity and avoid collapses due to damage resulting in devastating human fatalities and economic losses. In this article, a wavelet-based method called the Wavelet Energy Accumulation Method (WEAM) is developed in order to detect, locate and quantify damage in vehicular bridges. The WEAM consists of measuring the vibration signals on different points along the bridge while a vehicle crosses it, then those signals and the corresponding ones of the healthy bridge are subtracted and the Continuous Wavelet Transform (CWT) is applied on both, the healthy and the subtracted signals, to obtain the corresponding diagrams, which provide a clue about where the damage is located; then, the border effects must be eliminated. Finally, the Wavelet Energy (WE) is obtained by calculating the area under the curve along the selected range of scale for each point of the bridge deck. The energy of a healthy bridge is low and flat, whereas for a damaged bridge there is a WE accumulation at the damage location. The Rio Papaloapan Bridge (RPB) is considered for this research and the results obtained numerically and experimentally are very promissory to apply this method and avoid accidents. [ABSTRACT FROM AUTHOR]

Epilepsy is a brain disorder that affects about 50 million persons around the world and is characterized by generating recurrent seizures, which can put patients in permanent because of falls, drowning, burns, and prolonged seizures that they can suffer. Hence, it is of vital importance to propose a methodology with the capability of predicting a seizure with several minutes before the onset, allowing that the patients take their precautions against injuries. In this regard, a methodology based on the wavelet packet transform (WPT), statistical time features (STFs), and a decision tree classifier (DTC) for predicting an epileptic seizure using electrocardiogram (ECG) signals is presented. Seventeen STFs were analyzed to measure changes in the properties of ECG signals and find characteristics capable of differentiating between healthy and 15 min prior to seizure signals. The effectiveness of the proposed methodology for predicting an epileptic event is demonstrated using a database of seven patients with 10 epileptic seizures, which was provided by the Massachusetts Institute of Technology–Beth Israel Hospital (MIT–BIH). The results show that the proposed methodology is capable of predicting an epileptic seizure 15 min before with an accuracy of 100%. Our results suggest that the use of STFs at frequency bands related to heart activity to find parameters for the prediction of epileptic seizures is suitable. [ABSTRACT FROM AUTHOR]

The popularity of electric springs (ESs) has been grown in the last years mainly due to the boost in the growth of smart grids (SGs) and micro‐grids (μGs), as well as the high penetration of renewable energy sources. In general, ESs have a hardware structure similar to other compensator devices such as distributed flexible AC transmission systems or active filters. ESs offer the exchange of active and reactive powers with the electrical grid and the demand‐side load management. Their ability to simultaneously perform multiple tasks without the need of modifying their hardware structure classifies them as smart loads, distinguishing them from other types of compensators such as active filters, hybrid filters, dynamic voltage restorers, and distribution static VAR compensators, among others. In this work, an exhaustive review of ESs is carried out to cover their three main research trends, i.e. hardware structure evolution, different types of controllers, and applications in electric power distribution systems, SGs, and μGs. Moreover, their main advantages, disadvantages, and contributions in the power quality improvement are discussed. This review clarifies future research trends that have to be explored to advance the subject. [ABSTRACT FROM AUTHOR]

During the last years, civil infrastructure has experienced an increasing development to satisfy the society's demands such as communication, transportation, work and living spaces, among others. In this sense, the development and application of methods to guarantee the structure optimal operation, known as Structural Health Monitoring schemes, are necessary in order to avoid economic and human losses. Modern schemes employ the structure vibration response as any damage will modify the structure physical properties, which will be reflected in the vibration response. Thus, by measuring the waveform changes of the response, the structure condition can be determined. Considering this fact, this paper investigates the effectiveness of Katz fractal dimension, Higuchi fractal dimension, Box fractal dimension, Petrosian fractal dimension, and Sevcik fractal dimension which are nonlinear measurements to extract features of vibration signals in order to determine the health condition of a 3D 9-bay truss-type bridge. The obtained results show that the algorithms corresponding to Higuchi and Petrosian fractal dimension algorithms exceed the other nonlinear measurements in efficiency to discriminate between a healthy structure and a damage produced by corrosion. [ABSTRACT FROM AUTHOR]

A new multiple signal classification (MUSIC)-based methodology is presented for detecting and locating multiple damage types in a truss-type structure subjected to dynamic excitations. The methodology is based mainly on two steps: in step 1, the MUSIC method is employed to obtain the pseudo-spectra of vibration signatures, healthy and damaged, to be used for damage detection. In step 2, a new damage index, based on the obtained pseudo-spectra, is proposed to measure the structure condition. Furthermore, the damage location is estimated according to the variation in the amplitudes of the estimated pseudo-spectra. The presented results show that the proposed methodology can make an accurate and reliable estimation of the condition and location of three specific damage conditions, i.e., loosened bolts, internal corrosion, and external corrosion. [ABSTRACT FROM AUTHOR]

Voltage unbalance and mechanical overload generate negative effects on induction motors, producing thermal damages in the stator insulation and a reduction of the motor lifetime. In this regard, the development of protection devices is crucial as they can help to maintain the integrity of the motor and avoid irreversible damages such as insulation system breakdown, short-circuits, and so on. In this work, a methodology to obtain a time-protection model for induction motors from voltage unbalance is presented. The methodology is based on the thermal impact on the motor produced by mechanical overload and voltage unbalance conditions. In general, it consists of the following steps: i) induce in the motor different overloads and voltage unbalance levels and monitor their thermal profiles at the stator winding, ii) obtain the time–overload curve of the motor, iii) determine both the thermal level according to both the time-overload curve and the overload thermal profile, and iv) estimate the parameters of the time-unbalance model using both the thermal level obtained and the unbalance thermal profiles. The model is validated through its implementation in an online protection scheme, the results show a similar behavior between overload and unbalance protections in the safe thermal level defined by the protection models. The protection scheme is carried out over a 750 W three-phase induction motor under different overload and voltage unbalance operating conditions. [ABSTRACT FROM AUTHOR]

A methodology for automatic incipient broken rotor bar detection in induction motors (IMs) is presented. Sparse representations of signals are applied as a diagnosis technique. The novelty of this technique is that it can analyze the frequency spectra from vibration signals even when the differences among signals are small. This representation allows decomposing or reconstructing signals through a trained dictionary that has learned the features of one specific group/class. The main feature of this paper is the use of overcomplete dictionaries trained from sets of signals with faults to be detected. In this way, trained dictionaries perform the decomposition of signals using the orthogonal matching pursuit (OMP) algorithm. The decomposition is evaluated and classified by error-based criteria and a majority decision classifier, allowing the detection of early damage, ranging from 1 mm to one broken bar. The detection is performed by the decomposition of vibration signals from three axes ( ${x}$ , ${y}$ , and ${z}$ ) of IMs under three load conditions (unloaded, half loaded, and three-fourths loaded) and different levels of damage (healthy or 0 mm, 1–9 mm, and one broken bar). These signals are processed by the Fourier transform and the spectrum obtained is evaluated by the OMP algorithm. Finally, the retrieved information is evaluated and the diagnosis is given. All algorithms are developed in MATLAB software and the detection accuracy is higher than 90% for damages as small as 1 mm. [ABSTRACT FROM AUTHOR]

Motor current signals analysis (MCSA) is a widely used approach for fault diagnostics in induction motors (IMs). It consists of detecting a specific signature or pattern associated to a fault condition from current signals. In particular, the fault of broken rotor bars (BRBs) is featured by a V-shaped pattern in the time-frequency domain during the startup transient. Although many techniques and methodologies have been presented in literature, most of them have been focused on analyzing consolidated faults such as one- BRB and multiple BRBs; in contrast, the BRB incipient detection, such as half BRB, has been rarely investigated. Hence, a methodology based on a new technique named Tooth-fast Fourier transform (FFT) to detect both incipient and consolidated BRB conditions is presented in this work. It consists of two windows moving along the analyzed current signal, where the FFT is performed for each window. Next, the spectra are subtracted for minimizing the stationary frequencies and maximizing the moving-ones. The signature of the moving frequencies in the resulting spectrogram has a “teeth” shape, giving the name to the proposed technique. Next, a weight function and a classification stage employing four indicators are presented for automatic diagnostics. The proposal is validated and tested using both synthetic and real signals. For the latter, different levels of BRB, i.e., half BRB, one BRB, and two BRBs, are considered. Results demonstrate the effectiveness and usefulness of the proposal to detect both incipient and consolidated BRB faults in IMs. [ABSTRACT FROM AUTHOR]

Overheating is a negative effect that induction motors suffer under either mechanical or electrical anomalous conditions, which can decrement the motors' useful life or produce catastrophic damage. In this sense, a new approach for assisting in the analysis and solution of problems related to overheating in induction motors is the correlation of thermal effects between the abovementioned conditions. In this study, a methodology for the experimental extraction of mathematical models that correlate the thermal effect on the induction motor stator due to voltage unbalance and mechanical overload is developed. In general, the proposed methodology is based on thermal gradients at different points of an induction motor stator. These thermal gradients are generated from either voltage unbalance or mechanical overload. For voltage unbalance estimation, the definitions of IEEE standard 141, IEEE standard 1159, and NEMA norm are considered. The correlation models are carried out and validated experimentally on a three-phase induction motor of 746 W (1 hp). Results demonstrate effectiveness in the correlation at different stator points of both variables: unbalance voltage and mechanical overload. [ABSTRACT FROM AUTHOR]

Diverse conditions in power systems, such as massive use of nonlinear loads, continuous switching and operation of large electrical loads, and the integration of renewable energies, among others, have adversely affected the power quality (PQ) because they produce undesirable distortions in the waveforms of voltage and current. The conventional way to quantify the PQ is using the PQ indices (PQIs). Yet, the nonstationary properties of voltage and current signals degrade the PQIs estimation whenever classical techniques are used. In this paper, a methodology based on single-sideband modulation method and the Wavelet and Hilbert transforms for the estimation of instantaneous PQIs is proposed. It is shown that the proposal yields better tracking of transitory changes in the voltage/current signals than classical techniques such as the short-time Fourier transform. The PQIs used are the root-mean-square values, frequency, total harmonic distortion, active power, reactive power, apparent power, distortion power, power factor, and total power factor. PQIs performance is validated using synthetic and real signals. [ABSTRACT FROM PUBLISHER]

A major trust of modal parameters identification (MPI) research in recent years has been based on using artificial and natural vibrations sources because vibration measurements can reflect the true dynamic behavior of a structure while analytical prediction methods, such as finite element models, are less accurate due to the numerous structural idealizations and uncertainties involved in the simulations. This paper presents a state-of-the-art review of the time-frequency techniques for modal parameters identification of civil structures from acquired dynamic signals as well as the factors that affect the estimation accuracy. Further, the latest signal processing techniques proposed since 2012 are also reviewed. These algorithms are worth being researched for MPI of large real-life structures because they provide good time-frequency resolution and noise-immunity. [ABSTRACT FROM AUTHOR]

Many applications related to modeling, control and condition assessment of smart structures require an accurate identification of natural frequencies and damping ratios. This identification is generally carried out through artificial and natural vibration sources. The latter is often preferred in many situations; yet their analysis represents a challenge since the measured data are non-stationary with a high noise level. In this paper, a new methodology is proposed based on the synchrosqueezed wavelet transform (SWT). First, the random decrement technique (RDT) is applied to estimate the free vibration response from measured ambient vibration signals. Then, the SWT algorithm is used to decompose the vibration response into individual mode components. Finally, the Hilbert transform (HT) and the Kalman filter (KF) are used to estimate the natural frequencies and damping ratios of each mode and to filter and smoothen the results. The effectiveness of the proposed approach is first validated through numerical simulation of damped free vibration response of a 3-degree of freedom (DOF) system with two closely-spaced frequencies. Then, numerical and experimental data of a benchmark 4-story 2×2 bay 3D steel frame structure subjected to ambient vibrations is analyzed. Finally, the natural frequencies and damping ratios of a real-life bridge located in Queretaro, Mexico are obtained. For comparison purposes, two recent and advanced signal processing techniques, the complete ensemble empirical mode decomposition (CEEMD) technique and the short-time multiple signal classification (ST-MUSIC) are also tested. Numerical and experimental results show accurate identification of the natural frequencies and damping ratios even when the signal is embedded in high-level noise demonstrating that the proposed methodology provides a powerful approach to estimate the modal parameters of a civil structure using ambient vibration excitations. [ABSTRACT FROM AUTHOR]

Induction motors (IMs) are one of the most commonly used rotating machines in industry. In order to avoid downtimes and economical losses, development of condition monitoring systems is of paramount importance. Inter-turn short circuit (ITSC) faults are one of the most commonly occurring faults in IMs. In this regard, motor current signature analysis (MCSA) is a low cost and noninvasive technique, requiring only the current signal to perform the condition monitoring. Also, the development of tools that emulate IM faults, allow the design, calibration and validation of MCSA based techniques. In this work, a multilayer neural network-based model to reproduce the current signatures associated with ITSC fault conditions is presented. The model considers both five severities of ITSC faults and four torque levels. This model is also implemented on a power hardware in the loop scheme to provide a novel tool to test condition monitoring systems of IMs. [Display omitted] [ABSTRACT FROM AUTHOR]

One of the most critical devices in an electrical system is the transformer. It is continuously under different electrical and mechanical stresses that can produce failures in its components and other electrical network devices. The short-circuited turns (SCTs) are a common winding failure. This type of fault has been widely studied in literature employing the vibration signals produced in the transformer. Although promising results have been obtained, it is not a trivial task if different severity levels and a common high-level noise are considered. This paper presents a methodology based on statistical time features (STFs) and support vector machines (SVM) to diagnose a transformer under several SCTs conditions. As STFs, 19 indicators from the transformer vibration signals are computed; then, the most discriminant features are selected using the Fisher score analysis, and the linear discriminant analysis is used for dimension reduction. Finally, a support vector machine classifier is employed to carry out the diagnosis in an automatic way. Once the methodology has been developed, it is implemented on a field-programmable gate array (FPGA) to provide a system-on-a-chip solution. A modified transformer capable of emulating different SCTs severities is employed to validate and test the methodology and its FPGA implementation. Results demonstrate the effectiveness of the proposal for diagnosing the transformer condition as an accuracy of 96.82% is obtained. [ABSTRACT FROM AUTHOR]

• The online detection of short-circuited turns in transformers is presented. • The PMU scheme of the IEEE Std. C37.118.1 is extended to harmonic PMU (HPMU). • A fuzzy logic classifier is developed for automatic short-circuit fault detection. • HPMU provides magnitude, phase, and frequency of any harmonic component. Transformers in modern power systems often experience different types of faults due to electrical, mechanical, and thermal stresses during their service life. In this regard, online diagnosis is particularly important to ensure reliable and economic operation of power system by avoiding equipment damages and outages. In this work, a new methodology for online diagnosis of inter-turn faults in transformers is presented. In general, the proposal determines the fault condition through the differential current spectrum and a fuzzy logic (FL) system. For the spectrum estimation, a phasor measurement unit (PMU)-based scheme is expanded to a harmonic PMU (HPMU)-scheme to obtain the magnitude and phase of the harmonic content for the voltage and current signals in a synchronized way. The FL system diagnoses the transformer condition in a continuous and online way. As the proposed method is focused on the online diagnosis of fault, its performance is tested under different operating conditions, including no-load, linear loads, and non-linear loads. To increase the analysis, a simulated three-phase transformer and a real single-phase transformer that present different levels of fault severity for the inter-turn fault condition are analyzed. Results show the effectiveness and usefulness of the proposal. [ABSTRACT FROM AUTHOR]

The electric spring (ES) is a contemporary device that has emerged as a viable alternative for solving problems associated with voltage and power stability in distributed generation-based smart grids (SG). In order to study the integration of ESs into the electrical network, the steady-state simulation models have been developed as an essential tool. Typically, these models require an equivalent electrical circuit of the in-test networks, which implies adding restrictions for its implementation in simulation software. These restrictions generate simplified models, limiting their application to specific scenarios, which, in some cases, do not fully apply to the needs of modern power systems. Therefore, a robust steady-state model for the ES is proposed in this work to adequately represent the power exchange of multiples ESs in radial micro-grids (µGs) and renewable energy sources regardless of their physical location and without the need of additional restrictions. For solving and controlling the model simulation, a modified backward–forward sweep method (MBFSM) is implemented. In contrast, the voltage control determines the operating conditions of the ESs from the steady-state solution and the reference voltages established for each ES. The model and algorithms of the solution and the control are validated with dynamic simulations. For the quasi-stationary case with distributed renewable generation, the results show an improvement higher than 95% when the ESs are installed. On the other hand, the MBFSM reduces the number of iterations by 34% on average compared to the BFSM. [ABSTRACT FROM AUTHOR]

Phasor measurement units (PMUs) are important elements in power systems to monitor and know the real network condition. In order to regulate the performance of PMUs, the IEEE Std. C37.118.1 stablishes two classes—P and M, where the phasor estimation is carried out using a quadrature oscillator and a low-pass (LP) filter for modulation and demodulation, respectively. The LP filter plays the most important role since it determines the accuracy, response time and rejection capability of both harmonics and aliased signals. In this regard and by considering that the M-class filters are used for more accurate measurements, the IEEE Std. presents different M-class filters for different reporting rates (when a result is given). However, they can degrade their performance under frequency deviations if the LP frequency response is not properly considered. In this work, a unified model for magnitude compensation under frequency deviations for all the M-class filters is proposed, providing the necessary values of compensation to improve their performance. The model considers the magnitude response of the M-class filters for different reporting rates, a normalized frequency range based on frequency dilation and a fitted two-variable function. The effectiveness of the proposal is verified using both static and dynamic conditions for frequency deviations. Besides that, a real-time simulator to generate test signals is also used to validate the proposed methodology. [ABSTRACT FROM AUTHOR]

Transformers are vital and indispensable elements in electrical systems, and therefore, their correct operation is fundamental; despite being robust electrical machines, they are susceptible to present different types of faults during their service life. Although there are different faults, the fault of short-circuited turns (SCTs) has attracted the interest of many researchers around the world since the windings in a transformer are one of the most vulnerable parts. In this regard, several works in literature have analyzed the vibration signals that generate a transformer as a source of information to carry out fault diagnosis; however this analysis is not an easy task since the information associated with the fault is embedded in high level noise. This problem becomes more difficult when low levels of fault severity are considered. In this work, as the main contribution, the nonlinear mode decomposition (NMD) method is investigated as a potential signal processing technique to extract features from vibration signals, and thus, detect SCTs in transformers, even in early stages, i.e., low levels of fault severity. Also, the instantaneous root mean square (RMS) value computed using the Hilbert transform is proposed as a fault indicator, demonstrating to be sensitive to fault severity. Finally, a fuzzy logic system is developed for automatic fault diagnosis. To test the proposal, a modified transformer representing diverse levels of SCTs is used. These levels consist of 0 (healthy condition), 5, 10, 15, 20, and 25 SCTs. Results demonstrate the capability of the proposal to extract features from vibration signals and perform automatic fault diagnosis. [ABSTRACT FROM AUTHOR]

Heart diseases are among the most common death causes in the population. Particularly, sudden cardiac death (SCD) is the cause of 10% of the deaths around the world. For this reason, it is necessary to develop new methodologies that can predict this event in the earliest possible stage. This work presents a novel methodology to predict when a person can develop an SCD episode before it occurs. It is based on the adroit combination of the empirical mode decomposition, nonlinear measurements, such as the Higuchi fractal and permutation entropy, and a neural network. The obtained results show that the proposed methodology is capable of detecting an SCD episode 25 min before it appears with a 94% accuracy. The main benefits of the proposal are: (1) an improved detection time of 25% compared with previously published works, (2) moderate computational complexity since only two features are used, and (3) it uses the raw ECG without any preprocessing stage, unlike recent previous works. [ABSTRACT FROM AUTHOR]