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Transformers are key elements in electrical systems. Although they are robust machines, different faults can appear due to their inherent operating conditions, e.g., the presence of different electrical and mechanical stresses. Among the different elements that compound a transformer, the winding is one of the most vulnerable parts, where the damage of turn-to-turn short circuits is one of the most studied faults since low-level damage (i.e., a low number of short-circuited turns—SCTs) can lead to the overall fault of the transformer; therefore, early fault detection has become a fundamental task. In this regard, this paper presents a machine learning-based method to diagnose SCTs in the transformer windings by using their vibrational response. In general, the vibration signals are firstly decomposed by means of the variational mode decomposition method, where a comparison with the empirical mode decomposition (EMD) method and the ensemble empirical mode decomposition (EEMD) method is also carried out. Then, entropy, energy, and kurtosis indices are obtained from each decomposition as fault indicators, where both the combination of features and the dimensionality reduction by using the principal component analysis (PCA) method are analyzed for the global effectiveness improvement and the computational burden reduction. Finally, a pattern recognition algorithm based on artificial neural networks (ANNs) is used for automatic fault detection. The obtained results show 100% effectiveness in detecting seven fault conditions, i.e., 0 (healthy), 5, 10, 15, 20, 25, and 30 SCTs. [ABSTRACT FROM AUTHOR]

Short circuits occurring between turns within the windings are widely known as one of the primary causes of damage in electrical transformers; as a result, early detection plays a fundamental role in preventing further and more serious damage. This study introduces a novel approach that relies on the analysis of current and vibration signals, specifically employing the analysis of quaternion signals, to effectively detect short circuits within electrical transformers., offering an identification of conditions ranging from a healthy state to six levels of short circuit turns. in a no-load transformer, i.e., 0, 5, 10, 15, 20, 25 and 30 SCT. This proposed method employs quaternion rotation to extract statistical features that can be used to classify the condition of the transformer. To evaluate the effectiveness of the proposed methodology, an experimental validation is carried out using a 1.5 kVA transformer, comparing its performance against other existing methods. The results demonstrate the feasibility of the proposal, accurately identifying various levels of SCT, achieving an accuracy of 97.5%, using only 100 samples with the k nearest neighbors method. [ABSTRACT FROM AUTHOR]

Sudden cardiac death (SCD) is a global health problem, which represents 15–20% of global deaths. This type of death can be due to different heart conditions, where ventricular fibrillation has been reported as the main one. These cardiac alterations can be seen in an electrocardiogram (ECG) record, where the heart's electrical activity is altered. The present research uses these variations to be able to predict 30 min in advance when the SCD event will occur. In this regard, a methodology based on the complete ensemble empirical mode decomposition (CEEMD) method to decompose the cardiac signal into its intrinsic mode functions (IMFs) and a convolutional neural network (CNN) for automatic diagnosis is proposed. Results for the ensemble empirical mode decomposition (EEMD) method and the empirical mode decomposition (EMD) method are also compared. Results demonstrate that the combination of the CEEMD and the CNN is a potential solution for SCD prediction since 97.5% of accuracy is achieved up to 30 min in advance of the SCD event. [ABSTRACT FROM AUTHOR]

Featured Application: The proposed method can be used for monitoring the healthy condition of civil structures. Truss-type designs are widely used in civil structures. Despite the fact that they are robust and reliable structures, different kinds of damage can appear. In order to avoid human and economic losses, the development and application of damage-detection methodologies are paramount. In this work, a methodology based on the empirical mode decomposition (EMD) method and the Shannon Entropy Index (SEI) to detect incipient damages associated with corrosion in a 3D 9-bay truss-type bridge is presented. As different EMD methods are presented in literature, the most representative methods are investigated in order to evaluate their performance for this task. To this end, the vibration signals generated in the truss-type bridge at different conditions are analyzed. For the damage condition, four severity levels of simulated corrosion (1 mm, 3 mm, 5 mm, and 8 mm of diameter reduction) generated into the elements of truss-type bridge are considered. Results demonstrate the effectiveness of the proposal in terms of detecting corrosion in its very early stage (1 mm of reduction in the element). [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]

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]

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]

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]

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]

• 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]

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]