David Camarena‐Martinez
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View article: A Gramian Angular Field-Based Convolutional Neural Network Approach for Crack Detection in Low-Power Turbines from Vibration Signals
A Gramian Angular Field-Based Convolutional Neural Network Approach for Crack Detection in Low-Power Turbines from Vibration Signals Open
The detection of damage in wind turbine blades is critical for ensuring their operational efficiency and longevity. This study presents a novel method for wind turbine blade damage detection, utilizing Gramian Angular Field (GAF) transform…
View article: Early Detection of Inter-Turn Short Circuits in Induction Motors Using the Derivative of Stator Current and a Lightweight 1D-ResNet
Early Detection of Inter-Turn Short Circuits in Induction Motors Using the Derivative of Stator Current and a Lightweight 1D-ResNet Open
This work presents a lightweight and practical methodology for detecting inter-turn short-circuit faults in squirrel-cage induction motors under different mechanical load conditions. The proposed approach utilizes a one-dimensional convolu…
View article: A Computational Methodology Based on Maximum Overlap Discrete Wavelet Transform and Autoencoders for Early Prediction of Sudden Cardiac Death
A Computational Methodology Based on Maximum Overlap Discrete Wavelet Transform and Autoencoders for Early Prediction of Sudden Cardiac Death Open
Cardiovascular diseases are among the major global health problems. For example, sudden cardiac death (SCD) accounts for approximately 4 million deaths worldwide. In particular, an SCD event can subtly change the electrocardiogram (ECG) si…
View article: Field-Programmable Gate Array Architecture for the Discrete Orthonormal Stockwell Transform (DOST) Hardware Implementation
Field-Programmable Gate Array Architecture for the Discrete Orthonormal Stockwell Transform (DOST) Hardware Implementation Open
Time–frequency analysis is critical in studying linear and non-linear signals that exhibit variations across both time and frequency domains. Such analysis not only facilitates the identification of transient events and extraction of key f…
View article: Variational Mode Decomposition-Based Processing for Detection of Short-Circuited Turns in Transformers Using Vibration Signals and Machine Learning
Variational Mode Decomposition-Based Processing for Detection of Short-Circuited Turns in Transformers Using Vibration Signals and Machine Learning Open
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 d…
View article: Short-Circuit Damage Diagnosis in Transformer Windings Using Quaternions: Severity Assessment through Current and Vibration Signals
Short-Circuit Damage Diagnosis in Transformer Windings Using Quaternions: Severity Assessment through Current and Vibration Signals Open
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 dam…
View article: Time-Frequency Analysis and Neural Networks for Detecting Short-Circuited Turns in Transformers in Both Transient and Steady-State Regimes Using Vibration Signals
Time-Frequency Analysis and Neural Networks for Detecting Short-Circuited Turns in Transformers in Both Transient and Steady-State Regimes Using Vibration Signals Open
Transformers are vital elements in electrical networks, but they are prone to various faults throughout their service life. Among these, a winding short-circuit fault is of particular concern to researchers, as it is a crucial and vulnerab…
View article: Short-Circuited Turn Fault Diagnosis in Transformers by Using Vibration Signals, Statistical Time Features, and Support Vector Machines on FPGA
Short-Circuited Turn Fault Diagnosis in Transformers by Using Vibration Signals, Statistical Time Features, and Support Vector Machines on FPGA Open
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-…
View article: Hardware structures, control strategies, and applications of electric springs: a state‐of‐the‐art review
Hardware structures, control strategies, and applications of electric springs: a state‐of‐the‐art review Open
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…
View article: Location of Multiple Damage Types in a Truss-Type Structure Using Multiple Signal Classification Method and Vibration Signals
Location of Multiple Damage Types in a Truss-Type Structure Using Multiple Signal Classification Method and Vibration Signals Open
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 …
View article: Vibration Signal Processing-Based Detection of Short-Circuited Turns in Transformers: A Nonlinear Mode Decomposition Approach
Vibration Signal Processing-Based Detection of Short-Circuited Turns in Transformers: A Nonlinear Mode Decomposition Approach Open
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 thei…
View article: Complete Ensemble Empirical Mode Decomposition on FPGA for Condition Monitoring of Broken Bars in Induction Motors
Complete Ensemble Empirical Mode Decomposition on FPGA for Condition Monitoring of Broken Bars in Induction Motors Open
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 no…
View article: Demodulation Technique for Broken Rotor Bar Detection in Inverter-Fed Induction Motor Under Non-Stationary Conditions
Demodulation Technique for Broken Rotor Bar Detection in Inverter-Fed Induction Motor Under Non-Stationary Conditions Open
Producción Científica
View article: Reassigned Short Time Fourier Transform and K-means Method for Diagnosis of Broken Rotor Bar Detection in VSD-fed Induction Motors
Reassigned Short Time Fourier Transform and K-means Method for Diagnosis of Broken Rotor Bar Detection in VSD-fed Induction Motors Open
Over the years induction motors have established an uncanny knack for providing a plethora of utilities in the industry, where the fault monitoring and detection has become necessary. Several techniques could be applied for the monitoring …
View article: Analysis of various inverters feeding induction motors with incipient rotor fault using high-resolution spectral analysis
Analysis of various inverters feeding induction motors with incipient rotor fault using high-resolution spectral analysis Open
Recently, there has been an increased interest in fault detection on electrical machines in steady-state regimes. Several frequency estimation techniques have been developed to assist the early detection of faults in induction motors, espe…
View article: Shannon Entropy and<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>K</mml:mi></mml:mrow></mml:math>-Means Method for Automatic Diagnosis of Broken Rotor Bars in Induction Motors Using Vibration Signals
Shannon Entropy and-Means Method for Automatic Diagnosis of Broken Rotor Bars in Induction Motors Using Vibration Signals Open
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 attenti…
View article: Fused Empirical Mode Decomposition and MUSIC Algorithms for Detecting Multiple Combined Faults in Induction Motors
Fused Empirical Mode Decomposition and MUSIC Algorithms for Detecting Multiple Combined Faults in Induction Motors Open
Detection of failures in induction motors is one of the most important concerns in industry. An unexpected fault in the induction motors can cause a loss of financial resources and waste of time that most companies cannot afford. The contr…