E
Enrique López Droguett
Researcher at University of California, Los Angeles
Publications - 175
Citations - 2648
Enrique López Droguett is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Computer science & Reliability (statistics). The author has an hindex of 21, co-authored 151 publications receiving 1717 citations. Previous affiliations of Enrique López Droguett include University of Maryland, College Park & University of Chile.
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Journal ArticleDOI
Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings
TL;DR: The proposed CNN architecture achieves better results with less learnable parameters than similar architectures used for fault detection, including cases with experimental noise.
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Failure and reliability prediction by support vector machines regression of time series data
TL;DR: A comparative analysis of SVM effectiveness in forecasting time-to-failure and reliability of engineered components based on time series data shows that in the analyzed cases, SVM outperforms or is comparable to other techniques.
Journal ArticleDOI
Convolutional neural networks for automated damage recognition and damage type identification
Ceena Modarres,Nicolas Astorga,Enrique López Droguett,Enrique López Droguett,Viviana Meruane +4 more
TL;DR: A convolutional neural network‐based approach to identify the presence and type of structural damage and outperforms several other machine learning algorithms in completing the same task is proposed.
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Bayesian methodology for model uncertainty using model performance data.
TL;DR: A Bayesian methodology for the assessment of model uncertainties is described, where models are treated as sources of information on the unknown of interest and where information about models are available in form of homogeneous and nonhomogeneous performance data.
Journal ArticleDOI
Deep variational auto-encoders: A promising tool for dimensionality reduction and ball bearing elements fault diagnosis
Gabriel San Martin,Enrique López Droguett,Enrique López Droguett,Viviane Meruane,Márcio das Chagas Moura +4 more
TL;DR: The results show that variational auto-encoders are a competent and promising tool for dimensionality reduction for use in fault diagnosis and worth further exploring their capabilities beyond vibration signals of ball bearing elements.