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RENE VINICIO SANCHEZ LOJA

DOCENTE

UNIVERSIDAD POLITÉCNICA SALESIAN

CUECA, Ecuador

Líneas de Investigación


Sistemas neumáticos hidráulicos, monitoreo de la condición en sistemas mecánicos mediante la aplicación de técnicas de inteligencia artificial. Educación en ingeniería.

Educación

  •  MECANICA, UNIVERSIDAD NACIONAL DE EDUCACION A DISTANCIA. España, 2017
  •  AUTOMATICA, PONTIFICIA UNIVERSIDAD BOLIVARIANA. Colombia, 2018
  •  MECANICA, UNIVERSIDAD NACIONAL DE EDUCACION A DISTANCIA. Chile, 2012
  •  MECANICA, UNIVERSIDAD POLITECNICA SALESIANA . Ecuador, 2004
  •  AUTOMATICA, PONTIFICIA UNIVERSIDAD BOLIVARIANA. Colombia, 2018
  •  AUDITORIA DE LA GESTION DE LA CALIDAD, UNIVERSIDAD TECNICA PARTICULAR DE LOJA. Ecuador, 2008

Experiencia Académica

  •   DOCENTE Full Time

    UNIVERSIDAD POLITECNICA SALESIANA

    MECANICA

    CUENCA, Ecuador

    2004 - At present

Formación de Capital Humano


Trabajo en equipo



 

Article (44)

A hybrid heuristic algorithm for evolving models in simultaneous scenarios of classification and clustering
Generative Adversarial Networks Selection Approach for Extremely Imbalanced Fault Diagnosis of Reciprocating Machinery
Spur Gear Fault Diagnosis Using a Multilayer Gated Recurrent Unit Approach With Vibration Signal
Using a Support Vector Machine Based Decision Stage to Improve the Fault Diagnosis on Gearboxes
Vibration signal analysis using symbolic dynamics for gearbox fault diagnosis
A comparative feature analysis for gear pitting level classification by using acoustic emission, vibration and current signals
A comparison of fuzzy clustering algorithms for bearing fault diagnosis
A fuzzy transition based approach for fault severity prediction in helical gearboxes
A review on data-driven fault severity assessment in rolling bearings
A semi-supervised approach based on evolving clusters for discovering unknown abnormal condition patterns in gearboxes
Convolutional Neural Networks using Fourier Transform Spectrogram to Classify the Severity of Gear Tooth Breakage
Echo state network and variational autoencoder for efficient one-class learning on dynamical systems
Feature engineering based on ANOVA, cluster validity assessment and KNN for fault diagnosis in bearings
Feature ranking for multi-fault diagnosis of rotating machinery by using random forest and KNN
Gear crack level classification by using KNN and time-domain features from acoustic emission signals under different motor speeds and loads
Gearbox fault classification using dictionary sparse based representations of vibration signals
Gearbox Fault Diagnosis Based on a Novel Hybrid Feature Reduction Method
GKFP: A new fuzzy clustering method applied to bearings diagnosis
A Dictionary Sparse Based Representation of Vibration Signals for Gearbox Fault Detection
ANOVA and cluster distance based contributions for feature empirical analysis to fault diagnosis in rotating machinery
Attribute clustering using rough set theory for feature selection in fault severity classification of rotating machinery
Automatic feature extraction of time-series applied to fault severity assessment of helical gearbox in stationary and non-stationary speed operation
Deep neural networks-based rolling bearing fault diagnosis
Framework for discovering unknown abnormal condition patterns in gearboxes using a semi-supervised approach
Multi-fault Diagnosis of Rotating Machinery by Using Feature Ranking Methods and SVM-based Classifiers
SOA based integrated software to develop fault diagnosis models using machine learning in rotating machinery
Some preliminary results on the comparison of FCM, GK, FCMFP, and FN-DBSCAN for bearing fault diagnosis
Vibration-based gearbox fault diagnosis using deep neural networks
A statistical comparison of neuroclassifiers and feature selection methods for gearbox fault diagnosis under realistic conditions
Clustering algorithm using rough set theory for unsupervised feature selection
Extracting repetitive transients for rotating machinery diagnosis using multiscale clustered grey infogram
Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning
Fault diagnosis in spur gears based on genetic algorithm and random forest
Fuzzy determination of informative frequency band for bearing fault detection
Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals
Hierarchical feature selection based on relative dependency for gear fault diagnosis
Observer-biased bearing condition monitoring: From fault detection to multi-fault classification
Rolling element bearing defect detection using the generalized synchrosqueezing transform guided by time-frequency ridge enhancement
Fault diagnosis for controlled continuous systems from a hybrid approach: a case of study
Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition
Gearbox Fault Identification and Classification with Convolutional Neural Networks
Introduction to the special issue on the VIII Latin-American Congress on Mechanical Engineering
Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal
Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis
44
RENE SANCHEZ

DOCENTE

MECANICA

UNIVERSIDAD POLITÉCNICA SALESIAN

CUECA, Ecuador

1
Felipe Tobar

Associate Professor

Initiative for Data and Artificial Intelligence

Universidad de Chile

Santiago, Chile