R
René-Vinicio Sánchez
Researcher at Politecnica Salesiana University
Publications - 77
Citations - 3393
René-Vinicio Sánchez is an academic researcher from Politecnica Salesiana University. The author has contributed to research in topics: Fault (power engineering) & Fault detection and isolation. The author has an hindex of 22, co-authored 66 publications receiving 2372 citations. Previous affiliations of René-Vinicio Sánchez include University of Cuenca & National University of Distance Education.
Papers
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Journal ArticleDOI
A review on data-driven fault severity assessment in rolling bearings
Mariela Cerrada,Mariela Cerrada,René-Vinicio Sánchez,Chuan Li,Fannia Pacheco,Diego Cabrera,José Valente de Oliveira,Rafael E. Vásquez +7 more
TL;DR: In this article, a review of fault severity assessment of rolling bearing components is presented, focusing on data-driven approaches such as signal processing for extracting proper fault signatures associated with the damage degradation, and learning approaches that are used to identify degradation patterns with regards to health conditions.
Journal ArticleDOI
Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals
Chuan Li,Chuan Li,René-Vinicio Sánchez,Grover Zurita,Mariela Cerrada,Diego Cabrera,Rafael E. Vásquez +6 more
TL;DR: In this article, the authors proposed a deep random forest fusion (DRFF) technique to improve fault diagnosis performance for gearboxes by using measurements of an acoustic emission (AE) sensor and an accelerometer that are used for monitoring the gearbox condition simultaneously.
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Gearbox Fault Identification and Classification with Convolutional Neural Networks
TL;DR: An implementation of deep learning algorithm convolutional neural network used for fault identification and classification in gearboxes using different combinations of condition patterns based on some basic fault conditions indicates that the proposed approach is highly reliable and applicable in fault diagnosis of industrial reciprocating machinery.
Journal ArticleDOI
Fault diagnosis in spur gears based on genetic algorithm and random forest
Mariela Cerrada,Mariela Cerrada,Grover Zurita,Diego Cabrera,René-Vinicio Sánchez,Mariano Artés,Chuan Li,Chuan Li +7 more
TL;DR: The main aim of this research is to build up a robust system for the multi-class fault diagnosis in spur gears, by selecting the best set of condition parameters on time, frequency and time–frequency domains, which are extracted from vibration signals.
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Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis
TL;DR: This paper addresses a multimodal deep support vector classification (MDSVC) approach, which employs separation-fusion based deep learning in order to perform fault diagnosis tasks for gearboxes, and shows that the proposed model achieves the best fault classification rate in experiments when compared to representative deep and shallow learning methods.