Institution
Politecnica Salesiana University
Education•Cuenca, Ecuador•
About: Politecnica Salesiana University is a education organization based out in Cuenca, Ecuador. It is known for research contribution in the topics: Population & Smart grid. The organization has 2102 authors who have published 1847 publications receiving 7665 citations.
Papers published on a yearly basis
Papers
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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.
453 citations
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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.
324 citations
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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.
238 citations
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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.
235 citations
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TL;DR: A model for deep statistical feature learning from vibration measurements of rotating machinery with an essential improvement potential for diagnosing rotating machinery faults is presented.
Abstract: Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults.
181 citations
Authors
Showing all 2138 results
Name | H-index | Papers | Citations |
---|---|---|---|
Chuan Li | 42 | 183 | 5457 |
René-Vinicio Sánchez | 22 | 66 | 2372 |
Mariela Cerrada | 22 | 89 | 2125 |
Diego Cabrera | 21 | 65 | 1951 |
José J. Pazos-Arias | 20 | 170 | 1684 |
Jose Restrepo | 19 | 136 | 1613 |
Jose M. Aller | 19 | 95 | 1971 |
José Valente de Oliveira | 18 | 43 | 1483 |
Roque Saltaren | 18 | 101 | 1124 |
Roberto Hincapie | 15 | 65 | 627 |
Ramón Pérez Pérez | 14 | 41 | 1187 |
Grover Zurita | 13 | 20 | 1270 |
Giovanni Bernacchia | 13 | 42 | 771 |
Monica Huerta | 13 | 120 | 668 |
Sabino Armenise | 12 | 16 | 450 |