Browsing by Autor "Diego Cabrera"

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A REVIEW OF VIBRATION MACHINE DIAGNOSTICS BY USING ARTIFICIAL INTELLIGENCE METHODS
(2016) Grover Zurita; René–Vinicio Sánchez; Diego Cabrera
In the industry, gears and rolling bearings failures are one of the foremost causes of breakdown in rotating machines, reducing availability time of the production and resulting in costly systems downtime. Therefore, there are growing demands for vibration condition based monitoring of gears and bearings, and any method in order to improve the effectiveness, reliability, and accuracy of the bearing faults diagnosis ought to be evaluated. In order to perform machine diagnosis efficiently, researchers have extensively investigated different advanced digital signal processing techniques and artificial intelligence methods to accurately extract fault characteristics from vibration signals. The main goal of this article is to present the state-of-the-art development in vibration analysis for machine diagnosis based on artificial intelligence methods.
A Systematic Review of Fuzzy Formalisms for Bearing Fault Diagnosis
(Institute of Electrical and Electronics Engineers, 2018) Chuan Li; José Valente de Oliveira; Mariela Cerrada; Diego Cabrera; René–Vinicio Sánchez; Grover Zurita
Bearings are fundamental mechanical components in rotary machines (engines, gearboxes, generators, radars, turbines, etc.) that have been identified as one of the primary causes of failure in these machines. This makes bearing fault diagnosis (detection, classification, and prognosis) an economic very relevant topic, as well as a technically challenging one as evaluated by the extensive research literature on the subject. This paper employs a systematic methodology to identify, summarize, analyze, and interpret the primary literature on fuzzy formalisms for bearing fault diagnosis from 2000 to 2017 (March). The main contribution is an updated, unbiased, and (to a higher extend) repeatable search, review, and analysis (summary, classification, and critique) of the available approaches resorting to fuzzy formalisms in this trendy topic. A discussion on a new promising future research direction is provided. A comprehensive list of references is also included.
Fault diagnosis in spur gears based on genetic algorithm and random forest
(Elsevier BV, 2015) Mariela Cerrada; Grover Zurita; Diego Cabrera; René–Vinicio Sánchez; Mariano Artés; Chuan Li
Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition
(Higher Education Press, 2015) Diego Cabrera; Fernando Sancho; René–Vinicio Sánchez; Grover Zurita; Mariela Cerrada; Chuan Li; Rafael E. Vásquez
Observer-biased bearing condition monitoring: From fault detection to multi-fault classification
(Elsevier BV, 2016) Chuan Li; José Valente de Oliveira; Mariela Cerrada; Fannia Pacheco; Diego Cabrera; René–Vinicio Sánchez; Grover Zurita
UNA REVISIÓN BIBLIOGRÁFICA DEL ANÁLISIS VIBRACIONAL PARA EL DIAGNÓSTICO DE MÁQUINAS MEDIANTE EL USO DE MÉTODOS DE INTELIGENCIA ARTIFICIAL
(2016) Grover Zurita; Vinicio Mora Sánchez; Diego Cabrera