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Vibration Signal Forecasting on Rotating Machinery by means of Signal Decomposition and Neurofuzzy Modeling
Joint Authors
Romero-Troncoso, R. D. J.
Osornio-Rios, Roque A.
Cariño-Corrales, Jesus Adolfo
Saucedo-Dorantes, Juan Jose
Zurita-Millán, Daniel
Delgado-Prieto, Miguel
Ortega-Redondo, Juan Antonio
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-09-20
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Vibration monitoring plays a key role in the industrial machinery reliability since it allows enhancing the performance of the machinery under supervision through the detection of failure modes.
Thus, vibration monitoring schemes that give information regarding future condition, that is, prognosis approaches, are of growing interest for the scientific and industrial communities.
This work proposes a vibration signal prognosis methodology, applied to a rotating electromechanical system and its associated kinematic chain.
The method combines the adaptability of neurofuzzy modeling with a signal decomposition strategy to model the patterns of the vibrations signal under different fault scenarios.
The model tuning is performed by means of Genetic Algorithms along with a correlation based interval selection procedure.
The performance and effectiveness of the proposed method are validated experimentally with an electromechanical test bench containing a kinematic chain.
The results of the study indicate the suitability of the method for vibration forecasting in complex electromechanical systems and their associated kinematic chains.
American Psychological Association (APA)
Zurita-Millán, Daniel& Delgado-Prieto, Miguel& Saucedo-Dorantes, Juan Jose& Cariño-Corrales, Jesus Adolfo& Osornio-Rios, Roque A.& Ortega-Redondo, Juan Antonio…[et al.]. 2016. Vibration Signal Forecasting on Rotating Machinery by means of Signal Decomposition and Neurofuzzy Modeling. Shock and Vibration،Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1118919
Modern Language Association (MLA)
Zurita-Millán, Daniel…[et al.]. Vibration Signal Forecasting on Rotating Machinery by means of Signal Decomposition and Neurofuzzy Modeling. Shock and Vibration No. 2016 (2016), pp.1-13.
https://search.emarefa.net/detail/BIM-1118919
American Medical Association (AMA)
Zurita-Millán, Daniel& Delgado-Prieto, Miguel& Saucedo-Dorantes, Juan Jose& Cariño-Corrales, Jesus Adolfo& Osornio-Rios, Roque A.& Ortega-Redondo, Juan Antonio…[et al.]. Vibration Signal Forecasting on Rotating Machinery by means of Signal Decomposition and Neurofuzzy Modeling. Shock and Vibration. 2016. Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1118919
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1118919