Anova and cluster distance based contributions for feature empirical analysis to fault diagnosis in rotating machinery

Mario Pena, Ximena Alvarez, Diana Jadan, Pablo Lucero, Milton Barragan, Rodrigo Guaman, Vinicio Sanchez, Mariela Cerrada

Research output: Contribution to conferencePaperResearch

1 Citation (Scopus)
Original languageEnglish (US)
Pages69-74
Number of pages6
DOIs
StatePublished - 9 Dec 2017
EventProceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017 -
Duration: 9 Dec 2017 → …

Conference

ConferenceProceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017
Period9/12/17 → …

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Rotating machinery
Failure analysis
Feature extraction
Bearings (structural)
Analysis of variance (ANOVA)
Classifiers

Cite this

Pena, M., Alvarez, X., Jadan, D., Lucero, P., Barragan, M., Guaman, R., ... Cerrada, M. (2017). Anova and cluster distance based contributions for feature empirical analysis to fault diagnosis in rotating machinery. 69-74. Paper presented at Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017, . https://doi.org/10.1109/SDPC.2017.23
Pena, Mario ; Alvarez, Ximena ; Jadan, Diana ; Lucero, Pablo ; Barragan, Milton ; Guaman, Rodrigo ; Sanchez, Vinicio ; Cerrada, Mariela. / Anova and cluster distance based contributions for feature empirical analysis to fault diagnosis in rotating machinery. Paper presented at Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017, .6 p.
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title = "Anova and cluster distance based contributions for feature empirical analysis to fault diagnosis in rotating machinery",
author = "Mario Pena and Ximena Alvarez and Diana Jadan and Pablo Lucero and Milton Barragan and Rodrigo Guaman and Vinicio Sanchez and Mariela Cerrada",
year = "2017",
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Pena, M, Alvarez, X, Jadan, D, Lucero, P, Barragan, M, Guaman, R, Sanchez, V & Cerrada, M 2017, 'Anova and cluster distance based contributions for feature empirical analysis to fault diagnosis in rotating machinery' Paper presented at Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017, 9/12/17, pp. 69-74. https://doi.org/10.1109/SDPC.2017.23

Anova and cluster distance based contributions for feature empirical analysis to fault diagnosis in rotating machinery. / Pena, Mario; Alvarez, Ximena; Jadan, Diana; Lucero, Pablo; Barragan, Milton; Guaman, Rodrigo; Sanchez, Vinicio; Cerrada, Mariela.

2017. 69-74 Paper presented at Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017, .

Research output: Contribution to conferencePaperResearch

TY - CONF

T1 - Anova and cluster distance based contributions for feature empirical analysis to fault diagnosis in rotating machinery

AU - Pena, Mario

AU - Alvarez, Ximena

AU - Jadan, Diana

AU - Lucero, Pablo

AU - Barragan, Milton

AU - Guaman, Rodrigo

AU - Sanchez, Vinicio

AU - Cerrada, Mariela

PY - 2017/12/9

Y1 - 2017/12/9

U2 - 10.1109/SDPC.2017.23

DO - 10.1109/SDPC.2017.23

M3 - Paper

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EP - 74

ER -

Pena M, Alvarez X, Jadan D, Lucero P, Barragan M, Guaman R et al. Anova and cluster distance based contributions for feature empirical analysis to fault diagnosis in rotating machinery. 2017. Paper presented at Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017, . https://doi.org/10.1109/SDPC.2017.23