Poincaré plot features from vibration signal for gearbox fault diagnosis

Ruben Medina; Ximena Alvarez; Diana Jadan; Mariela Cerrada; Rene Vinicio Sanchez; Jean Carlo Macancela

doi:10.1109/ETCM.2017.8247500

Poincaré plot features from vibration signal for gearbox fault diagnosis

Ruben Medina
, Ximena Alvarez
, Diana Jadan
, Mariela Cerrada
, Rene Vinicio Sanchez
, Jean Carlo Macancela

Universidad de los Andes Mérida
Universidad Politécnica Salesiana

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

Abstract

This paper describes a method for fault diagnosis in gearboxes using features extracted from the Poincare plot of the vibration signal. Several features describing the geometrical shape of the Poincare plot are calculated and three of these features are selected for performing the classification of 10 types of faults recorded in the gearbox vibration signal dataset. A multi-class Error-Correcting Output Code Support Vector Machine is trained for performing the classification of faults. The cross-validation performed show that the highest accuracy attained is 95.3% when signals recorded using the load L1 are considered.

Original language	English
Title of host publication	2017 IEEE 2nd Ecuador Technical Chapters Meeting, ETCM 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-6
Number of pages	6
ISBN (Electronic)	9781538638941
DOIs	https://doi.org/10.1109/ETCM.2017.8247500
State	Published - 4 Jan 2018
Event	2nd IEEE Ecuador Technical Chapters Meeting, ETCM 2017 - Salinas, Ecuador Duration: 16 Oct 2017 → 20 Oct 2017

Publication series

Name	2017 IEEE 2nd Ecuador Technical Chapters Meeting, ETCM 2017
Volume	2017-January

Conference

Conference	2nd IEEE Ecuador Technical Chapters Meeting, ETCM 2017
Country/Territory	Ecuador
City	Salinas
Period	16/10/17 → 20/10/17

Keywords

Gearbox faults classification
Multi-class Support Vector Machines
Poincare plots
Rotatory machines

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10.1109/ETCM.2017.8247500

Cite this

Medina, R., Alvarez, X., Jadan, D., Cerrada, M., Sanchez, R. V., & Macancela, J. C. (2018). Poincaré plot features from vibration signal for gearbox fault diagnosis. In 2017 IEEE 2nd Ecuador Technical Chapters Meeting, ETCM 2017 (pp. 1-6). (2017 IEEE 2nd Ecuador Technical Chapters Meeting, ETCM 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ETCM.2017.8247500

@inproceedings{2318e8a979794971ae2b7e1b49af25d0,

title = "Poincar{\'e} plot features from vibration signal for gearbox fault diagnosis",

abstract = "This paper describes a method for fault diagnosis in gearboxes using features extracted from the Poincare plot of the vibration signal. Several features describing the geometrical shape of the Poincare plot are calculated and three of these features are selected for performing the classification of 10 types of faults recorded in the gearbox vibration signal dataset. A multi-class Error-Correcting Output Code Support Vector Machine is trained for performing the classification of faults. The cross-validation performed show that the highest accuracy attained is 95.3\% when signals recorded using the load L1 are considered.",

keywords = "Gearbox faults classification, Multi-class Support Vector Machines, Poincare plots, Rotatory machines",

author = "Ruben Medina and Ximena Alvarez and Diana Jadan and Mariela Cerrada and Sanchez, \{Rene Vinicio\} and Macancela, \{Jean Carlo\}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2nd IEEE Ecuador Technical Chapters Meeting, ETCM 2017 ; Conference date: 16-10-2017 Through 20-10-2017",

year = "2018",

month = jan,

day = "4",

doi = "10.1109/ETCM.2017.8247500",

language = "Ingl{\'e}s",

series = "2017 IEEE 2nd Ecuador Technical Chapters Meeting, ETCM 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1--6",

booktitle = "2017 IEEE 2nd Ecuador Technical Chapters Meeting, ETCM 2017",

}

Medina, R, Alvarez, X , Jadan, D, Cerrada, M, Sanchez, RV & Macancela, JC 2018, Poincaré plot features from vibration signal for gearbox fault diagnosis. in 2017 IEEE 2nd Ecuador Technical Chapters Meeting, ETCM 2017. 2017 IEEE 2nd Ecuador Technical Chapters Meeting, ETCM 2017, vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-6, 2nd IEEE Ecuador Technical Chapters Meeting, ETCM 2017, Salinas, Ecuador, 16/10/17. https://doi.org/10.1109/ETCM.2017.8247500

Poincaré plot features from vibration signal for gearbox fault diagnosis. / Medina, Ruben; Alvarez, Ximena ; Jadan, Diana et al.
2017 IEEE 2nd Ecuador Technical Chapters Meeting, ETCM 2017. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-6 (2017 IEEE 2nd Ecuador Technical Chapters Meeting, ETCM 2017; Vol. 2017-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Poincaré plot features from vibration signal for gearbox fault diagnosis

AU - Medina, Ruben

AU - Alvarez, Ximena

AU - Jadan, Diana

AU - Cerrada, Mariela

AU - Sanchez, Rene Vinicio

AU - Macancela, Jean Carlo

PY - 2018/1/4

Y1 - 2018/1/4

N2 - This paper describes a method for fault diagnosis in gearboxes using features extracted from the Poincare plot of the vibration signal. Several features describing the geometrical shape of the Poincare plot are calculated and three of these features are selected for performing the classification of 10 types of faults recorded in the gearbox vibration signal dataset. A multi-class Error-Correcting Output Code Support Vector Machine is trained for performing the classification of faults. The cross-validation performed show that the highest accuracy attained is 95.3% when signals recorded using the load L1 are considered.

AB - This paper describes a method for fault diagnosis in gearboxes using features extracted from the Poincare plot of the vibration signal. Several features describing the geometrical shape of the Poincare plot are calculated and three of these features are selected for performing the classification of 10 types of faults recorded in the gearbox vibration signal dataset. A multi-class Error-Correcting Output Code Support Vector Machine is trained for performing the classification of faults. The cross-validation performed show that the highest accuracy attained is 95.3% when signals recorded using the load L1 are considered.

KW - Gearbox faults classification

KW - Multi-class Support Vector Machines

KW - Poincare plots

KW - Rotatory machines

UR - https://www.scopus.com/pages/publications/85045739957

U2 - 10.1109/ETCM.2017.8247500

DO - 10.1109/ETCM.2017.8247500

M3 - Contribución a la conferencia

AN - SCOPUS:85045739957

T3 - 2017 IEEE 2nd Ecuador Technical Chapters Meeting, ETCM 2017

SP - 1

EP - 6

BT - 2017 IEEE 2nd Ecuador Technical Chapters Meeting, ETCM 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd IEEE Ecuador Technical Chapters Meeting, ETCM 2017

Y2 - 16 October 2017 through 20 October 2017

ER -

Poincaré plot features from vibration signal for gearbox fault diagnosis

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