Gearbox Broken Tooth Severity Classification using EMD of Acoustic Emission Signals

Ruben Medina; René Vinicio Sánchez; Diego Cabrera; Luis Renato Ortega; Mariela Cerrada

doi:10.1109/ETCM56276.2022.9935765

Gearbox Broken Tooth Severity Classification using EMD of Acoustic Emission Signals

Ruben Medina, René Vinicio Sánchez, Diego Cabrera, Luis Renato Ortega, Mariela Cerrada

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

1 Scopus citations

Abstract

Fault severity classification is a critical task necessary for optimal predictive maintenance to reduce costs and avoid catastrophic accidents in the industry. In this research, we propose a methodology for broken tooth severity classification in a gearbox using digital signal processing techniques of acoustic emission signals. The method uses empirical mode decomposition of the signal and extraction of time-domain features from a set of Intrinsic Mode Functions. The extracted features are fed to random forest and linear discriminant analysis models for attaining the classification of nine different severity conditions. The method provides classification accuracies higher than 90% with both machine learning models.

Original language	English
Title of host publication	6th IEEE Ecuador Technical Chapters Meeting, ETCM 2022
Editors	David Rivas Lalaleo, Monica Karel Huerta
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665487443
DOIs	https://doi.org/10.1109/ETCM56276.2022.9935765
State	Published - 2022
Event	6th IEEE Ecuador Technical Chapters Meeting, ETCM 2022 - Quito, Ecuador Duration: 11 Oct 2022 → 14 Oct 2022

Publication series

Name	6th IEEE Ecuador Technical Chapters Meeting, ETCM 2022

Conference

Conference	6th IEEE Ecuador Technical Chapters Meeting, ETCM 2022
Country/Territory	Ecuador
City	Quito
Period	11/10/22 → 14/10/22

Bibliographical note

Funding Information:
This research was funded by the MoST Science and Technology Partnership Program (KY201802006) and National Research Base of Intelligent Manufacturing Service, Chongqing

Funding Information:
This research was funded by the MoST Science and Technology Partnership Program (KY201802006) and National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University and by the Universidad Politécnica Salesiana through the GIDTEC research group.

Funding Information:
Technology and Business University and by the Universidad Politécnica Salesiana through the GIDTEC research group.

Publisher Copyright:
© 2022 IEEE.

Keywords

Acoustic emission
Broken tooth severity
Empirical Mode Decomposition
Gearbox
Linear Discriminant Analysis
Random Forest

Access to Document

10.1109/ETCM56276.2022.9935765

Cite this

Medina, R., Sánchez, R. V., Cabrera, D., Ortega, L. R., & Cerrada, M. (2022). Gearbox Broken Tooth Severity Classification using EMD of Acoustic Emission Signals. In D. R. Lalaleo, & M. K. Huerta (Eds.), 6th IEEE Ecuador Technical Chapters Meeting, ETCM 2022 (6th IEEE Ecuador Technical Chapters Meeting, ETCM 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ETCM56276.2022.9935765

Medina, Ruben ; Sánchez, René Vinicio ; Cabrera, Diego et al. / Gearbox Broken Tooth Severity Classification using EMD of Acoustic Emission Signals. 6th IEEE Ecuador Technical Chapters Meeting, ETCM 2022. editor / David Rivas Lalaleo ; Monica Karel Huerta. Institute of Electrical and Electronics Engineers Inc., 2022. (6th IEEE Ecuador Technical Chapters Meeting, ETCM 2022).

@inproceedings{437f12b9c9764b23ad1de34d66a57149,

title = "Gearbox Broken Tooth Severity Classification using EMD of Acoustic Emission Signals",

abstract = "Fault severity classification is a critical task necessary for optimal predictive maintenance to reduce costs and avoid catastrophic accidents in the industry. In this research, we propose a methodology for broken tooth severity classification in a gearbox using digital signal processing techniques of acoustic emission signals. The method uses empirical mode decomposition of the signal and extraction of time-domain features from a set of Intrinsic Mode Functions. The extracted features are fed to random forest and linear discriminant analysis models for attaining the classification of nine different severity conditions. The method provides classification accuracies higher than 90% with both machine learning models.",

keywords = "Acoustic emission, Broken tooth severity, Empirical Mode Decomposition, Gearbox, Linear Discriminant Analysis, Random Forest",

author = "Ruben Medina and S{\'a}nchez, {Ren{\'e} Vinicio} and Diego Cabrera and Ortega, {Luis Renato} and Mariela Cerrada",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 6th IEEE Ecuador Technical Chapters Meeting, ETCM 2022 ; Conference date: 11-10-2022 Through 14-10-2022",

year = "2022",

doi = "10.1109/ETCM56276.2022.9935765",

language = "English",

series = "6th IEEE Ecuador Technical Chapters Meeting, ETCM 2022",

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

editor = "Lalaleo, {David Rivas} and Huerta, {Monica Karel}",

booktitle = "6th IEEE Ecuador Technical Chapters Meeting, ETCM 2022",

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Medina, R, Sánchez, RV , Cabrera, D, Ortega, LR & Cerrada, M 2022, Gearbox Broken Tooth Severity Classification using EMD of Acoustic Emission Signals. in DR Lalaleo & MK Huerta (eds), 6th IEEE Ecuador Technical Chapters Meeting, ETCM 2022. 6th IEEE Ecuador Technical Chapters Meeting, ETCM 2022, Institute of Electrical and Electronics Engineers Inc., 6th IEEE Ecuador Technical Chapters Meeting, ETCM 2022, Quito, Ecuador, 11/10/22. https://doi.org/10.1109/ETCM56276.2022.9935765

Gearbox Broken Tooth Severity Classification using EMD of Acoustic Emission Signals. / Medina, Ruben; Sánchez, René Vinicio ; Cabrera, Diego et al.
6th IEEE Ecuador Technical Chapters Meeting, ETCM 2022. ed. / David Rivas Lalaleo; Monica Karel Huerta. Institute of Electrical and Electronics Engineers Inc., 2022. (6th IEEE Ecuador Technical Chapters Meeting, ETCM 2022).

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

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T1 - Gearbox Broken Tooth Severity Classification using EMD of Acoustic Emission Signals

AU - Medina, Ruben

AU - Sánchez, René Vinicio

AU - Cabrera, Diego

AU - Ortega, Luis Renato

AU - Cerrada, Mariela

PY - 2022

Y1 - 2022

N2 - Fault severity classification is a critical task necessary for optimal predictive maintenance to reduce costs and avoid catastrophic accidents in the industry. In this research, we propose a methodology for broken tooth severity classification in a gearbox using digital signal processing techniques of acoustic emission signals. The method uses empirical mode decomposition of the signal and extraction of time-domain features from a set of Intrinsic Mode Functions. The extracted features are fed to random forest and linear discriminant analysis models for attaining the classification of nine different severity conditions. The method provides classification accuracies higher than 90% with both machine learning models.

AB - Fault severity classification is a critical task necessary for optimal predictive maintenance to reduce costs and avoid catastrophic accidents in the industry. In this research, we propose a methodology for broken tooth severity classification in a gearbox using digital signal processing techniques of acoustic emission signals. The method uses empirical mode decomposition of the signal and extraction of time-domain features from a set of Intrinsic Mode Functions. The extracted features are fed to random forest and linear discriminant analysis models for attaining the classification of nine different severity conditions. The method provides classification accuracies higher than 90% with both machine learning models.

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KW - Broken tooth severity

KW - Empirical Mode Decomposition

KW - Gearbox

KW - Linear Discriminant Analysis

KW - Random Forest

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BT - 6th IEEE Ecuador Technical Chapters Meeting, ETCM 2022

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Medina R, Sánchez RV , Cabrera D, Ortega LR, Cerrada M. Gearbox Broken Tooth Severity Classification using EMD of Acoustic Emission Signals. In Lalaleo DR, Huerta MK, editors, 6th IEEE Ecuador Technical Chapters Meeting, ETCM 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (6th IEEE Ecuador Technical Chapters Meeting, ETCM 2022). doi: 10.1109/ETCM56276.2022.9935765

Gearbox Broken Tooth Severity Classification using EMD of Acoustic Emission Signals

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