Motor bearing failures detection by using vibration data

Jose Ignacio Rodríguez-Rodríguez; Oscar Núñez-Mata; Gustavo Gómez-Ramírez

doi:10.1109/CONCAPAN48024.2022.9997595

Motor bearing failures detection by using vibration data

Jose Ignacio Rodríguez-Rodríguez
, Oscar Núñez-Mata
, Gustavo Gómez-Ramírez

Escuela de Ingeniería Electromecánica

University of Costa Rica

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

5 Scopus citations

Abstract

The use of methodologies for condition monitoring of rotating machines has been growing to reduce unplanned downtime and to increase the reliability of the industrial processes. The companies must select a correct maintenance strategy to follow the evolution of rotating machines. Condition monitoring is the collection of data related to the health status of the machine and it has been widely studied so far. Different methodologies have been developed to identify specific behaviors in the condition of induction motors. This paper proposes a methodology for bearing failure detection by using vibrations data, based on the frequency spectrum applied to induction motors. This methodology allows the use of vibration data obtained from motor bearings to establish their condition and therefore determine the type of damage to the bearing. The effectiveness of the proposed methodology is validated using a data set obtained from NASA (National Aeronautics and Space Administration). The results showed that this type of approach is very useful for analyzing bearings and in this way creating maintenance routes based on the condition of the electric machines.

Original language	English
Title of host publication	Proceedings of the 2022 IEEE 40th Central America and Panama Convention, CONCAPAN 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728167152
DOIs	https://doi.org/10.1109/CONCAPAN48024.2022.9997595
State	Published - 2022
Event	40th IEEE Central America and Panama Convention, CONCAPAN 2022 - Panama City, Panama Duration: 9 Nov 2022 → 12 Nov 2022

Publication series

Name	Proceedings of the 2022 IEEE 40th Central America and Panama Convention, CONCAPAN 2022

Conference

Conference	40th IEEE Central America and Panama Convention, CONCAPAN 2022
Country/Territory	Panama
City	Panama City
Period	9/11/22 → 12/11/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

AC motors
ball bearings
condition monitoring
failure analysis
vibrations

Access to Document

10.1109/CONCAPAN48024.2022.9997595

Cite this

Rodríguez-Rodríguez, J. I., Núñez-Mata, O., & Gómez-Ramírez, G. (2022). Motor bearing failures detection by using vibration data. In Proceedings of the 2022 IEEE 40th Central America and Panama Convention, CONCAPAN 2022 (Proceedings of the 2022 IEEE 40th Central America and Panama Convention, CONCAPAN 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CONCAPAN48024.2022.9997595

Rodríguez-Rodríguez, Jose Ignacio ; Núñez-Mata, Oscar ; Gómez-Ramírez, Gustavo. / Motor bearing failures detection by using vibration data. Proceedings of the 2022 IEEE 40th Central America and Panama Convention, CONCAPAN 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (Proceedings of the 2022 IEEE 40th Central America and Panama Convention, CONCAPAN 2022).

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title = "Motor bearing failures detection by using vibration data",

abstract = "The use of methodologies for condition monitoring of rotating machines has been growing to reduce unplanned downtime and to increase the reliability of the industrial processes. The companies must select a correct maintenance strategy to follow the evolution of rotating machines. Condition monitoring is the collection of data related to the health status of the machine and it has been widely studied so far. Different methodologies have been developed to identify specific behaviors in the condition of induction motors. This paper proposes a methodology for bearing failure detection by using vibrations data, based on the frequency spectrum applied to induction motors. This methodology allows the use of vibration data obtained from motor bearings to establish their condition and therefore determine the type of damage to the bearing. The effectiveness of the proposed methodology is validated using a data set obtained from NASA (National Aeronautics and Space Administration). The results showed that this type of approach is very useful for analyzing bearings and in this way creating maintenance routes based on the condition of the electric machines.",

keywords = "AC motors, ball bearings, condition monitoring, failure analysis, vibrations",

author = "Rodr{\'i}guez-Rodr{\'i}guez, \{Jose Ignacio\} and Oscar N{\'u}{\~n}ez-Mata and Gustavo G{\'o}mez-Ram{\'i}rez",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 40th IEEE Central America and Panama Convention, CONCAPAN 2022 ; Conference date: 09-11-2022 Through 12-11-2022",

year = "2022",

doi = "10.1109/CONCAPAN48024.2022.9997595",

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Rodríguez-Rodríguez, JI, Núñez-Mata, O & Gómez-Ramírez, G 2022, Motor bearing failures detection by using vibration data. in Proceedings of the 2022 IEEE 40th Central America and Panama Convention, CONCAPAN 2022. Proceedings of the 2022 IEEE 40th Central America and Panama Convention, CONCAPAN 2022, Institute of Electrical and Electronics Engineers Inc., 40th IEEE Central America and Panama Convention, CONCAPAN 2022, Panama City, Panama, 9/11/22. https://doi.org/10.1109/CONCAPAN48024.2022.9997595

Motor bearing failures detection by using vibration data. / Rodríguez-Rodríguez, Jose Ignacio; Núñez-Mata, Oscar; Gómez-Ramírez, Gustavo.
Proceedings of the 2022 IEEE 40th Central America and Panama Convention, CONCAPAN 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (Proceedings of the 2022 IEEE 40th Central America and Panama Convention, CONCAPAN 2022).

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

TY - GEN

T1 - Motor bearing failures detection by using vibration data

AU - Rodríguez-Rodríguez, Jose Ignacio

AU - Núñez-Mata, Oscar

AU - Gómez-Ramírez, Gustavo

PY - 2022

Y1 - 2022

N2 - The use of methodologies for condition monitoring of rotating machines has been growing to reduce unplanned downtime and to increase the reliability of the industrial processes. The companies must select a correct maintenance strategy to follow the evolution of rotating machines. Condition monitoring is the collection of data related to the health status of the machine and it has been widely studied so far. Different methodologies have been developed to identify specific behaviors in the condition of induction motors. This paper proposes a methodology for bearing failure detection by using vibrations data, based on the frequency spectrum applied to induction motors. This methodology allows the use of vibration data obtained from motor bearings to establish their condition and therefore determine the type of damage to the bearing. The effectiveness of the proposed methodology is validated using a data set obtained from NASA (National Aeronautics and Space Administration). The results showed that this type of approach is very useful for analyzing bearings and in this way creating maintenance routes based on the condition of the electric machines.

AB - The use of methodologies for condition monitoring of rotating machines has been growing to reduce unplanned downtime and to increase the reliability of the industrial processes. The companies must select a correct maintenance strategy to follow the evolution of rotating machines. Condition monitoring is the collection of data related to the health status of the machine and it has been widely studied so far. Different methodologies have been developed to identify specific behaviors in the condition of induction motors. This paper proposes a methodology for bearing failure detection by using vibrations data, based on the frequency spectrum applied to induction motors. This methodology allows the use of vibration data obtained from motor bearings to establish their condition and therefore determine the type of damage to the bearing. The effectiveness of the proposed methodology is validated using a data set obtained from NASA (National Aeronautics and Space Administration). The results showed that this type of approach is very useful for analyzing bearings and in this way creating maintenance routes based on the condition of the electric machines.

KW - AC motors

KW - ball bearings

KW - condition monitoring

KW - failure analysis

KW - vibrations

UR - https://www.scopus.com/pages/publications/85146582872

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DO - 10.1109/CONCAPAN48024.2022.9997595

M3 - Contribución a la conferencia

AN - SCOPUS:85146582872

T3 - Proceedings of the 2022 IEEE 40th Central America and Panama Convention, CONCAPAN 2022

BT - Proceedings of the 2022 IEEE 40th Central America and Panama Convention, CONCAPAN 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 40th IEEE Central America and Panama Convention, CONCAPAN 2022

Y2 - 9 November 2022 through 12 November 2022

ER -

Rodríguez-Rodríguez JI, Núñez-Mata O, Gómez-Ramírez G. Motor bearing failures detection by using vibration data. In Proceedings of the 2022 IEEE 40th Central America and Panama Convention, CONCAPAN 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (Proceedings of the 2022 IEEE 40th Central America and Panama Convention, CONCAPAN 2022). doi: 10.1109/CONCAPAN48024.2022.9997595

Motor bearing failures detection by using vibration data

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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