Classification of Mechanical Faults in Rotating Machines Using SMOTE Method and Deep Neural Networks

Maher Messaoudi; Shady S. Refaat; Mohamed Massaoudi; Ali Ghrayeb; Haitham Abu-Rub

doi:10.1109/IECON49645.2022.9968875

Classification of Mechanical Faults in Rotating Machines Using SMOTE Method and Deep Neural Networks

Maher Messaoudi, Shady S. Refaat, Mohamed Massaoudi, Ali Ghrayeb, Haitham Abu-Rub

School of Physics, Engineering & Computer Science

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

Abstract

Condition monitoring of electrical Rotating Machines (RM) serves in structural changes detection during machine's operation. However, the frequent fault occurrence reduces the RM remaining useful life and accelerates their deterioration. Therefore, this paper proposes an effective multi-fault classification system for the faults in electric rotating machines. The proposed method employs an Artificial Neural Network (ANN) and Synthetic Minority Over-sampling (SMOTE) technique for automatically detecting rotating machines failures. This model's efficacy stems from the use of the relief feature selection approach to identify the most affecting features and improve the model's performance. A case study analysis uses the Machinery Fault Dataset (MAFAULDA) to test the models' performance. Simulation results are obtained to demonstrate that the proposed paradigm provides outstanding performance based on a fair assessment using the MAFAULDA dataset and shows that the proposed model has a high potential to detect rotating machine state.

Original language	English
Title of host publication	IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)	9781665480253
DOIs	https://doi.org/10.1109/IECON49645.2022.9968875
Publication status	Published - 2022
Event	48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022 - Brussels, Belgium Duration: 17 Oct 2022 → 20 Oct 2022

Publication series

Name	IECON Proceedings (Industrial Electronics Conference)
Volume	2022-October

Conference

Conference	48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022
Country/Territory	Belgium
City	Brussels
Period	17/10/22 → 20/10/22

Keywords

Data pre-processing
machine learning
machinery fault dataset
neural network
rotating machine faults
synthetic minority over-sampling technique

Access to Document

10.1109/IECON49645.2022.9968875

Cite this

Messaoudi, M., Refaat, S. S., Massaoudi, M., Ghrayeb, A., & Abu-Rub, H. (2022). Classification of Mechanical Faults in Rotating Machines Using SMOTE Method and Deep Neural Networks. In IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society (IECON Proceedings (Industrial Electronics Conference); Vol. 2022-October). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/IECON49645.2022.9968875

@inproceedings{4afdefc43e2e4b3680cba9a433543836,

title = "Classification of Mechanical Faults in Rotating Machines Using SMOTE Method and Deep Neural Networks",

abstract = "Condition monitoring of electrical Rotating Machines (RM) serves in structural changes detection during machine's operation. However, the frequent fault occurrence reduces the RM remaining useful life and accelerates their deterioration. Therefore, this paper proposes an effective multi-fault classification system for the faults in electric rotating machines. The proposed method employs an Artificial Neural Network (ANN) and Synthetic Minority Over-sampling (SMOTE) technique for automatically detecting rotating machines failures. This model's efficacy stems from the use of the relief feature selection approach to identify the most affecting features and improve the model's performance. A case study analysis uses the Machinery Fault Dataset (MAFAULDA) to test the models' performance. Simulation results are obtained to demonstrate that the proposed paradigm provides outstanding performance based on a fair assessment using the MAFAULDA dataset and shows that the proposed model has a high potential to detect rotating machine state.",

keywords = "Data pre-processing, machine learning, machinery fault dataset, neural network, rotating machine faults, synthetic minority over-sampling technique",

author = "Maher Messaoudi and Refaat, {Shady S.} and Mohamed Massaoudi and Ali Ghrayeb and Haitham Abu-Rub",

note = "Funding Information: ACKNOWLEDGMENT This publication was made possible by NPRP grant [NPRP13S-0116-200085] from the Qatar National Research Fund (a member of Qatar Foundation). The statementsmadehereinaresolely the responsibility of the authors. Publisher Copyright: {\textcopyright} 2022 IEEE.; 48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022 ; Conference date: 17-10-2022 Through 20-10-2022",

year = "2022",

doi = "10.1109/IECON49645.2022.9968875",

language = "English",

series = "IECON Proceedings (Industrial Electronics Conference)",

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Messaoudi, M, Refaat, SS, Massaoudi, M, Ghrayeb, A & Abu-Rub, H 2022, Classification of Mechanical Faults in Rotating Machines Using SMOTE Method and Deep Neural Networks. in IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society. IECON Proceedings (Industrial Electronics Conference), vol. 2022-October, Institute of Electrical and Electronics Engineers (IEEE), 48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022, Brussels, Belgium, 17/10/22. https://doi.org/10.1109/IECON49645.2022.9968875

Classification of Mechanical Faults in Rotating Machines Using SMOTE Method and Deep Neural Networks. / Messaoudi, Maher; Refaat, Shady S.; Massaoudi, Mohamed et al.
IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society. Institute of Electrical and Electronics Engineers (IEEE), 2022. (IECON Proceedings (Industrial Electronics Conference); Vol. 2022-October).

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

TY - GEN

T1 - Classification of Mechanical Faults in Rotating Machines Using SMOTE Method and Deep Neural Networks

AU - Messaoudi, Maher

AU - Refaat, Shady S.

AU - Massaoudi, Mohamed

AU - Ghrayeb, Ali

AU - Abu-Rub, Haitham

N1 - Funding Information: ACKNOWLEDGMENT This publication was made possible by NPRP grant [NPRP13S-0116-200085] from the Qatar National Research Fund (a member of Qatar Foundation). The statementsmadehereinaresolely the responsibility of the authors. Publisher Copyright: © 2022 IEEE.

PY - 2022

Y1 - 2022

N2 - Condition monitoring of electrical Rotating Machines (RM) serves in structural changes detection during machine's operation. However, the frequent fault occurrence reduces the RM remaining useful life and accelerates their deterioration. Therefore, this paper proposes an effective multi-fault classification system for the faults in electric rotating machines. The proposed method employs an Artificial Neural Network (ANN) and Synthetic Minority Over-sampling (SMOTE) technique for automatically detecting rotating machines failures. This model's efficacy stems from the use of the relief feature selection approach to identify the most affecting features and improve the model's performance. A case study analysis uses the Machinery Fault Dataset (MAFAULDA) to test the models' performance. Simulation results are obtained to demonstrate that the proposed paradigm provides outstanding performance based on a fair assessment using the MAFAULDA dataset and shows that the proposed model has a high potential to detect rotating machine state.

AB - Condition monitoring of electrical Rotating Machines (RM) serves in structural changes detection during machine's operation. However, the frequent fault occurrence reduces the RM remaining useful life and accelerates their deterioration. Therefore, this paper proposes an effective multi-fault classification system for the faults in electric rotating machines. The proposed method employs an Artificial Neural Network (ANN) and Synthetic Minority Over-sampling (SMOTE) technique for automatically detecting rotating machines failures. This model's efficacy stems from the use of the relief feature selection approach to identify the most affecting features and improve the model's performance. A case study analysis uses the Machinery Fault Dataset (MAFAULDA) to test the models' performance. Simulation results are obtained to demonstrate that the proposed paradigm provides outstanding performance based on a fair assessment using the MAFAULDA dataset and shows that the proposed model has a high potential to detect rotating machine state.

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KW - machine learning

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KW - neural network

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KW - synthetic minority over-sampling technique

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M3 - Conference contribution

AN - SCOPUS:85143891484

T3 - IECON Proceedings (Industrial Electronics Conference)

BT - IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

Y2 - 17 October 2022 through 20 October 2022

ER -

Messaoudi M, Refaat SS, Massaoudi M, Ghrayeb A, Abu-Rub H. Classification of Mechanical Faults in Rotating Machines Using SMOTE Method and Deep Neural Networks. In IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society. Institute of Electrical and Electronics Engineers (IEEE). 2022. (IECON Proceedings (Industrial Electronics Conference)). doi: 10.1109/IECON49645.2022.9968875

Classification of Mechanical Faults in Rotating Machines Using SMOTE Method and Deep Neural Networks

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Keywords

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