Improved Detection of Broken Rotor Bars by 1-D Self-ONNs

Levent Eren; Ozer Can Devecioglu; Turker Ince; Murat Askar

doi:10.1109/IECON49645.2022.9968348

Improved Detection of Broken Rotor Bars by 1-D Self-ONNs

Levent Eren
, Ozer Can Devecioglu
, Turker Ince
, Murat Askar

Computing Sciences

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

1 Citation (Scopus)

Abstract

Recently, machine learning techniques have been increasingly applied to the detection of both mechanical and electrical faults in induction motors. Broken rotor bars are one of the most common fault types that seriously affect the efficiency and lifetime of induction motors. In this study, compact 1-D self-organized operational neural networks (Self-ONNs) are applied to improve the detection and classification of broken rotor bars in induction motors. 1-D convolutional neural networks (CNNs) are a special case of Self-ONNs and they are usually preferred to traditional fault diagnosis systems with separately designed feature extraction and classification blocks as they provide cost-effective and practical hardware implementation. The proposed system improves the detection and classification performance of 1-D CNNs while still providing similar advantages and preserving real-time computational ability.

Original language	English
Title of host publication	IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society
Publisher	IEEE
ISBN (Electronic)	9781665480253
DOIs	https://doi.org/10.1109/IECON49645.2022.9968348
Publication status	Published - 2022
Publication type	A4 Article in conference proceedings
Event	Annual Conference of the IEEE Industrial Electronics Society - Brussels , Belgium Duration: 17 Oct 2022 → 20 Oct 2022 https://iecon2022.org/

Publication series

Name	Proceedings of the Annual Conference of the IEEE Industrial Electronics Society
Volume	2022-October
ISSN (Electronic)	2577-1647

Conference

Conference	Annual Conference of the IEEE Industrial Electronics Society
Abbreviated title	IECON 2022
Country/Territory	Belgium
City	Brussels
Period	17/10/22 → 20/10/22
Internet address	https://iecon2022.org/

Keywords

Broken rotor bar detection
induction motors
operational neural networks

Publication forum classification

Publication forum level 1

ASJC Scopus subject areas

Control and Systems Engineering
Electrical and Electronic Engineering

Access to Document

10.1109/IECON49645.2022.9968348

Cite this

@inproceedings{bb3b9d70c2624797bec87b87a0ebe32d,

title = "Improved Detection of Broken Rotor Bars by 1-D Self-ONNs",

abstract = "Recently, machine learning techniques have been increasingly applied to the detection of both mechanical and electrical faults in induction motors. Broken rotor bars are one of the most common fault types that seriously affect the efficiency and lifetime of induction motors. In this study, compact 1-D self-organized operational neural networks (Self-ONNs) are applied to improve the detection and classification of broken rotor bars in induction motors. 1-D convolutional neural networks (CNNs) are a special case of Self-ONNs and they are usually preferred to traditional fault diagnosis systems with separately designed feature extraction and classification blocks as they provide cost-effective and practical hardware implementation. The proposed system improves the detection and classification performance of 1-D CNNs while still providing similar advantages and preserving real-time computational ability.",

keywords = "Broken rotor bar detection, induction motors, operational neural networks",

author = "Levent Eren and Devecioglu, \{Ozer Can\} and Turker Ince and Murat Askar",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE. jufoid=57361; Annual Conference of the IEEE Industrial Electronics Society, IECON 2022 ; Conference date: 17-10-2022 Through 20-10-2022",

year = "2022",

doi = "10.1109/IECON49645.2022.9968348",

language = "English",

series = "Proceedings of the Annual Conference of the IEEE Industrial Electronics Society",

publisher = "IEEE",

booktitle = "IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society",

address = "United States",

url = "https://iecon2022.org/",

}

Eren, L, Devecioglu, OC, Ince, T & Askar, M 2022, Improved Detection of Broken Rotor Bars by 1-D Self-ONNs. in IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society. Proceedings of the Annual Conference of the IEEE Industrial Electronics Society, vol. 2022-October, IEEE, Annual Conference of the IEEE Industrial Electronics Society, Brussels , Belgium, 17/10/22. https://doi.org/10.1109/IECON49645.2022.9968348

Improved Detection of Broken Rotor Bars by 1-D Self-ONNs. / Eren, Levent; Devecioglu, Ozer Can; Ince, Turker et al.
IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2022. (Proceedings of the Annual Conference of the IEEE Industrial Electronics Society; Vol. 2022-October).

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

TY - GEN

T1 - Improved Detection of Broken Rotor Bars by 1-D Self-ONNs

AU - Eren, Levent

AU - Devecioglu, Ozer Can

AU - Ince, Turker

AU - Askar, Murat

PY - 2022

Y1 - 2022

N2 - Recently, machine learning techniques have been increasingly applied to the detection of both mechanical and electrical faults in induction motors. Broken rotor bars are one of the most common fault types that seriously affect the efficiency and lifetime of induction motors. In this study, compact 1-D self-organized operational neural networks (Self-ONNs) are applied to improve the detection and classification of broken rotor bars in induction motors. 1-D convolutional neural networks (CNNs) are a special case of Self-ONNs and they are usually preferred to traditional fault diagnosis systems with separately designed feature extraction and classification blocks as they provide cost-effective and practical hardware implementation. The proposed system improves the detection and classification performance of 1-D CNNs while still providing similar advantages and preserving real-time computational ability.

AB - Recently, machine learning techniques have been increasingly applied to the detection of both mechanical and electrical faults in induction motors. Broken rotor bars are one of the most common fault types that seriously affect the efficiency and lifetime of induction motors. In this study, compact 1-D self-organized operational neural networks (Self-ONNs) are applied to improve the detection and classification of broken rotor bars in induction motors. 1-D convolutional neural networks (CNNs) are a special case of Self-ONNs and they are usually preferred to traditional fault diagnosis systems with separately designed feature extraction and classification blocks as they provide cost-effective and practical hardware implementation. The proposed system improves the detection and classification performance of 1-D CNNs while still providing similar advantages and preserving real-time computational ability.

KW - Broken rotor bar detection

KW - induction motors

KW - operational neural networks

U2 - 10.1109/IECON49645.2022.9968348

DO - 10.1109/IECON49645.2022.9968348

M3 - Conference contribution

AN - SCOPUS:85143895948

T3 - Proceedings of the Annual Conference of the IEEE Industrial Electronics Society

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

PB - IEEE

T2 - Annual Conference of the IEEE Industrial Electronics Society

Y2 - 17 October 2022 through 20 October 2022

ER -

Improved Detection of Broken Rotor Bars by 1-D Self-ONNs

Abstract

Publication series

Conference

Keywords

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ASJC Scopus subject areas

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Cite this