Flux-based Detection and Classification of Induction Motor Eccentricity, Rotor Cage, and Load Defects

Jaehoon Shin; Yonghyun Park; Sang Bin Lee

doi:10.1109/ECCE44975.2020.9235866

Flux-based Detection and Classification of Induction Motor Eccentricity, Rotor Cage, and Load Defects

Jaehoon Shin, Yonghyun Park, Sang Bin Lee

School of Electrical Engineering

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

Abstract

Motor current signature analysis (MCSA) has been accepted in the field as a reliable means of detecting rotor cage faults in induction motors. However, there are many limitations to MCSA-based detection for other types of faults including rotor eccentricity and load defects. Recently, there has been increasing interest in airgap or leakage flux monitoring as a low cost alternative to replace or complement MCSA. Flux monitoring can provide a reliable means of detecting rotor faults since anomalies in the rotor magneto-motive force or airgap can be directly measured. In this paper, a new method based on monitoring the attenuation of rotor rotational frequency sidebands in the flux spectrum is proposed for reliable detection and classification of rotor cage and eccentricity faults. It is also shown that flux monitoring can provide reliable detection of rotor faults independent of load defects. An experimental study under controlled broken bar, eccentricity, misalignment, and load unbalance conditions are given to support the claims. The test results show that rotor cage, eccentricity, and load defects can be detected and distinguished for cases where MCSA alone is ineffective. A comparative evaluation between conventional and proposed flux monitoring methods is also given.

Original language	English
Title of host publication	ECCE 2020 - IEEE Energy Conversion Congress and Exposition
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4491-4498
Number of pages	8
ISBN (Electronic)	9781728158266
DOIs	https://doi.org/10.1109/ECCE44975.2020.9235866
Publication status	Published - 2020 Oct 11
Event	12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020 - Virtual, Detroit, United States Duration: 2020 Oct 11 → 2020 Oct 15

Publication series

Name	ECCE 2020 - IEEE Energy Conversion Congress and Exposition

Conference

Conference	12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020
Country	United States
City	Virtual, Detroit
Period	20/10/11 → 20/10/15

Keywords

Airgap Flux
Eccentricity
Fault Diagnostics
Leakage Flux
Load Unbalance
Misalignment
Search Coil
Spectral Analysis
Squirrel Cage Induction Motor
Vibration

ASJC Scopus subject areas

Electrical and Electronic Engineering
Mechanical Engineering
Control and Optimization
Energy Engineering and Power Technology

Access to Document

10.1109/ECCE44975.2020.9235866

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Shin, J., Park, Y., & Lee, S. B. (2020). Flux-based Detection and Classification of Induction Motor Eccentricity, Rotor Cage, and Load Defects. In ECCE 2020 - IEEE Energy Conversion Congress and Exposition (pp. 4491-4498). [9235866] (ECCE 2020 - IEEE Energy Conversion Congress and Exposition). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ECCE44975.2020.9235866

Flux-based Detection and Classification of Induction Motor Eccentricity, Rotor Cage, and Load Defects. / Shin, Jaehoon; Park, Yonghyun; Lee, Sang Bin.

ECCE 2020 - IEEE Energy Conversion Congress and Exposition. Institute of Electrical and Electronics Engineers Inc., 2020. p. 4491-4498 9235866 (ECCE 2020 - IEEE Energy Conversion Congress and Exposition).

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

Shin, J, Park, Y & Lee, SB 2020, Flux-based Detection and Classification of Induction Motor Eccentricity, Rotor Cage, and Load Defects. in ECCE 2020 - IEEE Energy Conversion Congress and Exposition., 9235866, ECCE 2020 - IEEE Energy Conversion Congress and Exposition, Institute of Electrical and Electronics Engineers Inc., pp. 4491-4498, 12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020, Virtual, Detroit, United States, 20/10/11. https://doi.org/10.1109/ECCE44975.2020.9235866

Shin J, Park Y, Lee SB. Flux-based Detection and Classification of Induction Motor Eccentricity, Rotor Cage, and Load Defects. In ECCE 2020 - IEEE Energy Conversion Congress and Exposition. Institute of Electrical and Electronics Engineers Inc. 2020. p. 4491-4498. 9235866. (ECCE 2020 - IEEE Energy Conversion Congress and Exposition). https://doi.org/10.1109/ECCE44975.2020.9235866

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title = "Flux-based Detection and Classification of Induction Motor Eccentricity, Rotor Cage, and Load Defects",

abstract = "Motor current signature analysis (MCSA) has been accepted in the field as a reliable means of detecting rotor cage faults in induction motors. However, there are many limitations to MCSA-based detection for other types of faults including rotor eccentricity and load defects. Recently, there has been increasing interest in airgap or leakage flux monitoring as a low cost alternative to replace or complement MCSA. Flux monitoring can provide a reliable means of detecting rotor faults since anomalies in the rotor magneto-motive force or airgap can be directly measured. In this paper, a new method based on monitoring the attenuation of rotor rotational frequency sidebands in the flux spectrum is proposed for reliable detection and classification of rotor cage and eccentricity faults. It is also shown that flux monitoring can provide reliable detection of rotor faults independent of load defects. An experimental study under controlled broken bar, eccentricity, misalignment, and load unbalance conditions are given to support the claims. The test results show that rotor cage, eccentricity, and load defects can be detected and distinguished for cases where MCSA alone is ineffective. A comparative evaluation between conventional and proposed flux monitoring methods is also given.",

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N2 - Motor current signature analysis (MCSA) has been accepted in the field as a reliable means of detecting rotor cage faults in induction motors. However, there are many limitations to MCSA-based detection for other types of faults including rotor eccentricity and load defects. Recently, there has been increasing interest in airgap or leakage flux monitoring as a low cost alternative to replace or complement MCSA. Flux monitoring can provide a reliable means of detecting rotor faults since anomalies in the rotor magneto-motive force or airgap can be directly measured. In this paper, a new method based on monitoring the attenuation of rotor rotational frequency sidebands in the flux spectrum is proposed for reliable detection and classification of rotor cage and eccentricity faults. It is also shown that flux monitoring can provide reliable detection of rotor faults independent of load defects. An experimental study under controlled broken bar, eccentricity, misalignment, and load unbalance conditions are given to support the claims. The test results show that rotor cage, eccentricity, and load defects can be detected and distinguished for cases where MCSA alone is ineffective. A comparative evaluation between conventional and proposed flux monitoring methods is also given.

AB - Motor current signature analysis (MCSA) has been accepted in the field as a reliable means of detecting rotor cage faults in induction motors. However, there are many limitations to MCSA-based detection for other types of faults including rotor eccentricity and load defects. Recently, there has been increasing interest in airgap or leakage flux monitoring as a low cost alternative to replace or complement MCSA. Flux monitoring can provide a reliable means of detecting rotor faults since anomalies in the rotor magneto-motive force or airgap can be directly measured. In this paper, a new method based on monitoring the attenuation of rotor rotational frequency sidebands in the flux spectrum is proposed for reliable detection and classification of rotor cage and eccentricity faults. It is also shown that flux monitoring can provide reliable detection of rotor faults independent of load defects. An experimental study under controlled broken bar, eccentricity, misalignment, and load unbalance conditions are given to support the claims. The test results show that rotor cage, eccentricity, and load defects can be detected and distinguished for cases where MCSA alone is ineffective. A comparative evaluation between conventional and proposed flux monitoring methods is also given.

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Flux-based Detection and Classification of Induction Motor Eccentricity, Rotor Cage, and Load Defects

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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