Stator Resistance Imbalance Diagnosis Using Teager-Kaiser Energy Operator Based on Motor Current Signature Analysis

Haiyang Li; Huaqing Wang; Funso Otuyemi; Dong Zhen; Fengshou Gu; Andrew D. Ball

doi:10.1007/978-3-030-57745-2_78

Stator Resistance Imbalance Diagnosis Using Teager-Kaiser Energy Operator Based on Motor Current Signature Analysis

Haiyang Li, Huaqing Wang, Funso Otuyemi, Dong Zhen, Fengshou Gu, Andrew D. Ball

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

Abstract

Stator resistance imbalance fault is one of the most common faults happens in induction motors (IMs). Effective stator resistance imbalance detection plays significant roles in reducing maintenance costs and unscheduled downtimes. Stator resistance imbalance faults information can be detected through analysing the motor supply current signals. Various motor current signal processing methods have been investigated to extract the weak diagnostic feature from noisy current signals based on advanced signal processing analysis. In this paper, Teager-Kaiser energy operator (TKEO) is proposed to identify fault signatures based on motor current signal analysis (MCSA). TKEO is able to increase the signal-to-noise ratio and has the ability of demodulating FM as well AM signal for fault feature extraction. Experimental analysis results show that TKEO can successfully identify and extract the fault characteristic frequency for the condition monitoring and fault diagnosis of IMs operating under different operations based on MCSA.

Original language	English
Title of host publication	Advances in Asset Management and Condition Monitoring, COMADEM 2019
Editors	Andrew Ball, Len Gelman, B.K.N. Rao
Publisher	Springer, Cham
Pages	953-962
Number of pages	10
Volume	166
ISBN (Electronic)	9783030577452
ISBN (Print)	9783030577445
DOIs	https://doi.org/10.1007/978-3-030-57745-2_78
Publication status	Published - 28 Aug 2020
Event	32nd International Congress and Exhibition on Conditioning Monitoring and Diagnostic Engineering Management Conference - University of Huddersfield, Huddersfield, United Kingdom Duration: 3 Sep 2019 → 5 Sep 2019 Conference number: 32 http://www.comadem2019.com/ (Link to Conference Website)

Publication series

Name	Smart Innovation, Systems and Technologies
Publisher	Springer
Volume	166
ISSN (Print)	2190-3018
ISSN (Electronic)	2190-3026

Conference

Conference	32nd International Congress and Exhibition on Conditioning Monitoring and Diagnostic Engineering Management Conference
Abbreviated title	COMADEM 2019
Country/Territory	United Kingdom
City	Huddersfield
Period	3/09/19 → 5/09/19
Internet address	http://www.comadem2019.com/ (Link to Conference Website)

UN SDGs

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

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Li, H., Wang, H., Otuyemi, F., Zhen, D., Gu, F., & Ball, A. D. (2020). Stator Resistance Imbalance Diagnosis Using Teager-Kaiser Energy Operator Based on Motor Current Signature Analysis. In A. Ball, L. Gelman, & B. K. N. Rao (Eds.), Advances in Asset Management and Condition Monitoring, COMADEM 2019 (Vol. 166, pp. 953-962). (Smart Innovation, Systems and Technologies; Vol. 166). Springer, Cham. https://doi.org/10.1007/978-3-030-57745-2_78

Li, Haiyang ; Wang, Huaqing ; Otuyemi, Funso et al. / Stator Resistance Imbalance Diagnosis Using Teager-Kaiser Energy Operator Based on Motor Current Signature Analysis. Advances in Asset Management and Condition Monitoring, COMADEM 2019. editor / Andrew Ball ; Len Gelman ; B.K.N. Rao. Vol. 166 Springer, Cham, 2020. pp. 953-962 (Smart Innovation, Systems and Technologies).

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title = "Stator Resistance Imbalance Diagnosis Using Teager-Kaiser Energy Operator Based on Motor Current Signature Analysis",

abstract = "Stator resistance imbalance fault is one of the most common faults happens in induction motors (IMs). Effective stator resistance imbalance detection plays significant roles in reducing maintenance costs and unscheduled downtimes. Stator resistance imbalance faults information can be detected through analysing the motor supply current signals. Various motor current signal processing methods have been investigated to extract the weak diagnostic feature from noisy current signals based on advanced signal processing analysis. In this paper, Teager-Kaiser energy operator (TKEO) is proposed to identify fault signatures based on motor current signal analysis (MCSA). TKEO is able to increase the signal-to-noise ratio and has the ability of demodulating FM as well AM signal for fault feature extraction. Experimental analysis results show that TKEO can successfully identify and extract the fault characteristic frequency for the condition monitoring and fault diagnosis of IMs operating under different operations based on MCSA.",

keywords = "Induction motor, Motor current signal analysis, Stator resistance imbalance, Teager-Kaiser energy operator",

author = "Haiyang Li and Huaqing Wang and Funso Otuyemi and Dong Zhen and Fengshou Gu and Ball, {Andrew D.}",

year = "2020",

month = aug,

day = "28",

doi = "10.1007/978-3-030-57745-2_78",

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series = "Smart Innovation, Systems and Technologies",

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pages = "953--962",

editor = "Andrew Ball and Len Gelman and B.K.N. Rao",

booktitle = "Advances in Asset Management and Condition Monitoring, COMADEM 2019",

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note = "32nd International Congress and Exhibition on Conditioning Monitoring and Diagnostic Engineering Management Conference, COMADEM 2019 ; Conference date: 03-09-2019 Through 05-09-2019",

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Li, H, Wang, H, Otuyemi, F, Zhen, D, Gu, F & Ball, AD 2020, Stator Resistance Imbalance Diagnosis Using Teager-Kaiser Energy Operator Based on Motor Current Signature Analysis. in A Ball, L Gelman & BKN Rao (eds), Advances in Asset Management and Condition Monitoring, COMADEM 2019. vol. 166, Smart Innovation, Systems and Technologies, vol. 166, Springer, Cham, pp. 953-962, 32nd International Congress and Exhibition on Conditioning Monitoring and Diagnostic Engineering Management Conference, Huddersfield, United Kingdom, 3/09/19. https://doi.org/10.1007/978-3-030-57745-2_78

Stator Resistance Imbalance Diagnosis Using Teager-Kaiser Energy Operator Based on Motor Current Signature Analysis. / Li, Haiyang; Wang, Huaqing; Otuyemi, Funso et al.

Advances in Asset Management and Condition Monitoring, COMADEM 2019. ed. / Andrew Ball; Len Gelman; B.K.N. Rao. Vol. 166 Springer, Cham, 2020. p. 953-962 (Smart Innovation, Systems and Technologies; Vol. 166).

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

TY - GEN

T1 - Stator Resistance Imbalance Diagnosis Using Teager-Kaiser Energy Operator Based on Motor Current Signature Analysis

AU - Li, Haiyang

AU - Wang, Huaqing

AU - Otuyemi, Funso

AU - Zhen, Dong

AU - Gu, Fengshou

AU - Ball, Andrew D.

N1 - Conference code: 32

PY - 2020/8/28

Y1 - 2020/8/28

N2 - Stator resistance imbalance fault is one of the most common faults happens in induction motors (IMs). Effective stator resistance imbalance detection plays significant roles in reducing maintenance costs and unscheduled downtimes. Stator resistance imbalance faults information can be detected through analysing the motor supply current signals. Various motor current signal processing methods have been investigated to extract the weak diagnostic feature from noisy current signals based on advanced signal processing analysis. In this paper, Teager-Kaiser energy operator (TKEO) is proposed to identify fault signatures based on motor current signal analysis (MCSA). TKEO is able to increase the signal-to-noise ratio and has the ability of demodulating FM as well AM signal for fault feature extraction. Experimental analysis results show that TKEO can successfully identify and extract the fault characteristic frequency for the condition monitoring and fault diagnosis of IMs operating under different operations based on MCSA.

AB - Stator resistance imbalance fault is one of the most common faults happens in induction motors (IMs). Effective stator resistance imbalance detection plays significant roles in reducing maintenance costs and unscheduled downtimes. Stator resistance imbalance faults information can be detected through analysing the motor supply current signals. Various motor current signal processing methods have been investigated to extract the weak diagnostic feature from noisy current signals based on advanced signal processing analysis. In this paper, Teager-Kaiser energy operator (TKEO) is proposed to identify fault signatures based on motor current signal analysis (MCSA). TKEO is able to increase the signal-to-noise ratio and has the ability of demodulating FM as well AM signal for fault feature extraction. Experimental analysis results show that TKEO can successfully identify and extract the fault characteristic frequency for the condition monitoring and fault diagnosis of IMs operating under different operations based on MCSA.

KW - Induction motor

KW - Motor current signal analysis

KW - Stator resistance imbalance

KW - Teager-Kaiser energy operator

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U2 - 10.1007/978-3-030-57745-2_78

DO - 10.1007/978-3-030-57745-2_78

M3 - Conference contribution

AN - SCOPUS:85091316036

SN - 9783030577445

VL - 166

T3 - Smart Innovation, Systems and Technologies

SP - 953

EP - 962

BT - Advances in Asset Management and Condition Monitoring, COMADEM 2019

A2 - Ball, Andrew

A2 - Gelman, Len

A2 - Rao, B.K.N.

PB - Springer, Cham

T2 - 32nd International Congress and Exhibition on Conditioning Monitoring and Diagnostic Engineering Management Conference

Y2 - 3 September 2019 through 5 September 2019

ER -

Li H, Wang H, Otuyemi F, Zhen D, Gu F , Ball AD. Stator Resistance Imbalance Diagnosis Using Teager-Kaiser Energy Operator Based on Motor Current Signature Analysis. In Ball A, Gelman L, Rao BKN, editors, Advances in Asset Management and Condition Monitoring, COMADEM 2019. Vol. 166. Springer, Cham. 2020. p. 953-962. (Smart Innovation, Systems and Technologies). Epub 2020 Aug 28. doi: 10.1007/978-3-030-57745-2_78

Stator Resistance Imbalance Diagnosis Using Teager-Kaiser Energy Operator Based on Motor Current Signature Analysis

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