Bond Graph Modelling for Condition Monitoring of Induction Motors

Aisha Alashter, Yunpeng Cao, Khalid Rabeyee, Samir Alabied, Fengshou Gu, Andrew Ball

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The complication of existing electromechanical systems and the increased demands on their operational performances of efficiency and reliability motivate the need for monitoring and fault diagnosis of these systems. Motor Current Analysis (MCA) is a cost-effective technique for the detection of motor faults. To our knowledge, MCA has not been used with bond graph (BG) modeling for developing accurate diagnostic information. In this paper, a BG model is developed for fault detection of AC Induction Motors (ACIM) based on motor current analysis. BG is a single language for unified domains, which allows the dynamics of electrical and mechanical effects to be modeled directly. In the proposed model the physical components of the electro-mechanical system are constructed by including three different levels of modeling, conceptual behavior, cause and effect relations, and numerical model. This BG model was examined based on the behavior of the ACIM and confirmed the high efficiency of BG based approach in achieving diagnostics of different fault cases. In particular, the focus is on the impact of both the broken rotor bars (BRB) and stator short circuit (SSC) that commonly occur in ACIM. The simulation results indicate that the proposed BG approach is an effective method for extracting diagnostic information based on current analysis. The relationship between the sideband components and the system behavior can be used as an indicator to distinguish between healthy condition, BRB and SSC. The results were evaluated using experiments data. Faults in ACIM are investigated actively.
Original languageEnglish
Title of host publicationProceedings of COMADEM
Subtitle of host publication32nd International Congress and Exhibition on Condition Monitoring and Diagnostic Engineering Management: COMADEM 2019
PublisherCOMADEM International
Publication statusAccepted/In press - 24 Jun 2019
Event32nd International Congress and Exhibition on Conditioning Monitoring and Diagnostic Engineering Management Conference - University of Huddersfield, Huddersfield, United Kingdom
Duration: 3 Sep 20195 Sep 2019
Conference number: 32
http://www.comadem2019.com/ (Link to Conference Website)

Conference

Conference32nd International Congress and Exhibition on Conditioning Monitoring and Diagnostic Engineering Management Conference
Abbreviated titleCOMADEM 2019
CountryUnited Kingdom
CityHuddersfield
Period3/09/195/09/19
Internet address

Fingerprint

AC motors
Condition monitoring
Induction motors
Short circuit currents
Stators
Rotors
Fault detection
Failure analysis
Numerical models
Monitoring
Costs
Experiments

Cite this

Alashter, A., Cao, Y., Rabeyee, K., Alabied, S., Gu, F., & Ball, A. (Accepted/In press). Bond Graph Modelling for Condition Monitoring of Induction Motors. In Proceedings of COMADEM: 32nd International Congress and Exhibition on Condition Monitoring and Diagnostic Engineering Management: COMADEM 2019 COMADEM International.
Alashter, Aisha ; Cao, Yunpeng ; Rabeyee, Khalid ; Alabied, Samir ; Gu, Fengshou ; Ball, Andrew. / Bond Graph Modelling for Condition Monitoring of Induction Motors. Proceedings of COMADEM: 32nd International Congress and Exhibition on Condition Monitoring and Diagnostic Engineering Management: COMADEM 2019. COMADEM International, 2019.
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title = "Bond Graph Modelling for Condition Monitoring of Induction Motors",
abstract = "The complication of existing electromechanical systems and the increased demands on their operational performances of efficiency and reliability motivate the need for monitoring and fault diagnosis of these systems. Motor Current Analysis (MCA) is a cost-effective technique for the detection of motor faults. To our knowledge, MCA has not been used with bond graph (BG) modeling for developing accurate diagnostic information. In this paper, a BG model is developed for fault detection of AC Induction Motors (ACIM) based on motor current analysis. BG is a single language for unified domains, which allows the dynamics of electrical and mechanical effects to be modeled directly. In the proposed model the physical components of the electro-mechanical system are constructed by including three different levels of modeling, conceptual behavior, cause and effect relations, and numerical model. This BG model was examined based on the behavior of the ACIM and confirmed the high efficiency of BG based approach in achieving diagnostics of different fault cases. In particular, the focus is on the impact of both the broken rotor bars (BRB) and stator short circuit (SSC) that commonly occur in ACIM. The simulation results indicate that the proposed BG approach is an effective method for extracting diagnostic information based on current analysis. The relationship between the sideband components and the system behavior can be used as an indicator to distinguish between healthy condition, BRB and SSC. The results were evaluated using experiments data. Faults in ACIM are investigated actively.",
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Alashter, A, Cao, Y, Rabeyee, K, Alabied, S, Gu, F & Ball, A 2019, Bond Graph Modelling for Condition Monitoring of Induction Motors. in Proceedings of COMADEM: 32nd International Congress and Exhibition on Condition Monitoring and Diagnostic Engineering Management: COMADEM 2019. COMADEM International, 32nd International Congress and Exhibition on Conditioning Monitoring and Diagnostic Engineering Management Conference, Huddersfield, United Kingdom, 3/09/19.

Bond Graph Modelling for Condition Monitoring of Induction Motors. / Alashter, Aisha; Cao, Yunpeng; Rabeyee, Khalid; Alabied, Samir; Gu, Fengshou; Ball, Andrew.

Proceedings of COMADEM: 32nd International Congress and Exhibition on Condition Monitoring and Diagnostic Engineering Management: COMADEM 2019. COMADEM International, 2019.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Bond Graph Modelling for Condition Monitoring of Induction Motors

AU - Alashter, Aisha

AU - Cao, Yunpeng

AU - Rabeyee, Khalid

AU - Alabied, Samir

AU - Gu, Fengshou

AU - Ball, Andrew

PY - 2019/6/24

Y1 - 2019/6/24

N2 - The complication of existing electromechanical systems and the increased demands on their operational performances of efficiency and reliability motivate the need for monitoring and fault diagnosis of these systems. Motor Current Analysis (MCA) is a cost-effective technique for the detection of motor faults. To our knowledge, MCA has not been used with bond graph (BG) modeling for developing accurate diagnostic information. In this paper, a BG model is developed for fault detection of AC Induction Motors (ACIM) based on motor current analysis. BG is a single language for unified domains, which allows the dynamics of electrical and mechanical effects to be modeled directly. In the proposed model the physical components of the electro-mechanical system are constructed by including three different levels of modeling, conceptual behavior, cause and effect relations, and numerical model. This BG model was examined based on the behavior of the ACIM and confirmed the high efficiency of BG based approach in achieving diagnostics of different fault cases. In particular, the focus is on the impact of both the broken rotor bars (BRB) and stator short circuit (SSC) that commonly occur in ACIM. The simulation results indicate that the proposed BG approach is an effective method for extracting diagnostic information based on current analysis. The relationship between the sideband components and the system behavior can be used as an indicator to distinguish between healthy condition, BRB and SSC. The results were evaluated using experiments data. Faults in ACIM are investigated actively.

AB - The complication of existing electromechanical systems and the increased demands on their operational performances of efficiency and reliability motivate the need for monitoring and fault diagnosis of these systems. Motor Current Analysis (MCA) is a cost-effective technique for the detection of motor faults. To our knowledge, MCA has not been used with bond graph (BG) modeling for developing accurate diagnostic information. In this paper, a BG model is developed for fault detection of AC Induction Motors (ACIM) based on motor current analysis. BG is a single language for unified domains, which allows the dynamics of electrical and mechanical effects to be modeled directly. In the proposed model the physical components of the electro-mechanical system are constructed by including three different levels of modeling, conceptual behavior, cause and effect relations, and numerical model. This BG model was examined based on the behavior of the ACIM and confirmed the high efficiency of BG based approach in achieving diagnostics of different fault cases. In particular, the focus is on the impact of both the broken rotor bars (BRB) and stator short circuit (SSC) that commonly occur in ACIM. The simulation results indicate that the proposed BG approach is an effective method for extracting diagnostic information based on current analysis. The relationship between the sideband components and the system behavior can be used as an indicator to distinguish between healthy condition, BRB and SSC. The results were evaluated using experiments data. Faults in ACIM are investigated actively.

KW - Bond graph

KW - Induction

KW - Motor

KW - Fault diagnosis

KW - Condition monitoring

UR - http://www.comadem2019.com/

M3 - Conference contribution

BT - Proceedings of COMADEM

PB - COMADEM International

ER -

Alashter A, Cao Y, Rabeyee K, Alabied S, Gu F, Ball A. Bond Graph Modelling for Condition Monitoring of Induction Motors. In Proceedings of COMADEM: 32nd International Congress and Exhibition on Condition Monitoring and Diagnostic Engineering Management: COMADEM 2019. COMADEM International. 2019