Broken Rotor Bar Detection Using Mathematical Morphology Based on Instantaneous Induction Motor Electrical Signals Analysis

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

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

Abstract

AC induction machines (IM) are the most popular motors in many manufacturing processes and industrial applications. They are widely applied in petrochemical, military, aerospace and nuclear plants and mining industry. Broken rotor bar (BRB) is one of the most frequent AC IM faults. Accurate and timely diagnosis of BRB can help to lower maintenance costs and prevent unscheduled downtimes. BRB faults will cause rotor resistance change and then affect stator supply current directly. Therefore, it is an effective way to detect BRB fault by analysing instantaneous electrical signature (IES). However, most of the analysis results achieved by the common methods are time consuming and cannot process geometric information for BRB fault diagnosis with high accuracy. Mathematical morphology (MM) has attracted considerable attention due to its advantages of extracting the geometric structure of the modulation feature with less computation. In this paper, a novel fault detector is developed for BRB fault detection using MM based motor IES analysis. A novel morphological gradient is improved to analyse the motor current signal for fault related modulation components enhancement. And then the max value in the low frequency range of the MM filtered signal is extracted as a fault detector for BRB diagnosis. The performance of the proposed method is evaluated by experimental study. The test motors are setup with different broken bar level and operating under different loads. The analysis results prove the efficiency and performance of the proposed method on the BRB faults diagnosis for IMs.

Original languageEnglish
Title of host publication2019 25th International Conference on Automation and Computing, ICAC 2019
Subtitle of host publicationImproving Productivity through Automation and Computing
EditorsHui Yu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781861376657
ISBN (Print)9781728125183
DOIs
Publication statusPublished - 11 Nov 2019
Event25th IEEE International Conference on Automation and Computing: Improving Productivity through Automation and Computing - Lancaster University, Lancaster, United Kingdom
Duration: 5 Sep 20197 Sep 2019
Conference number: 25
http://www.research.lancs.ac.uk/portal/en/activities/25th-ieee-international-conference-on-automation-and-computing-icac19-57-september-2019-lancaster-university-uk(679d94ff-4efb-46b5-9c80-c6d34a13bae4).html

Conference

Conference25th IEEE International Conference on Automation and Computing
Abbreviated titleICAC 2019
CountryUnited Kingdom
CityLancaster
Period5/09/197/09/19
Internet address

Fingerprint

Mathematical morphology
Mathematical Morphology
Signal Analysis
Induction Motor
Signal analysis
Induction motors
Rotor
Instantaneous
Rotors
Fault
Induction Machine
Fault Diagnosis
Failure analysis
Modulation
Signature
Detector
Geometric Process
Detectors
Mineral industry
Geometric Structure

Cite this

Li, H., Zhen, D., Otuyemi, F., Gu, F., & Ball, A. D. (2019). Broken Rotor Bar Detection Using Mathematical Morphology Based on Instantaneous Induction Motor Electrical Signals Analysis. In H. Yu (Ed.), 2019 25th International Conference on Automation and Computing, ICAC 2019: Improving Productivity through Automation and Computing [8894928] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/IConAC.2019.8894928
Li, Haiyang ; Zhen, Dong ; Otuyemi, Funso ; Gu, Fengshou ; Ball, Andrew D. / Broken Rotor Bar Detection Using Mathematical Morphology Based on Instantaneous Induction Motor Electrical Signals Analysis. 2019 25th International Conference on Automation and Computing, ICAC 2019: Improving Productivity through Automation and Computing. editor / Hui Yu. Institute of Electrical and Electronics Engineers Inc., 2019.
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title = "Broken Rotor Bar Detection Using Mathematical Morphology Based on Instantaneous Induction Motor Electrical Signals Analysis",
abstract = "AC induction machines (IM) are the most popular motors in many manufacturing processes and industrial applications. They are widely applied in petrochemical, military, aerospace and nuclear plants and mining industry. Broken rotor bar (BRB) is one of the most frequent AC IM faults. Accurate and timely diagnosis of BRB can help to lower maintenance costs and prevent unscheduled downtimes. BRB faults will cause rotor resistance change and then affect stator supply current directly. Therefore, it is an effective way to detect BRB fault by analysing instantaneous electrical signature (IES). However, most of the analysis results achieved by the common methods are time consuming and cannot process geometric information for BRB fault diagnosis with high accuracy. Mathematical morphology (MM) has attracted considerable attention due to its advantages of extracting the geometric structure of the modulation feature with less computation. In this paper, a novel fault detector is developed for BRB fault detection using MM based motor IES analysis. A novel morphological gradient is improved to analyse the motor current signal for fault related modulation components enhancement. And then the max value in the low frequency range of the MM filtered signal is extracted as a fault detector for BRB diagnosis. The performance of the proposed method is evaluated by experimental study. The test motors are setup with different broken bar level and operating under different loads. The analysis results prove the efficiency and performance of the proposed method on the BRB faults diagnosis for IMs.",
keywords = "Broken Rotor Bar, Fault Diagnosis, Induction Motor, Instantaneous Electrical Signature, Mathematical Morphology (MM)",
author = "Haiyang Li and Dong Zhen and Funso Otuyemi and Fengshou Gu and Ball, {Andrew D.}",
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Li, H, Zhen, D, Otuyemi, F, Gu, F & Ball, AD 2019, Broken Rotor Bar Detection Using Mathematical Morphology Based on Instantaneous Induction Motor Electrical Signals Analysis. in H Yu (ed.), 2019 25th International Conference on Automation and Computing, ICAC 2019: Improving Productivity through Automation and Computing., 8894928, Institute of Electrical and Electronics Engineers Inc., 25th IEEE International Conference on Automation and Computing, Lancaster, United Kingdom, 5/09/19. https://doi.org/10.23919/IConAC.2019.8894928

Broken Rotor Bar Detection Using Mathematical Morphology Based on Instantaneous Induction Motor Electrical Signals Analysis. / Li, Haiyang; Zhen, Dong; Otuyemi, Funso; Gu, Fengshou; Ball, Andrew D.

2019 25th International Conference on Automation and Computing, ICAC 2019: Improving Productivity through Automation and Computing. ed. / Hui Yu. Institute of Electrical and Electronics Engineers Inc., 2019. 8894928.

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

TY - GEN

T1 - Broken Rotor Bar Detection Using Mathematical Morphology Based on Instantaneous Induction Motor Electrical Signals Analysis

AU - Li, Haiyang

AU - Zhen, Dong

AU - Otuyemi, Funso

AU - Gu, Fengshou

AU - Ball, Andrew D.

PY - 2019/11/11

Y1 - 2019/11/11

N2 - AC induction machines (IM) are the most popular motors in many manufacturing processes and industrial applications. They are widely applied in petrochemical, military, aerospace and nuclear plants and mining industry. Broken rotor bar (BRB) is one of the most frequent AC IM faults. Accurate and timely diagnosis of BRB can help to lower maintenance costs and prevent unscheduled downtimes. BRB faults will cause rotor resistance change and then affect stator supply current directly. Therefore, it is an effective way to detect BRB fault by analysing instantaneous electrical signature (IES). However, most of the analysis results achieved by the common methods are time consuming and cannot process geometric information for BRB fault diagnosis with high accuracy. Mathematical morphology (MM) has attracted considerable attention due to its advantages of extracting the geometric structure of the modulation feature with less computation. In this paper, a novel fault detector is developed for BRB fault detection using MM based motor IES analysis. A novel morphological gradient is improved to analyse the motor current signal for fault related modulation components enhancement. And then the max value in the low frequency range of the MM filtered signal is extracted as a fault detector for BRB diagnosis. The performance of the proposed method is evaluated by experimental study. The test motors are setup with different broken bar level and operating under different loads. The analysis results prove the efficiency and performance of the proposed method on the BRB faults diagnosis for IMs.

AB - AC induction machines (IM) are the most popular motors in many manufacturing processes and industrial applications. They are widely applied in petrochemical, military, aerospace and nuclear plants and mining industry. Broken rotor bar (BRB) is one of the most frequent AC IM faults. Accurate and timely diagnosis of BRB can help to lower maintenance costs and prevent unscheduled downtimes. BRB faults will cause rotor resistance change and then affect stator supply current directly. Therefore, it is an effective way to detect BRB fault by analysing instantaneous electrical signature (IES). However, most of the analysis results achieved by the common methods are time consuming and cannot process geometric information for BRB fault diagnosis with high accuracy. Mathematical morphology (MM) has attracted considerable attention due to its advantages of extracting the geometric structure of the modulation feature with less computation. In this paper, a novel fault detector is developed for BRB fault detection using MM based motor IES analysis. A novel morphological gradient is improved to analyse the motor current signal for fault related modulation components enhancement. And then the max value in the low frequency range of the MM filtered signal is extracted as a fault detector for BRB diagnosis. The performance of the proposed method is evaluated by experimental study. The test motors are setup with different broken bar level and operating under different loads. The analysis results prove the efficiency and performance of the proposed method on the BRB faults diagnosis for IMs.

KW - Broken Rotor Bar

KW - Fault Diagnosis

KW - Induction Motor

KW - Instantaneous Electrical Signature

KW - Mathematical Morphology (MM)

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DO - 10.23919/IConAC.2019.8894928

M3 - Conference contribution

AN - SCOPUS:85075782053

SN - 9781728125183

BT - 2019 25th International Conference on Automation and Computing, ICAC 2019

A2 - Yu, Hui

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Li H, Zhen D, Otuyemi F, Gu F, Ball AD. Broken Rotor Bar Detection Using Mathematical Morphology Based on Instantaneous Induction Motor Electrical Signals Analysis. In Yu H, editor, 2019 25th International Conference on Automation and Computing, ICAC 2019: Improving Productivity through Automation and Computing. Institute of Electrical and Electronics Engineers Inc. 2019. 8894928 https://doi.org/10.23919/IConAC.2019.8894928