Motor Current Signal Analysis Based on Machine Learning for Centrifugal Pump Fault Diagnosis

Samir Alabied, Alsadak Daraz, Khalid Rabeyee, Ibrahim Alqatawneh, Fengshou Gu, Andrew D. Ball

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

Abstract

Centrifugal pumps are widely used in various industrial processes, such as petrochemical production and power plants. It is important to develop an accurate and cost-effective diagnosis approach for ensuring effective condition monitoring methods. In this paper, the motor current signal has been investigated for the condition monitoring of the centrifugal pump. In specific, a combined approach is proposed based on intrinsic time scale decomposition (ITD) for feature extraction and Support Vector Machine (SVM) for classifying the health conditions. The classification accuracy of SVM was improved significantly when the ITD method is applied to extract the diagnostic features. The results have shown that ITD is efficient and effective for extracting the most informative features from the motor current signals. Also, this indicates that the proposed diagnostic method based on ITD with SVM is more effective to classify the simulated faults of the centrifugal pump.

Original languageEnglish
Title of host publication2019 25th IEEE 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 pages7
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

Centrifugal Pump
Signal Analysis
Centrifugal pumps
Signal analysis
Fault Diagnosis
Failure analysis
Learning systems
Machine Learning
Time Scales
Decomposition
Support vector machines
Support Vector Machine
Condition Monitoring
Condition monitoring
Decompose
Diagnostics
Power Plant
Decomposition Method
Petrochemicals
Feature Extraction

Cite this

Alabied, S., Daraz, A., Rabeyee, K., Alqatawneh, I., Gu, F., & Ball, A. D. (2019). Motor Current Signal Analysis Based on Machine Learning for Centrifugal Pump Fault Diagnosis. In H. Yu (Ed.), 2019 25th IEEE International Conference on Automation and Computing, ICAC 2019 : Improving Productivity through Automation and Computing [8895057] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/IConAC.2019.8895057
Alabied, Samir ; Daraz, Alsadak ; Rabeyee, Khalid ; Alqatawneh, Ibrahim ; Gu, Fengshou ; Ball, Andrew D. / Motor Current Signal Analysis Based on Machine Learning for Centrifugal Pump Fault Diagnosis. 2019 25th IEEE 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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abstract = "Centrifugal pumps are widely used in various industrial processes, such as petrochemical production and power plants. It is important to develop an accurate and cost-effective diagnosis approach for ensuring effective condition monitoring methods. In this paper, the motor current signal has been investigated for the condition monitoring of the centrifugal pump. In specific, a combined approach is proposed based on intrinsic time scale decomposition (ITD) for feature extraction and Support Vector Machine (SVM) for classifying the health conditions. The classification accuracy of SVM was improved significantly when the ITD method is applied to extract the diagnostic features. The results have shown that ITD is efficient and effective for extracting the most informative features from the motor current signals. Also, this indicates that the proposed diagnostic method based on ITD with SVM is more effective to classify the simulated faults of the centrifugal pump.",
keywords = "Centrifugal Pump, Motor Current Signature Analysis, Support Vector Machine",
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Alabied, S, Daraz, A, Rabeyee, K, Alqatawneh, I, Gu, F & Ball, AD 2019, Motor Current Signal Analysis Based on Machine Learning for Centrifugal Pump Fault Diagnosis. in H Yu (ed.), 2019 25th IEEE International Conference on Automation and Computing, ICAC 2019 : Improving Productivity through Automation and Computing., 8895057, 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.2019.8895057

Motor Current Signal Analysis Based on Machine Learning for Centrifugal Pump Fault Diagnosis. / Alabied, Samir; Daraz, Alsadak; Rabeyee, Khalid; Alqatawneh, Ibrahim; Gu, Fengshou; Ball, Andrew D.

2019 25th IEEE 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. 8895057.

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

TY - GEN

T1 - Motor Current Signal Analysis Based on Machine Learning for Centrifugal Pump Fault Diagnosis

AU - Alabied, Samir

AU - Daraz, Alsadak

AU - Rabeyee, Khalid

AU - Alqatawneh, Ibrahim

AU - Gu, Fengshou

AU - Ball, Andrew D.

PY - 2019/11/11

Y1 - 2019/11/11

N2 - Centrifugal pumps are widely used in various industrial processes, such as petrochemical production and power plants. It is important to develop an accurate and cost-effective diagnosis approach for ensuring effective condition monitoring methods. In this paper, the motor current signal has been investigated for the condition monitoring of the centrifugal pump. In specific, a combined approach is proposed based on intrinsic time scale decomposition (ITD) for feature extraction and Support Vector Machine (SVM) for classifying the health conditions. The classification accuracy of SVM was improved significantly when the ITD method is applied to extract the diagnostic features. The results have shown that ITD is efficient and effective for extracting the most informative features from the motor current signals. Also, this indicates that the proposed diagnostic method based on ITD with SVM is more effective to classify the simulated faults of the centrifugal pump.

AB - Centrifugal pumps are widely used in various industrial processes, such as petrochemical production and power plants. It is important to develop an accurate and cost-effective diagnosis approach for ensuring effective condition monitoring methods. In this paper, the motor current signal has been investigated for the condition monitoring of the centrifugal pump. In specific, a combined approach is proposed based on intrinsic time scale decomposition (ITD) for feature extraction and Support Vector Machine (SVM) for classifying the health conditions. The classification accuracy of SVM was improved significantly when the ITD method is applied to extract the diagnostic features. The results have shown that ITD is efficient and effective for extracting the most informative features from the motor current signals. Also, this indicates that the proposed diagnostic method based on ITD with SVM is more effective to classify the simulated faults of the centrifugal pump.

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KW - Motor Current Signature Analysis

KW - Support Vector Machine

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Alabied S, Daraz A, Rabeyee K, Alqatawneh I, Gu F, Ball AD. Motor Current Signal Analysis Based on Machine Learning for Centrifugal Pump Fault Diagnosis. In Yu H, editor, 2019 25th IEEE International Conference on Automation and Computing, ICAC 2019 : Improving Productivity through Automation and Computing. Institute of Electrical and Electronics Engineers Inc. 2019. 8895057 https://doi.org/10.23919/IConAC.2019.8895057