Fault Diagnosis of Centrifugal Pumps based on the Intrinsic Time-scale Decomposition of Motor Current signals

Samir Alabied, Osama Hamomd, Alsadak Daraz, Fengshou Gu, Andrew Ball

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

2 Citations (Scopus)

Abstract

Centrifugal pumps are widely used in various manufacturing processes, such as power plants, and chemistry. However, pump problems are responsible for large amount of the maintenance budget. An early detection of such problems would provide timely information to take appropriate preventive actions. This paper investigates the application of Machine Learning Techniques (MLT) in monitoring and diagnosing fault in centrifugal pump. In particular, the focus is on utilising motor current signals since they can be measured remotely for easy and low-cost deployment. Moreover, because the signals are usually produced by a nonlinear process and contaminated by various noises, it is difficult to obtain accurate diagnostic features with conventional signal processing methods such as Fourier spectrum and wavelet transforms as they rely heavily on standard basis functions and often capture limited nonlinear weak fault signatures. Therefore, a data-driven method: Intrinsic Time-scale Decomposition (ITD) is adopted in this study to process motor current signals from different pump fault cases. The results indicate that the proposed ITD technique is an effective method for extracting useful diagnostic information, leading to accurate diagnosis by combining the RMS values of the first Proper Rotation Component (PRC) with the raw signal RMS values.
Original languageEnglish
Title of host publicationProceedings of the 23rd International Conference on Automation & Computing, (University of Huddersfield, 7-8 September 2017)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9780701702601
ISBN (Print)9781509050406
DOIs
Publication statusPublished - 26 Oct 2017
Event23rd International Conference on Automation and Computing: Addressing Global Challenges through Automation and Computing - University of Huddersfield, Huddersfield, United Kingdom
Duration: 7 Sep 20178 Sep 2017
Conference number: 23
https://www.ieee.org/conferences_events/conferences/conferencedetails/index.html?Conf_ID=41042 (Link to Conference Website)

Conference

Conference23rd International Conference on Automation and Computing
Abbreviated titleICAC 2017
CountryUnited Kingdom
CityHuddersfield
Period7/09/178/09/17
OtherThe scope of the conference covers a broad spectrum of areas with multi-disciplinary interests in the fields of automation, control engineering, computing and information systems, ranging from fundamental research to real-world applications.
Internet address

Fingerprint

Centrifugal pumps
Failure analysis
Pumps
Decomposition
Wavelet transforms
Learning systems
Power plants
Signal processing
Monitoring
Costs

Cite this

Alabied, S., Hamomd, O., Daraz, A., Gu, F., & Ball, A. (2017). Fault Diagnosis of Centrifugal Pumps based on the Intrinsic Time-scale Decomposition of Motor Current signals. In Proceedings of the 23rd International Conference on Automation & Computing, (University of Huddersfield, 7-8 September 2017) Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/IConAC.2017.8082027
Alabied, Samir ; Hamomd, Osama ; Daraz, Alsadak ; Gu, Fengshou ; Ball, Andrew. / Fault Diagnosis of Centrifugal Pumps based on the Intrinsic Time-scale Decomposition of Motor Current signals. Proceedings of the 23rd International Conference on Automation & Computing, (University of Huddersfield, 7-8 September 2017). Institute of Electrical and Electronics Engineers Inc., 2017.
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title = "Fault Diagnosis of Centrifugal Pumps based on the Intrinsic Time-scale Decomposition of Motor Current signals",
abstract = "Centrifugal pumps are widely used in various manufacturing processes, such as power plants, and chemistry. However, pump problems are responsible for large amount of the maintenance budget. An early detection of such problems would provide timely information to take appropriate preventive actions. This paper investigates the application of Machine Learning Techniques (MLT) in monitoring and diagnosing fault in centrifugal pump. In particular, the focus is on utilising motor current signals since they can be measured remotely for easy and low-cost deployment. Moreover, because the signals are usually produced by a nonlinear process and contaminated by various noises, it is difficult to obtain accurate diagnostic features with conventional signal processing methods such as Fourier spectrum and wavelet transforms as they rely heavily on standard basis functions and often capture limited nonlinear weak fault signatures. Therefore, a data-driven method: Intrinsic Time-scale Decomposition (ITD) is adopted in this study to process motor current signals from different pump fault cases. The results indicate that the proposed ITD technique is an effective method for extracting useful diagnostic information, leading to accurate diagnosis by combining the RMS values of the first Proper Rotation Component (PRC) with the raw signal RMS values.",
keywords = "Centrifugal pump, Current signal analysis, Intrinsic time-scale decomposition",
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Alabied, S, Hamomd, O, Daraz, A, Gu, F & Ball, A 2017, Fault Diagnosis of Centrifugal Pumps based on the Intrinsic Time-scale Decomposition of Motor Current signals. in Proceedings of the 23rd International Conference on Automation & Computing, (University of Huddersfield, 7-8 September 2017). Institute of Electrical and Electronics Engineers Inc., 23rd International Conference on Automation and Computing, Huddersfield, United Kingdom, 7/09/17. https://doi.org/10.23919/IConAC.2017.8082027

Fault Diagnosis of Centrifugal Pumps based on the Intrinsic Time-scale Decomposition of Motor Current signals. / Alabied, Samir; Hamomd, Osama; Daraz, Alsadak; Gu, Fengshou; Ball, Andrew.

Proceedings of the 23rd International Conference on Automation & Computing, (University of Huddersfield, 7-8 September 2017). Institute of Electrical and Electronics Engineers Inc., 2017.

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

TY - GEN

T1 - Fault Diagnosis of Centrifugal Pumps based on the Intrinsic Time-scale Decomposition of Motor Current signals

AU - Alabied, Samir

AU - Hamomd, Osama

AU - Daraz, Alsadak

AU - Gu, Fengshou

AU - Ball, Andrew

PY - 2017/10/26

Y1 - 2017/10/26

N2 - Centrifugal pumps are widely used in various manufacturing processes, such as power plants, and chemistry. However, pump problems are responsible for large amount of the maintenance budget. An early detection of such problems would provide timely information to take appropriate preventive actions. This paper investigates the application of Machine Learning Techniques (MLT) in monitoring and diagnosing fault in centrifugal pump. In particular, the focus is on utilising motor current signals since they can be measured remotely for easy and low-cost deployment. Moreover, because the signals are usually produced by a nonlinear process and contaminated by various noises, it is difficult to obtain accurate diagnostic features with conventional signal processing methods such as Fourier spectrum and wavelet transforms as they rely heavily on standard basis functions and often capture limited nonlinear weak fault signatures. Therefore, a data-driven method: Intrinsic Time-scale Decomposition (ITD) is adopted in this study to process motor current signals from different pump fault cases. The results indicate that the proposed ITD technique is an effective method for extracting useful diagnostic information, leading to accurate diagnosis by combining the RMS values of the first Proper Rotation Component (PRC) with the raw signal RMS values.

AB - Centrifugal pumps are widely used in various manufacturing processes, such as power plants, and chemistry. However, pump problems are responsible for large amount of the maintenance budget. An early detection of such problems would provide timely information to take appropriate preventive actions. This paper investigates the application of Machine Learning Techniques (MLT) in monitoring and diagnosing fault in centrifugal pump. In particular, the focus is on utilising motor current signals since they can be measured remotely for easy and low-cost deployment. Moreover, because the signals are usually produced by a nonlinear process and contaminated by various noises, it is difficult to obtain accurate diagnostic features with conventional signal processing methods such as Fourier spectrum and wavelet transforms as they rely heavily on standard basis functions and often capture limited nonlinear weak fault signatures. Therefore, a data-driven method: Intrinsic Time-scale Decomposition (ITD) is adopted in this study to process motor current signals from different pump fault cases. The results indicate that the proposed ITD technique is an effective method for extracting useful diagnostic information, leading to accurate diagnosis by combining the RMS values of the first Proper Rotation Component (PRC) with the raw signal RMS values.

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KW - Current signal analysis

KW - Intrinsic time-scale decomposition

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DO - 10.23919/IConAC.2017.8082027

M3 - Conference contribution

SN - 9781509050406

BT - Proceedings of the 23rd International Conference on Automation & Computing, (University of Huddersfield, 7-8 September 2017)

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Alabied S, Hamomd O, Daraz A, Gu F, Ball A. Fault Diagnosis of Centrifugal Pumps based on the Intrinsic Time-scale Decomposition of Motor Current signals. In Proceedings of the 23rd International Conference on Automation & Computing, (University of Huddersfield, 7-8 September 2017). Institute of Electrical and Electronics Engineers Inc. 2017 https://doi.org/10.23919/IConAC.2017.8082027