Electrical Fault Detection Using Machine Learning Algorithm For Centrifugal Water Pumps

Ranganatha Chakravarthy H.S.; Sai Charan Bharadwaj; Umashankar S.; Sanjeevikumar Padmanaban; Nabanita Dutta; Jens Bo Holm-Nielsen

doi:10.1109/EEEIC.2019.8783841

Electrical Fault Detection Using Machine Learning Algorithm For Centrifugal Water Pumps

Ranganatha Chakravarthy H.S., Sai Charan Bharadwaj, Umashankar S., Sanjeevikumar Padmanaban, Nabanita Dutta, Jens Bo Holm-Nielsen

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

10 Citationer (Scopus)

Abstract

The most essential part of any living being is water. Humans utilize water for various purposes such as cooking, bathing, washing, drinking, cultivating, cleaning, power generation and so on. Water pumps are employed to facilitate easy access near the requirement. Pumps can be classified into many groups according to the method of fluid displacement they inculcate to transport the fluid. Various faults are associated with the working of the water pumps due to the way it is handled, environmental conditions, supply imbalance, poor power quality or due to any other mechanical failures. Hence an effective process to determine the different various faults so as to mitigate the damage is required. This paper analyses the various faults in the centrifugal water pumps driven by induction motors used in agriculture fields and proposes a new algorithm to effectively and efficiently identify the fault and classify it according to its category using machine learning algorithms.

Originalsprog	Engelsk
Titel	2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe
Antal sider	6
Forlag	IEEE (Institute of Electrical and Electronics Engineers)
Publikationsdato	jun. 2019
Artikelnummer	8783841
ISBN (Trykt)	978-1-7281-0654-0
DOI	https://doi.org/10.1109/EEEIC.2019.8783841
Status	Udgivet - jun. 2019
Begivenhed	2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) - NH Collection Genoa Marina Hotel, Genoa, Italien Varighed: 11 jun. 2019 → 14 jun. 2019 Konferencens nummer: 19 https://www.showsbee.com/fairs/IEEE-EEEIC.html

Konference

Konference	2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)
Nummer	19
Lokation	NH Collection Genoa Marina Hotel
Land/Område	Italien
By	Genoa
Periode	11/06/2019 → 14/06/2019
Internetadresse	https://www.showsbee.com/fairs/IEEE-EEEIC.html

Emneord

Circuit faults
Induction motors
Pumps
Training
Stator windings
Support vector machines

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10.1109/EEEIC.2019.8783841

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8783841

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Citationsformater

H.S., R. C., Bharadwaj, S. C., S., U., Padmanaban, S., Dutta, N., & Holm-Nielsen, J. B. (2019). Electrical Fault Detection Using Machine Learning Algorithm For Centrifugal Water Pumps. I 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe Artikel 8783841 IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/EEEIC.2019.8783841

H.S., Ranganatha Chakravarthy ; Bharadwaj, Sai Charan ; S., Umashankar et al. / Electrical Fault Detection Using Machine Learning Algorithm For Centrifugal Water Pumps. 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe . IEEE (Institute of Electrical and Electronics Engineers), 2019.

@inproceedings{218bff5e39ea44c092e8a4bc45fe7bb8,

title = "Electrical Fault Detection Using Machine Learning Algorithm For Centrifugal Water Pumps",

abstract = "The most essential part of any living being is water. Humans utilize water for various purposes such as cooking, bathing, washing, drinking, cultivating, cleaning, power generation and so on. Water pumps are employed to facilitate easy access near the requirement. Pumps can be classified into many groups according to the method of fluid displacement they inculcate to transport the fluid. Various faults are associated with the working of the water pumps due to the way it is handled, environmental conditions, supply imbalance, poor power quality or due to any other mechanical failures. Hence an effective process to determine the different various faults so as to mitigate the damage is required. This paper analyses the various faults in the centrifugal water pumps driven by induction motors used in agriculture fields and proposes a new algorithm to effectively and efficiently identify the fault and classify it according to its category using machine learning algorithms.",

keywords = "Circuit faults, Induction motors, Pumps, Training, Stator windings, Support vector machines",

author = "H.S., {Ranganatha Chakravarthy} and Bharadwaj, {Sai Charan} and Umashankar S. and Sanjeevikumar Padmanaban and Nabanita Dutta and Holm-Nielsen, {Jens Bo}",

year = "2019",

month = jun,

doi = "10.1109/EEEIC.2019.8783841",

language = "English",

isbn = "978-1-7281-0654-0",

booktitle = "2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe ",

publisher = "IEEE (Institute of Electrical and Electronics Engineers)",

address = "United States",

note = "2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) , 2019 EEEIC/ICPS Europe ; Conference date: 11-06-2019 Through 14-06-2019",

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}

H.S., RC, Bharadwaj, SC, S., U, Padmanaban, S, Dutta, N & Holm-Nielsen, JB 2019, Electrical Fault Detection Using Machine Learning Algorithm For Centrifugal Water Pumps. i 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe ., 8783841, IEEE (Institute of Electrical and Electronics Engineers), 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) , Genoa, Italien, 11/06/2019. https://doi.org/10.1109/EEEIC.2019.8783841

Electrical Fault Detection Using Machine Learning Algorithm For Centrifugal Water Pumps. / H.S., Ranganatha Chakravarthy; Bharadwaj, Sai Charan; S., Umashankar et al.
2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe . IEEE (Institute of Electrical and Electronics Engineers), 2019. 8783841.

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

TY - GEN

T1 - Electrical Fault Detection Using Machine Learning Algorithm For Centrifugal Water Pumps

AU - H.S., Ranganatha Chakravarthy

AU - Bharadwaj, Sai Charan

AU - S., Umashankar

AU - Padmanaban, Sanjeevikumar

AU - Dutta, Nabanita

AU - Holm-Nielsen, Jens Bo

N1 - Conference code: 19

PY - 2019/6

Y1 - 2019/6

N2 - The most essential part of any living being is water. Humans utilize water for various purposes such as cooking, bathing, washing, drinking, cultivating, cleaning, power generation and so on. Water pumps are employed to facilitate easy access near the requirement. Pumps can be classified into many groups according to the method of fluid displacement they inculcate to transport the fluid. Various faults are associated with the working of the water pumps due to the way it is handled, environmental conditions, supply imbalance, poor power quality or due to any other mechanical failures. Hence an effective process to determine the different various faults so as to mitigate the damage is required. This paper analyses the various faults in the centrifugal water pumps driven by induction motors used in agriculture fields and proposes a new algorithm to effectively and efficiently identify the fault and classify it according to its category using machine learning algorithms.

AB - The most essential part of any living being is water. Humans utilize water for various purposes such as cooking, bathing, washing, drinking, cultivating, cleaning, power generation and so on. Water pumps are employed to facilitate easy access near the requirement. Pumps can be classified into many groups according to the method of fluid displacement they inculcate to transport the fluid. Various faults are associated with the working of the water pumps due to the way it is handled, environmental conditions, supply imbalance, poor power quality or due to any other mechanical failures. Hence an effective process to determine the different various faults so as to mitigate the damage is required. This paper analyses the various faults in the centrifugal water pumps driven by induction motors used in agriculture fields and proposes a new algorithm to effectively and efficiently identify the fault and classify it according to its category using machine learning algorithms.

KW - Circuit faults

KW - Induction motors

KW - Pumps

KW - Training

KW - Stator windings

KW - Support vector machines

UR - https://ieeexplore.ieee.org/document/8783841/

U2 - 10.1109/EEEIC.2019.8783841

DO - 10.1109/EEEIC.2019.8783841

M3 - Article in proceeding

SN - 978-1-7281-0654-0

BT - 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe

PB - IEEE (Institute of Electrical and Electronics Engineers)

T2 - 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)

Y2 - 11 June 2019 through 14 June 2019

ER -

H.S. RC, Bharadwaj SC, S. U, Padmanaban S, Dutta N, Holm-Nielsen JB. Electrical Fault Detection Using Machine Learning Algorithm For Centrifugal Water Pumps. I 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe . IEEE (Institute of Electrical and Electronics Engineers). 2019. 8783841 doi: 10.1109/EEEIC.2019.8783841

Electrical Fault Detection Using Machine Learning Algorithm For Centrifugal Water Pumps

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