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

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

9 Citations (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.

Original language	English
Title of host publication	2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe
Number of pages	6
Publisher	IEEE
Publication date	Jun 2019
Article number	8783841
ISBN (Print)	978-1-7281-0654-0
DOIs	https://doi.org/10.1109/EEEIC.2019.8783841
Publication status	Published - Jun 2019
Event	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, Italy Duration: 11 Jun 2019 → 14 Jun 2019 Conference number: 19 https://www.showsbee.com/fairs/IEEE-EEEIC.html

Conference

Conference	2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)
Number	19
Location	NH Collection Genoa Marina Hotel
Country/Territory	Italy
City	Genoa
Period	11/06/2019 → 14/06/2019
Internet address	https://www.showsbee.com/fairs/IEEE-EEEIC.html

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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. In 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe Article 8783841 IEEE. https://doi.org/10.1109/EEEIC.2019.8783841

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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",

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. in 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe ., 8783841, IEEE, 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) , Genoa, Italy, 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, 2019. 8783841.

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › 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

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 -

Electrical Fault Detection Using Machine Learning Algorithm For Centrifugal Water Pumps

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