A Novel Feature Extraction and Fault Detection Technique for the Intelligent Fault Identification of Water Pump Bearings

Muhammad Irfan; Abdullah Saeed Alwadie; Adam Glowacz; Muhammad Awais; Saifur Rahman; Mohammad Kamal Asif Khan; Mohammad Jalalah; Omar Alshorman; Wahyu Caesarendra

doi:10.3390/s21124225

A Novel Feature Extraction and Fault Detection Technique for the Intelligent Fault Identification of Water Pump Bearings

Muhammad Irfan^*, Abdullah Saeed Alwadie, Adam Glowacz, Muhammad Awais, Saifur Rahman, Mohammad Kamal Asif Khan, Mohammad Jalalah, Omar Alshorman, Wahyu Caesarendra

^*Corresponding author for this work

Computer Science

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Abstract

The reliable and cost-effective condition monitoring of the bearings installed in water pumps is a real challenge in the industry. This paper presents a novel strong feature selection and extraction algorithm (SFSEA) to extract fault-related features from the instantaneous power spectrum (IPS). The three features extracted from the IPS using the SFSEA are fed to an extreme gradient boosting (XBG) classifier to reliably detect and classify the minor bearing faults. The experiments performed on a lab-scale test setup demonstrated classification accuracy up to 100%, which is better than the previously reported fault classification accuracies and indicates the effectiveness of the proposed method.

Original language	English
Article number	4225
Journal	Sensors
Volume	21
Issue number	12
Early online date	20 Jun 2021
DOIs	https://doi.org/10.3390/s21124225
Publication status	Published - 20 Jun 2021

Keywords

Feature selection
Induction motors
Instantaneous power measurement
Stator current sensing
Vibration measurement
Voltage measurement

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sensors-21-04225.pdfFinal published version, 3.25 MBLicence: CC BY

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title = "A Novel Feature Extraction and Fault Detection Technique for the Intelligent Fault Identification of Water Pump Bearings",

abstract = "The reliable and cost-effective condition monitoring of the bearings installed in water pumps is a real challenge in the industry. This paper presents a novel strong feature selection and extraction algorithm (SFSEA) to extract fault-related features from the instantaneous power spectrum (IPS). The three features extracted from the IPS using the SFSEA are fed to an extreme gradient boosting (XBG) classifier to reliably detect and classify the minor bearing faults. The experiments performed on a lab-scale test setup demonstrated classification accuracy up to 100%, which is better than the previously reported fault classification accuracies and indicates the effectiveness of the proposed method.",

keywords = "Feature selection, Induction motors, Instantaneous power measurement, Stator current sensing, Vibration measurement, Voltage measurement",

author = "Muhammad Irfan and Alwadie, {Abdullah Saeed} and Adam Glowacz and Muhammad Awais and Saifur Rahman and Khan, {Mohammad Kamal Asif} and Mohammad Jalalah and Omar Alshorman and Wahyu Caesarendra",

note = "Funding Information: Funding: This research was supported by the Ministry of Education, Saudi Arabia, under the institutional funding committee of Najran University, Saudi Arabia. Funding Information: Acknowledgments: The authors would like to acknowledge the support of the Deputy for Research and Innovation of the Ministry of Education, Kingdom of Saudi Arabia, for this research through a grant (NU/IFC/ENT/01/011) under the institutional Funding Committee at Najran University, Kingdom of Saudi Arabia. Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.",

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doi = "10.3390/s21124225",

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AU - Irfan, Muhammad

AU - Alwadie, Abdullah Saeed

AU - Glowacz, Adam

AU - Awais, Muhammad

AU - Rahman, Saifur

AU - Khan, Mohammad Kamal Asif

AU - Jalalah, Mohammad

AU - Alshorman, Omar

AU - Caesarendra, Wahyu

N1 - Funding Information: Funding: This research was supported by the Ministry of Education, Saudi Arabia, under the institutional funding committee of Najran University, Saudi Arabia. Funding Information: Acknowledgments: The authors would like to acknowledge the support of the Deputy for Research and Innovation of the Ministry of Education, Kingdom of Saudi Arabia, for this research through a grant (NU/IFC/ENT/01/011) under the institutional Funding Committee at Najran University, Kingdom of Saudi Arabia. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2021/6/20

Y1 - 2021/6/20

N2 - The reliable and cost-effective condition monitoring of the bearings installed in water pumps is a real challenge in the industry. This paper presents a novel strong feature selection and extraction algorithm (SFSEA) to extract fault-related features from the instantaneous power spectrum (IPS). The three features extracted from the IPS using the SFSEA are fed to an extreme gradient boosting (XBG) classifier to reliably detect and classify the minor bearing faults. The experiments performed on a lab-scale test setup demonstrated classification accuracy up to 100%, which is better than the previously reported fault classification accuracies and indicates the effectiveness of the proposed method.

AB - The reliable and cost-effective condition monitoring of the bearings installed in water pumps is a real challenge in the industry. This paper presents a novel strong feature selection and extraction algorithm (SFSEA) to extract fault-related features from the instantaneous power spectrum (IPS). The three features extracted from the IPS using the SFSEA are fed to an extreme gradient boosting (XBG) classifier to reliably detect and classify the minor bearing faults. The experiments performed on a lab-scale test setup demonstrated classification accuracy up to 100%, which is better than the previously reported fault classification accuracies and indicates the effectiveness of the proposed method.

KW - Feature selection

KW - Induction motors

KW - Instantaneous power measurement

KW - Stator current sensing

KW - Vibration measurement

KW - Voltage measurement

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A Novel Feature Extraction and Fault Detection Technique for the Intelligent Fault Identification of Water Pump Bearings

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