A Novel Feature Extraction and Fault Detection Technique for the Intelligent Fault Identification of Water Pump Bearings
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...
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MDPI AG
2021-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/12/4225 |
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doaj-63db71f78e4f4f00bdab252f23bef9a72021-07-01T00:41:32ZengMDPI AGSensors1424-82202021-06-01214225422510.3390/s21124225A Novel Feature Extraction and Fault Detection Technique for the Intelligent Fault Identification of Water Pump BearingsMuhammad Irfan0Abdullah Saeed Alwadie1Adam Glowacz2Muhammad Awais3Saifur Rahman4Mohammad Kamal Asif Khan5Mohammad Jalalah6Omar Alshorman7Wahyu Caesarendra8Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi ArabiaElectrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi ArabiaDepartment of Automatic, Control and Robotics, AGH University of Science and Technology, 30-059 Kraków, PolandDepartment of Computer Science, Edge Hill University, St Helens Road, Ormskirk L39 4QP, UKElectrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi ArabiaMechanical Engineering Department, College of Engineering, Najran University Saudi Arabia, Najran 61441, Saudi ArabiaElectrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi ArabiaElectrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi ArabiaFaculty of Integrated Technologies, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, BruneiThe 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.https://www.mdpi.com/1424-8220/21/12/4225induction motorsstator current sensingvoltage measurementinstantaneous power measurementvibration measurementfeature selection |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Muhammad Irfan Abdullah Saeed Alwadie Adam Glowacz Muhammad Awais Saifur Rahman Mohammad Kamal Asif Khan Mohammad Jalalah Omar Alshorman Wahyu Caesarendra |
| spellingShingle |
Muhammad Irfan Abdullah Saeed Alwadie Adam Glowacz Muhammad Awais Saifur Rahman Mohammad Kamal Asif Khan Mohammad Jalalah Omar Alshorman Wahyu Caesarendra A Novel Feature Extraction and Fault Detection Technique for the Intelligent Fault Identification of Water Pump Bearings Sensors induction motors stator current sensing voltage measurement instantaneous power measurement vibration measurement feature selection |
| author_facet |
Muhammad Irfan Abdullah Saeed Alwadie Adam Glowacz Muhammad Awais Saifur Rahman Mohammad Kamal Asif Khan Mohammad Jalalah Omar Alshorman Wahyu Caesarendra |
| author_sort |
Muhammad Irfan |
| title |
A Novel Feature Extraction and Fault Detection Technique for the Intelligent Fault Identification of Water Pump Bearings |
| title_short |
A Novel Feature Extraction and Fault Detection Technique for the Intelligent Fault Identification of Water Pump Bearings |
| title_full |
A Novel Feature Extraction and Fault Detection Technique for the Intelligent Fault Identification of Water Pump Bearings |
| title_fullStr |
A Novel Feature Extraction and Fault Detection Technique for the Intelligent Fault Identification of Water Pump Bearings |
| title_full_unstemmed |
A Novel Feature Extraction and Fault Detection Technique for the Intelligent Fault Identification of Water Pump Bearings |
| title_sort |
novel feature extraction and fault detection technique for the intelligent fault identification of water pump bearings |
| publisher |
MDPI AG |
| series |
Sensors |
| issn |
1424-8220 |
| publishDate |
2021-06-01 |
| description |
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. |
| topic |
induction motors stator current sensing voltage measurement instantaneous power measurement vibration measurement feature selection |
| url |
https://www.mdpi.com/1424-8220/21/12/4225 |
| work_keys_str_mv |
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