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

Research output: Contribution to journalArticlepeer-review

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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 languageEnglish
Article numbere4225
JournalSensors
Volume21
Issue number12
Early online date20 Jun 2021
DOIs
Publication statusE-pub ahead of print - 20 Jun 2021

Keywords

  • induction motors
  • stator current sensing
  • voltage measurement
  • instantaneous power measurement
  • vibration measurement
  • feature selection

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