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, Mohammed Jalalah, Omar Alshorman, Wahyu Caesarendra

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)
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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 number4225
Issue number12
Publication statusPublished - 20 Jun 2021


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

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