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Faults Feature Extraction Using Discrete Wavelet Transform and Artificial Neural Network for Induction Motor Availability Monitoring—Internet of Things Enabled Environment

Author

Listed:
  • Muhammad Zuhaib

    (Department of Electrical Engineering, PNEC, National University of Science and Technology, Karachi 75350, Pakistan)

  • Faraz Ahmed Shaikh

    (Department of Electrical Engineering, Nazeer Hussain University (NHU), Karachi 75950, Pakistan)

  • Wajiha Tanweer

    (Department of Electrical Engineering, PNEC, National University of Science and Technology, Karachi 75350, Pakistan
    Department of Electrical Engineering, Nazeer Hussain University (NHU), Karachi 75950, Pakistan)

  • Abdullah M. Alnajim

    (Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia)

  • Saleh Alyahya

    (Department of Electrical Engineering, College of Engineering & Information Technology, Onaizah Colleges, Unayzah 51911, Saudi Arabia)

  • Sheroz Khan

    (Department of Electrical Engineering, College of Engineering & Information Technology, Onaizah Colleges, Unayzah 51911, Saudi Arabia)

  • Muhammad Usman

    (Department of Mechatronics and Control Engineering, Faisalabad Campus, University of Engineering and Technology (UET), Lahore 39161, Pakistan)

  • Muhammad Islam

    (Department of Electrical Engineering, College of Engineering & Information Technology, Onaizah Colleges, Unayzah 51911, Saudi Arabia)

  • Mohammad Kamrul Hasan

    (Center for Cyber Security, Faculty of Information Science & Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia)

Abstract

Motivation: This paper presents the high contact resistance (HCR) and rotor bar faults by an extraction method for an induction motor using Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN). The root mean square (RMS) and mean features are obtained using DWT, and ANN is used for classification using activation functions. Activation provides output by assigning the specific input with respect to the transfer function according to the nature and type of the activation function. Method: The faulty conditions are induced using MATLAB by adopting the motor current signature analysis (MCSA) method to achieve current signature signals of the healthy and faulty motors. Results: The DWT technique has been applied to obtain fault-specific features of the average continuously varying signal (RMS) and an average of the data points (mean) at levels 5, 7, 8, and 9, followed by ANN to classify the faults for condition monitoring. Utility: The utility of the results is to reduce unscheduled downtime in the industry, thus saving revenue and reducing production losses. This work will help provide support to ensure early indication of faults in induction motors under operating conditions, enabling in-service engineers to take timely preventive measures as part of the availability of resources in IoT-enabled systems. Application: Resource availability and cybersecurity are becoming vital in an environment that supports the Internet of Things (IoT) as the essential components of Industry 4.0 scenarios. The novelty of this research lies in the implementation of high contact resistance and rotor bar faults using DWT and ANN with different activation functions to achieve accuracy up to 98%.

Suggested Citation

  • Muhammad Zuhaib & Faraz Ahmed Shaikh & Wajiha Tanweer & Abdullah M. Alnajim & Saleh Alyahya & Sheroz Khan & Muhammad Usman & Muhammad Islam & Mohammad Kamrul Hasan, 2022. "Faults Feature Extraction Using Discrete Wavelet Transform and Artificial Neural Network for Induction Motor Availability Monitoring—Internet of Things Enabled Environment," Energies, MDPI, vol. 15(21), pages 1-32, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:7888-:d:951886
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    References listed on IDEAS

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    1. Qiao, Zijian & Shu, Xuedao, 2021. "Coupled neurons with multi-objective optimization benefit incipient fault identification of machinery," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    2. Ana L. Martinez-Herrera & Edna R. Ferrucho-Alvarez & Luis M. Ledesma-Carrillo & Ruth I. Mata-Chavez & Misael Lopez-Ramirez & Eduardo Cabal-Yepez, 2022. "Multiple Fault Detection in Induction Motors through Homogeneity and Kurtosis Computation," Energies, MDPI, vol. 15(4), pages 1-11, February.
    3. Yuri Merizalde & Luis Hernández-Callejo & Oscar Duque-Perez, 2017. "State of the Art and Trends in the Monitoring, Detection and Diagnosis of Failures in Electric Induction Motors," Energies, MDPI, vol. 10(7), pages 1-34, July.
    4. Shrinathan Esakimuthu Pandarakone & Yukio Mizuno & Hisahide Nakamura, 2019. "A Comparative Study between Machine Learning Algorithm and Artificial Intelligence Neural Network in Detecting Minor Bearing Fault of Induction Motors," Energies, MDPI, vol. 12(11), pages 1-14, June.
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