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Neuro-Fuzzy Framework for Fault Prediction in Electrical Machines via Vibration Analysis

Author

Listed:
  • Karolina Kudelina

    (Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia)

  • Hadi Ashraf Raja

    (Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia)

Abstract

The advent of Industry 4.0 has ushered in a new era of technological advancements, particularly in integrating information technology with physical devices. This convergence has given rise to smart devices and the Internet of Things (IoT), revolutionizing industrial processes. However, despite the push towards predictive maintenance, there still is a significant gap in fault prediction algorithms for electrical machines. This paper proposes a signal spectrum-based machine learning approach for fault prediction, specifically focusing on bearing faults. This study compares the effectiveness of traditional neural network algorithms with a novel approach integrating fuzzy logic. Through extensive experimentation and analysis of vibration spectra from various mechanical faults in bearings, it is demonstrated that the fuzzy-neuro network model outperforms traditional neural networks, achieving a validation accuracy of 99.40% compared to 94.34%. Incorporating fuzzy logic within the neural network framework offers advantages in handling complex fault combinations, showing promise for applications requiring higher accuracy in fault detection. While initial results are encouraging, further validation with more complex fault scenarios and additional fuzzy layers is recommended to fully explore the potential of fuzzy-neuro networks in fault prediction for electrical machines.

Suggested Citation

  • Karolina Kudelina & Hadi Ashraf Raja, 2024. "Neuro-Fuzzy Framework for Fault Prediction in Electrical Machines via Vibration Analysis," Energies, MDPI, vol. 17(12), pages 1-11, June.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:12:p:2818-:d:1411095
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    Cited by:

    1. Paolo Visconti & Giuseppe Rausa & Carolina Del-Valle-Soto & Ramiro Velázquez & Donato Cafagna & Roberto De Fazio, 2024. "Machine Learning and IoT-Based Solutions in Industrial Applications for Smart Manufacturing: A Critical Review," Future Internet, MDPI, vol. 16(11), pages 1-42, October.

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