INDUCTION MOTOR BEARING FAULTS DIAGNOSIS USING AN IMPROVED KURTOGRAM METHOD

Authors

  • MOHAMMED-EL-AMINE KHODJA National School of Built and Ground Works Engineering image/svg+xml , Preparatory Classes Department, National School of Built and Ground Works Engineering, Algeria. , LDEE Laboratory, Department of Electrical Engineering, University of Sciences and Technology USTO, Algeria. Author https://orcid.org/0000-0002-1973-4501 (unauthenticated)
  • AHMED HAMIDA BOUDINAR LDEE Laboratory, Department of Electrical Engineering, University of Sciences and Technology USTO, Algeria. Author
  • AMEUR FETHI AIMER LDEE Laboratory, Department of Electrical Engineering, University of Saida, Algeria. Author
  • AZEDDINE BENDIABDELLAH LDEE Laboratory, Department of Electrical Engineering, University of Sciences and Technology USTO, Algeria. Author

DOI:

https://doi.org/10.59277/RRST-EE.2026.2.7

Keywords:

Induction motor, Stator current, Rolling-elements bearing, Fault diagnosis, Spectral kurtosis, Kurtogram

Abstract

It is well known that the bearing’s outer race is the most failing part of the rolling-element bearings used in induction motors, which causes, in most cases, the complete shutdown of the entire industrial process. Moreover, the early diagnosis of this fault is essential to improve the operational reliability of these motors and in order to avoid huge financial losses. In this aim, demodulating the stator current envelope is a promising diagnostic approach, allowing direct extraction of the fault signature without being affected by the fundamental frequency. In addition, the Kurtogram, a statistical tool of 4th order spectral analysis, makes it possible to extract the signature of the searched faults even in the case of non-stationary signals. Therefore, the purpose of this paper is to address a comparative study between two Kurtogram-based computation algorithms: Fast-Kurtogram and Wavelet-Kurtogram. The experimental results obtained by the stator current spectral analysis show the superiority of the Wavelet-Kurtogram compared to the Fast-Kurtogram in the detection and localization of the outer race fault.

Author Biography

  • MOHAMMED-EL-AMINE KHODJA, National School of Built and Ground Works Engineering, Preparatory Classes Department, National School of Built and Ground Works Engineering, Algeria., LDEE Laboratory, Department of Electrical Engineering, University of Sciences and Technology USTO, Algeria.

    With a deep-rooted passion for Electrical Engineering education, my role at ENSTP and USTO-MB encompasses university lecturing and management, driven by a commitment to academic excellence. Spearheading innovative teaching methods, my focus lies in the creation of dynamic learning environments and the development of a curriculum that aligns with industry standards.

    At USTO-MB, my research in advanced signal processing for fault detection in electrical motors and drives has been instrumental in advancing the field. Collaborating with colleagues and mentoring students, we strive to contribute meaningful advancements to Electrical Engineering. My leadership in the classroom and lab reflects a dedication to fostering the next generation of engineers and researchers.

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Published

02.06.2026

Issue

Vol. 71 No. 2 (2026): RRST-EE

Section

Électrotechnique et électroénergétique | Electrical and Power Engineering

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How to Cite

INDUCTION MOTOR BEARING FAULTS DIAGNOSIS USING AN IMPROVED KURTOGRAM METHOD. (2026). REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE, 71(2), 211-216. https://doi.org/10.59277/RRST-EE.2026.2.7