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Application of AdaBoost for stator fault diagnosis in three-phase permanent magnet synchronous motors based on vibration–current data fusion analysis

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Abstract

Permanent magnet synchronous motors (PMSMs) are of wide utilization in various industrial applications for their precise control capabilities. Hereby, these PMSMs are consistently subjected to operational faults that might lead to heavy consequences effecting overall safety. Responsively, health state monitoring techniques for early failure detection are of paramount necessity to ensure optimum performance and longevity of such applications. A qualification-based methodology is presented in the current study in response to fault diagnosis of three-phased PMSMs using vibration–current fusion of data analytics. Stator faults were induced as inter-turn short circuits by the use of bypassing resistances where experimental datasets from a manufactured test rig were acquired which are current and vibration time-domain signals then progressed into statistical features. Different operating cases were diagnosed and classified based on AdaBoost, a currently-growing machine learning model. Utilizing vibration statistical features alone has improved fault detection to 83.0%, while the vibration–current data fusion has achieved an accuracy of 90.7% which was the highest of them all. The precision, F1 score, and recall values were all of 0.907 that sill validated the accuracy results of the data fusion methodology. This study highlights the potential of the data fusion analysis in early fault diagnosis of which enables proactive maintenance strategies, and enhances reliability of PMSMs in various applications of industrial machinery in addition to renewable energy systems.

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Authors and Affiliations

  1. Training and Workshops Center, University of Technology-Iraq, Baghdad, Iraq

    Luttfi A. Al-Haddad, Sameera Sadey Shijer, Safaa Taha Al-Ani & Eyad Taha Abd

  2. Mechanical Engineering Department, University of Technology-Iraq, Baghdad, Iraq

    Alaa Abdulhady Jaber & Ahmed A. Al-Zubaidi

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  1. Luttfi A. Al-Haddad
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  2. Sameera Sadey Shijer
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  3. Alaa Abdulhady Jaber
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  4. Safaa Taha Al-Ani
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  5. Ahmed A. Al-Zubaidi
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Contributions

SSS helped in conceptualization; AAJ helped in methodology; STAl-A, ETA, and LAAl-h helped in formal analysis; LAAl-H contributed to writing—original draft preparation; and LAAl-H and AAAl-Z contributed to writing—review and editing. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Luttfi A. Al-Haddad.

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The authors have no conflicts of interest to declare. All co-authors have seen and agree with the contents of this manuscript, and there is no financial interest to report.

Ethical and informed consent for data used

In this paper, the authors affirm acquiring the test rig experimental data from a published article in a journal concerned in publishing experimental datasets. The data are online and free to use as the article is published in open-access format and, hence, already subjected to regulations and guidelines of publisher. We hereby consent on complying with ethical data practices and have cited the article accordingly [53].

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Al-Haddad, L.A., Shijer, S.S., Jaber, A.A. et al. Application of AdaBoost for stator fault diagnosis in three-phase permanent magnet synchronous motors based on vibration–current data fusion analysis. Electr Eng (2024). https://doi.org/10.1007/s00202-023-02195-6

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