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Evolution of Shipboard Motor Failure Monitoring Technology: Multi-Physics Field Mechanism Modeling and Intelligent Operation and Maintenance System Integration

Author

Listed:
  • Jun Sun

    (College of Electrical Engineering, Naval University of Engineering, Wuhan 430033, China)

  • Pan Sun

    (College of Electrical Engineering, Naval University of Engineering, Wuhan 430033, China)

  • Boyu Lin

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China)

  • Weibo Li

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China
    College of Electrical Engineering, Northwest Minzu University, Lanzhou 730124, China)

Abstract

As a core component of both the ship propulsion system and mission-critical equipment, shipboard motors are undergoing a technological transition from traditional fault diagnosis to multi-physical-field collaborative modeling and integrated intelligent maintenance systems. This paper provides a systematic review of recent advances in shipboard motor fault monitoring, with a focus on key technical challenges under complex service environments, and offers several innovative insights and analyses in the following aspects. First, regarding the fault evolution under electromagnetic–thermal–mechanical coupling, this study summarizes the typical fault mechanisms, such as bearing electrical erosion, rotor eccentricity, permanent magnet demagnetization, and insulation aging, and analyzes their modeling approaches and multi-physics coupling evolution paths. Second, in response to the problem of multi-source signal fusion, the applicability and limitations of feature extraction methods—including current analysis, vibration demodulation, infrared thermography, and Dempster–Shafer (D-S) evidence theory—are evaluated, providing a basis for designing subsequent signal fusion strategies. With respect to intelligent diagnostic models, this paper compares model-driven and data-driven approaches in terms of their suitability for different scenarios, highlighting their complementarity and integration potential in the complex operating conditions of shipboard motors. Finally, considering practical deployment needs, the key aspects of monitoring platform implementation under shipborne edge computing environments are discussed. The study also identifies current research gaps and proposes future directions, such as digital twin-driven intelligent maintenance, fleet-level PHM collaborative management, and standardized health data transmission. In summary, this paper offers a comprehensive analysis in the areas of fault mechanism modeling, feature extraction method evaluation, and system deployment frameworks, aiming to provide a theoretical reference and engineering insights for the advancement of shipboard motor health management technologies.

Suggested Citation

  • Jun Sun & Pan Sun & Boyu Lin & Weibo Li, 2025. "Evolution of Shipboard Motor Failure Monitoring Technology: Multi-Physics Field Mechanism Modeling and Intelligent Operation and Maintenance System Integration," Energies, MDPI, vol. 18(16), pages 1-25, August.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:16:p:4336-:d:1724552
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    References listed on IDEAS

    as
    1. Junxiang Li & Ziang Li & Jian Zhang & Shuyuan Zhao & Feitian Cheng & Chuan Qian & Xingyu Hu & Guoxiang Zhou, 2023. "Automated Monitoring of the Uniform Demagnetization Faults in Permanent-Magnet Synchronous Motors: Practical Methods and Challenges," Sustainability, MDPI, vol. 15(23), pages 1-22, November.
    2. Muhammad Usman Sardar & Toomas Vaimann & Lauri Kütt & Ants Kallaste & Bilal Asad & Siddique Akbar & Karolina Kudelina, 2023. "Inverter-Fed Motor Drive System: A Systematic Analysis of Condition Monitoring and Practical Diagnostic Techniques," Energies, MDPI, vol. 16(15), pages 1-41, July.
    3. Rahul R. Kumar & Mauro Andriollo & Giansalvo Cirrincione & Maurizio Cirrincione & Andrea Tortella, 2022. "A Comprehensive Review of Conventional and Intelligence-Based Approaches for the Fault Diagnosis and Condition Monitoring of Induction Motors," Energies, MDPI, vol. 15(23), pages 1-36, November.
    4. Lorin Jenkel & Stefan Jonas & Angela Meyer, 2023. "Privacy-Preserving Fleet-Wide Learning of Wind Turbine Conditions with Federated Learning," Energies, MDPI, vol. 16(17), pages 1-29, September.
    5. Karolina Kudelina & Bilal Asad & Toomas Vaimann & Anton Rassõlkin & Ants Kallaste & Huynh Van Khang, 2021. "Methods of Condition Monitoring and Fault Detection for Electrical Machines," Energies, MDPI, vol. 14(22), pages 1-20, November.
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