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A Study on the Predictive Maintenance Algorithms Considering Load Characteristics of PMSMs to Drive EGR Blowers for Smart Ships

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  • Sung-An Kim

    (High Power Electric Propulsion Center, Korea Maine Equipment Research Institute, Ulsan 44776, Korea)

Abstract

Exhaust gas recirculation (EGR) is a NOx reduction technology that can meet stringent environmental regulatory requirements. EGR blower systems must be used to increase the exhaust gas pressure at a lower rate than the scavenging air pressure. Electric motor drive systems are essential to rotate the EGR blowers. For the effective management of the EGR blower systems in smart ships, there is a growing need for predictive maintenance technology fused with information and communication technology (ICT). Since an electric motor accounts for about 80% of electric loads driven by the EGR, it is essential to apply the predictive maintenance technology of the electric motor to maximize the reliability and operation time of the EGR blower system. Therefore, this paper presents the predictive maintenance algorithm to prevent the stator winding turn faults, which is the most significant cause of the electrical failure of the electric motors. The proposed algorithm predicts the remaining useful life (RUL) by obtaining the winding temperature value by considering the load characteristics of the electric motor. The validity of the proposed algorithm is verified through the simulation results of an EGR blower system model and the experimental results derived from using a test rig.

Suggested Citation

  • Sung-An Kim, 2021. "A Study on the Predictive Maintenance Algorithms Considering Load Characteristics of PMSMs to Drive EGR Blowers for Smart Ships," Energies, MDPI, vol. 14(18), pages 1-13, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5744-:d:634084
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    References listed on IDEAS

    as
    1. Sung-An Kim & Kyung-Pyo Hong, 2021. "Analysis and Experimental Verification of a Variable Speed Turbo Air Centrifugal Compressor System for Energy Saving," Energies, MDPI, vol. 14(4), pages 1-11, February.
    2. Wei, Li & Yan, Fuwu & Hu, Jie & Xi, Guangwei & Liu, Bo & Zeng, Jiawei, 2017. "Nox conversion efficiency optimization based on NSGA-II and state-feedback nonlinear model predictive control of selective catalytic reduction system in diesel engine," Applied Energy, Elsevier, vol. 206(C), pages 959-971.
    3. Sung-An Kim, 2021. "Improved Modeling of a Multi-Level Inverter for TACS to Reduce Computational Time and Improve Accuracy," Energies, MDPI, vol. 14(4), pages 1-13, February.
    4. Lao, Chung Ting & Akroyd, Jethro & Eaves, Nickolas & Smith, Alastair & Morgan, Neal & Nurkowski, Daniel & Bhave, Amit & Kraft, Markus, 2020. "Investigation of the impact of the configuration of exhaust after-treatment system for diesel engines," Applied Energy, Elsevier, vol. 267(C).
    5. Sokratis Stoumpos & Gerasimos Theotokatos, 2020. "Multiobjective Optimisation of a Marine Dual Fuel Engine Equipped with Exhaust Gas Recirculation and Air Bypass Systems," Energies, MDPI, vol. 13(19), pages 1-20, September.
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    Cited by:

    1. Myoung-Su Kim & Sung-An Kim, 2023. "Design and Experimental Verification of Hubless Rim-Driven Propulsor Consisting of Bearingless Propeller for an Unmanned Underwater Drone," Energies, MDPI, vol. 16(21), pages 1-16, November.

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