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A Review on Fault Diagnosis Technology of Key Components in Cold Ironing System

Author

Listed:
  • Kai Ding

    (State Grid Hubei Electric Power Company Limited Research Institute, Wuhan 430000, China)

  • Chen Yao

    (No. 2 Institute of Water Transportation, Anhui Transport Survey and Design Institute Co., Ltd., Hefei 230011, China)

  • Yifan Li

    (School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430000, China)

  • Qinglong Hao

    (School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430000, China)

  • Yaqiong Lv

    (School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430000, China)

  • Zengrui Huang

    (State Grid Hubei Electric Power Company Limited Research Institute, Wuhan 430000, China)

Abstract

Nowadays, cold ironing technology has been demonstrated to be an effective solution to deal with the environmental and social problems brought by port ship emissions and relevant effects. The working states of cold ironing equipment, especially the key components such as circuit breakers, transformers and frequency converters, have a significant effect on the safety and reliability of the whole system. However, due to the harsh working environment of cold ironing equipment, they are prone to a high risk of failure. In this respect, fault diagnosis methods can play a significant role in detecting potential failure in time and guarantee the safe and reliable operation of the cold ironing system. In recent years, research on the fault diagnosis of a cold ironing system has been rapidly growing, and this paper aims to present a comprehensive review of this literature, with an emphasis on the fault diagnosis technology applied to the key components in a cold ironing system. This review classifies the literature according to the type of key component, and, for each special type of component, the fault diagnosis methods are further categorized and analyzed. This paper provides useful references for professionals and researchers working on the fault diagnosis of a cold ironing system and points out valuable research directions in the future.

Suggested Citation

  • Kai Ding & Chen Yao & Yifan Li & Qinglong Hao & Yaqiong Lv & Zengrui Huang, 2022. "A Review on Fault Diagnosis Technology of Key Components in Cold Ironing System," Sustainability, MDPI, vol. 14(10), pages 1-28, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:6197-:d:819356
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    References listed on IDEAS

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    7. Maciej Skowron & Teresa Orlowska-Kowalska & Marcin Wolkiewicz & Czeslaw T. Kowalski, 2020. "Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor," Energies, MDPI, vol. 13(6), pages 1-21, March.
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    Cited by:

    1. Saud Altaf & Shafiq Ahmad & Mazen Zaindin & Shamsul Huda & Sofia Iqbal & Muhammad Waseem Soomro, 2022. "Multiple Industrial Induction Motors Fault Diagnosis Model within Powerline System Based on Wireless Sensor Network," Sustainability, MDPI, vol. 14(16), pages 1-29, August.

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