Surface Discharge Detection and Severity Assessment of Printed Circuit Boards for Electric Aircraft Using Fluorescent Fiber

Yiming Zang*, Mohamad Ghaffarian Niasar, Ze Li, Yaocheng Li, Qinglin Qian, Xiuchen Jiang, Peter Vaessen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

More and more printed circuit boards (PCBs) will be used in electric aircraft to achieve higher power density of the on-board electric system, while PCBs in aircraft are more likely to generate partial discharges (PDs) due to external factors such as compact structure and low air pressure. Therefore, this paper proposes a fluorescent fiber-based method for detecting and evaluating PDs on PCBs. The detection method is effectively immune against the presence of the electromagnetic, acoustic and vibration interference. Based on the optical detection, the evolution regularities of PCB surface appearance changes, optical PRPD patterns and optical pulses during the aging process of PCB under different air pressure are analyzed in this paper. Then, 12 assessment features are extracted for the PD aging process, and the contribution of these 12 features to the severity assessment at different air pressures is obtained using the minimal-redundancy-maximal-relevance (mRMR) algorithm. Finally, different numbers of PD features are tested for PD severity assessment by the SVM algorithm. The evaluation results show that the severity assessment method proposed in this paper can achieve an assessment accuracy of at least 91.1% and up to 94.4% under all three air pressures, which has good application and guidance value.

Original languageEnglish
JournalIEEE Transactions on Dielectrics and Electrical Insulation
DOIs
Publication statusAccepted/In press - 2024

Keywords

  • Aging
  • Aircraft
  • electric aircraft
  • Fluorescence
  • fluorescent fiber
  • Optical fiber sensors
  • Optical fiber theory
  • Optical fibers
  • Optical pulses
  • partial discharge
  • printed circuit boards
  • severity assessment

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