Open AccessIncipient fault diagnosis method for DC–DC converters based on sensitive fault features
Author(s) -
Yu Yang,
Jiang Yueming,
Liu Yanlong,
Peng Xiyuan
Publication year - 2020
Publication title -
iet power electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.637
H-Index - 77
eISSN - 1755-4543
pISSN - 1755-4535
DOI - 10.1049/iet-pel.2020.0857
Subject(s) - fault (geology) , converters , fault indicator , stuck at fault , wavelet , fault detection and isolation , time domain , engineering , computer science , control theory (sociology) , electronic engineering , artificial intelligence , voltage , electrical engineering , control (management) , seismology , geology , actuator , computer vision
DC–DC converters play an important role in electrical systems. The fault state of a DC–DC converter has a major impact on the operation of the back‐end components and the entire electrical system. Therefore, the fault diagnosis is very necessary when the fault state of DC–DC is in the early stage. It can decrease the likelihood of happening the serious faults that may result in the enormous economic loss. To effectively diagnose the incipient fault in DC–DC converters, an incipient fault diagnosis method based on sensitive fault features is proposed. Firstly, for each type of the incipient fault, this study obtains the statistical features in time domain and the wavelet analysis local energy values in the frequency domain of the output. Then to further improve the incipient fault diagnosis accuracy, this study removes the redundant fault features based on overlap calculation, the unique fault features for each type of incipient fault will be selected. Finally, these sensitive fault features are used to build support vector data description models, which can diagnose the incipient faults. Simulation and hardware experimental results validate the practicability and effectiveness of the proposed method.