Mengdi Li
Beijing Jiaotong University
2 Papers
Mengdi Li is an academic researcher from Beijing Jiaotong University. The author has contributed to research in topics: Fault (power engineering) & Computer science. The author has an hindex of 1, co-authored 2 publications.
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Papers
Misalignment Fault Diagnosis for Wind Turbines Based on Information Fusion.
TL;DR: In this article, multiple types of signals including vibration, temperature, and stator current are used simultaneously for wind turbine misalignment diagnosis, and the model is constructed by integrated methods based on Dempster-Shafer (D-S) evidence theory.
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Low-Pass Filtering Empirical Wavelet Transform Machine Learning Based Fault Diagnosis for Combined Fault of Wind Turbines.
TL;DR: Wang et al. as discussed by the authors proposed a low-pass filtering empirical wavelet transform (LPFEWT) machine learning based fault diagnosis method for combined fault of wind turbines, which can identify the fault type of wind turbine simply and efficiently without human experience and with low computation costs.
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