Data-driven fault diagnosis for wind turbines using modified multiscale fluctuation dispersion entropy and cosine pairwise-constrained supervised manifold mapping-Reference-Cited by-同舟云学术

Data-driven fault diagnosis for wind turbines using modified multiscale fluctuation dispersion entropy and cosine pairwise-constrained supervised manifold mapping

Published:2021-09 Issue: Volume:228 Page:107276
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Wang Zhenya,Li Gaosong,Yao Ligang,Qi Xiaoli,Zhang Jun

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Management Information Systems,Software

Reference48 articles.

1. Mahalanobis semi-supervised mapping and beetle antennae search based support vector machine for wind turbine rolling bearings fault diagnosis;Wang;Renew. Energy,2020

2. Vibration based condition monitoring and fault diagnosis of wind turbine planetary gearbox: A review;Wang;Mech. Syst. Signal Process.,2019

3. Multichannel fault diagnosis of wind turbine driving system using multivariate singular spectrum decomposition and improved Kolmogorov complexity;Yan;Renew. Energy,2021

4. Application of an improved MCKDA for fault detection of wind turbine gear based on encoder signal;Miao;Renew. Energy,2020

5. Universal domain adaptation in fault diagnostics with hybrid weighted deep adversarial learning;Zhang;IEEE Trans. Ind. Inf.,2021

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