Literature DB >> 33804512

Vibration Analysis for Fault Detection of Wind Turbine Drivetrains-A Comprehensive Investigation.

Wei Teng1, Xian Ding2, Shiyao Tang3, Jin Xu2, Bingshuai Shi1, Yibing Liu1.   

Abstract

Vibration analysis is an effective tool for the condition monitoring and fault diagnosis of wind turbine drivetrains. It enables the defect location of mechanical subassemblies and health indicator construction for remaining useful life prediction, which is beneficial to reducing the operation and maintenance costs of wind farms. This paper analyzes the structure features of different drivetrains of mainstream wind turbines and introduces a vibration data acquisition system. Almost all the research on the vibration-based diagnosis algorithm for wind turbines in the past decade is reviewed, with its effects being discussed. Several challenging tasks and their solutions in the vibration-based fault detection of wind turbine drivetrains are proposed from the perspective of practicality for wind turbines, including the fault detection of planetary subassemblies in multistage wind turbine gearboxes, fault feature extraction under nonstationary conditions, fault information enhancement techniques and health indicator construction. Numerous naturally damaged cases representing the real operational features of industrial wind turbines are given, with a discussion of the failure mechanism of defective parts in wind turbine drivetrains as well.

Entities:  

Keywords:  failure mechanism; fault detection; signal processing; vibration analysis; wind turbine drivetrain

Year:  2021        PMID: 33804512      PMCID: PMC7957485          DOI: 10.3390/s21051686

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  1 in total

1.  Fault diagnosis for wind turbine planetary ring gear via a meshing resonance based filtering algorithm.

Authors:  Tianyang Wang; Fulei Chu; Qinkai Han
Journal:  ISA Trans       Date:  2016-11-25       Impact factor: 5.468

  1 in total
  3 in total

1.  A Model-Agnostic Meta-Baseline Method for Few-Shot Fault Diagnosis of Wind Turbines.

Authors:  Xiaobo Liu; Wei Teng; Yibing Liu
Journal:  Sensors (Basel)       Date:  2022-04-25       Impact factor: 3.847

2.  A Probabilistic Bayesian Parallel Deep Learning Framework for Wind Turbine Bearing Fault Diagnosis.

Authors:  Liang Meng; Yuanhao Su; Xiaojia Kong; Xiaosheng Lan; Yunfeng Li; Tongle Xu; Jinying Ma
Journal:  Sensors (Basel)       Date:  2022-10-09       Impact factor: 3.847

3.  Fault Diagnosis of a Wind Turbine Gearbox Based on Improved Variational Mode Algorithm and Information Entropy.

Authors:  Fan Zhang; Wenlei Sun; Hongwei Wang; Tiantian Xu
Journal:  Entropy (Basel)       Date:  2021-06-23       Impact factor: 2.524

  3 in total

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