Skip to main content

Advanced Search

Projected result for your current search: 0

Search Operators:

You can use operators in your search query to narrow down your results even more. Click on the search operator to see an explanation of how it works.
Finds documents with exactly this word group, in exactly this word order and spelling (e.g. "employer branding").
Finds documents with both search terms (e.g. sales AND bonus).
Finds documents with one or the other search term (e.g. porsche OR volkswagen).
Finds documents with all search terms. Blank space is understood as AND (e.g. man robot production).
Finds documents with no appearance of the word behind NOT (e.g. ford NOT "harrison ford").
Finds documents where the search term is mentioned at least "n" times."n" may be any number (e.g. COUNT(gear)>8).
Finds documents with both search terms in any word order, permitting "n" words as a maximum distance between them. Best choose between 15 and 30 (e.g. NEAR(recruit, professionals, 20)).
Finds documents with the search term in word versions or composites. The asterisk * marks whether you wish them BEFORE, BEHIND, or BEFORE and BEHIND the search term (e.g. lightweight*, *lightweight, *lightweight*).
Finds documents with the search term in different spellings. "?" allows only one character (e.g. organi?ation).
Special characters are understood as AND (e.g. Miller Bros. & Sons).

Semantic Search

powered by (Link opens in a new window)
loading …
Log in
Top
Annals of Data Science

Research on Wind Turbine Fault Diagnosis Method Realized by Vibration Monitoring

  • 10-10-2023
Published in:
Author
Xiuhua Jiang
Published in
Annals of Data Science | Issue 2/2024

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The article delves into the challenges of diagnosing wind turbine faults due to their remote installation and harsh environments. It introduces a novel method using vibration monitoring and a genetic algorithm-enhanced back-propagation neural network (GA-BPNN) for fault diagnosis. The study analyzes typical wind turbine faults and uses time-domain analysis of vibration signals to identify fault characteristic parameters. Through simulation experiments, the GA-BPNN is shown to have high accuracy in diagnosing various faults, outperforming other algorithms like support vector machines and random forests. The article concludes by highlighting the feasibility and superior performance of the GA-BPNN in wind turbine fault diagnosis, paving the way for future research with real-world data.
AI Generated

This summary of the content was generated with the help of AI.

Already a customer? Then log in here to gain access.

Customer login

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Engineering + Technology"

Online-Abonnement

Springer Professional "Engineering + Technology" gives you access to:

  • more than 75.000 books
  • more than 390 journals

from the following specialised fileds:

  • Automotive
  • Business IT + Informatics
  • Construction + Real Estate
  • Electrical Engineering + Electronics
  • Energy + Sustainability
  • Mechanical Engineering + Materials
  • Surfaces + Materials Technology





 

Secure your knowledge advantage now!

inform now

Springer Professional "Business + Economics & Engineering + Technology"

Online-Abonnement

Springer Professional "Business + Economics & Engineering + Technology" gives you access to:

  • more than 130.000 books
  • more than 540 journals

from the following subject areas:

  • Automotive
  • Construction + Real Estate
  • Business IT + Informatics
  • Electrical Engineering + Electronics
  • Energy + Sustainability
  • Finance + Banking
  • Management + Leadership
  • Marketing + Sales
  • Mechanical Engineering + Materials
  • Surfaces + Materials Technology
  • Insurance + Risk


Secure your knowledge advantage now!

inform now

Springer Professional "Business + Economics"

Online-Abonnement

Springer Professional "Business + Economics" gives you access to:

  • more than 100.000 books
  • more than 340 journals

from the following specialised fileds:

  • Construction + Real Estate
  • Business IT + Informatics
  • Finance + Banking
  • Management + Leadership
  • Marketing + Sales
  • Insurance + Risk



Secure your knowledge advantage now!

inform now

For explorers: Get to know our platforms better with a free registration.

Register for free
previous article A Survey on Differential Privacy for Medical Data Analysis
next article A Generalized Gompertz Distribution with Hazard Power Parameter and Its Bivariate Extension: Properties and Applications
Title
Research on Wind Turbine Fault Diagnosis Method Realized by Vibration Monitoring
Author
Xiuhua Jiang
Publication date
10-10-2023
Publisher
Springer Berlin Heidelberg
Published in
Annals of Data Science / Issue 2/2024
Print ISSN: 2198-5804
Electronic ISSN: 2198-5812
DOI
https://doi.org/10.1007/s40745-023-00497-x
This content is only visible if you are logged in and have the appropriate permissions.

Other articles of this Issue 2/2024

Student Trend Analysis for Foreign Education Employing Machine Learning: A Case Study from ‘Disha Consultants’, Gujarat, India

Improving Bayesian Classifier Using Vine Copula and Fuzzy Clustering Technique

Problem of Compromise Allocation in Multivariate Stratified Sampling Using Intuitionistic Fuzzy Programming

A Survey on Differential Privacy for Medical Data Analysis

Large Scopus Data Sets and Its Analysis for Decision Making

Data Analysis by Adaptive Progressive Hybrid Censored Under Bivariate Model

Premium Partner

    Image Credits
    Schmalkalden/© Schmalkalden, NTT Data/© NTT Data, Verlagsgruppe Beltz/© Verlagsgruppe Beltz, EGYM Wellpass GmbH/© EGYM Wellpass GmbH, rku.it GmbH/© rku.it GmbH, zfm/© zfm, ibo Software GmbH/© ibo Software GmbH, Sovero/© Sovero, Axians Infoma GmbH/© Axians Infoma GmbH, OEDIV KG/© OEDIV KG, Rundstedt & Partner GmbH/© Rundstedt & Partner GmbH