Wind Turbine Fault Detection Using Machine Learning and Neural Networks

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Wind Turbine Fault Detection Using Machine Learning and Neural Networks

Description

Offshore wind turbines in farm locations are hard to reach and may pose several faults, causing problems in maintenance cycles as well as costly fault repairs and procedures. The smart solution to fault detection in wind turbines is to utilize remote monitoring and diagnostics based on the sensor data. Faults using sensor data can be detected by artificial intelligence techniques such as machine learning and neural networks. This white paper explains the entire process of detecting faults in wind turbines using machine learning and neural networks.

Highlights

  • Remote Fault Monitoring and Detection Concept
  • Fault Diagnostics Process Flow
  • Fault Detection Model Development using AI
  • Support Vector Machines (SVM)
  • Artificial Neural Networks
  • Case study: Fault Detection in Wind Turbines using SVM and NN
  • Wind Turbine Model Diagnostics
  • ault Detection Algorithms using AI
  • Comparison of Kalman Filter Algorithm and Machine Learning Algorithms

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    Description

    Offshore wind turbines in farm locations are hard to reach and may pose several faults, causing problems in maintenance cycles as well as costly fault repairs and procedures. The smart solution to fault detection in wind turbines is to utilize remote monitoring and diagnostics based on the sensor data. Faults using sensor data can be detected by artificial intelligence techniques such as machine learning and neural networks. This white paper explains the entire process of detecting faults in wind turbines using machine learning and neural networks.

    Project Highlights

    • Remote Fault Monitoring and Detection Concept
    • Fault Diagnostics Process Flow
    • Fault Detection Model Development using AI
    • Support Vector Machines (SVM)
    • Artificial Neural Networks
    • Case study: Fault Detection in Wind Turbines using SVM and NN
    • Wind Turbine Model Diagnostics
    • ault Detection Algorithms using AI
    • Comparison of Kalman Filter Algorithm and Machine Learning Algorithms

    To read more, download the copy

    arrows-new-1

    To download this resource

    Fill in the details below






      I have read and understand the Privacy Policy By submitting this form, I acknowledge that I have read and understand the Privacy Policy

      I wish to be contacted by eInfochips I wish to be contacted by eInfochips

      For all career related inquiries, kindly visit our careers page or write to career@einfochips.com

      Description

      Offshore wind turbines in farm locations are hard to reach and may pose several faults, causing problems in maintenance cycles as well as costly fault repairs and procedures. The smart solution to fault detection in wind turbines is to utilize remote monitoring and diagnostics based on the sensor data. Faults using sensor data can be detected by artificial intelligence techniques such as machine learning and neural networks. This white paper explains the entire process of detecting faults in wind turbines using machine learning and neural networks.

      Project Highlights

      • Remote Fault Monitoring and Detection Concept
      • Fault Diagnostics Process Flow
      • Fault Detection Model Development using AI
      • Support Vector Machines (SVM)
      • Artificial Neural Networks
      • Case study: Fault Detection in Wind Turbines using SVM and NN
      • Wind Turbine Model Diagnostics
      • ault Detection Algorithms using AI
      • Comparison of Kalman Filter Algorithm and Machine Learning Algorithms

      To read more, download the copy

      arrows-new-1

      To download this resource

      Fill in the details below






        I have read and understand the Privacy Policy By submitting this form, I acknowledge that I have read and understand the Privacy Policy

        I wish to be contacted by eInfochips I wish to be contacted by eInfochips

        For all career related inquiries, kindly visit our careers page or write to career@einfochips.com