A Multi-Level-Denoising Autoencoder Approach for Wind Turbine Fault Detection
نویسندگان
چکیده
منابع مشابه
Structured Denoising Autoencoder for Fault Detection and Analysis
This paper proposes a new fault detection and analysis approach which can leverage incomplete prior information. Conventional data-driven approaches suffer from the problem of overfitting and result in high rates of false positives, and model-driven approaches suffer from a lack of specific information about complex systems. We overcome these problems by modifying the denoising autoencoder (DA)...
متن کاملWavelet based Fault Detection for Wind Turbine
Renewable energy sources are gaining high prominence in today’s world. However, these sources do not supply energy throughout the year and hence efficiency is required when extracting energy from them. Wind energy is a recently developing area of common interest. Efficiency of a wind turbine is however, very low. Hence, detection of fault in the system becomes very essential so as to increase t...
متن کاملRobust Model- Based Fault Detection and Isolation for V47/660kW Wind Turbine
In this paper, in order to increase the efficiency, to reduce the cost and to prevent the failures of wind turbines, which lead to an extensive break down, a robust fault diagnosis system is proposed for V47/660kW wind turbine operated in Manjil wind farm, Gilan province, Iran. According to the acquired data from Iran wind turbine industry, common faults of the wind turbine such as sensor fault...
متن کاملFault detection and isolation for a wind turbine benchmark using a mixed Bayesian/Set-membership approach
This paper addresses the problem of fault detection and isolation of wind turbines using a mixed Bayesian/Setmembership approach. Modeling errors are assumed to be unknown but bounded, following the set-membership approach. On the other hand, measurement noise is also assumed to be bounded, but following a statistical distribution inside the bounds. To avoid false alarms, the fault detection pr...
متن کاملRepresentational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach
Reliable and quick response fault diagnosis is crucial for the wind turbine generator system (WTGS) to avoid unplanned interruption and to reduce the maintenance cost. However, the conditional data generated from WTGS operating in a tough environment is always dynamical and high-dimensional. To address these challenges, we propose a new fault diagnosis scheme which is composed of multiple extre...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2914731