scholarly journals Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach

Energies ◽  
2016 ◽  
Vol 9 (6) ◽  
pp. 379 ◽  
Author(s):  
Zhi-Xin Yang ◽  
Xian-Bo Wang ◽  
Jian-Hua Zhong
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Extreme Learning Machines ◽  
Learning Machines ◽  
Turbine Equipment

A novel doublet extreme learning machines for Delta 3D printer fault diagnosis using attitude sensor

2020 ◽  
Author(s):  
Jianwen Guo ◽  
Xiaoyan Li ◽  
Zhiyuan Liu ◽  
Shaohui Zhang ◽  
Jiapeng Wu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
3D Printer ◽  
Extreme Learning Machines ◽  
Learning Machines ◽  
Attitude Sensor

scholarly journals Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines

2021 ◽  
Vol 11 (2) ◽  
pp. 590
Author(s):  
Pere Marti-Puig ◽  
Alejandro Blanco-M. ◽  
Moisès Serra-Serra ◽  
Jordi Solé-Casals
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Feedforward Neural Networks ◽  
Model Parameters ◽  
Extreme Learning Machines ◽  
Target Variable ◽  
Learning Machines ◽  
Realistic Option ◽  
Hidden Layer ◽  
Search Approach

In this paper, a method to build models to monitor and evaluate the health status of wind turbines using Single-hidden Layer Feedforward Neural networks (SLFN) is presented. The models are trained using the Extreme Learning Machines (ELM) strategy. The data used is obtained from the SCADA systems, easily available in modern wind turbines. The ELM technique requires very low computational costs for the training of the models, and thus allows for the integration of a grid-search approach with parallelized instances to find out the optimal model parameters. These models can be built both individually, considering the turbines separately, or as an aggregate for the whole wind plant. The followed strategy consists in predicting a target variable using the rest of the variables of the system/subsystem, computing the error deviation from the real target variable and finally comparing high error values with a selection of alarm events for that system, therefore validating the performance of the model. The experimental results indicate that this methodology leads to the detection of mismatches in the stages of the system’s failure, thus making it possible to schedule the maintenance operation before a critical failure occurs. The simplicity of the ELM systems and the ease with which the parameters can be adjusted make it a realistic option to be implemented in wind turbine models to work in real time.

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