scholarly journals A fault detection framework using recurrent neural networks for condition monitoring of wind turbines

Wind Energy ◽  
2021 ◽  
Author(s):  
Yue Cui ◽  
Pramod Bangalore ◽  
Lina Bertling Tjernberg
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Recurrent Neural Networks

Condition monitoring and fault detection in wind turbines based on cointegration analysis of SCADA data

2018 ◽  
Vol 116 ◽  
pp. 107-122 ◽  
Author(s):  
Phong B. Dao ◽  
Wieslaw J. Staszewski ◽  
Tomasz Barszcz ◽  
Tadeusz Uhl
Keyword(s):  
Fault Detection ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Cointegration Analysis

scholarly journals A Novel Approach to Condition Monitoring of the Cutting Process Using Recurrent Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4493
Author(s):  
Rui Silva ◽  
António Araújo
Keyword(s):  
Neural Networks ◽  
Condition Monitoring ◽  
Recurrent Neural Networks ◽  
Cutting Process ◽  
Time Dependent ◽  
Industrial Processes ◽  
Fundamental Part ◽  
Novel Approach ◽  
Noisy Signals ◽  
Proper Condition

Condition monitoring is a fundamental part of machining, as well as other manufacturing processes where, generally, there are parts that wear out and have to be replaced. Devising proper condition monitoring has been a concern of many researchers, but there is still a lack of robustness and efficiency, most often hindered by the system’s complexity or otherwise limited by the inherent noisy signals, a characteristic of industrial processes. The vast majority of condition monitoring approaches do not take into account the temporal sequence when modelling and hence lose an intrinsic part of the context of an actual time-dependent process, fundamental to processes such as cutting. The proposed system uses a multisensory approach to gather information from the cutting process, which is then modelled by a recurrent neural network, capturing the evolutive pattern of wear over time. The system was tested with realistic cutting conditions, and the results show great effectiveness and accuracy with just a few cutting tests. The use of recurrent neural networks demonstrates the potential of such an approach for other time-dependent industrial processes under noisy conditions.

Recurrent Neural Networks for Nonlinear System Modelling in Fault Detection

1997 ◽  
Vol 30 (18) ◽  
pp. 701-706 ◽  
Author(s):  
Markus Schubert ◽  
Birgit Köppen-Seliger ◽  
Paul M. Frank
Keyword(s):  
Neural Networks ◽  
Nonlinear System ◽  
Fault Detection ◽  
Recurrent Neural Networks ◽  
System Modelling

scholarly journals Neural networks applications for fault detection on wind turbines

2011 ◽  
pp. 1138-1141 ◽  
Author(s):  
R.F. Mesquita Brandao ◽  
J.A. Beleza Carvalho ◽  
F.P. Maciel Barbosa
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Wind Turbines

Condition monitoring and fault detection of wind turbines and related algorithms: A review

2009 ◽  
Vol 13 (1) ◽  
pp. 1-39 ◽  
Author(s):  
Z. Hameed ◽  
Y.S. Hong ◽  
Y.M. Cho ◽  
S.H. Ahn ◽  
C.K. Song
Keyword(s):  
Fault Detection ◽  
Condition Monitoring ◽  
Wind Turbines
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