scholarly journals A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3411 ◽  
Author(s):  
Peng Qian ◽  
Xiange Tian ◽  
Jamil Kanfoud ◽  
Joash Lee ◽  
Tat-Hean Gan
Keyword(s):  
Condition Monitoring ◽  
Wind Turbines ◽  
Defect Detection ◽  
Short Term Memory ◽  
Wind Farms ◽  
Economic Benefits ◽  
Short Term ◽  
Monitoring Method ◽  
Term Memory ◽  
Long Short Term Memory

Effective intelligent condition monitoring, as an effective technique to enhance the reliability of wind turbines and implement cost-effective maintenance, has been the object of extensive research and development to improve defect detection from supervisory control and data acquisition (SCADA) data, relying on perspective signal processing and statistical algorithms. The development of sophisticated machine learning now allows improvements in defect detection from historic data. This paper proposes a novel condition monitoring method for wind turbines based on Long Short-Term Memory (LSTM) algorithms. LSTM algorithms have the capability of capturing long-term dependencies hidden within a sequence of measurements, which can be exploited to increase the prediction accuracy. LSTM algorithms are therefore suitable for application in many diverse fields. The residual signal obtained by comparing the predicted values from a prediction model and the actual measurements from SCADA data can be used for condition monitoring. The effectiveness of the proposed method is validated in the case study. The proposed method can increase the economic benefits and reliability of wind farms.

scholarly journals Extraction of local and global features by a convolutional neural network–long short-term memory network for diagnosing bearing faults

2021 ◽  
pp. 095440622110165
Author(s):  
Zhang Chao ◽  
Wang Wei-zhi ◽  
Zhang Chen ◽  
Fan Bin ◽  
Wang Jian-guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Short Term Memory ◽  
Vibration Signal ◽  
Short Term ◽  
Global Features ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Lstm Network

Accurate and reliable fault diagnosis is one of the key and difficult issues in mechanical condition monitoring. In recent years, Convolutional Neural Network (CNN) has been widely used in mechanical condition monitoring, which is also a great breakthrough in the field of bearing fault diagnosis. However, CNN can only extract local features of signals. The model accuracy and generalization of the original vibration signals are very low in the process of vibration signal processing only by CNN. Based on the above problems, this paper improves the traditional convolution layer of CNN, and builds the learning module (local feature learning block, LFLB) of the local characteristics. At the same time, the Long Short-Term Memory (LSTM) is introduced into the network, which is used to extract the global features. This paper proposes the new neural network—improved CNN-LSTM network. The extracted deep feature is used for fault classification. The improved CNN-LSTM network is applied to the processing of the vibration signal of the faulty bearing collected by the bearing failure laboratory of Inner Mongolia University of science and technology. The results show that the accuracy of the improved CNN-LSTM network on the same batch test set is 98.75%, which is about 24% higher than that of the traditional CNN. The proposed network is applied to the bearing data collection of Western Reserve University under the condition that the network parameters remain unchanged. The experiment shows that the improved CNN-LSTM network has better generalization than the traditional CNN.

scholarly journals Prediction of Wind Turbine-Grid Interaction Based on a Principal Component Analysis-Long Short Term Memory Model

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3221 ◽  
Author(s):  
Yining Wang ◽  
Da Xie ◽  
Xitian Wang ◽  
Yu Zhang
Keyword(s):  
Principal Component Analysis ◽  
Wind Turbine ◽  
Short Term Memory ◽  
Principal Component ◽  
Wind Farms ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Lstm Network ◽  
Oscillation Frequencies

The interaction between the gird and wind farms has significant impact on the power grid, therefore prediction of the interaction between gird and wind farms is of great significance. In this paper, a wind turbine-gird interaction prediction model based on long short term memory (LSTM) network under the TensorFlow framework is presented. First, the multivariate time series was screened by principal component analysis (PCA) to reduce the data dimensionality. Secondly, the LSTM network is used to model the nonlinear relationship between the selected sequence of wind turbine network interactions and the actual output sequence of the wind farms, it is proved that it has higher accuracy and applicability by comparison with single LSTM model, Autoregressive Integrated Moving Average (ARIMA) model and Back Propagation Neural Network (BPNN) model, the Mean Absolute Percentage Error (MAPE) is 0.617%, 0.703%, 1.397% and 3.127%, respectively. Finally, the Prony algorithm was used to analyze the predicted data of the wind turbine-grid interactions. Based on the actual data, it is found that the oscillation frequencies of the predicted data from PCA-LSTM model are basically the same as the oscillation frequencies of the actual data, thus the feasibility of the model proposed for analyzing interaction between grid and wind turbines is verified.

Defect Detection of Bridge based on Impact-Echo Signals and Long Short-Term Memory

2021 ◽  
Vol 48 (9) ◽  
pp. 988-997
Author(s):  
Byoung-Doo Oh ◽  
Hyung Choi ◽  
Young Jin Kim ◽  
Won Jong Chin ◽  
Yu-Seop Kim
Keyword(s):  
Defect Detection ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Impact Echo ◽  
Long Short Term Memory
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