Attention Mechanism-Based CNN-LSTM Model for Wind Turbine Fault Prediction Using SSN Ontology Annotation

Yuan Xie,Chenghua Tang,Longge Li,Luzhong Mi,Baohua Qiang,Jisheng Zhao,Xingsi Xue

doi:10.1155/2021/6627588

Yuan Xie, Chenghua Tang + Show 5 more

Open Access

https://doi.org/10.1155/2021/6627588

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Abstract

The traditional model for wind turbine fault prediction is not sensitive to the time sequence data and cannot mine the deep connection between the time series data, resulting in poor generalization ability of the model. To solve this problem, this paper proposes an attention mechanism-based CNN-LSTM model. The semantic sensor data annotated by SSN ontology is used as input data. Firstly, CNN extracts features to get high-level feature representation from input data. Then, the latent time sequence connection of features in different time periods is learned by LSTM. Finally, the output of LSTM is input into the attention mechanism module to obtain more fault-related target information, which improves the efficiency, accuracy, and generalization ability of the model. In addition, in the data preprocessing stage, the random forest algorithm analyzes the feature correlation degree of the data to get the features of high correlation degree with the wind turbine fault, which further improves the efficiency, accuracy, and generalization ability of the model. The model is validated on the icing fault dataset of No. 21 wind turbine and the yaw dataset of No. 4 wind turbine. The experimental results show that the proposed model has better efficiency, accuracy, and generalization ability than RNN, LSTM, and XGBoost.

Highlights

In recent years, with the development of human beings, the exploitation and utilization of petroleum and fossil fuels have promoted the development of various fields
Zheng et al [23] put forward the fault prediction method for wind turbine gearbox based on K-means clustering and LSTM
According to the experimental results of Experiment 1 and Experiment 2, the CLA model has the best performance in A, P, R, and F1 compared with the LSTM and RNN algorithm models; the results show that the temporal relationship of fault features can be learned to improve the accuracy and generalization ability of the model by fusion of CNN and LSTM and introducing attention mechanism

Summary

Introduction

With the development of human beings, the exploitation and utilization of petroleum and fossil fuels have promoted the development of various fields. Zheng et al [23] put forward the fault prediction method for wind turbine gearbox based on K-means clustering and LSTM It can process effectively time series data, it inputs directly the original features of the wind turbine into the model for training, resulting in model training too long. In accordance with shortcomings of traditional machine learning in processing time series data and advantages of CNN [26] in feature extraction and LSTM [27] in processing time series data, an attention mechanism-based CNN-LSTM model for wind turbine fault prediction is constructed The contributions of this method are summarized as follows:.

Data Acquisition

Introduction to the Model

Experiment and Analysis

Optimization of CLA Model

Conclusions