Recurrent neural network optimization for wind turbine condition prognosis

Kerboua, Adlen; Kelaiaia, Ridha

doi:10.29354/diag/151608

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…This method demonstrates the effectiveness of this predictive approach. Adlen et al [24] conducted research using LSTM, Bi-LSTM, and GRU to predict the condition of wind turbine operations based on vibration time series data. In this study, Bayesian optimization was used to fine-tune the training parameters.…”

Section: Related Workmentioning

confidence: 99%

Comparative Performance Analysis of RNN Techniques for Predicting Concatenated Normal and Abnormal Vibrations

Lee,

Hong

2023

Electronics

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We analyze the comparative performance of predicting the transition from normal to abnormal vibration states, simulating the motor’s condition before a drone crash, by proposing a concatenated vibration prediction model (CVPM) based on recurrent neural network (RNN) techniques. Subsequently, using the proposed CVPM, the prediction performances of six RNN techniques: long short-term memory (LSTM), attention-LSTM (Attn.-LSTM), bidirectional-LSTM (Bi-LSTM), gate recurrent unit (GRU), attention-GRU (Attn.-GRU), and bidirectional-GRU (Bi-GRU), are analyzed comparatively. In order to assess the prediction accuracy of these RNN techniques in predicting concatenated vibrations, both normal and abnormal vibration data are collected from the motors connected to the drone’s propellers. Consequently, a concatenated vibration dataset is generated by combining 50% of normal vibration data with 50% of abnormal vibration data. This dataset is then used to compare and analyze vibration prediction performance and simulation runtime across the six RNN techniques. The goal of this analysis is to comparatively analyze the performances of the six RNN techniques for vibration prediction. According to the simulation results, it is observed that Attn.-LSTM and Attn.-GRU, incorporating the attention mechanism technique to focus on information highly relevant to the prediction target through unidirectional learning, demonstrate the most promising predictive performance among the six RNN techniques. This implies that employing the attention mechanism enhances the concentration of relevant information, resulting in superior predictive accuracy compared to the other RNN techniques.

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Section: Related Workmentioning

confidence: 99%

Comparative Performance Analysis of RNN Techniques for Predicting Concatenated Normal and Abnormal Vibrations

Lee,

Hong

2023

Electronics

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Intelligent Techniques Employing LSTM and ANFIS Models for Fault Detection and Isolation in a Wind Turbine Machine

Zemali,

Cherroun,

Hadroug

et al. 2023

2023 2nd International Conference on Electronics, Energy and Measurement (IC2EM)

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Symmetry-Enhanced LSTM-Based Recurrent Neural Network for Oscillation Minimization of Overhead Crane Systems during Material Transportation

Cui,

Chipusu,

Ashraf

et al. 2024

Symmetry

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This paper introduces a novel methodology for mitigating undesired oscillations in overhead crane systems used in material handling operations in the industry by leveraging Long Short-Term Memory (LSTM)-based Recurrent Neural Networks (RNNs). Oscillations during material transportation, particularly at the end location, pose safety risks and prolong carrying times. The methodology involves collecting sensor data from an overhead crane system, preprocessing the data, training an LSTM-based RNN model that incorporates symmetrical features, and integrating the model into a control algorithm. The control algorithm utilizes swing angle predictions from the symmetry-enhanced LSTM-based RNN model to dynamically adjust crane motion in real time, minimizing oscillations. Symmetry in this framework refers to the balanced and consistent handling of oscillatory data, ensuring that the model can generalize better across different scenarios and load conditions. The LSTM-based RNN model accurately predicts swing angles, enabling proactive control actions to be taken. Experimental validation demonstrates the effectiveness of the proposed approach, achieving an accuracy of approximately 98.6% in swing angle prediction. This innovative approach holds promise for transforming material transportation processes in industrial settings, enhancing operational safety, and optimizing efficiency.

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Recurrent neural network optimization for wind turbine condition prognosis

Cited by 3 publications

References 18 publications

Comparative Performance Analysis of RNN Techniques for Predicting Concatenated Normal and Abnormal Vibrations

Comparative Performance Analysis of RNN Techniques for Predicting Concatenated Normal and Abnormal Vibrations

Intelligent Techniques Employing LSTM and ANFIS Models for Fault Detection and Isolation in a Wind Turbine Machine

Symmetry-Enhanced LSTM-Based Recurrent Neural Network for Oscillation Minimization of Overhead Crane Systems during Material Transportation

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