Bayesian Estimation of Remaining Useful Life for Wind Turbine Blades

Nielsen, Jannie Sønderkær; Sørensen, John Dalsgaard

doi:10.3390/en10050664

Cited by 56 publications

(44 citation statements)

References 17 publications

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“…Every gate has its own weights to be adjusted and thus the most important historical information can be stored in the memory cell for producing the most proper output. The equations for the LSTM network are Equations (1)- (6). In the equations, σ(•) stands for the sigmoid function, tanh(•) stands for the hyperbolic tangent function, and || stands for the concatenation operation.…”

Section: Tsmc-cnn-alstmmentioning

confidence: 99%

Time Series Multiple Channel Convolutional Neural Network with Attention-Based Long Short-Term Memory for Predicting Bearing Remaining Useful Life

Jiang

Lee

Zeng

2019

Sensors

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This paper proposes two deep learning methods for remaining useful life (RUL) prediction of bearings. The methods have the advantageous end-to-end property that they take raw data as input and generate the predicted RUL directly. They are TSMC-CNN, which stands for the time series multiple channel convolutional neural network, and TSMC-CNN-ALSTM, which stands for the TSMC-CNN integrated with the attention-based long short-term memory (ALSTM) network. The proposed methods divide a time series into multiple channels and take advantage of the convolutional neural network (CNN), the long short-term memory (LSTM) network, and the attention-based mechanism for boosting performance. The CNN performs well for extracting features from data with multiple channels; dividing a time series into multiple channels helps the CNN extract relationship among far-apart data points. The LSTM network is excellent for processing temporal data; the attention-based mechanism allows the LSTM network to focus on different features at different time steps for better prediction accuracy. PRONOSTIA bearing operation datasets are applied to the proposed methods for the purpose of performance evaluation and comparison. The comparison results show that the proposed methods outperform the others in terms of the mean absolute error (MAE) and the root mean squared error (RMSE) of RUL prediction.

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Section: Tsmc-cnn-alstmmentioning

confidence: 99%

Time Series Multiple Channel Convolutional Neural Network with Attention-Based Long Short-Term Memory for Predicting Bearing Remaining Useful Life

Jiang

Lee

Zeng

2019

Sensors

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“…The proposed model for planning and learning in uncertain dynamic systems is based on the Bayes-adaptive partially observable Markov decision process model and is capable of learning from the environment, updating the distribution of the model parameters and selecting the optimal strategy under conditions of uncertainty. In [177], the authors apply maximum the likelihood method of BNs to obtain the transition probabilities between the states of a semi-Markov model used for estimating the RUL of the blades of a WT. Using the real and observed values of the wind turbulence intensity and RPM of the electrical generator, [178] constructs a BN model that can calculate the failure probability at any point in time and the impact of the possible maintenance actions and quantify the deterioration level during a time period for the gearbox of a WT.…”

Section: Bayesian Networkmentioning

confidence: 99%

Maintenance Models Applied to Wind Turbines. A Comprehensive Overview

et al. 2019

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Wind power generation has been the fastest-growing energy alternative in recent years, however, it still has to compete with cheaper fossil energy sources. This is one of the motivations to constantly improve the efficiency of wind turbines and develop new Operation and Maintenance (O&M) methodologies. The decisions regarding O&M are based on different types of models, which cover a wide range of scenarios and variables and share the same goal, which is to minimize the Cost of Energy (COE) and maximize the profitability of a wind farm (WF). In this context, this review aims to identify and classify, from a comprehensive perspective, the different types of models used at the strategic, tactical, and operational decision levels of wind turbine maintenance, emphasizing mathematical models (MatMs). The investigation allows the conclusion that even though the evolution of the models and methodologies is ongoing, decision making in all the areas of the wind industry is currently based on artificial intelligence and machine learning models.

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“…The validity of the questionnaire is based on the Consistency Index (CI), as shown in Equation (12). The tolerance value is CI ≤ 0.1.…”

Section: 468mentioning

confidence: 99%

Research on CBM of the Intelligent Substation SCADA System

Liu

Chou

et al. 2019

Energies

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An equipment status management and maintenance platform of an intelligent substation monitoring and control system is built in the Tai-Tam substation of the Taipower company. The real-time operating status of the equipment, such as the server, supervisory control and data acquisition (SCADA) human–machine interface (HMI) software, switches, intelligent electronic devices (IEDs), merging units (MUs), as well as the entire SCADA system, are evaluated comprehensively. First, the status information of all equipment is collected, and the theory of relative deterioration degree (RDD) and fuzzy theory (FT) are applied to calculate the fuzzy evaluation matrix of the equipment influencing factors. Then, the subjective analytic hierarchy process (AHP) and the objective entropy method for weighting are combined to calculate the comprehensive weights of the equipment influencing factors. Finally, the result of the equipment status evaluation is obtained using the fuzzy comprehensive evaluation (FCE) method and is presented at the equipment status management and maintenance platform. Such equipment status evaluation results can be used by the inspection and maintenance personnel to determine the priority for equipment maintenance and repair. The result of this study may serve as a valuable reference to utility companies when making maintenance plans.

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Bayesian Estimation of Remaining Useful Life for Wind Turbine Blades

Cited by 56 publications

References 17 publications

Time Series Multiple Channel Convolutional Neural Network with Attention-Based Long Short-Term Memory for Predicting Bearing Remaining Useful Life

Time Series Multiple Channel Convolutional Neural Network with Attention-Based Long Short-Term Memory for Predicting Bearing Remaining Useful Life

Maintenance Models Applied to Wind Turbines. A Comprehensive Overview

Research on CBM of the Intelligent Substation SCADA System

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