An ensemble framework based on convolutional bi-directional LSTM with multiple time windows for remaining useful life estimation

Xia, Tangbin; Song, Yang; Zheng, Yu; Pan, Ershun; Xi, Lifeng

doi:10.1016/j.compind.2019.103182

Cited by 191 publications

(67 citation statements)

References 32 publications

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“…In the resent past, machine learning models are very popular to solve various problems like image classification [11], text processing [12], real-time fault diagnosis [13] and healthcare [14,15]. It is very common to use ML algorithms to address disease prediction [16,17] [18].…”

Section: Related Workmentioning

confidence: 99%

Blended Multi-Modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction

et al. 2020

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Diabetic Retinopathy (DR) is one of the major causes of visual impairment and blindness across the world. It is usually found in patients who suffer from diabetes for a long period. Major focus of this work is to derive optimal representation of retinal images that further helps to improve the performance of DR recognition models. In order to extract optimal representation, features extracted from multiple pre-trained ConvNet models are blended using proposed multi-modal fusion module. These final representations are used to train a Deep Neural Network (DNN) used for DR identification and severity level prediction. As each ConvNet extract different features, fusing them using 1-D pooling, and cross pooling lead to better representation than using features extracted from a single ConvNet. Experimental studies on benchmark Kaggle APTOS 2019 contest dataset reveals that the model trained on proposed blended feature representations is superior to the existing methods. In addition, we notice that cross average pooling based fusion of features from Xception and VGG16 is the most appropriate for DR recognition. With the proposed model, we achieve an accuracy of 97.41%, and a kappa statistic of 94.82 for DR identification and an accuracy of 81.7% and a kappa statistic of 71.1% for severity level prediction. Another interesting observation is that, DNN with dropout at input layer converges faster when trained using blended features, than compared to the same model trained using uni-modal deep features.

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Section: Related Workmentioning

confidence: 99%

Blended Multi-Modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction

et al. 2020

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“…Their empirical study verified its superiority by comparing it with individual benchmark models. 16 Ma et al 17 combined the Grid concept and LSTM (G-LSTM) for the forecasting of fuel cell degradation. More than that, the latest researches applied LSTM to more hot areas of prediction, for example, electricity price forecasting, 18 flood forecasting, 19 wind speed forecasting, 20 air pollution forecasting, 21 voltages forecasting, 22 demand forecasting, 23 photovoltaic power forecasting, 24 and so forth.…”

Section: Overview Of Studies On Carbon Price Forecasting and Other mentioning

confidence: 99%

“…Additionally, the values of A MAE , A MAPE , A RMSE , and A R2 are closer to 0, the smaller amelioration the proposed model renders. The definition of, A MAE , A MAPE , A RMSE , and A R2 is described as Equations (16)(17)(18)(19). where the subscript 1 and 2 denote the statistic index of the proposed model and other benchmark models.…”

Section: Performance Evaluation Criteriamentioning

confidence: 99%

An ensemble‐driven long short‐term memory model based on mode decomposition for carbon price forecasting of all eight carbon trading pilots in China

Sun

2020

Energy Science & Engineering

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Global climate change, which was triggered by the excessive emissions of greenhouse gases, has become an obstacle in sustainable development. Among all greenhouse gases, carbon dioxide is the most prominent and is artificially controllable. China ranks as the largest emitter of carbon dioxide worldwide and is a pioneer in economic growth. 1 As the international community is advocating in reducing carbon emissions, Chinese carbon emissions and its corresponding

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“…Wang et al (2020a) proposed a new recurrent convolutional neural network that could integrate variational inference for giving a probabilistic RUL result. Xia et al (2020) presented an ensemble framework with convolutional bi-directional LSTM for RUL prediction which could adaptively select trained base models for ensemble and further predicting RUL. An et al (2020) utilized convolutional stacked LSTM for RUL prediction of milling tools where time-domain and frequency-domain features were combined, encoded and denoised through unidirectional LSTM.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction Based on Improved Temporal Convolutional Network for Nuclear Power Plant Valves

Wang

Peng

et al. 2020

Front. Energy Res.

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Proper risk assessment and monitoring of critical component is crucial to the safe operation of Nuclear Power Plants. One of the ways to ensure real-time monitoring is the development of Prognostics and Health Management systems for safety-critical equipment. Recently, the remaining useful life prediction (RUL) has been found to be important in ensuring predictive maintenance and avoiding critical component failure. With the development of artificial intelligent techniques, deep learning algorithms are becoming popular for RUL prediction. Consequently, this paper presents RUL prediction techniques for nuclear plant electric gate valves with a temporal convolution network (TCN). The main advantage of using TCN is its ability to capture and process useful information in short-term sensor measurement changes. Moreover, the efficiency of the proposed TCN is enhanced by incorporating a convolution auto-encoder as a preprocessing layer in its structure, which greatly improved the residual convolution mode. The proposed method is verified on the electric gate valves experimental dataset that represents the real-world operation of the valve, and the result obtained is compared with other conventional data-driven approaches. The evaluation result shows impressive performance of the proposed model in predicting the remaining service life of the gate valves used in the nuclear reactor control system. Moreover, the generalization of the proposed model is evaluated on the turbofan engine benchmark dataset. The evaluation result also shows improved performance in the predicted RUL. Broader application of the proposed TCN is envisaged for critical components in other industries.

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An ensemble framework based on convolutional bi-directional LSTM with multiple time windows for remaining useful life estimation

Cited by 191 publications

References 32 publications

Blended Multi-Modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction

Blended Multi-Modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction

An ensemble‐driven long short‐term memory model based on mode decomposition for carbon price forecasting of all eight carbon trading pilots in China

Remaining Useful Life Prediction Based on Improved Temporal Convolutional Network for Nuclear Power Plant Valves

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