Remaining Useful Life Estimation of Aircraft Engines Using a Joint Deep Learning Model Based on TCNN and Transformer

Wang, Hai-Kun; Yi, Cheng; Song, Ke

doi:10.1155/2021/5185938

Cited by 34 publications

(16 citation statements)

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“…During the downsampling of neighborhood coding, the input resolution of coders could be effectively reduced [37]. Wang et al (2021) [38] proposed a novel joint deep learning architecture, which consisted of two main parts. A transformer encoder that uses scaled dot product attention to extract dependencies across distances in time series.…”

Section: Discussionmentioning

confidence: 99%

Medical Ultrasound Image Segmentation With Deep Learning Models

2023

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This work aimed to adopt a transformer model combined with deep learning neural network to discuss the segmentation of medical ultrasound images. A network combining a transformer model with a deep neural network model (ConvTrans-Net) is proposed. The image content is preselected based on a multilayer perceptron and attention mechanism, different feature vectors are concatenated and fed into the multilayer perceptron, and the results of multiple attentions are mapped to a larger dimensional space using a feed-forward network. The lesion areas segmented by ultrasonic scan were analysed, and an attention mechanism and multilayer perceptron were combined to preselect image content. The performance and convergence of the model were analysed, and the Jaccard similarity coefficient precision and recall of the model were measured. In the experiment, two different iterative step sizes were selected, the convergence trend of the model increased with the increase in the number of iterative steps, and the model gradually stabilized. The Jaccard of ConvTrans-Net was 85.21%. The precision (85.17%) and recall (89.65%) were significantly higher than those of EfficientNet and DeepViT-L, and the differences were significant (P < 0.05). The experimental results show that the proposed model is stable, and the combination of Transformer model and deep learning neural network has a good effect on ultrasound image segmentation, which has some practical application value. INDEX TERMS deep learning; medical images; ultrasound; neural networks; image features

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Section: Discussionmentioning

confidence: 99%

Medical Ultrasound Image Segmentation With Deep Learning Models

2023

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“…The past decade has witnessed the emergence of AI as a practicable tool in clinical management [20]. Preliminary studies have confirmed that AI-aided chest CTs had good sensitivities for detecting COVID-19 lung pathologies [43][44][45]. Sen et al proposed a model to extract features from chest CT images via CNN and then accurately screened out the most significant COVID-19 characteristics-from the patients' chest CT images [46].…”

Section: Discussionmentioning

confidence: 99%

Identification of Asymptomatic COVID-19 Patients on Chest CT Images Using Transformer-Based or Convolutional Neural Network–Based Deep Learning Models

et al. 2023

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Novel coronavirus disease 2019 (COVID-19) has rapidly spread throughout the world; however, it is difficult for clinicians to make early diagnoses. This study is to evaluate the feasibility of using deep learning (DL) models to identify asymptomatic COVID-19 patients based on chest CT images. In this retrospective study, six DL models (Xception, NASNet, ResNet, EfficientNet, ViT, and Swin), based on convolutional neural networks (CNNs) or transformer architectures, were trained to identify asymptomatic patients with COVID-19 on chest CT images. Data from Yangzhou were randomly split into a training set (n = 2140) and an internal-validation set (n = 360). Data from Suzhou was the external-test set (n = 200). Model performance was assessed by the metrics accuracy, recall, and specificity and was compared with the assessments of two radiologists. A total of 2700 chest CT images were collected in this study. In the validation dataset, the Swin model achieved the highest accuracy of 0.994, followed by the EfficientNet model (0.954). The recall and the precision of the Swin model were 0.989 and 1.000, respectively. In the test dataset, the Swin model was still the best and achieved the highest accuracy (0.980). All the DL models performed remarkably better than the two experts. Last, the time on the test set diagnosis spent by two experts—42 min, 17 s (junior); and 29 min, 43 s (senior)—was significantly higher than those of the DL models (all below 2 min). This study evaluated the feasibility of multiple DL models in distinguishing asymptomatic patients with COVID-19 from healthy subjects on chest CT images. It found that a transformer-based model, the Swin model, performed best.

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“…The third principle [43,44,45] includes the addition of any form of the attention mechanism to the previously presented approaches. For example, in [43] attention mechanism was used both in the sensor allocation phase and in accounting for the influence of time series position.…”

Section: Novel Sota Approachesmentioning

confidence: 99%

Interaction models for remaining useful life estimation

Zhevnenko¹,

Kazantsev²,

Makarov³

2023

Preprint

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The paper deals with the problem of controlling the state of industrial devices according to the readings of their sensors. The current methods rely on one approach to feature extraction in which the prediction occurs. We proposed a technique to build a scalable model that combines multiple different feature extractor blocks. A new model based on sequential sensor space analysis achieves state-of-the-art results on the C-MAPSS benchmark for equipment remaining useful life estimation. The resulting model performance was validated including the prediction changes with scaling.

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Remaining Useful Life Estimation of Aircraft Engines Using a Joint Deep Learning Model Based on TCNN and Transformer

Cited by 34 publications

References 50 publications

Medical Ultrasound Image Segmentation With Deep Learning Models

Medical Ultrasound Image Segmentation With Deep Learning Models

Identification of Asymptomatic COVID-19 Patients on Chest CT Images Using Transformer-Based or Convolutional Neural Network–Based Deep Learning Models

Interaction models for remaining useful life estimation

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