Precise cutterhead torque prediction for shield tunneling machines using a novel hybrid deep neural network

Qin, Chengjin; Shi, Gang; Tao, Jianfeng; Yu, Honggan; Jin, Yanrui; Lei, Jiehao; Liu, Chengliang

doi:10.1016/j.ymssp.2020.107386

Cited by 110 publications

(30 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CNNs strong feature extraction capabilities also come in here. Such as, Qin et al [110] used a deep twin CNN to detect the misfire of diesel engines under strong environmental noise and different working conditions; In order to better mine meaningful deep information and apply it to the prediction of cutter head torque, Qin et al [111] also utilized CNN and LSTM to extract the implicit features and sequential features of relevant parameters of shield tunneling machines.…”

Section: The Application Of Neural Network-based Detection Methods During the Pandemic Of Covid-19mentioning

confidence: 99%

Deep Learning Applications for COVID-19 Analysis: A State-of-the-Art Survey

Li¹,

Deng²,

Shao³

et al. 2021

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

The COVID-19 has resulted in catastrophic situation and the deaths of millions of people all over the world. In this paper, the predictions of epidemiological propagation models, such as SIR and SEIR, are introduced to analyze the earlier COVID-19 propagation. The deep learning methods combined with transfer learning are familiar with classification-detection approaches based on chest X-ray and CT images are presented in detail. Besides, deep learning approaches have also been applied to lung ultrasound (LUS), which has been shown to be more sensitive than chest X-ray and CT images in detecting COVID-19. In the absence of a vaccine, the machine learning-related approaches are applied to analyze vaccine candidates in the realm of biology and medicine. The telehealth system played a major role in combating the pandemic from all aspects and reducing contact with patients during this period. Natural language processing-related methods are utilized to analyze tweets related to the COVID-19 epidemic on social media, and further analyze public sentiment and subject modeling, so as to arrange corresponding measures to appease public sentiment. In particular, this survey is to summarize and analyze the contributions made in various fields during the COVID-19 pandemic by considering both the contribution of deep learning in chest X-ray and CT images, as well as the application of the latest LUS during the COVID-19 pandemic. Telehealth and the importance of public sentiment analysis during a pandemic were also described in detail.

show abstract

Section: The Application Of Neural Network-based Detection Methods During the Pandemic Of Covid-19mentioning

confidence: 99%

Deep Learning Applications for COVID-19 Analysis: A State-of-the-Art Survey

Li¹,

Deng²,

Shao³

et al. 2021

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

show abstract

“…A series of open-source CNN architectures built by statistical modelers, such as AlexNet [29], VGG [30], GoogLeNet [31], and DenseNet [32], etc., enable researchers in different fields to use a wide variety of networks to solve problems in their respective fields. Compared to manual feature extraction, the convolutional kernel of CNN can automatically extract deep-level features [33]. Thomas et al [1] used gray-scale images of drill cores to classify three lithologies, carbonate-cement, shale and sandstone, and obtained a classification accuracy of 94%, which is an early attempt to predict lithology from drill core images.…”

Section: Introductionmentioning

confidence: 99%

Deep learning based lithology classification of drill core images

Wang

et al. 2022

PLoS ONE

View full text Add to dashboard Cite

Drill core lithology is an important indicator reflecting the geological conditions of the drilling area. Traditional lithology identification usually relies on manual visual inspection, which is time-consuming and professionally demanding. In recent years, the rapid development of convolutional neural networks has provided an innovative way for the automatic prediction of drill core images. In this work, a core dataset containing a total of 10 common lithology categories in underground engineering was constructed. ResNeSt-50 we adopted uses a strategy of combining channel-wise attention and multi-path network to achieve cross-channel feature correlations, which significantly improves the model accuracy without high model complexity. Transfer learning was used to initialize the model parameters, to extract the feature of core images more efficiently. The model achieved superior performance on testing images compared with other discussed CNN models, the average value of its Precision, Recall, F1−score for each category of lithology is 99.62%, 99.62%, and 99.59%, respectively, and the prediction accuracy is 99.60%. The test results show that the proposed method is optimal and effective for automatic lithology classification of borehole cores.

show abstract

“…The CNN-LSTM network is constructed to analyze the vibration signals; when the CNN method finishes the feature of one-dimensional singles, LSTM continues to process this important information for diagnosis classification [ 18 , 19 , 20 ]. LSTM can extract special correlations for the stronger self-learning ability of CNN for prediction, and these architectures produce many ideas that are useful for this paper [ 21 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Deep Learning Method for Fault Diagnosis of Rotating Machinery Based on Extended WDCNN and Long Short-Term Memory

Gao

Kim

2021

Sensors

View full text Add to dashboard Cite

Deep learning (DL) plays a very important role in the fault diagnosis of rotating machinery. To enhance the self-learning capacity and improve the intelligent diagnosis accuracy of DL for rotating machinery, a novel hybrid deep learning method (NHDLM) based on Extended Deep Convolutional Neural Networks with Wide First-layer Kernels (EWDCNN) and long short-term memory (LSTM) is proposed for complex environments. First, the EWDCNN method is presented by extending the convolution layer of WDCNN, which can further improve automatic feature extraction. The LSTM then changes the geometric architecture of the EWDCNN to produce a novel hybrid method (NHDLM), which further improves the performance for feature classification. Compared with CNN, WDCNN, and EWDCNN, the proposed NHDLM method has the greatest performance and identification accuracy for the fault diagnosis of rotating machinery.

show abstract

Precise cutterhead torque prediction for shield tunneling machines using a novel hybrid deep neural network

Cited by 110 publications

References 44 publications

Deep Learning Applications for COVID-19 Analysis: A State-of-the-Art Survey

Deep Learning Applications for COVID-19 Analysis: A State-of-the-Art Survey

Deep learning based lithology classification of drill core images

A Novel Hybrid Deep Learning Method for Fault Diagnosis of Rotating Machinery Based on Extended WDCNN and Long Short-Term Memory

Contact Info

Product

Resources

About