A novel LSTM-autoencoder and enhanced transformer-based detection method for shield machine cutterhead clogging

Qin, Chengjin; Wu, RuiHong; Huang, Guoqiang; Tao, Jianfeng; Liu, Chengliang

doi:10.1007/s11431-022-2218-9

Cited by 38 publications

(11 citation statements)

References 47 publications

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“…Additionally, gradient disappears easily during RNN training. To overcome these drawbacks, the LSTM network was designed in [51] and has been widely used [52][53][54][55]. It is capable of effectively learning long-distance time information using the gate mechanism and internal memory unit and overcoming gradient disappearance in traditional RNN.…”

Section: Cnn and Lstmmentioning

confidence: 99%

Anti‐noise diesel engine misfire diagnosis using a multi‐scale CNN‐LSTM neural network with denoising module

Qin

Jin

Zhang

et al. 2023

CAAI Trans on Intel Tech

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Currently, accuracy of existing diesel engine fault diagnosis methods under strong noise and generalisation performance between different noise levels are still limited. A novel multi‐scale CNN‐LSTM neural network (MSCNN‐LSTMNet) is proposed with a residual‐CNN denoising module for anti‐noise diesel engine misfire diagnosis. First, a residual‐CNN module is designed for denoising the original vibration signal measured from the diesel engine cylinder and residual loss for constructing a new loss function is utilised. Considering the essential characteristics of measured vibration signals at different scales, a multi‐scale convolutional NN (CNN) block is designed to realize multi‐scale feature extraction. Specifically, multiple convolution layers with different branches and different convolution kernel sizes are utilised to extract different time scales features, enhancing the robustness of the model. On this basis, the LSTM is utilised to further extract sequential features for improving anti‐noise and generalisation performances. The effectiveness of MSCNN‐LSTMNet is validated by experimental results of both one‐ and hybrid‐cylinder diesel engine misfires diagnosis under various noise levels and working conditions. The results demonstrate that MSCNN‐LSTMNet achieved much better anti‐noise and generalisation performances than the existing methods. Under strong noise conditions (−10 dB signal‐to‐noise ratio) for four datasets, MSCNN‐LSTMNet obtained 97.561% average accuracy, while average accuracy for random forest, deep neural network, CNN and MSCNNNet were 73.828%, 79.544%, 82.247%, and 89.741%, respectively. Moreover, for 11 noise generalisation tasks between different noise levels, MSCNN‐LSTMNet obtained at least 96.679%, 97.849%, 98.892%, and 94.010% accuracy on the four datasets, which are much higher than those of the existing methods.

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Section: Cnn and Lstmmentioning

confidence: 99%

Anti‐noise diesel engine misfire diagnosis using a multi‐scale CNN‐LSTM neural network with denoising module

Qin

Jin

Zhang

et al. 2023

CAAI Trans on Intel Tech

Self Cite

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“…The LSTM is a recurrent neural network type that can learn order dependence in sequence prediction tasks; this is essential for solving challenging problems like machine translation, voice identification [46]. The LSTM architecture consists of three types of layers: input, LSTM (hidden), and output.…”

Section: The Long Short Term Memory Architecturementioning

confidence: 99%

The Key Criteria for Predicting Unusual Behavior in the Elderly With Deep Learning Models Under 5G Technology

Zamzami

2023

IEEE Access

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Deep learning algorithms and technology based on 5G networks may be able to help identify unusual behavior in elderly people. Because 5G networks have a lower latency and a greater bandwidth, it is possible to use more complex algorithms and larger data sets for training and detection in a real-time. On top of that, real-time analysis of the data gathered through in-home monitoring of the elderly can become much simpler to carry out with the help of 5G's potential to simplify the process. However, the system needs to be developed in a way that it considers the preferences of the most important criteria of elderly people, who have more requirements in terms of their living situation and the environment in which they live. Therefore, this study advocated using ''Decision Making Trial and Evaluation Laboratory'' (DEMATEL) to analyze the most crucial criteria (feature) required for creating a model for identifying odd behavior in elderly persons. Convolutional Neural Networks (CNNs) and Long Short-Term Memories (LSTMs) are adopted in detecting unusual behavior in the elderly after the analysis key criteria for predicting unusual behavior in the elderly by DEMATEL. The research established a concept by linking the SIMADL dataset with the dimension of elderly people's behavior and performed an experimental analysis using both CNN and LSTM. Performance evaluations show that the LSTM performs better in detecting unusual behavior in elderly persons with 96% accuracy. Depressive disorder is the most significant aspect of ageing that might lead to a typical unusual behavior in elderly persons, according to DEMATEL analysis.

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“…Transformer [19] is designed for machine translation tasks in the NLP field. It completes the recognition and transformation of complex sequence models through the self-attention mechanism, which makes up for the shortcomings of RNN [20] and LSTM [21] and other methods. For example, BERT [22,23], GPT [24] and other methods have been improved based on Transformer and obtained advanced indicators in NLP tasks.…”

Section: School Of Electronics and Information Xi'an Polytechnic Univ...mentioning

confidence: 99%

Intelligent Identification Method of Hydrophobic Grade of Composite Insulator Based on Efficient GA‐YOLO Former Network

Song

Huang

et al. 2023

IEEJ Transactions Elec Engng

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The hydrophobia of composite insulators is a parameter closely related to insulation performance. As an essential index to measure the pollution flashover resistance of composite insulators, the hydrophobic grade is of great significance to maintaining transmission line insulators. To judge the hydrophobic grade of composite insulators accurately and efficiently and to solve the problem of difficult extraction of fine‐grained feature information and high similarity between classes in the data sets of anti‐hydrophobic grade recognition, an end‐to‐end intelligent identification method of the hydrophobic grade of composite insulators based on Efficient GA‐YOLO Former network is proposed. This method introduces Swin Transformer—a multi‐head self‐attention mechanism with shifted windows, into CNN, which helps the model better understand the multi‐scale global semantic information of images through cross‐layer interaction. The efficient BiFPN structure is introduced in the neck part to realize the bidirectional cross‐scale connection and weighted feature fusion in the feature extraction process. By integrating the GAM attention mechanism to amplify the significant cross‐dimensional acceptance region, the model can learn more subtle details that are more difficult to distinguish, improving model efficiency and detection accuracy. Combined with the target detection algorithm, a system for evaluating the hydrophobic grade of composite insulators on transmission lines is designed, which can realize the real‐time measurement of the hydrophobic grade of composite insulators and has practical engineering application value. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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A novel LSTM-autoencoder and enhanced transformer-based detection method for shield machine cutterhead clogging

Cited by 38 publications

References 47 publications

Anti‐noise diesel engine misfire diagnosis using a multi‐scale CNN‐LSTM neural network with denoising module

Anti‐noise diesel engine misfire diagnosis using a multi‐scale CNN‐LSTM neural network with denoising module

The Key Criteria for Predicting Unusual Behavior in the Elderly With Deep Learning Models Under 5G Technology

Intelligent Identification Method of Hydrophobic Grade of Composite Insulator Based on Efficient GA‐YOLO Former Network

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