Multiple Wavelet Coefficients Fusion in Deep Residual Networks for Fault Diagnosis

Zhao, Minghang; Kang, Myeongsu; Tang, Baoping; Pecht, Michael

doi:10.1109/tie.2018.2866050

Cited by 147 publications

(52 citation statements)

References 29 publications

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“…Some of the other works using transfer learning or domain adaptation strategy using the CWRU bearing dataset for machinery fault detection are tabulated in Table 10. Some of the works employing DL-based transfer learning and domainadaptation approaches using the dataset other than the CWRU dataset are [145] - [147].…”

Section: Deep Transfer Learning and Domain Adaptation Methodsmentioning

confidence: 99%

Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review

2020

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A smart factory is a highly digitized and connected production facility that relies on smart manufacturing. Additionally, artificial intelligence is the core technology of smart factories. The use of machine learning and deep learning algorithms has produced fruitful results in many fields like image processing, speech recognition, fault detection, object detection, or medical sciences. With the increment in the use of smart machinery, the faults in the machinery equipment are expected to increase. Machinery fault detection and diagnosis through various deep learning algorithms has increased day by day. Many types of research have been done and published using both open-source and closed-source datasets, implementing the deep learning algorithms. Out of many publicly available datasets, Case Western Reserve University (CWRU) bearing dataset has been widely used to detect and diagnose machinery bearing fault and is accepted as a standard reference for validating the models. This paper summarizes the recent works which use the CWRU bearing dataset in machinery fault detection and diagnosis employing deep learning algorithms. We have reviewed the published works and presented the working algorithm, result, and other necessary details in this paper. This paper, we believe, can be of good help for future researchers to start their work on machinery fault detection and diagnosis using the CWRU dataset. INDEX TERMS Bearing, deep learning, machine learning, machinery fault detection and diagnosis, CWRU dataset.

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Section: Deep Transfer Learning and Domain Adaptation Methodsmentioning

confidence: 99%

Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review

2020

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“…In our study, we use the coefficients directly as input features. This method has been tested in other fields such as fault diagnosis [40].…”

Section: Eeg Noise Removal Andmentioning

confidence: 99%

DWT-Net: Seizure Detection System with Structured EEG Montage and Multiple Feature Extractor in Convolution Neural Network

et al. 2020

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Automated seizure detection system based on electroencephalograms (EEG) is an interdisciplinary research problem between computer science and neuroscience. Epileptic seizure affects 1% of the worldwide population and can lead to severe long-term harm to safety and life quality. The automation of seizure detection can greatly improve the treatment of patients. In this work, we propose a neural network model to extract features from EEG signals with a method of arranging the dimension of feature extraction inspired by the traditional method of neurologists. A postprocessor is used to improve the output of the classifier. The result of our seizure detection system on the TUSZ dataset reaches a false alarm rate of 12 per 24 hours with a sensitivity of 59%, which approaches the performance of average human detector based on qEEG tools.

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“…Thus, a ResNet can effectively train networks, even with 1001 layers [32]. Moreover, ResNet incorporates many techniques for better training of neural networks, such as momentum, batch normalization, regularization, and weight initialization [30,33].…”

Section: Introductionmentioning

confidence: 99%

Sleep State Classification Using Power Spectral Density and Residual Neural Network with Multichannel EEG Signals

Hasan

Shon

et al. 2020

Applied Sciences

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This paper proposes a classification framework for automatic sleep stage detection in both male and female human subjects by analyzing the electroencephalogram (EEG) data of polysomnography (PSG) recorded for three regions of the human brain, i.e., the pre-frontal, central, and occipital lobes. Without considering any artifact removal approach, the residual neural network (ResNet) architecture is used to automatically learn the distinctive features of different sleep stages from the power spectral density (PSD) of the raw EEG data. The residual block of the ResNet learns the intrinsic features of different sleep stages from the EEG data while avoiding the vanishing gradient problem. The proposed approach is validated using the sleep dataset of the Dreams database, which comprises of EEG signals for 20 healthy human subjects, 16 female and 4 male. Our experimental results demonstrate the effectiveness of the ResNet based approach in identifying different sleep stages in both female and male subjects compared to state-of-the-art methods with classification accuracies of 87.8% and 83.7%, respectively.

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Multiple Wavelet Coefficients Fusion in Deep Residual Networks for Fault Diagnosis

Cited by 147 publications

References 29 publications

Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review

Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review

DWT-Net: Seizure Detection System with Structured EEG Montage and Multiple Feature Extractor in Convolution Neural Network

Sleep State Classification Using Power Spectral Density and Residual Neural Network with Multichannel EEG Signals

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