Research on intelligent diagnostic techniques for rolling bearings based on unbalanced data sets

In this paper, based on the combination of comprehensive sampling and one-dimensional convolutional neural network, a bearing fault intelligent diagnosis technique is proposed for the classification of rolling bearing vibration data. At first, the fault data set is expanded by ADASYN method. Then, the data is cleaned up by Tomek link under sampling technique, the risk of overfitting caused by overlap of different classes is reduced and the data of different categories is more apparent, and finally the normal data set and fault data set after comprehensive sampling are classified by one-dimensional convolutional neural network algorithm. Compared with random forests and support vector machines, the results show that the method has a high accuracy in identifying classifications and can effectively solve the classification problem of unbalanced bearing data.

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Security of E-Health Systems Using Face Recognition Based on Convolutional Neural Network

International Journal of Extreme Automation and Connectivity in Healthcare ◽

10.4018/ijeach.2020070104 ◽

2020 ◽

Vol 2 (2) ◽

pp. 37-41

Author(s):

Zhixian Chen ◽

Jialin Tang ◽

Xueyuan Gong ◽

Qinglang Su

Keyword(s):

Neural Network ◽

Face Recognition ◽

Convolutional Neural Network ◽

Principal Component ◽

Activation Function ◽

Support Vector ◽

Data Set ◽

Novel Approach ◽

Rectified Linear Unit ◽

The Face

In order to improve the low accuracy of the face recognition methods in the case of e-health, this paper proposed a novel face recognition approach, which is based on convolutional neural network (CNN). In detail, through resolving the convolutional kernel, rectified linear unit (ReLU) activation function, dropout, and batch normalization, this novel approach reduces the number of parameters of the CNN model, improves the non-linearity of the CNN model, and alleviates overfitting of the CNN model. In these ways, the accuracy of face recognition is increased. In the experiments, the proposed approach is compared with principal component analysis (PCA) and support vector machine (SVM) on ORL, Cohn-Kanade, and extended Yale-B face recognition data set, and it proves that this approach is promising.

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Multi-Regional Online Car-Hailing Order Quantity Forecasting Based on the Convolutional Neural Network

Information ◽

10.3390/info10060193 ◽

2019 ◽

Vol 10 (6) ◽

pp. 193 ◽

Cited By ~ 1

Author(s):

Zihao Huang ◽

Gang Huang ◽

Zhijun Chen ◽

Chaozhong Wu ◽

Xiaofeng Ma ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Travel Demand ◽

Short Term Memory ◽

Demand Forecasting ◽

Image Feature ◽

Support Vector ◽

Data Set ◽

Demand Distribution ◽

Demand Forecasting Model

With the development of online cars, the demand for travel prediction is increasing in order to reduce the information asymmetry between passengers and drivers of online car-hailing. This paper proposes a travel demand forecasting model named OC-CNN based on the convolutional neural network to forecast the travel demand. In order to make full use of the spatial characteristics of the travel demand distribution, this paper meshes the prediction area and creates a travel demand data set of the graphical structure to preserve its spatial properties. Taking advantage of the convolutional neural network in image feature extraction, the historical demand data of the first twenty-five minutes of the entire region are used as a model input to predict the travel demand for the next five minutes. In order to verify the performance of the proposed method, one-month data from online car-hailing of the Chengdu Fourth Ring Road are used. The results show that the model successfully extracts the spatiotemporal features of the data, and the prediction accuracies of the proposed method are superior to those of the representative methods, including the Bayesian Ridge Model, Linear Regression, Support Vector Regression, and Long Short-Term Memory networks.

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A Spectral Feature Based Convolutional Neural Network for Classification of Sea Surface Oil Spill

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8040160 ◽

2019 ◽

Vol 8 (4) ◽

pp. 160 ◽

Cited By ~ 11

Author(s):

Bingxin Liu ◽

Ying Li ◽

Guannan Li ◽

Anling Liu

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Convolutional Neural Networks ◽

Machine Learning Algorithms ◽

Classification Model ◽

Support Vector ◽

Oil Film ◽

One Dimensional

Spectral characteristics play an important role in the classification of oil film, but the presence of too many bands can lead to information redundancy and reduced classification accuracy. In this study, a classification model that combines spectral indices-based band selection (SIs) and one-dimensional convolutional neural networks was proposed to realize automatic oil films classification using hyperspectral remote sensing images. Additionally, for comparison, the minimum Redundancy Maximum Relevance (mRMR) was tested for reducing the number of bands. The support vector machine (SVM), random forest (RF), and Hu’s convolutional neural networks (CNN) were trained and tested. The results show that the accuracy of classifications through the one dimensional convolutional neural network (1D CNN) models surpassed the accuracy of other machine learning algorithms such as SVM and RF. The model of SIs+1D CNN could produce a relatively higher accuracy oil film distribution map within less time than other models.

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Effect on speech emotion classification of a feature selection approach using a convolutional neural network

PeerJ Computer Science ◽

10.7717/peerj-cs.766 ◽

2021 ◽

Vol 7 ◽

pp. e766

Author(s):

Ammar Amjad ◽

Lal Khan ◽

Hsien-Tsung Chang

Keyword(s):

Neural Network ◽

Feature Selection ◽

Convolutional Neural Network ◽

Feature Selection Method ◽

Classification Problem ◽

Speech Emotion Recognition ◽

Support Vector ◽

Emotion Classification ◽

K Nearest Neighbors ◽

Feature Selection Technique

Speech emotion recognition (SER) is a challenging issue because it is not clear which features are effective for classification. Emotionally related features are always extracted from speech signals for emotional classification. Handcrafted features are mainly used for emotional identification from audio signals. However, these features are not sufficient to correctly identify the emotional state of the speaker. The advantages of a deep convolutional neural network (DCNN) are investigated in the proposed work. A pretrained framework is used to extract the features from speech emotion databases. In this work, we adopt the feature selection (FS) approach to find the discriminative and most important features for SER. Many algorithms are used for the emotion classification problem. We use the random forest (RF), decision tree (DT), support vector machine (SVM), multilayer perceptron classifier (MLP), and k-nearest neighbors (KNN) to classify seven emotions. All experiments are performed by utilizing four different publicly accessible databases. Our method obtains accuracies of 92.02%, 88.77%, 93.61%, and 77.23% for Emo-DB, SAVEE, RAVDESS, and IEMOCAP, respectively, for speaker-dependent (SD) recognition with the feature selection method. Furthermore, compared to current handcrafted feature-based SER methods, the proposed method shows the best results for speaker-independent SER. For EMO-DB, all classifiers attain an accuracy of more than 80% with or without the feature selection technique.

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Fault diagnosis of rolling bearing based on multi-scale one-dimensional convolutional neural network

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1207/1/012003 ◽

2021 ◽

Vol 1207 (1) ◽

pp. 012003

Author(s):

Xukun Hou ◽

Pengjie Hu ◽

Wenliao Du ◽

Xiaoyun Gong ◽

Hongchao Wang ◽

...

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Rolling Bearing ◽

One Dimensional ◽

Bearing Fault ◽

Multi Scale ◽

Reconstructed Signal ◽

Using Data

Abstract Aiming at the typical non-stationary and nonlinear characteristics of rolling bearing vibration signals, a multi-scale convolutional neural network method for bearing fault diagnosis based on wavelet transform and one-dimensional convolutional neural network is proposed. First, the signal is decomposed into multi scale components with wavelet transform, and then each scale component is reconstructed. The reconstructed signal is subjected to the Fourier transform to obtain the frequency spectrum representation, which is used as the input of the one-dimensional convolutional neural network. Finally, one-dimensional convolution neural network is used to learn the features of the input data and recognize the bearing fault. The performance of the model is verified by using data sets of rolling bearing. The results show that this method can intelligent feature extraction and obtain 99.94% diagnostic accuracy.

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Auxiliary Diagnostic Method for Patellofemoral Pain Syndrome Based on One-Dimensional Convolutional Neural Network

Frontiers in Public Health ◽

10.3389/fpubh.2021.615597 ◽

2021 ◽

Vol 9 ◽

Author(s):

Wuxiang Shi ◽

Yurong Li ◽

Dujian Xu ◽

Chen Lin ◽

Junlin Lan ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Diagnostic Method ◽

Pain Syndrome ◽

Patellofemoral Pain Syndrome ◽

Patellofemoral Pain ◽

Flexion Angle ◽

Data Set ◽

One Dimensional ◽

Semg Signals

Early accurate diagnosis of patellofemoral pain syndrome (PFPS) is important to prevent the further development of the disease. However, traditional diagnostic methods for PFPS mostly rely on the subjective experience of doctors and subjective feelings of the patient, which do not have an accurate-unified standard, and the clinical accuracy is not high. With the development of artificial intelligence technology, artificial neural networks are increasingly applied in medical treatment to assist doctors in diagnosis, but selecting a suitable neural network model must be considered. In this paper, an intelligent diagnostic method for PFPS was proposed on the basis of a one-dimensional convolutional neural network (1D CNN), which used surface electromyography (sEMG) signals and lower limb joint angles as inputs, and discussed the model from three aspects, namely, accuracy, interpretability, and practicability. This article utilized the running and walking data of 41 subjects at their selected speed, including 26 PFPS patients (16 females and 10 males) and 16 painless controls (8 females and 7 males). In the proposed method, the knee flexion angle, hip flexion angle, ankle dorsiflexion angle, and sEMG signals of the seven muscles around the knee of three different data sets (walking data set, running data set, and walking and running mixed data set) were used as input of the 1D CNN. Focal loss function was introduced to the network to solve the problem of imbalance between positive and negative samples in the data set and make the network focus on learning the difficult-to-predict samples. Meanwhile, the attention mechanism was added to the network to observe the dimension feature that the network pays more attention to, thereby increasing the interpretability of the model. Finally, the depth features extracted by 1D CNN were combined with the traditional gender features to improve the accuracy of the model. After verification, the 1D CNN had the best performance on the running data set (accuracy = 92.4%, sensitivity = 97%, specificity = 84%). Compared with other methods, this method could provide new ideas for the development of models that assisted doctors in diagnosing PFPS without using complex biomechanical modeling and with high objective accuracy.

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Rolling Bearing Fault Diagnosis Based on Convolutional Neural Network and Support Vector Machine

IEEE Access ◽

10.1109/access.2020.3012053 ◽

2020 ◽

Vol 8 ◽

pp. 137395-137406 ◽

Cited By ~ 2

Author(s):

Laohu Yuan ◽

Dongshan Lian ◽

Xue Kang ◽

Yuanqiang Chen ◽

Kejia Zhai

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Rolling Bearing ◽

Support Vector ◽

Bearing Fault ◽

Bearing Fault Diagnosis

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A Novel Fault Diagnosis Algorithm for Rolling Bearings Based on One-Dimensional Convolutional Neural Network and INPSO-SVM

Applied Sciences ◽

10.3390/app10124303 ◽

2020 ◽

Vol 10 (12) ◽

pp. 4303

Author(s):

Yang Shao ◽

Xianfeng Yuan ◽

Chengjin Zhang ◽

Yong Song ◽

Qingyang Xu

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Experimental Results ◽

Learning Ability ◽

Support Vector ◽

Intelligent Fault Diagnosis ◽

Rolling Bearings ◽

One Dimensional ◽

Hybrid Framework

Deep learning based intelligent fault diagnosis methods have become a research hotspot in the fields of fault diagnosis and the health management of rolling bearings in recent years. To effectively identify incipient faults in rotating machinery, this paper proposes a novel hybrid intelligent fault diagnosis framework based on a convolutional neural network and support vector machine (SVM). First, an improved one-dimensional convolutional neural network (1DCNN) was adopted to extract fault features, and the state information and intrinsic properties of the raw vibration signals were mined. Second, the extracted features were used to train the SVM, which was applied to classify the fault category. The proposed hybrid framework combined the excellent classification performance of the SVM for small samples and the strong feature-learning ability of CNN network. In order to tune the parameters of the SVM, an improved novel particle swarm optimization algorithm (INPSO) which combined the Tent map and Lévy flight strategy was proposed. Numerical experimental results indicated that the proposed PSO variant had a better performance in searching accuracy and convergence speed. At last, multiple groups of rolling bearing fault diagnosis experiments were carried out and experimental results showed that, with the proposed 1DCNN-INPSO-SVM model, the hybrid framework was capable of diagnosing with high precision for rolling bearings and superior to some traditional fault diagnosis methods.

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A One-Dimensional CNN-LSTM Model for Epileptic Seizure Recognition Using EEG Signal Analysis

Frontiers in Neuroscience ◽

10.3389/fnins.2020.578126 ◽

2020 ◽

Vol 14 ◽

Author(s):

Gaowei Xu ◽

Tianhe Ren ◽

Yu Chen ◽

Wenliang Che

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Epileptic Seizure ◽

Signal Analysis ◽

Epileptic Seizures ◽

Support Vector ◽

Eeg Signal ◽

Learning Methods ◽

One Dimensional ◽

Eeg Signal Analysis

Frequent epileptic seizures cause damage to the human brain, resulting in memory impairment, mental decline, and so on. Therefore, it is important to detect epileptic seizures and provide medical treatment in a timely manner. Currently, medical experts recognize epileptic seizure activity through the visual inspection of electroencephalographic (EEG) signal recordings of patients based on their experience, which takes much time and effort. In view of this, this paper proposes a one-dimensional convolutional neural network-long short-term memory (1D CNN-LSTM) model for automatic recognition of epileptic seizures through EEG signal analysis. Firstly, the raw EEG signal data are pre-processed and normalized. Then, a 1D convolutional neural network (CNN) is designed to effectively extract the features of the normalized EEG sequence data. In addition, the extracted features are then processed by the LSTM layers in order to further extract the temporal features. After that, the output features are fed into several fully connected layers for final epileptic seizure recognition. The performance of the proposed 1D CNN-LSTM model is verified on the public UCI epileptic seizure recognition data set. Experiments results show that the proposed method achieves high recognition accuracies of 99.39% and 82.00% on the binary and five-class epileptic seizure recognition tasks, respectively. Comparing results with traditional machine learning methods including k-nearest neighbors, support vector machines, and decision trees, other deep learning methods including standard deep neural network and CNN further verify the superiority of the proposed method.

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Electrocardiogram Classification Based on Faster Regions with Convolutional Neural Network

Sensors ◽

10.3390/s19112558 ◽

2019 ◽

Vol 19 (11) ◽

pp. 2558 ◽

Cited By ~ 13

Author(s):

Yinsheng Ji ◽

Sen Zhang ◽

Wendong Xiao

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

System Development ◽

Support Vector ◽

One Dimensional ◽

Ecg Signals ◽

Two Dimensional Image ◽

Average Accuracy ◽

Effective System ◽

The One

The classification of electrocardiograms (ECG) plays an important role in the clinical diagnosis of heart disease. This paper proposes an effective system development and implementation for ECG classification based on faster regions with a convolutional neural network (Faster R-CNN) algorithm. The original one-dimensional ECG signals contain the preprocessed patient ECG signals and some ECG recordings from the MIT-BIH database in this experiment. Each ECG beat of one-dimensional ECG signals was transformed into a two-dimensional image for experimental training sets and test sets. As a result, we classified the ECG beats into five categories with an average accuracy of 99.21%. In addition, we did a comparative experiment using the one versus rest support vector machine (OVR SVM) algorithm, and the classification accuracy of the proposed Faster R-CNN was shown to be 2.59% higher.

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