scholarly journals Condition Monitoring of Chain Sprocket Drive System Based on IoT Device and Convolutional Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Sang Kwon Lee ◽  
Jiseon Back ◽  
Kanghyun An ◽  
Sunwon Kim ◽  
Changho Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Condition Monitoring ◽  
Defect Detection ◽  
Learning Algorithm ◽  
Gear Tooth ◽  
Classification Performance ◽  
Two Dimensional ◽  
Feature Maps ◽  
Monitoring Method

This paper proposes a condition monitoring method for the early defect detection in a chain sprocket drive (CSD) system and classification of fault types before a catastrophic failure occurs. In the operation of a CSD system, early defect detection is very useful in preventing system failure. In this work, eight fault types associated with the CSD system components, such as the gear tooth, bearings, and drive motor shaft, were arbitrarily damaged and incorporated into the CSD system. To detect the fault signals during the CSD system operation, the vibration was measured using an Internet of Things (IoT) device, which features a wireless MEMS accelerometer, Bluetooth function, Wi-Fi function, and battery. The IoT device was mounted on the gearbox housing. The measured one-dimensional vibration time-series was transformed into time-scale images using continuous wavelet transform (CWT). A convolution neural network (CNN) was employed to extract deep features embedded in the images, which are closely related to fault types. To update the learning parameters of the CNN, the RMSprop learning algorithm was applied, and the CNN was trained using 500 image samples. Multiple-classification performance of the trained network was tested using 100 image samples. Feature maps for different fault types were obtained from the final CNN convolution layer. For the visualization of fault types, t-stochastic neighbor embedding was employed and applied to the feature maps to convert high-dimensional data into two-dimensional data. Two-dimensional features enabled excellent classification of the eight fault types and one normal type.

Health Monitoring of Chain Sprocket Drive System based on IoT Device and Convolutional Neural Network

2020 ◽  
Author(s):  
sang kwon lee ◽  
jiseon Back ◽  
Kanghyun An ◽  
Sunwon Kim ◽  
Changho Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Early Detection ◽  
Health Monitoring ◽  
Learning Algorithm ◽  
Gear Tooth ◽  
Two Dimensional ◽  
Feature Maps ◽  
Monitoring Method ◽  
Detection Of Defects

This paper proposes a health monitoring method for the early detection of defects in a chain sprocket drive (CSD) system and classification of fault types before a catastrophic failure occurs. In the operation of a CSD system, the early detection of defects is very useful to prevent system failure. Specially, if the type of defect is known, it will be easy to find a method to fix it. In this work, eight fault types in the components of the CSD system, such as the gear tooth, bearings, and shaft of the drive motor, were arbitrarily made and assembled. To detect the fault signals during the CSD system operation, the vibration is measured by an Internet of Things (IoT) device, which features a wireless MEMS accelerometer, Bluetooth function, Wi-Fi function, and battery. The IoT device is mounted on the gearbox housing. The measured one-dimensional vibration time-series is transformed into time-scale images by continuous wavelet transform (CWT). A convolution neural network (CNN) is employed to extract deep features embedded in the images, which are closely related to fault types. To update the learning parameters of the CNN, the RMSprop learning algorithm is applied, and the CNN is trained using 500 image samples. Multiple classification performance of the trained network is tested using 100 image samples. Feature maps for different fault types are obtained from the final convolution layer of the CNN. For the visualization of fault types, t-stochastic neighbor embedding is employed and applied to the feature maps to convert high-dimensional data into two-dimensional data. Two-dimensional features enabled excellent classification of the eight fault types and one normal type.

Pattern Classification Based on Self-Organizing Feature Mapping Neural Network

2013 ◽  
Vol 448-453 ◽  
pp. 3645-3649 ◽  
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang ◽  
Qing Hui Wu
Keyword(s):  
Neural Network ◽  
Pattern Classification ◽  
Classification Performance ◽  
Adjustment Process ◽  
Data Sets ◽  
Two Dimensional ◽  
Sample Data ◽  
Sofm Network ◽  
Traditional Pattern

Traditional pattern classification methods are not always efficient because sample data sets are sometimes incomplete and there are exceptions and counter examples. In this paper, SOFM neural network is applied in pattern classification of two-dimensional vectors after analysis of its structure and algorithm. The method to establish SOFM network via MATLAB7.0 is introduced before the network is applied to classify two-dimensional vectors. The adjustment process of weight vectors together with classification performance of SOFM model are also tested in the condition of different number of training steps. The simulation results show that the classification approach based on SOFM model is effective because of its fast speed, high accuracy and strong generalization ability.

scholarly journals Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

2021 ◽  
Vol 11 (3) ◽  
pp. 352
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu ◽  
Imran Javaid ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Medical Image ◽  
Resonance Imaging ◽  
Feature Maps ◽  
Deep Cnn

The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

scholarly journals Spectral Convolution Feature-Based SPD Matrix Representation for Signal Detection Using a Deep Neural Network

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 949
Author(s):  
Jiangyi Wang ◽  
Min Liu ◽  
Xinwu Zeng ◽  
Xiaoqiang Hua
Keyword(s):  
Neural Network ◽  
Signal Detection ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Detection Method ◽  
Learning Algorithm ◽  
Simulated Data ◽  
Data Sets ◽  
Feature Maps ◽  
Simulated Data Sets

Convolutional neural networks have powerful performances in many visual tasks because of their hierarchical structures and powerful feature extraction capabilities. SPD (symmetric positive definition) matrix is paid attention to in visual classification, because it has excellent ability to learn proper statistical representation and distinguish samples with different information. In this paper, a deep neural network signal detection method based on spectral convolution features is proposed. In this method, local features extracted from convolutional neural network are used to construct the SPD matrix, and a deep learning algorithm for the SPD matrix is used to detect target signals. Feature maps extracted by two kinds of convolutional neural network models are applied in this study. Based on this method, signal detection has become a binary classification problem of signals in samples. In order to prove the availability and superiority of this method, simulated and semi-physical simulated data sets are used. The results show that, under low SCR (signal-to-clutter ratio), compared with the spectral signal detection method based on the deep neural network, this method can obtain a gain of 0.5–2 dB on simulated data sets and semi-physical simulated data sets.

scholarly journals Classification of chosen orchard pests using the self-organizing feature maps neural network

2012 ◽  
Vol 7 (47) ◽  
pp. 6357-6362 ◽  
Author(s):  
Pilarski Krzysztof ◽  
Boniecki Piotr ◽  
Slosarz Piotr ◽  
Dach Jacek ◽  
Boniecka Piekarska Hanna ◽  
...  
Keyword(s):  
Neural Network ◽  
The Self ◽  
Feature Maps ◽  
Orchard Pests ◽  
Self Organizing

scholarly journals Condition Monitoring of Wind Turbine Based on Copula Function and Autoregressive Neural Network

2018 ◽  
Vol 198 ◽  
pp. 04008
Author(s):  
Zhongshan Huang ◽  
Ling Tian ◽  
Dong Xiang ◽  
Sichao Liu ◽  
Yaozhong Wei
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Copula Function ◽  
Single Step ◽  
Fusion Model ◽  
Monitoring Method ◽  
Monitoring Model ◽  
Autoregressive Neural Network

The traditional wind turbine fault monitoring is often based on a single monitoring signal without considering the overall correlation between signals. A global condition monitoring method based on Copula function and autoregressive neural network is proposed for this problem. Firstly, the Copula function was used to construct the binary joint probability density function of the power and wind speed in the fault-free state of the wind turbine. The function was used as the data fusion model to output the fusion data, and a fault-free condition monitoring model based on the auto-regressive neural network in the faultless state was established. The monitoring model makes a single-step prediction of wind speed and power, and statistical analysis of the residual values of the prediction determines whether the value is abnormal, and then establishes a fault warning mechanism. The experimental results show that this method can provide early warning and effectively realize the monitoring of wind turbine condition.

scholarly journals Text Classification of Public Feedbacks using Convolutional Neural Network Based on Differential Evolution Algorithm

2019 ◽  
Vol 14 (1) ◽  
pp. 124-134 ◽  
Author(s):  
Shuai Zhang ◽  
Yong Chen ◽  
Xiaoling Huang ◽  
Yishuai Cai
Keyword(s):  
Neural Network ◽  
Differential Evolution ◽  
Convolutional Neural Network ◽  
Text Classification ◽  
Differential Evolution Algorithm ◽  
Classification Performance ◽  
Classification Model ◽  
Evolution Algorithm ◽  
Classification Prediction

Online feedback is an effective way of communication between government departments and citizens. However, the daily high number of public feedbacks has increased the burden on government administrators. The deep learning method is good at automatically analyzing and extracting deep features of data, and then improving the accuracy of classification prediction. In this study, we aim to use the text classification model to achieve the automatic classification of public feedbacks to reduce the work pressure of administrator. In particular, a convolutional neural network model combined with word embedding and optimized by differential evolution algorithm is adopted. At the same time, we compared it with seven common text classification models, and the results show that the model we explored has good classification performance under different evaluation metrics, including accuracy, precision, recall, and F1-score.

A Health Condition Monitoring Method of Aeroengine Gas Path System Based on Consistency Fusion and Neural Network

2013 ◽  
Vol 385-386 ◽  
pp. 981-984
Author(s):  
Jian Guo Cui ◽  
Can Wu ◽  
Li Ying Jiang ◽  
Yi Wen Qi ◽  
Guo Qiang Li
Keyword(s):  
Neural Network ◽  
Condition Monitoring ◽  
Probabilistic Neural Network ◽  
Complex Structure ◽  
Reference Value ◽  
Health Condition ◽  
Monitoring Method ◽  
Accurate Localization ◽  
Safety And Reliability ◽  
Path System

Because of the complex structure, poor working conditions and lots of fault modes of aeroengine , it is necessary to monitor the operational status, accurate localization of aeroengine fault and identify fault to improve the safety and reliability of aircraft. Based on consistency fusion, this paper uses probabilistic neural network to monitor health condition of aeroengine and puts forward a combined method of health condition monitoring based on the consistency fusion and the neural network. The results of test show that this method can quickly monitor the health condition of the aeroengine and has certain reference value for other mechanical equipments condition monitoring.

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