Small‐batch‐size convolutional neural network based fault diagnosis system for nuclear energy production safety with big‐data environment

2020 ◽  
Vol 44 (7) ◽  
pp. 5841-5855 ◽  
Author(s):  
Yuantao Yao ◽  
Jin Wang ◽  
Pengcheng Long ◽  
Min Xie ◽  
Jianye Wang
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Energy Production ◽  
Nuclear Energy ◽  
Batch Size ◽  
Production Safety ◽  
Fault Diagnosis System ◽  
Data Environment

scholarly journals Design of the Piano Score Recommendation Image Analysis System Based on the Big Data and Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yuanyuan Zhang
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Big Data ◽  
Convolutional Neural Network ◽  
Recommendation System ◽  
Piano Music ◽  
Music Recommendation ◽  
Recommendation Algorithm ◽  
Data Environment ◽  
Music Recommendation System

In the era of big data, the problem of information overload is becoming more and more obvious. A piano music image analysis and recommendation system based on the CNN classifier and user preference is designed by using the convolutional neural network (CNN), which can realize accurate piano music recommendation for users in the big data environment. The piano music recommendation system based on the CNN is mainly composed of user modeling, music feature extraction, recommendation algorithm, and so on. In the recommendation algorithm module, the potential characteristics of music are predicted by the regression model, and the matching degree between users and music is calculated according to user preferences. Then, music that users may be interested in is generated and sorted in order to recommend new piano music to relevant users. The image analysis model contains four “convolution + pooling” layers. The classification accuracy and gradient change law of the CNN under RMSProp and Adam optimal controllers are compared. The image analysis results show that the Adam optimal controller can quickly find the direction, and the gradient decreases greatly. In addition, the accuracy of the recommendation system is 55.84%. Compared with the traditional CNN algorithm, this paper uses the convolutional neural network (CNN) to analyze and recommend piano music images according to users’ preferences, which can realize more accurate piano music recommendation for users in the big data environment. Therefore, the piano music recommendation system based on the CNN has strong feature learning ability and good prediction and recommendation ability.

scholarly journals Development of Compound Fault Diagnosis System for Gearbox Based on Convolutional Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6169
Author(s):  
Ming-Chang Lin ◽  
Po-Yu Han ◽  
Yi-Hua Fan ◽  
Chih-Hung G. Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Local Area Network ◽  
Mechanical Equipment ◽  
Area Network ◽  
Diagnosis System ◽  
Training Time ◽  
Convolution Kernels ◽  
Fault Diagnosis System

Gear transmission is widely used in mechanical equipment. In practice, if the gearbox is damaged, it not only affects the yield rate but also damages other parts of machines; thus, increases the cost and difficulty of maintenance. With the advancement of technology, the concept of unmanned factories has been proposed; an automatic diagnosis system for the health management of gearboxes becomes necessary. In this paper, a compound fault diagnosis system for the gearbox based on convolutional neural network (CNN) is developed. Specifically, three-axis vibration signals measured by accelerometers are used as the input of the one-dimensional CNN; the detection of the existence and type of the fault is directly output. In testing, the model achieved nearly 100% accuracy on the fault samples we captured. Experimental evidence also shows that the frequency-domain data can provide better diagnostic results than the time-domain data due to the stable characteristics in the frequency spectrum. For practical usage, we demonstrated a remote fault diagnosis system through a local area network on an embedded platform. Furthermore, optimization of convolution kernels was also investigated. When moderately reducing the number of convolution kernels, it does not affect the diagnostic accuracy but greatly reduces the training time of the model.

scholarly journals Fault Diagnosis of Induction Motor Using Convolutional Neural Network

2019 ◽  
Vol 9 (15) ◽  
pp. 2950 ◽  
Author(s):  
Jong-Hyun Lee ◽  
Jae-Hyung Pack ◽  
In-Soo Lee
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Induction Motor ◽  
Induction Motors ◽  
Vibration Signal ◽  
Operating Conditions ◽  
Industrial Equipment ◽  
Diagnosis System ◽  
Fault Diagnosis System

Induction motors are among the most important components of modern machinery and industrial equipment. Therefore, it is necessary to develop a fault diagnosis system that detects the operating conditions of and faults in induction motors early. This paper presents an induction motor fault diagnosis system based on a CNN (convolutional neural network) model. In the proposed method, vibration signal data are obtained from the induction motor experimental environment, and these values are input into the CNN. Then, the CNN performs fault diagnosis. In this study, fault diagnosis of an induction motor is performed in three states, namely, normal, rotor fault, and bearing fault. In addition, a GUI (graphical user interface) for the proposed fault diagnosis system is presented. The experimental results confirm that the proposed method is suitable for diagnosing rotor and bearing faults of induction motors.

A Novel Approach to Fault Diagnosis of High Voltage Transmission line - A Self Attentive Convolutional Neural Network Model

2020 ◽  
Author(s):  
Shahriar Rahman Fahim ◽  
Md. Rabiul Islam Sarker ◽  
Md. Arifuzzaman ◽  
Md. Sakhawat Hosen ◽  
Subrata K Sarker ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Transmission Line ◽  
Convolutional Neural Network ◽  
Network Model ◽  
High Voltage ◽  
Neural Network Model ◽  
High Voltage Transmission Line ◽  
Novel Approach ◽  
High Voltage Transmission

scholarly journals Fault Diagnosis of Bearings Based on Multi-Sensor Information Fusion and 2D Convolutional Neural Network

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 23717-23725
Author(s):  
Jiaxing Wang ◽  
Dazhi Wang ◽  
Sihan Wang ◽  
Wenhui Li ◽  
Keling Song
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Information Fusion ◽  
Sensor Information
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