Compound fault diagnosis model for Photovoltaic array using multi-scale SE-ResNet

2022 ◽  
Vol 50 ◽  
pp. 101785
Author(s):  
Peijie Lin ◽  
Zhuang Qian ◽  
Xiaoyang Lu ◽  
Yaohai Lin ◽  
Yunfeng Lai ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Multi Scale ◽  
Photovoltaic Array ◽  
Diagnosis Model

A Novel Multi-Scale Lightweight Fault Diagnosis Model Based on the Idea of Adversarial Learning

2021 ◽  
pp. 1-1
Author(s):  
Ping Zhang ◽  
Guangrui Wen ◽  
Shuzhi Dong ◽  
Hailong Lin ◽  
Xin Huang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Adversarial Learning ◽  
Multi Scale ◽  
Model Based ◽  
Diagnosis Model

scholarly journals Application of Deep Learning in Fault Diagnosis of Rotating Machinery

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 919
Author(s):  
Wanlu Jiang ◽  
Chenyang Wang ◽  
Jiayun Zou ◽  
Shuqing Zhang
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Generative Adversarial Networks ◽  
Extraction Ability ◽  
One Dimensional ◽  
Adversarial Networks ◽  
Diagnosis Model ◽  
The One

The field of mechanical fault diagnosis has entered the era of “big data”. However, existing diagnostic algorithms, relying on artificial feature extraction and expert knowledge are of poor extraction ability and lack self-adaptability in the mass data. In the fault diagnosis of rotating machinery, due to the accidental occurrence of equipment faults, the proportion of fault samples is small, the samples are imbalanced, and available data are scarce, which leads to the low accuracy rate of the intelligent diagnosis model trained to identify the equipment state. To solve the above problems, an end-to-end diagnosis model is first proposed, which is an intelligent fault diagnosis method based on one-dimensional convolutional neural network (1D-CNN). That is to say, the original vibration signal is directly input into the model for identification. After that, through combining the convolutional neural network with the generative adversarial networks, a data expansion method based on the one-dimensional deep convolutional generative adversarial networks (1D-DCGAN) is constructed to generate small sample size fault samples and construct the balanced data set. Meanwhile, in order to solve the problem that the network is difficult to optimize, gradient penalty and Wasserstein distance are introduced. Through the test of bearing database and hydraulic pump, it shows that the one-dimensional convolution operation has strong feature extraction ability for vibration signals. The proposed method is very accurate for fault diagnosis of the two kinds of equipment, and high-quality expansion of the original data can be achieved.

scholarly journals Composite multi-scale basic scale entropy based on CEEMDAN and its application in hydraulic pump fault diagnosis

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Xiaolin Liu ◽  
Xiaoqiang Yang ◽  
Faming Shao ◽  
Wuqiang Liu ◽  
Fuming Zhou ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Hydraulic Pump ◽  
Multi Scale ◽  
Basic Scale

Study on Remote Control and Fault Diagnosis for Ultrahigh Speed Grinding

2007 ◽  
Vol 359-360 ◽  
pp. 518-522
Author(s):  
Wan Shan Wang ◽  
Tian Biao Yu ◽  
Xing Yu Jiang ◽  
Jian Yu Yang
Keyword(s):  
Remote Control ◽  
Fault Diagnosis ◽  
Knowledge Acquisition ◽  
Rough Set ◽  
Theoretical Research ◽  
Reduction Rule ◽  
Diagnosis System ◽  
Linear Speed ◽  
Fault Diagnosis System ◽  
Diagnosis Model

Remote control and fault diagnosis of ultrahigh speeding grinding is studied, which is based on the theory of rough set. Knowledge acquisition and reduction rule of fault diagnosis, realization method of remote control for ultrahigh speed grinding are studied, diagnosis model is established. Based on the theoretical research and ultrahigh speed grinder with a linear speed of 250 m/s, the remote control and fault diagnosis system of ultrahigh speed grinding is developed. Results of the system running show that the environment is improved, the mental pressure of workers is relieved and the efficiency is improved. At the same time, it proves that the ability to diagnosis and the accuracy of diagnosis for the ultrahigh speed grinding are improved and the time for diagnosis is shortened by applying rough set.

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