Fault diagnosis of a logical circuit by use of a pseudorandom signal and a neural network

1997 ◽  
Vol 1 (4) ◽  
pp. 169-172
Author(s):  
H. Kashiwagi
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Logical Circuit ◽  
Pseudorandom Signal

Wind Turbine Gearbox Fault Diagnosis using SAE-BP Transfer Neural Network

2019 ◽  
Vol 15 (9) ◽  
pp. 2504
Author(s):  
Wang Yu ◽  
Yang Shuai ◽  
Sánchez RenéVinicio
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbine Gearbox

Application of Artficial Neural Network in Fault Diagnosis with Clearance Time

2007 ◽  
Vol 2 (1) ◽  
pp. 24-29
Author(s):  
Rengarajan N ◽  
◽  
Palani S ◽  
Ravichandiran C.S ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Clearance Time

Improved PSO-BP neural network for power transformer fault diagnosis

2010 ◽  
Vol 30 (3) ◽  
pp. 783-785 ◽  
Author(s):  
Zhong-yang XIONG ◽  
Qing-bo YANG ◽  
Yu-fang ZHANG
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Power Transformer ◽  
Transformer Fault ◽  
Improved Pso

PNN and BP Neural Network Fault Diagnosis Research on Electronic Accelerator Pedal Detection

2020 ◽  
Vol 14 (2) ◽  
pp. 205-220
Author(s):  
Yuxiu Jiang ◽  
Xiaohuan Zhao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Lower Limit ◽  
Bp Neural Network ◽  
Accelerator Pedal ◽  
Electronic Throttle ◽  
Measured Voltage ◽  
Upper Limit ◽  
Network Fault

Background: The working state of electronic accelerator pedal directly affects the safety of vehicles and drivers. Effective fault detection and judgment for the working state of the accelerator pedal can prevent accidents. Methods: Aiming at different working conditions of electronic accelerator pedal, this paper used PNN and BP diagnosis model to detect the state of electronic accelerator pedal according to the principle and characteristics of PNN and BP neural network. The fault diagnosis test experiment of electronic accelerator pedal was carried out to get the data acquisition. Results: After the patents for electronic accelerator pedals are queried and used, the first measured voltage, the upper limit of first voltage, the first voltage lower limit, the second measured voltage, the upper limit of second voltage and the second voltage lower limit are tested to build up the data samples. Then the PNN and BP fault diagnosis models of electronic accelerator pedal are established. Six fault samples are defined through the design of electronic accelerator pedal fault classifier and the fault diagnosis processes are executed to test. Conclusion: The fault diagnosis results were analyzed and the comparisons between the PNN and the BP research results show that BP neural network is an effective method for fault detection of electronic throttle pedal, which is obviously superior to PNN neural network based on the experiment data.

Research of Fault Diagnosis of Belt Conveyor Based on Fuzzy Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 916-921
Author(s):  
Yuan Yuan ◽  
Wenjun Meng ◽  
Xiaoxia Sun
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Fault Location ◽  
Fuzzy Neural Network ◽  
Fuzzy Theory ◽  
Belt Conveyor ◽  
Fuzzy Neural ◽  
Neural Network Algorithm ◽  
Network Fault

To address deficiencies in the process of fault diagnosis of belt conveyor, this study uses a BP neural network algorithm combined with fuzzy theory to provide an intelligent fault diagnosis method for belt conveyor and to establish a BP neural network fault diagnosis model with a predictive function. Matlab is used to simulate the fuzzy BP neural network fault diagnosis of the belt conveyor. Results show that the fuzzy neural network can filter out unnecessary information; save time and space; and improve the fault diagnosis recognition, classification, and fault location capabilities of belt conveyor. The proposed model has high practical value for engineering.

scholarly journals Fault diagnosis of DC-DC module of V2G charging pile based on Fuzzy Neural Network

2021 ◽  
Vol 772 (1) ◽  
pp. 012027
Author(s):  
Qunfei Wang ◽  
Xiaoli Lu ◽  
Dongjunming Yang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fuzzy Neural Network ◽  
Fuzzy Neural

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.

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