Efficient Network Fault Detection Using Adaptive Polling

2017 ◽  
Author(s):  
Ankur Gupta ◽  
Purnendu Prabhat
Keyword(s):  
Fault Detection ◽  
Adaptive Polling ◽  
Network Fault

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.

scholarly journals A Remote Educational Video Dynamic Image Sharp Processing Algorithm

2013 ◽  
Vol 760-762 ◽  
pp. 1562-1566
Author(s):  
Qian Jun Tang ◽  
Yan Zhang
Keyword(s):  
Distance Education ◽  
Fault Detection ◽  
Transmission Efficiency ◽  
Video Image ◽  
Transmission Network ◽  
Characteristic Analysis ◽  
Remote Education ◽  
Transmission Distance ◽  
Education Network ◽  
Network Fault

In distance education network transmission process, because transmission distance is too long, transmission network will be affected by complicated external environment factors, which leads to network failure and failure in remote education video image formation, and finally causes unsmooth transmission. This paper puts forward a distributed network fault detection technology to perform fault detection for remote education transmission network nodes and characteristic analysis of the use of network fault by using genetic neural network, accurately locate fault node area so as to realize the remote education networks fault detection. Experiments show that this method can avoid distance education network fault resulted from long transmission distance and improve the transmission efficiency of remote education video image.

Synchronization Optimization Simulation of Optical Fiber Network Fault Features and Model Parameters

2014 ◽  
Vol 986-987 ◽  
pp. 1596-1599
Author(s):  
Yong Huang ◽  
Heng Jun Liu ◽  
Zeng Liang Liu
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Optical Fiber ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Improved Genetic Algorithm ◽  
Fiber Network ◽  
Detection Model ◽  
Network Fault ◽  
Optical Fiber Network

The traditional optical fiber network fault detection method has not considered the relationship between the fault characteristics and KNN parameters, it is optimized separately, and the accuracy of optical fiber network fault diagnosis is low. The synchronous optimization fault detection model of fault characteristics detection model parameters is proposed. The candidate feature subsets and K adjacent parameters are used to construct the optical fiber network fault detection model. The improved genetic algorithm is used to solve the mathematical model, and the better accurate rate of fault diagnosis for optical fiber network is obtained. The simulation is taken for testing the performance of model, compared to the traditional model, the new model has better accurate detection rate, and the detection accuracy is improved greatly, the efficiency of optical fiber network fault detection is improved, it has great application value in practice.

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