Carrier Network Fault Diagnosis Algorithm Based on Network and Business Relationship

2021 ◽  
pp. 1361-1370
Author(s):  
Yutu Liang ◽  
Zhan Shi ◽  
Ying Zeng ◽  
Song Kang
Keyword(s):  
Fault Diagnosis ◽  
Business Relationship ◽  
Diagnosis Algorithm ◽  
Network Fault

Carrier Network Fault Diagnosis Algorithm Based on Dynamic Bayes Theory

2021 ◽  
pp. 1371-1380
Author(s):  
Zhan Shi ◽  
Ying Zeng ◽  
Yutu Liang ◽  
Keqin Zhang
Keyword(s):  
Fault Diagnosis ◽  
Bayes Theory ◽  
Diagnosis Algorithm ◽  
Network Fault

Carrier Network Fault Diagnosis Algorithm Based on Service Characteristics

2021 ◽  
pp. 1381-1391
Author(s):  
Zhan Shi ◽  
Zhengfeng Zhang ◽  
Yutu Liang ◽  
Weichao Gong
Keyword(s):  
Fault Diagnosis ◽  
Service Characteristics ◽  
Diagnosis Algorithm ◽  
Network Fault

scholarly journals Research on Fault Diagnosis Model of Generative Adss Based on Improved Semisupervised Diagnosis Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yi Qian
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Rapid Development ◽  
Network Models ◽  
Fault Classification ◽  
Neural Network Models ◽  
Diagnosis Algorithm ◽  
Diagnosis Model ◽  
Network Fault

With the advent of the era of big data and the rapid development of deep learning and other technologies, people can use complex neural network models to mine and extract key information in massive data with the support of powerful computing power. However, it also increases the complexity of heterogeneous network and greatly increases the difficulty of network maintenance and management. In order to solve the problem of network fault diagnosis, this paper first proposes an improved semisupervised inverse network fault diagnosis algorithm; the proposed algorithm effectively guarantees the convergence of generated against network model, makes full use of a large amount of trouble-free tag data, and obtains a good accuracy of fault diagnosis. Then, the diagnosis model is further optimized and the fault classification task is completed by the convolutional neural network, the discriminant function of the network is simplified, and the generation pair network is only responsible for generating fault samples. The simulation results also show that the fault diagnosis algorithm based on network generation and convolutional neural network achieves good fault diagnosis accuracy and saves the overhead of manually labeling a large number of data samples.

Diagnostic Fault Simulation for the Failure Analyst

2004 ◽  
Author(s):  
Dan Bodoh ◽  
Anthony Blakely ◽  
Terry Garyet
Keyword(s):  
Fault Diagnosis ◽  
Failure Analysis ◽  
Complete Solution ◽  
Fault Simulation ◽  
Building Blocks ◽  
Physical World ◽  
Design For Test ◽  
Fault Diagnosis System ◽  
Diagnosis Algorithm ◽  
Diagnosis Tool

Abstract Since failure analysis (FA) tools originated in the design-for-test (DFT) realm, most have abstractions that reflect a designer's viewpoint. These abstractions prevent easy application of diagnosis results in the physical world of the FA lab. This article presents a fault diagnosis system, DFS/FA, which bridges the DFT and FA worlds. First, it describes the motivation for building DFS/FA and how it is an improvement over off-the-shelf tools and explains the DFS/FA building blocks on which the diagnosis tool depends. The article then discusses the diagnosis algorithm in detail and provides an overview of some of the supporting tools that make DFS/FA a complete solution for FA. It also presents a FA example where DFS/FA has been applied. The example demonstrates how the consideration of physical proximity improves the accuracy without sacrificing precision.

Efficient Fault Diagnosis Algorithm in Dynamic Internet Service Environment

2009 ◽  
Vol 20 (9) ◽  
pp. 2520-2530
Author(s):  
Ling-Wei CHU ◽  
Shi-Hong ZOU ◽  
Shi-Duan CHENG ◽  
Chun-Qi TIAN ◽  
Wen-Dong WANG
Keyword(s):  
Fault Diagnosis ◽  
Internet Service ◽  
Service Environment ◽  
Diagnosis Algorithm

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.

Orthotopic-filtering-based hierarchical fault diagnosis algorithm for linear recursive models

2020 ◽  
Vol 14 (16) ◽  
pp. 2310-2318
Author(s):  
Ziyun Wang ◽  
Guixiang Xu ◽  
Yan Wang ◽  
Ju H. Park ◽  
Zhicheng Ji
Keyword(s):  
Fault Diagnosis ◽  
Recursive Models ◽  
Diagnosis Algorithm
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