Integrated Energy Virtual Network Service Fault Diagnosis Algorithm Under Disaster Event Environment

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

Journal of Software ◽

10.3724/sp.j.1001.2009.03448 ◽

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

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

IET Control Theory and Applications ◽

10.1049/iet-cta.2019.1229 ◽

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

Fault diagnosis algorithm of Electrical system based on Deep Learning

2020 2nd International Conference on Information Technology and Computer Application (ITCA) ◽

10.1109/itca52113.2020.00055 ◽

2020 ◽

Author(s):

Li Yifei ◽

Zhou Hangyan ◽

Yin Yujie

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

Electrical System ◽

Diagnosis Algorithm

Demo: AIML-as-a-Service for SLA management of a Digital Twin Virtual Network Service

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) ◽

10.1109/infocomwkshps51825.2021.9484610 ◽

2021 ◽

Author(s):

J. Baranda ◽

J. Mangues-Bafalluy ◽

E. Zeydan ◽

C. Casetti ◽

C. F. Chiasserini ◽

...

Keyword(s):

Virtual Network ◽

Network Service ◽

Digital Twin ◽

Sla Management

A Fault Diagnosis Algorithm for Wind Turbine Blades Based on BP Neural Network

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1043/2/022032 ◽

2021 ◽

Vol 1043 (2) ◽

pp. 022032

Author(s):

Jun-Xi Bi ◽

Wen-Ze Fan ◽

Ying Wang ◽

Jun Ren ◽

Hai-Bin Li

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Wind Turbine ◽

Bp Neural Network ◽

Turbine Blades ◽

Wind Turbine Blades ◽

Diagnosis Algorithm

A fault diagnosis algorithm using probabilistic neural network with particle fish swarm algorithm

Proceeding of the 11th World Congress on Intelligent Control and Automation ◽

10.1109/wcica.2014.7053334 ◽

2014 ◽

Cited By ~ 1

Author(s):

Mengmeng Liu ◽

Li Tian ◽

Li Zhang ◽

Dabo Zhang

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Probabilistic Neural Network ◽

Diagnosis Algorithm ◽

Swarm Algorithm

Fault diagnosis algorithm for distribution line based on wavelet singular entropy and wavelet energy entropy

2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) ◽

10.1109/iaeac.2017.8054451 ◽

2017 ◽

Cited By ~ 1

Author(s):

Rui Li ◽

Nan-hua Yu ◽

Ming Gong ◽

Yi-dan Su ◽

Bin-bin Zhang ◽

...

Keyword(s):

Fault Diagnosis ◽

Wavelet Energy ◽

Energy Entropy ◽

Distribution Line ◽

Diagnosis Algorithm ◽

Wavelet Energy Entropy

A Multistage Cutting Tool Fault Diagnosis Algorithm for the Involute form Cutter Using Cutting Force and Vibration Signals Spectrum Imaging and Convolutional Neural Networks

Arabian Journal for Science and Engineering ◽

10.1007/s13369-021-05709-1 ◽

2021 ◽

Author(s):

Gurkan Kucukyildiz ◽

Habibe Gursoy Demir

Keyword(s):

Neural Networks ◽

Fault Diagnosis ◽

Cutting Force ◽

Convolutional Neural Networks ◽

Cutting Tool ◽

Vibration Signals ◽

Spectrum Imaging ◽

Diagnosis Algorithm

Adaptive Diagnosis of Hamiltonian Networks under the Comparison Model

Parallel Processing Letters ◽

10.1142/s0129626421500158 ◽

2021 ◽

pp. 2150015

Author(s):

Wenjun Liu ◽

Wenjun Li

Keyword(s):

Fault Diagnosis ◽

Upper Bound ◽

Multiprocessor Systems ◽

Major Goal ◽

Comparison Model ◽

A Value ◽

Pmc Model ◽

Diagnosis Algorithm ◽

Test Outcomes

Adaptive diagnosis is an approach in which tests can be scheduled dynamically during the diagnosis process based on the previous test outcomes. Naturally, reducing the number of test rounds as well as the total number of tests is a major goal of an efficient adaptive diagnosis algorithm. The adaptive diagnosis of multiprocessor systems under the PMC model has been widely investigated, while adaptive diagnosis using comparison model has been independently discussed only for three networks, including hypercube, torus, and completely connected networks. In addition, adaptive diagnosis of general Hamiltonian networks is more meaningful than that of special graph. In this paper, the problem of adaptive fault diagnosis in Hamiltonian networks under the comparison model is explored. First, we propose an adaptive diagnostic scheme which takes five to six test rounds. Second, we derive a dynamic upper bound of the number of fault nodes instead of setting a value like normal. Finally, we present an algorithm such that at least one sequence obtained from cycle partition can be picked out and all nodes in this sequence can be identified based on the previous upper bound.