Self-organized Neighborhood Fault Detection Protocol under Dynamic Dependable Network Environments

2010 ◽  
Vol 32 (9) ◽  
pp. 2145-2150
Author(s):  
Guang-hui Chang ◽  
Shu-yu Chen ◽  
Guang-xia Xu ◽  
Hua-wei Lu
Keyword(s):  
Fault Detection ◽  
Self Organized ◽  
Network Environments

Self-organized Modeling for Vehicle Fleet Based Fault Detection

2008 ◽  
Author(s):  
Stefan Byttner ◽  
Thorsteinn Rögnvaldsson ◽  
Magnus Svensson
Keyword(s):  
Fault Detection ◽  
Self Organized ◽  
Vehicle Fleet

Consensus self-organized models for fault detection (COSMO)

2011 ◽  
Vol 24 (5) ◽  
pp. 833-839 ◽  
Author(s):  
S. Byttner ◽  
T. Rögnvaldsson ◽  
M. Svensson
Keyword(s):  
Fault Detection ◽  
Self Organized

Problem behavior in autism spectrum disorders: A paradigmatic self-organized perspective of network structures

2019 ◽  
Vol 42 ◽  
Author(s):  
Lucio Tonello ◽  
Luca Giacobbi ◽  
Alberto Pettenon ◽  
Alessandro Scuotto ◽  
Massimo Cocchi ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Problem Behaviors ◽  
Autism Spectrum ◽  
The Self ◽  
Network Structures ◽  
Self Organized ◽  
Critical Equilibrium ◽  
Self Organized Criticality ◽  
Acute Agitation ◽  
Spectrum Disorders

AbstractAutism spectrum disorder (ASD) subjects can present temporary behaviors of acute agitation and aggressiveness, named problem behaviors. They have been shown to be consistent with the self-organized criticality (SOC), a model wherein occasionally occurring “catastrophic events” are necessary in order to maintain a self-organized “critical equilibrium.” The SOC can represent the psychopathology network structures and additionally suggests that they can be considered as self-organized systems.

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

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