Fault models

2003 ◽  
pp. 26-48
Keyword(s):  
Author(s):  
Rebekah A. Austin ◽  
Nagabhushan Mahadevan ◽  
Arthur F. Witulski ◽  
Gabor Karsai ◽  
Brian D. Sierawski ◽  
...  

2019 ◽  
Vol 131 ◽  
pp. 101646 ◽  
Author(s):  
Gerassimos Papadopoulos ◽  
Apostolos Agalos ◽  
Marinos Charalampakis ◽  
Charalampos Kontoes ◽  
Ioannis Papoutsis ◽  
...  

Author(s):  
K. Eftekhari Shahroudi

Despite their seemingly impressive claims, current products for Condition Monitoring, Diagnostic and Decision Support Systems (CMD&D) do not provide the reliable bottom line information that end users and operators need. Instead they confuse the issue with gigabytes of logged trends, complex cause-effect matrices, fault signatures etc. The term “Intelligent Health Control” here refers to the next generation of such systems which provide usable information on: • the existence and severity of faults; • how their severity will progress with utilization; • how this progress can be influenced or controlled. In this paper the fundamental shortcomings of current approaches are discussed prior to introducing the basics of Intelligent Health Control in terms of fault models and how they can be used to close the diagnostic, prognostic and intelligent control triangle. The industry will unavoidably shift towards an “information centric” view from the currently predominant “data centric” view. Gigabytes of performance trends will no longer be relevant. Instead, reliable bottom line information will be required on how to minimize or control the costs associated with machinery health degradation or faults. In order to keep the discussion real, the current state of the art of enabling technologies are discussed, including: • Open Information Buses; • Adding real time data server functionality to the control system; • Computational Steering, Human-in-the-Loop Optimization (or semi-automatic problem solving); • Fault Models; • Faster than real time simulation; • Neural Nets.


2010 ◽  
Vol 52 (4) ◽  
Author(s):  
Ilia Polian ◽  
Bernd Becker
Keyword(s):  

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 851 ◽  
Author(s):  
Gil-Tomàs ◽  
Gracia-Morán ◽  
Saiz-Adalid ◽  
Gil-Vicente

Due to the increasing defect rates in highly scaled complementary metal–oxide–semiconductor (CMOS) devices, and the emergence of alternative nanotechnology devices, reliability challenges are of growing importance. Understanding and controlling the fault mechanisms associated with new materials and structures for both transistors and interconnection is a key issue in novel nanodevices. The graphene nanoribbon field-effect transistor (GNR FET) has revealed itself as a promising technology to design emerging research logic circuits, because of its outstanding potential speed and power properties. This work presents a study of fault causes, mechanisms, and models at the device level, as well as their impact on logic circuits based on GNR FETs. From a literature review of fault causes and mechanisms, fault propagation was analyzed, and fault models were derived for device and logic circuit levels. This study may be helpful for the prevention of faults in the design process of graphene nanodevices. In addition, it can help in the design and evaluation of defect- and fault-tolerant nanoarchitectures based on graphene circuits. Results are compared with other emerging devices, such as carbon nanotube (CNT) FET and nanowire (NW) FET.


2012 ◽  
Vol 466-467 ◽  
pp. 1186-1190
Author(s):  
Jun Bin Cao ◽  
Er Min Guo ◽  
Yan Li

In order to diagnose and exempt the fault of aircraft electrical system accurately and fast, on the basis of analyzing the lost mode and fault mechanism of certain aircraft electrical system, fault structure are built and structure are built and fault models are analyzed by adopting the analytical technology based on regular fault structure. Two induction mechanisms, namely directional and anti-directional inference are introduced and the component methods are studied based on the knowledge corpus of data corpus technique. The result shows the inference results of fault diagnosis system are in accordance to reality and improve the intelligentized level of fault diagnosis system for aircraft electrical power.


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