Compositionality of Component Fault Trees

2019 ◽  
pp. 125-140 ◽  
Author(s):  
Simon Greiner ◽  
Peter Munk ◽  
Arne Nordmann
Keyword(s):  
Fault Trees

CAT: a computer code for the automated construction of fault trees. [In FORTRAN]

1978 ◽  
Author(s):  
G. E. Apostolakis ◽  
S. L. Salem ◽  
J. S. Wu
Keyword(s):  
Computer Code ◽  
Fault Trees

Software safety analysis of function block diagrams using fault trees

2005 ◽  
Vol 88 (3) ◽  
pp. 215-228 ◽  
Author(s):  
Younju Oh ◽  
Junbeom Yoo ◽  
Sungdeok Cha ◽  
Han Seong Son
Keyword(s):  
Safety Analysis ◽  
Software Safety ◽  
Fault Trees ◽  
Function Block ◽  
Analysis Of Function

A new methodology for the computer-aided construction of fault trees

1977 ◽  
Vol 4 (9-10) ◽  
pp. 417-433 ◽  
Author(s):  
S.L. Salem ◽  
G.E. Apostolakis ◽  
D. Okrent
Keyword(s):  
Fault Trees ◽  
Computer Aided

scholarly journals Exploring Antarctic subglacial lakes with scientific probes: a formal probabilistic approach for operational risk management

2012 ◽  
Vol 58 (212) ◽  
pp. 1085-1097 ◽  
Author(s):  
M.P. Brito ◽  
G. Griffiths ◽  
M. Mowlem
Keyword(s):  
Markov Chain ◽  
Probabilistic Approach ◽  
Operational Risk ◽  
Formal Approach ◽  
Fault Trees ◽  
Subglacial Lake ◽  
Complex Processes ◽  
Formal Framework ◽  
Past Climate Change ◽  
Subglacial Lakes

AbstractSince their discovery, Antarctic subglacial lakes have become of great interest to the science community. It is hypothesized that they may hold unique forms of biological life and that they hold detailed sedimentary records of past climate change. According to the latest inventory, a total of 387 subglacial lakes have been identified in Antarctica (Wright and Siegert, 2011). However, exploration using scientific probes has yet to be performed. We propose a generic, formal approach to manage the operational risk of deploying probes during clean access to subglacial lake exploration. A representation of the entire probe deployment process is captured in a Markov chain. The transition from one state to the next depends on several factors, including reliability of components and processes. We use fault trees to quantify the probability of failure of the complex processes that must take place to facilitate the transition from one state to another. Therefore, the formal framework consists of integrating a Markov chain, fault trees, component and subsystem reliability data and expert judgment. To illustrate its application we describe how the approach can be used to address a series of what-if scenarios, using the intended Ellsworth Subglacial Lake probe deployment as a case study.

General Fault Monitoring and Diagnosis Expert System Based on Fault Tree and Multi-Sensor Information

2011 ◽  
Vol 121-126 ◽  
pp. 4481-4485
Author(s):  
Ai Yu Zhang ◽  
Xiao Guang Zhao ◽  
Lei Zhang
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Expert Knowledge ◽  
Sensor Failure ◽  
Fault Trees ◽  
Intelligent Algorithms ◽  
Monitoring And Diagnosis ◽  
Backward Reasoning ◽  
Fault Monitoring ◽  
Sensor Information

Due to the limited generality of traditional fault diagnosis expert system and its low accuracy of extracting failure symptoms, a general fault monitoring and diagnosis expert system has been built. For different devices, users can build fault trees in an interactive way and then the fault trees will be saved as expert knowledge. A variety of sensors are fixed to monitor the real-time condition of the device and intelligent algorithms such as wavelet transform and neural network are used to assist the extraction of failure symptoms. On the basis of integration of multi-sensor failure symptoms, the fault diagnosis is realized through forward and backward reasoning. The simulation diagnosis experiments of NC device have shown the effectiveness of the proposed method.

scholarly journals Mean Importance Measures for Groups of Events in Fault Trees

1994 ◽  
Vol 106 (2) ◽  
pp. 155-167 ◽  
Author(s):  
F. Eric Haskin ◽  
Min Huang ◽  
M. Kent Sasser ◽  
Desmond W. Stack
Keyword(s):  
Fault Trees
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