Validating Safety Models with Fault Trees

1993 ◽  
pp. 21-30 ◽  
Author(s):  
Glenn Bruns ◽  
Stuart Anderson
Keyword(s):  
1978 ◽  
Author(s):  
G. E. Apostolakis ◽  
S. L. Salem ◽  
J. S. Wu
Keyword(s):  

2005 ◽  
Vol 88 (3) ◽  
pp. 215-228 ◽  
Author(s):  
Younju Oh ◽  
Junbeom Yoo ◽  
Sungdeok Cha ◽  
Han Seong Son

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

2012 ◽  
Vol 58 (212) ◽  
pp. 1085-1097 ◽  
Author(s):  
M.P. Brito ◽  
G. Griffiths ◽  
M. Mowlem

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.


2011 ◽  
Vol 121-126 ◽  
pp. 4481-4485
Author(s):  
Ai Yu Zhang ◽  
Xiao Guang Zhao ◽  
Lei Zhang

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.


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