Component failure rate data sources for probabilistic safety and reliability

2010 ◽  
Vol 29 (3) ◽  
pp. 236-241 ◽  
Author(s):  
L.C. Cadwallader ◽  
S.A. Eide
Keyword(s):  
Failure Rate ◽  
Data Sources ◽  
Rate Data ◽  
Component Failure ◽  
Safety And Reliability ◽  
Probabilistic Safety

scholarly journals Comparison of Tritium Component Failure Rate Data

2005 ◽  
Vol 47 (4) ◽  
pp. 983-988 ◽  
Author(s):  
L. C. Cadwallader
Keyword(s):  
Failure Rate ◽  
Rate Data ◽  
Component Failure

Automatic Electronic Component Failure-Rate Prediction with MIL-HDBK-217B

1977 ◽  
Vol R-26 (3) ◽  
pp. 214-219 ◽  
Author(s):  
W.W. Gaertner ◽  
D.S. Elders ◽  
D.B. Ellingham ◽  
J.A. Kastning ◽  
W.M. Schreyer
Keyword(s):  
Failure Rate ◽  
Electronic Component ◽  
Component Failure ◽  
Rate Prediction

scholarly journals Research on Dynamic Reliability of a Jet Pipe Servo Valve Based on Generalized Stochastic Petri Nets

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yuanbo Chu ◽  
Zhaohui Yuan ◽  
Jia Chen
Keyword(s):  
Petri Nets ◽  
Failure Rate ◽  
Failure Modes ◽  
Stochastic Petri Nets ◽  
Rate Data ◽  
Repair Rate ◽  
Dynamic Reliability ◽  
Servo Valve ◽  
Reliability Parameters ◽  
Generalized Stochastic Petri Nets

The jet pipe servo valve is widely used in the military fields of aviation and ship, whose reliability has obvious randomness and dynamic. However, existing methods are either having complicated theory or analyzing static reliability. Based on the generalized stochastic petri nets (GSPN) theory and the collected basic failure modes and failure rate data of jet pipe servo valve, this paper proposes a novel modeling and simulating method for system’s dynamic behavior analysis. In this method, the dynamic reliability model considering failure’s random and repair is established and is simulated using GSPN software. Then, the steady state probability of servo valve is calculated, which is compared with the value calculated by Markov method. Finally, the dynamic reliability parameters of jet pipe servo valve are calculated using collected failure rate data and different repair rate data. Results show the probability that the maximum error between methods of GSPN and Markov is 2.07%, the optimal repair rate set is less than 1.71µi, and also the dynamic reliability parameters become better with increasing simulation time because of failure’s recovery. Therefore, research methods and results based on GSPN are concise and realistic, which can be used for failure’s qualitative forecast and dynamic reliability’s quantitative calculation of similar complicated system.

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