Dynamic Gates
The DFT is based on the developing of new gates, including Priority-AND (PAND) gate, Functional Dependency (FDEP) gate, Spare gate and Sequence Enforcing (SEQ) gate.
DFTs were developed based on SFTs with new types of gates, which are called dynamic gates.
The use of these new dynamic gates makes it feasible to involve time and cross dependencies in the calculations.
As mentioned, the DFT brings four new dynamic gates; including PAND gate (Fig. 2-a), which fails if all of its inputs fail in a pre-defined order (left-to-right in the visual presentation of the gate), FDEP gate (Fig. 2-b), which compels its secondary (dependent) inputs to fail when the primary input (trigger) occurs, along with the first event, SPARE gate (Fig. 2-c), which has one primary input and a number of spare inputs, and SEQ gate (Fig. 2-d), which compels its inputs to occur in a pre-defined order (left-to-right in the visual presentation of the gate).
DFT Solution Methods
Several researches have been conducted on altering old approaches for solving DFTs.
Existing methods for solving DFTs are generally based on the mapping the DFT into a different model [5].
In general, solution approaches to solve a DFT, are classified to four different methods; including analytical, simulation based, diagram representation
and hybrid methods.
There are three classes of quantitative analytical models, which are used to solve a DFT [6]:
combinatorial approaches, which are unable to handle dynamic dependencies among the system components [13];
state-space approaches, which improve static models for modeling complex systems; but the statespace model of a system can be too large and it may require too much computation time [12], and
modular approaches, which are combination of previous approaches and mostly used for DFT analysis [1,8].
Most of these methods are particular for a special case and it is difficult to extend that solution method for other cases.
In addition, the complexity of the real systems requires the modeling of their reliability with realistic considerations, which suggests the use of analytical methods very grinding and effortful [7].
Simulation based methods, especially Monte Carlo simulation based techniques, can solve these problems.
According to the researches, complex systems which may be difficult to solve with analytical methods are simply solved with Monte Carlo simulation approach [3,4,7,12].
The reliability methods, which are based on Monte Carlo simulation approach, because of their ability in modeling the real conditions and stochastic behavior of the system,
By: Mohammad Pourgol-Mohammad, Ph.D, P.E, CRE, mpourgol@gmail.com
Previously published in the September 2015 Volume 6, Issue 3 ASQ Reliability Division Newsletter
Picture © B. Poncelet https://bennyponcelet.wordpress.com
