<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD v1.0 20120330//EN" "JATS-archivearticle1.dtd">
<article xmlns:xlink="http://www.w3.org/1999/xlink">
  <front>
    <journal-meta />
    <article-meta>
      <title-group>
        <article-title>Comparative Reliability Analysis of Reactor Trip System Architectures: Industrial Case</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <string-name>Aleksei Vambol</string-name>
          <email>o.vambol@csn.khai.edu</email>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Vyacheslav Kharchenko</string-name>
          <email>v.kharchenko@csn.khai.edu</email>
          <xref ref-type="aff" rid="aff0">0</xref>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
        <aff id="aff0">
          <label>0</label>
          <institution>Centre for Safety Infrastructure-Oriented Research and Analysis, RPC Radiy</institution>
          ,
          <addr-line>Kropyvnytskyi</addr-line>
          ,
          <country country="UA">Ukraine</country>
        </aff>
        <aff id="aff1">
          <label>1</label>
          <institution>Department of Computer Systems, Networks and Cybersecurity, National Aerospace University «KhAI»</institution>
          ,
          <addr-line>Kharkiv</addr-line>
          ,
          <country country="UA">Ukraine</country>
        </aff>
      </contrib-group>
      <abstract>
        <p>The aim of this paper is to propose the approach to choosing the most reliable architecture of reactor trip system. The industrial case is based on the systems developed by the use of the platform «RadICS produced by RPC «Radiy». The two-channel three-chassis and three-channel two-chassis architectures were analyzed using their reliability block diagrams (RBDs). The results of analysis show that no architecture among the given ones can be unconditionally considered the most reliable. The choice of the best alternative in terms of reliability can be made using the formulae proposed in the given paper, which allow to take into account the reliabilities of the blocks of RBDs and the percents of common failures for certain types of elements. The analytical expressions for the mean of the advantage and the percent of superiority cases in terms of reliability were obtained for the considered architectures using the aforementioned formulae. The approach to searching the cases of maximal superiority in reliability for the analyzed architectures has been proposed. The aforesaid analysis can be conducted for an arbitrary pair of architectures represented by their RBDs.</p>
      </abstract>
      <kwd-group>
        <kwd>reliability</kwd>
        <kwd>reactor trip system</kwd>
        <kwd>comparative analysis</kwd>
        <kwd>common cause failure</kwd>
        <kwd>RadICS</kwd>
      </kwd-group>
    </article-meta>
  </front>
  <body>
    <sec id="sec-1">
      <title>-</title>
      <p>1.1</p>
    </sec>
    <sec id="sec-2">
      <title>Introduction</title>
      <sec id="sec-2-1">
        <title>Motivation</title>
        <p>
          Reliability of reactor trip systems (RTS) is of great importance for safety of a nuclear
power plant. Among such systems the ones based on the FPGA platform RadICS,
developed and produced by RPC «Radiy», deserve considerable attention [
          <xref ref-type="bibr" rid="ref1 ref2">1, 2</xref>
          ]. As
this platform allows implementation of systems with different architectures, it is
necessary to have an approach to their comparison in terms of reliability. The
twochannel three-chassis architecture (2C3), briefly described in [
          <xref ref-type="bibr" rid="ref1">1</xref>
          ], and the
threechannel two-chassis (3C2) one, outlined in [
          <xref ref-type="bibr" rid="ref2">2</xref>
          ], are good examples of the aforesaid
multeity. Their reliability block diagrams (RBDs) are given in Figure 1.
        </p>
        <p>
          The signals of RTSes considered in this paper are formed on the basis of output
signals of independent channels according to 1-out-of-2 (for 2C3) or 2-out-of-3 (for
3C2) voting logic. The elements implementing these logics are designated in Figure 1
as «1/2» and «2/3» blocks. Each channel uses output signals of its underlying chassis
to generate a signal in obedience to one of the aforesaid voting logics [
          <xref ref-type="bibr" rid="ref1 ref2">1, 2</xref>
          ].
        </p>
        <p>
          The chassis consist of five components: analogue and digital input modules, logic
module, analogue and digital output modules. Each of these components is based on
FPGA chips. All modules must be in working states to provide the failure-free
operation of the chassis, the reliability of which is affected by physical and design faults.
Therefore, the chassis are represented in Figure 1 as serially connected «pf» (physical
faults) and «df» (design faults) blocks [
          <xref ref-type="bibr" rid="ref2">2</xref>
          ].
        </p>
        <p>
          Usually RTS is developed using 2C3 architecture. It is caused by special
requirements to safety critical systems which are joint by the principle of independence. This
principle implies, firstly, independent forming of main and diverse signals for RTS by
redundant and diverse channels, and, secondly, their separate placing in different
constructions (for example, cabinets) [
          <xref ref-type="bibr" rid="ref3">3</xref>
          ]. However, in terms of reliability, 3C2
architecture can be more appropriate. Hence, there are two reasons to compare these
architectures:
        </p>
        <p>1) it’s possible that there are other application areas where requirements regarding
independence are not strong;</p>
        <p>2) in terms of safety, the benefits due to higher reliability could be more irrefutable
than ones caused by independence.
1.2</p>
        <p>Objectives and an approach
The aim of this research is to propose the approach to choosing the most reliable
version from the aforementioned pair of architectures. The proposed analysis algorithm
can be performed in case of arbitrary pair of RBDs.</p>
        <p>Within the scope of this work the elements of the same type are considered to
possess equal reliability. In the rest of this paper the RBDs and corresponding
architectures are designated as «left» and «right» for the purpose of brevity.</p>
        <p>
          The paper is structured as follows. Sections 2-4 are dedicated to consecutive
consideration of three cases, which differ in the number of parameters. The analysis
begins with the ideal case, where «1/2» and «2/3» blocks are absolutely reliable, and
each next occasion generalizes the previous one. Section 5 is devoted to study of the
case in which the underlying elements have such failure rates as in [
          <xref ref-type="bibr" rid="ref1">1</xref>
          ] and [
          <xref ref-type="bibr" rid="ref2">2</xref>
          ]. The
obtained results and possible directions for further research are discussed in Section 6.
Besides, this section gives some recommendations for choosing between the analyzed
architectures.
1.3
        </p>
        <p>
          Related work
There are a lot of papers [
          <xref ref-type="bibr" rid="ref4 ref5 ref6 ref7">4-7</xref>
          ] where typical KooN (1oo2, 2oo3, etc.) architectures
have been researched. However, the RBDs in Figure 1 are more complex and
implement the principle of a structural-version redundancy to minimize risk of common
cause failures [
          <xref ref-type="bibr" rid="ref8">8</xref>
          ].
        </p>
        <p>Besides, the reliability of such structures depends on more initial parameters, in
particular, rates of failures due to physical and design faults of channels (versions),
failure rates of voting units, diversity metrics and so on.</p>
        <p>Hence, choosing the most reliable architecture from the considered pair should be
based on a detailed analysis of the effect of the aforesaid parameters on reliability
indicators.
2</p>
        <p>
          Ideal case: Absolutely reliable «1/2» and «2/3» elements
Let r denote the reliability of inputs of «1/2» and «2/3» blocks. In this case the
reliability formulae for absolutely reliable «1/2» and «2/3» elements are 2r - r2 and
3r2 - 2r3 [
          <xref ref-type="bibr" rid="ref9">9</xref>
          ]. Thus, the reliability formulae for the left and right RBDs are
PL(p,q) = 2q(3p2 - 2p3) - q2(3p2 - 2p3)2,
PR(p,q) = 3(2pq - p2q2)2 - 2(2pq - p2q2)3,
(1)
(2)
where p and q are reliabilities of the blocks prone to potential failures caused by «pf»
(physical faults) and «df» (design faults), respectively.
        </p>
        <p>It can be supposed that for some values of p and q the right RBD surpasses the left
one in terms of reliability, while for other values of the parameters the left RBD is the
most reliable. Optimization algorithms for multivariate functions can be used to find
such a pair (p, q) for which the reliability advantage of the right RBD over the left one
is maximal (or minimal). This problem can be solved by the search of maximum and
minimum of ∆P(p,q) = PR(p,q) - PL(p,q) with the constraints p, q ∈ (0; 1]. The
computing environment MATLAB can be used for the given purpose.</p>
        <p>The script for searching maximum and minimum of ∆P(p,q) can be written in the
following way:</p>
        <p>
          The plot of ∆P(p,q), which is given in Figure 2, can be built using the given script:
dp = @(p, q) ((3*(2*p*q - p^2*q^2)^2 - ...
2*(2*p*q - p^2*q^2)^3) - (2*q*(3*p^2 - 2*p^3) - ...
q^2*(3*p^2 - 2*p^3)^2));
fsurf(dp, [
          <xref ref-type="bibr" rid="ref1 ref1">0, 1, 0, 1</xref>
          ], "EdgeColor", "none", ...
"MeshDensity", 200);
colormap(gray); xlabel("p"); ylabel("q"); box on;
        </p>
        <p>Brighter areas of the given plot correspond to higher values of ∆P(p,q), which
indicate a greater reliability advantage of the right RBD over the left one.</p>
        <p>Consider the expression H(∆P(p,q)), where H(x) is Heaviside step function, which
equals 0 for x &lt; 0 and 1 for x ≥ 0. This composite function is equal to 0 if for a pair
(p, q) the left RBD surpasses the right one in terms of reliability. In other cases its
value is 1.</p>
        <p>
          Figure 3 represents the plot of H(∆P(p,q)). The black area corresponds to the value
pairs of p and q for which the left RBD is more reliable than the right one. The script
for the given plot can be constructed as follows:
dp = @(p, q) heaviside((3*(2*p*q - p^2*q^2)^2 - ...
2*(2*p*q - p^2*q^2)^3) - (2*q*(3*p^2 - 2*p^3) - ...
q^2*(3*p^2 - 2*p^3)^2));
fsurf(dp, [
          <xref ref-type="bibr" rid="ref1 ref1">0, 1, 0, 1</xref>
          ], "EdgeColor", "none", ...
"MeshDensity", 200);
colormap(gray); xlabel("p"); ylabel("q"); box on;
        </p>
        <p>Let S denote the percent of cases in which the right RBD is more reliable than the
left one. It can be obtained as multiplied by 100 average value of H(∆P(p,q)) over all
p, q ∈ (0; 1]. By dint of the approach used for A, the following formula is found:
1 1
A = ∫ ∫ ∆P(p,q) dp dq
0 0</p>
        <p>
          Let A denote the average value of ∆P(p,q) over all p, q ∈ (0; 1]. It can be found
using the formula from [
          <xref ref-type="bibr" rid="ref10">10</xref>
          ] for the average value of a function over a region as follows:
        </p>
        <p>The script for calculating the value of A can be written in the following way:
syms p q
dp = @(p, q) (3*(2*p*q - p^2*q^2)^2 - ...
2*(2*p*q - p^2*q^2)^3) - (2*q*(3*p^2 - 2*p^3) - ...
q^2*(3*p^2 - 2*p^3)^2);
vpaintegral(vpaintegral(dp, p, [0 1]), q, [0 1])</p>
        <p>In the case of S the script can be built as follows:
syms p q
hdp = @(p, q) 100*heaviside((3*(2*p*q - p^2*q^2)^2 - ...
2*(2*p*q - p^2*q^2)^3) - (2*q*(3*p^2 - 2*p^3) - ...
q^2*(3*p^2 - 2*p^3)^2));
vpaintegral(vpaintegral(hdp, p, [0 1], "AbsTol", ...
0.001), q, [0 1], "AbsTol", 0.001)</p>
        <p>The given computations yield A = -0.00537415 and S = 48.15.
3</p>
        <p>Ordinary case: Partially reliable «1/2» and «2/3» elements
In the case of not absolutely reliable «1/2» and «2/3» elements, which have reliability
values u and v respectively, the reliability formulae for the left and right RBDs can be
written in the following way:</p>
        <p>PL(p,q,u,v) = u(2qv(3p2 - 2p3) - q2v2(3p2 - 2p3)2),
PR(p,q,u,v) = v(3u2(2pq - p2q2)2 - 2u3(2pq - p2q2)3).
(3)
(4)</p>
        <p>The aforesaid formulae can be obtained using the modification of the approach for
the ideal case, where the reliability formulae for «1/2» and «2/3» elements are
replaced with the results of their multiplication by u and v, respectively.</p>
        <p>
          The script for searching global extrema of ∆P(p,q,u,v) = PR(p,q,u,v) - PL(p,q,u,v)
with the constraints p, q, u, v ∈ (0; 1] can be written as follows:
dp = @(p, q, u, v)((v*(3*u^2*(2*p*q - p^2*q^2)^2 - ...
2*u^3*(2*p*q - p^2*q^2)^3) - u*(2*q*v*(3*p^2 - 2*p^3) ...
- q^2*v^2*(3*p^2 - 2*p^3)^2)));
lp = createOptimProblem('fmincon','x0',[0.5,0.5, ...
0.5,0.5],'objective',@(x)(dp(x(1),x(2),x(3),x(4))), ...
'lb',[0,0,0,0],'ub',[
          <xref ref-type="bibr" rid="ref1 ref1 ref1 ref1">1,1,1,1</xref>
          ]);
hp = createOptimProblem('fmincon','x0',[0.5,0.5, ...
0.5,0.5],'objective',@(x)(-dp(x(1),x(2),x(3),x(4))), ...
'lb',[0,0,0,0],'ub',[
          <xref ref-type="bibr" rid="ref1 ref1 ref1 ref1">1,1,1,1</xref>
          ]);
gs = GlobalSearch();
lr = run(gs, lp);
hr = run(gs, hp);
disp(join([" Minimum: ", lr, newline, "Maximum: ", hr]));
The aforesaid computations yield the results given in Table 2.
The mean of ∆P(p,q,u,v) is calculated according to the following formula:
1 1 1 1
A = ∫ ∫ ∫ ∫ ∆P(p,q,u,v)dpdq du dv
        </p>
        <p>0 0 0 0
The percent of cases in which the right RBD is more reliable:</p>
        <p>1 1 1 1
S = 100 ∫ ∫ ∫ ∫ H(∆P(p,q,u,v))dpdq du dv</p>
        <p>0 0 0 0
These formulae can be obtained using the corresponding approach for the ideal case.</p>
        <p>The script for calculating the value of A can be constructed in the following way:
syms p q u v
dp = @(p, q, u, v)((v*(3*u^2*(2*p*q - p^2*q^2)^2 - ...
2*u^3*(2*p*q - p^2*q^2)^3) - u*(2*q*v*(3*p^2 - 2*p^3) ...
- q^2*v^2*(3*p^2 - 2*p^3)^2)));
vpaintegral(vpaintegral(vpaintegral(vpaintegral(dp, ...
p, [0 1], "AbsTol", 0.001), q, [0 1], "AbsTol", ...
0.001), u, [0 1], "AbsTol", 0.001), v, [0 1], ...
"AbsTol", 0.001)
The value of S can be calculated using the following script:</p>
        <sec id="sec-2-1-1">
          <title>The given computations yield A ≈ -0.029 and S ≈ 5.</title>
          <p>4</p>
          <p>Generalized case: Common failures in «pf» and «df» elements
The previous case can be generalized by considering all «pf» and «df» blocks as
having 100h% and 100s% of common failures, respectively. The probability of
failurefree operation for the given RBDs under the condition of absence of common failure
can be calculated using (3) and (4) by substituting
x = p / (1 - h(1 - p)) for p,
y = q / (1 - s(1 - q)) for q.</p>
          <p>The formulae for x and y represent the reliability of «pf» and «df» elements provided
that situations leading to common failure do not happen.</p>
          <p>The probability of common failure absence equals</p>
          <p>(1 - h(1 - p))(1 - s(1 - q)) = (p / x)(q / y).</p>
          <p>The aforementioned RBDs are not able to function properly in case of common
failure, so their reliability formulae can be written in the following way:
PL(p,q,u,v,h,s) = u(2yv(3x2 - 2x3) - y2v2(3x2 - 2x3)2)(p / x)(q / y),
PR(p,q,u,v,h,s) = v(3u2(2xy - x2y2)2 - 2u3(2xy - x2y2)3)(p / x)(q / y).
(5)
(6)</p>
        </sec>
        <sec id="sec-2-1-2">
          <title>The mean of ∆P(p,q,u,v,h,s) is calculated as follows:</title>
          <p>1 1 1 1 1 1
A = ∫ ∫ ∫ ∫ ∫ ∫ ∆P(p,q,u,v,h,s)dpdqdu dvds dh</p>
          <p>0 0 0 0 0 0
The percent of cases in which the right RBD is more reliable:</p>
          <p>1 1 1 1 1 1
S = 100∫ ∫ ∫ ∫ ∫ ∫ H(∆P(p,q,u,v,h,s))dpdq dudvdsdh</p>
          <p>
            Case study: Specified failure rates of «pf» and «df» blocks
In the papers [
            <xref ref-type="bibr" rid="ref1">1</xref>
            ] and [
            <xref ref-type="bibr" rid="ref2">2</xref>
            ], where the aforesaid architectures have been investigated in
terms of Markov analysis, the considered failure rate of «pf» block is 10-4 h-1. For
«df» elements the examined values of this parameter, given in 10-6 h-1, are 10, 25, 50
and 75. Other blocks are regarded as absolutely reliable.
          </p>
          <p>Within the scope of this case study, the analyzed RBDs are considered for
parameters chosen as described above. Thus, the reliability formulae for the ideal case, which
are given in Section 2, can be used. The probability of failure-free operation during t
hours for «pf» and «df» blocks can be calculated using the following expressions:
Ppf (t) = exp(-λpf · t),</p>
          <p>
            Pdf (t) = exp(-λdf · t),
where λpf and λdf are failure rates of «pf» and «df» elements, respectively [
            <xref ref-type="bibr" rid="ref11">11</xref>
            ]. Hence,
the reliability values of the considered RBDs at the time moment t can be obtained
using (1) and (2) by substituting Ppf (t) for p and Pdf (t) for q.
          </p>
          <p>The aforementioned approach can be used to prove that if t ≥ 7000 h, each of the
analyzed RBDs has reliability less than 0.85 for any of the considered failure rate sets.
Thus, the given architectures should not be used during a larger time spans, if their
parameters are as described above. Consequently, in this case study it is sufficient to
analyze the given RBDs only for t less than 7000 h. Other time intervals are irrelevant
to choosing the preferable architecture and therefore not considered.</p>
          <p>The script for searching minimum of the difference between the reliability values
of the right and left RBDs in a specified range of time spans can be written as follows:
LP = 1e-4; LD = 10 * 1e-6; ST = 0; FN = 7000;
p = @(t) exp(-LP * t);
q = @(t) exp(-LD * t);
R = @(t) (3*(2*p(t)*q(t) - (p(t))^2*(q(t))^2)^2 - ...
2*(2*p(t)*q(t) - (p(t))^2*(q(t))^2)^3);
L = @(t) (2*q(t)*(3*(p(t))^2 - 2*(p(t))^3) - ...
(q(t))^2*(3*(p(t))^2 - 2*(p(t))^3)^2);
m = createOptimProblem('fmincon','x0',[(ST+FN)/2], ...
'objective',@(t)(R(t) - L(t)),'lb',[ST],'ub',[FN]);
g = GlobalSearch();
[x, y] = run(g, m);
disp(join([" Minimum: ", y]));</p>
          <p>The first line of the given code determines such parameters as λpf, λdf and the
examined range of time intervals, which in this study is set to (0; 7000) h. The result
returned by this script is nonnegative for each of the aforementioned failure rate sets.
Thus, for any time interval less than 7000 h the right RBD is more reliable in all cases
examined above. Hence, the right architecture is preferable for all pairs (λpf, λdf)
considered in this section.</p>
        </sec>
      </sec>
    </sec>
    <sec id="sec-3">
      <title>Conclusion</title>
      <sec id="sec-3-1">
        <title>Discussion and future steps</title>
        <p>No architecture among the given ones can be unconditionally considered the most
reliable, so the reliability formulae for their RBDs have been obtained in order to
make possible the choice of the most reliable alternative. These formulae allow to
take into account the reliabilities of the underlying elements of the aforementioned
RBDs and the percents of common failures for «pf» and «df» elements. The aforesaid
analytical expressions have been used to obtain the formula for the mean of the
reliability advantage of the right RBD over the left one as well as the expression for the
percent of cases in which the right architecture is more reliable. The approach to
finding the cases of maximal reliability advantage for the left and right architectures has
been proposed. The given analysis can be conducted for an arbitrary pair of RBDs.</p>
        <p>Future research can be dedicated to development of a decision-making system for
choosing between the given architectures, which considers all parameters and
standard requirements for RTS or other similar safety-critical systems.
6.2</p>
        <p>Recommendations for choosing an architecture
If reliability of the underlying elements can be estimated precisely, a preferable
architecture can be chosen using the aforementioned reliability formulas for the analyzed
RBDs. In particular, for the case of the underlying blocks having such failure rates as
described in the first paragraph of Section 5, the right architecture is recommended.</p>
        <p>However, the results of this research also allow to give guidances for some
occasions, where the reliability values for elements of the given RBDs are known only
partially. The most important of these recommendations are listed below.</p>
        <p>In the ideal case, which is described in Section 2, the right architecture should be
used if the reliability of «df» block is greater than 0.6, and the left one is preferable if
this parameter is less than 0.29.</p>
        <p>For the ordinary case, which is considered in Section 3, the left architecture is more
reliable for about 95% of all possible reliability parameter sets characterizing the
blocks of the given RBDs. Thus, if there is no information about the reliability of the
underlying elements (e.g., due to their degradation), the left architecture is preferable.</p>
      </sec>
    </sec>
    <sec id="sec-4">
      <title>Acknowledgements</title>
      <p>This research is supported by the project STARC (Methodology of SusTAinable
Development and InfoRmation Technologies of Green Computing and Communication)
funded by Department of Education and Science of Ukraine.</p>
    </sec>
  </body>
  <back>
    <ref-list>
      <ref id="ref1">
        <mixed-citation>
          1.
          <string-name>
            <surname>Butenko</surname>
            ,
            <given-names>V.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Kharchenko</surname>
            ,
            <given-names>V.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Odarushchenko</surname>
            ,
            <given-names>E.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Butenko</surname>
            ,
            <given-names>D.</given-names>
          </string-name>
          <article-title>Metric-based approach and tool for modeling the I&amp;C system using Markov chains</article-title>
          .
          <source>Proceedings of 23rd International Conference on Nuclear Engineering</source>
          ,
          <year>2015</year>
          , pp.
          <fpage>1</fpage>
          -
          <lpage>9</lpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref2">
        <mixed-citation>
          2.
          <string-name>
            <surname>Kharchenko</surname>
            ,
            <given-names>V.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Butenko</surname>
            ,
            <given-names>V.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Odarushchenko</surname>
            ,
            <given-names>O.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Odarushchenko</surname>
            ,
            <given-names>E.</given-names>
          </string-name>
          <article-title>Markov's Modeling of NPP I&amp;C Reliability and Safety: Optimization of Tool-and-Technique Selection</article-title>
          .
          <source>Second International Symposium on Stochastic Models in Reliability Engineering, Life Science and Operations Management</source>
          ,
          <year>2016</year>
          , pp.
          <fpage>328</fpage>
          -
          <lpage>336</lpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref3">
        <mixed-citation>
          3.
          <string-name>
            <surname>Yastrebenetsky</surname>
            ,
            <given-names>M.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Kharchenko</surname>
            ,
            <given-names>V.</given-names>
          </string-name>
          <article-title>Nuclear Power Plant Instrumentation and Control Systems for Safety and Security, 1st Edition</article-title>
          .
          <source>IGI Global</source>
          ,
          <year>2014</year>
          , 470 p.
        </mixed-citation>
      </ref>
      <ref id="ref4">
        <mixed-citation>
          4.
          <string-name>
            <surname>Geist</surname>
            ,
            <given-names>R.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Trivedi</surname>
            ,
            <given-names>K.</given-names>
          </string-name>
          <article-title>Reliability estimation of fault-tolerant systems: Tools and techniques</article-title>
          .
          <source>IEEE Computer</source>
          , vol.
          <volume>23</volume>
          ,
          <issue>iss</issue>
          . 7,
          <issue>1990</issue>
          , pp.
          <fpage>52</fpage>
          -
          <lpage>61</lpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref5">
        <mixed-citation>
          5.
          <string-name>
            <surname>Wang</surname>
            ,
            <given-names>D.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Trivedi</surname>
            ,
            <given-names>K.</given-names>
          </string-name>
          <article-title>Reliability analysis of phased-mission system with independent component repairs</article-title>
          .
          <source>IEEE Transactions on reliability</source>
          , vol.
          <volume>56</volume>
          ,
          <issue>iss</issue>
          . 3,
          <issue>2007</issue>
          , pp.
          <fpage>540</fpage>
          -
          <lpage>551</lpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref6">
        <mixed-citation>
          6.
          <string-name>
            <surname>Grottke</surname>
            ,
            <given-names>M.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Sun</surname>
            ,
            <given-names>H.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Fricks</surname>
            ,
            <given-names>R.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Trivedi</surname>
            ,
            <given-names>K.</given-names>
          </string-name>
          <article-title>Ten fallacies of availability and reliability analysis</article-title>
          .
          <source>Service Availability: 5th International Service Availability Symposium</source>
          ,
          <year>2008</year>
          , pp.
          <fpage>187</fpage>
          -
          <lpage>206</lpage>
        </mixed-citation>
      </ref>
      <ref id="ref7">
        <mixed-citation>
          7.
          <string-name>
            <surname>Popov</surname>
            ,
            <given-names>G.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Tashev</surname>
            ,
            <given-names>T.</given-names>
          </string-name>
          <article-title>Comparative Reliability Analysis for TMR «2 out of 3». Fault Tolerance Systems</article-title>
          .
          <source>Recent Advances in Applied Mathematics and Computational and Information Sciences</source>
          , vol.
          <volume>2</volume>
          ,
          <issue>2009</issue>
          , pp.
          <fpage>357</fpage>
          -
          <lpage>360</lpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref8">
        <mixed-citation>
          8.
          <string-name>
            <surname>Stott</surname>
            ,
            <given-names>J.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Britton</surname>
            ,
            <given-names>P.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Ring</surname>
            ,
            <given-names>R.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Hark</surname>
            ,
            <given-names>F.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Hatfield</surname>
            ,
            <given-names>G.</given-names>
          </string-name>
          <article-title>Common Cause Failure Modeling: Aerospace vs</article-title>
          .
          <source>Nuclear. 10th International Conference on Probabilistic Safety Assessment &amp; Management</source>
          , vol.
          <volume>3</volume>
          ,
          <issue>2010</issue>
          , pp.
          <fpage>2570</fpage>
          -
          <lpage>2581</lpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref9">
        <mixed-citation>
          9.
          <string-name>
            <surname>Ferrero</surname>
            ,
            <given-names>A.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Petri</surname>
            ,
            <given-names>D.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Carbone</surname>
            ,
            <given-names>P.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Catelani</surname>
            ,
            <given-names>M.</given-names>
          </string-name>
          <article-title>Modern Measurements: Fundamentals and Applications</article-title>
          . John Wiley &amp; Sons,
          <year>2015</year>
          , 571 p.
        </mixed-citation>
      </ref>
      <ref id="ref10">
        <mixed-citation>
          10.
          <string-name>
            <surname>Larson</surname>
            ,
            <given-names>R.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Edwards</surname>
            ,
            <given-names>B. Multivariable</given-names>
          </string-name>
          <string-name>
            <surname>Calculus</surname>
          </string-name>
          ,
          <source>11th Edition. Brooks Cole</source>
          ,
          <year>2017</year>
          , 1160 p.
        </mixed-citation>
      </ref>
      <ref id="ref11">
        <mixed-citation>
          11.
          <string-name>
            <surname>Gnedenko</surname>
            ,
            <given-names>B.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Belyayev</surname>
            ,
            <given-names>Yu.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Solovyev</surname>
            ,
            <given-names>A</given-names>
          </string-name>
          . Mathematical Methods of Reliability Theory. Academic Press,
          <year>1969</year>
          , 506 p.
        </mixed-citation>
      </ref>
    </ref-list>
  </back>
</article>