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Survey needs sorting out:

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Needs lots of detail, procedural and historic
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fmmd_concept/fmmd_concept.tex
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@ -1,7 +1,4 @@
\ifthenelse {\boolean{paper}} \ifthenelse {\boolean{paper}}
{ {
\abstract{ \abstract{
@ -278,7 +275,8 @@ system level outcomes.
\subsubsection{ FTA weaknesses } \subsubsection{ FTA weaknesses }
\begin{itemize} \begin{itemize}
\item Possibility to miss component failure modes. \item Complex component interaction effects are by definition modelled by FTA, but because of the top down approach, not all
base component failure modes are guaranteed to be included in the model.
\item Possibility to miss environmental affects. \item Possibility to miss environmental affects.
\item No possibility to model base component level double failure modes. \item No possibility to model base component level double failure modes.
\end{itemize} \end{itemize}
@ -305,8 +303,10 @@ a prioritised `todo list', with higher $RPN$ values being the most urgent.
\subsubsection{ FMEA weaknesses } \subsubsection{ FMEA weaknesses }
\begin{itemize} \begin{itemize}
\item Possibility to miss the effects of failure modes at SYSTEM level. \item Possibility to miss the effects of base component failure modes at SYSTEM level.
(because the its each individual component, not all its failure modes, that are considered for analysis).
\item Possibility to miss environmental effects. \item Possibility to miss environmental effects.
\item Complex component interaction effects can be missed.
\item No possibility to model base component level double failure modes. \item No possibility to model base component level double failure modes.
\end{itemize} \end{itemize}
@ -359,6 +359,7 @@ Again this essentially produces a prioritised `todo' list.
\begin{itemize} \begin{itemize}
\item Possibility to miss the effects of failure modes at SYSTEM level. \item Possibility to miss the effects of failure modes at SYSTEM level.
\item Possibility to miss environmental affects. \item Possibility to miss environmental affects.
\item Complex component interaction effects can be missed.
\item No possibility to model base component level double failure modes. \item No possibility to model base component level double failure modes.
\end{itemize} \end{itemize}
@ -381,9 +382,12 @@ The following gives an outline of the procedure.
\subsubsection{Two statistical perspectives} \subsubsection{Two statistical perspectives}
\ifthenelse {\boolean{paper}}
{
FMEDA is a statistical analysis methodology and is used from one of two perspectives, FMEDA is a statistical analysis methodology and is used from one of two perspectives,
Probability of Failure on Demand (PFD), and Probability of Failure Probability of Failure on Demand (PFD), and Probability of Failure
in continuous Operation, or Failure in Time (FIT). in continuous Operation, or Failure in Time (FIT).
\paragraph{Failure in Time (FIT).} Continuous operation is measured in failures per billion ($10^9$) hours of operation. \paragraph{Failure in Time (FIT).} Continuous operation is measured in failures per billion ($10^9$) hours of operation.
For a continuously running nuclear powerstation, industrial burner or aircraft engine For a continuously running nuclear powerstation, industrial burner or aircraft engine
we would be interested in its operational FIT values. we would be interested in its operational FIT values.
@ -392,6 +396,12 @@ we would be interested in its operational FIT values.
automobile braking, or other fail safe measure applied in an emergency, we would be interested in PFD. automobile braking, or other fail safe measure applied in an emergency, we would be interested in PFD.
That is to say the ratio of it failing That is to say the ratio of it failing
to succeeding to operate correctly on demand. to succeeding to operate correctly on demand.
}
{
FMEDA is a statistical analysis methodology and is used from one of two perspectives,
Probability of Failure on Demand (PFD) (see \ref{survey:pfd}), and Probability of Failure
in continuous Operation, or Failure in Time (FIT) (see \ref{survey:fit}).
}
\subsubsection{The FMEDA Analysis Process} \subsubsection{The FMEDA Analysis Process}
@ -406,18 +416,18 @@ environmental conditions. The SYSTEM errors are categorised as `safe' or `danger
% %
%Statistical data exists for most component types \cite{mil1992}. %Statistical data exists for most component types \cite{mil1992}.
% %
This phase is typically implemented on a spreadsheet %This phase is typically implemented on a spreadsheet
with rows representing each component. A typical component spreadsheet row would %with rows representing each component. A typical component spreadsheet row would
comprise of %comprise of
component type, placement, %component type, placement,
part number, environmental stress factors, MTTF, safe/dangerous etc. %part number, environmental stress factors, MTTF, safe/dangerous etc.
%will be a determination of whether the component failing will lead to a `safe' %%will be a determination of whether the component failing will lead to a `safe'
%or `unsafe' condition. %or `unsafe' condition.
\paragraph{Overall SYSTEM failure rate.} %\paragraph{Overall SYSTEM failure rate.}
The product failure rate is the sum of all component %The product failure rate is the sum of all component
failure rates. Typically the sum of all MTTF rates for all %failure rates. Typically the sum of all MTTF rates for all
components in an FMEDA spreadsheet. %components in an FMEDA spreadsheet.
%This is the sum of safe and unsafe %This is the sum of safe and unsafe
%failures. %failures.
@ -430,16 +440,16 @@ This is done by taking a component failure mode and determining
if the SYSTEM error it is tied to is dangerous or safe. if the SYSTEM error it is tied to is dangerous or safe.
The decision for this may be The decision for this may be
based on heuristics or field data. based on heuristics or field data.
EN61508 uses the $\lambda$ symbol to represent probabilities. %EN61508 uses the $\lambda$ symbol to represent probabilities.
Because we have statistics for each component failure mode, %Because we have statistics for each component failure mode,
we can now now classify these in terms of safe and dangerous lambda values. %we can now now classify these in terms of safe and dangerous lambda values.
Detectable failure probabilities are labelled `$\lambda_D$' (for %Detectable failure probabilities are labelled `$\lambda_D$' (for
dangerous) and `$\lambda_S$' (for safe) \cite{en61508}. %dangerous) and `$\lambda_S$' (for safe) \cite{en61508}.
\paragraph{Determine Detectable and Undetectable Failures.} \paragraph{Determine Detectable and Undetectable Failures.}
Each safe and dangerous failure mode is now Each safe and dangerous failure mode is now
classified as detectable or un-detectable. classified as detectable or un-detectable.
EN61508 assumes that products have a high level of For the higher integrity levels, EN61508 assumes that products have a high proportion of
self checking features. self checking features.
% %
This gives us four level failure mode classifications: This gives us four level failure mode classifications:
@ -454,7 +464,7 @@ analysis, the
% and guess how it will affect an ENTIRE complex SYSTEM % and guess how it will affect an ENTIRE complex SYSTEM
% Admission of failure of the process really !!!! % Admission of failure of the process really !!!!
next step is to investigate using an actual working SYSTEM. next step is to investigate using an actual working SYSTEM.
%
Failures are deliberately caused (by physical intervention), and any new SYSTEM level Failures are deliberately caused (by physical intervention), and any new SYSTEM level
failures are added to the model. failures are added to the model.
Heuristics and MTTF failure rates for the components Heuristics and MTTF failure rates for the components
@ -464,38 +474,39 @@ $\lambda_{SD}$, $\lambda_{SU}$, $\lambda_{DD}$, $\lambda_{DU}$).
These new failures are added to the model. These new failures are added to the model.
%SD, SU, DD, DU. %SD, SU, DD, DU.
With these classifications, and statistics for each component %With these classifications, and statistics for each component
we can now calculate statistics for the diagnostic coverage (how good at `self checking' the system is) %we can now calculate statistics for the diagnostic coverage (how good at `self checking' the system is)
and its safe failure fraction (how many of its failures are self detected or safe compared to %and its safe failure fraction (how many of its failures are self detected or safe compared to
all failures possible). %all failures possible).
%
The calculations for these are described below. %The calculations for these are described below.
\paragraph{Diagnostic Coverage.}
The diagnostic coverage is simply the ratio
of the dangerous detected probabilities
against the probability of all dangerous failures,
and is normally expressed as a percentage. $\Sigma\lambda_{DD}$ represents
the percentage of dangerous detected base component failure modes, and
$\Sigma\lambda_D$ the total number of dangerous base component failure modes.
$$ DiagnosticCoverage = \Sigma\lambda_{DD} / \Sigma\lambda_D $$
The diagnostic coverage for safe failures, where $\Sigma\lambda_{SD}$ represents the percentage of
safe detected base component failure modes,
and $\Sigma\lambda_S$ the total number of safe base component failure modes,
is given as
$$ SF = \frac{\Sigma\lambda_{SD}}{\Sigma\lambda_S} $$
%\paragraph{Diagnostic Coverage.}
%The diagnostic coverage is simply the ratio
%of the dangerous detected probabilities
%against the probability of all dangerous failures,
%and is normally expressed as a percentage.
%%$\Sigma\lambda_{DD}$ represents
%the percentage of dangerous detected base component failure modes, and
%$\Sigma\lambda_D$ the total number of dangerous base component failure modes.
%
%$$ DiagnosticCoverage = \Sigma\lambda_{DD} / \Sigma\lambda_D $$
%
%The diagnostic coverage for safe failures, where $\Sigma\lambda_{SD}$ represents the percentage of
%safe detected base component failure modes,
%and $\Sigma\lambda_S$ the total number of safe base component failure modes,
%is given as
%
%$$ SF = \frac{\Sigma\lambda_{SD}}{\Sigma\lambda_S} $$
%
%
\paragraph{Safe Failure Fraction.} \paragraph{Safe Failure Fraction.}
A key concept in FMEDA is Safe Failure Fraction (SFF). A key concept in FMEDA is Safe Failure Fraction (SFF).
This is the ratio of safe and dangerous detected failures This is the ratio of safe and dangerous detected failures
against all safe and dangerous failure probabilities. against all safe and dangerous failure probabilities.
Again this is usually expressed as a percentage. %Again this is usually expressed as a percentage.
$$ SFF = \big( \Sigma\lambda_S + \Sigma\lambda_{DD} \big) / \big( \Sigma\lambda_S + \Sigma\lambda_D \big) $$ %$$ SFF = \big( \Sigma\lambda_S + \Sigma\lambda_{DD} \big) / \big( \Sigma\lambda_S + \Sigma\lambda_D \big) $$
%This is the ratio of %This is the ratio of
%Step 4 Calculate SFF, SIL and PFD %Step 4 Calculate SFF, SIL and PFD
@ -610,7 +621,8 @@ and its international analog standard IOC5108.
\subsubsection{ FMEDA weaknesses } \subsubsection{ FMEDA weaknesses }
\begin{itemize} \begin{itemize}
\item Possibility to miss the effects of failure modes at SYSTEM level. \item Possibility to miss the effects of failure modes at SYSTEM level.
\item Statistical nature allows a proportion of undetected failures for given S.I.L. level. \item Statistical nature allows a proportion of undetected failures for given S.I.L. level. These could be catostophic failures, as long as the percieved probability is low enough, they are considered acceptable for EN61508.
\item Complex component interaction effects are more likely to be seen (because self diagnostic capability is considered), than FMEA or FMECA but can still be missed.
\item Allows a small proportion of `undetectable' error conditions. \item Allows a small proportion of `undetectable' error conditions.
\item No possibility to model base component level double failure modes. \item No possibility to model base component level double failure modes.
\end{itemize} \end{itemize}

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@ -81,6 +81,7 @@
\newcommand{\pin}{\ensuremath{\stackrel{pi}{\longleftrightarrow}}} \newcommand{\pin}{\ensuremath{\stackrel{pi}{\longleftrightarrow}}}
%\newcommand{\pic}{\em pure~intersection~chain} %\newcommand{\pic}{\em pure~intersection~chain}
\newcommand{\pic}{\em pair-wise~intersection~chain} \newcommand{\pic}{\em pair-wise~intersection~chain}
\newcommand{\wrt}{\em with-respect~to}
%----- Display example text (#1) in typewriter font %----- Display example text (#1) in typewriter font

389
survey/survey.tex
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@ -12,20 +12,391 @@ A survey of Static Failure Mode analysis Methodologies applicable to saefty crit
A survey of Static Failure Mode analysis Methodologies applicable to saefty critical systems. A survey of Static Failure Mode analysis Methodologies applicable to saefty critical systems.
} }
\section{FMEA} There are four methodologies in common use for failure mode modelling.
These are FTA, FMEA, FMECA
and FMEDA (a form of statistical assessment).
%
These methodologies date from the 1940's onwards, and were designed for
different application areas and reasons; all have drawbacks and
advantages that are discussed in the next section.
%In short
%FTA, due to its top down nature, can overlook error conditions. FMEA and the Statistical Methods
%lack precision in predicting failure modes at the SYSTEM level.
Two meanings, a general one Fault Mode Effects Analysis, meaning general statics diagnosis of a design, looking \ifthenelse {\boolean{paper}}
at faults that can occur and their effect. {
paper
}
{
chapter
}
presents the design considerations that motivated and provided the specification for
the FMMD methodology.
%
\subsection { FTA }
This, like all top~down methodologies introduces the very serious problem
of missing component failure modes \cite{faa}[Ch.9].
%, or modelling at
%a too high level of failure mode abstraction.
FTA was invented for use on the minuteman nuclear defence missile
systems in the early 1960s and was not designed as a rigorous
fault/failure mode methodology.
It was designed to look for disastrous top level hazards and
determine how they could be caused.
It is more like a procedure to
be applied when discussing the safety of a system, with a top down hierarchical
notation using logic symbols, that guides the analysis.
This methodology was designed for
experienced engineers sitting around a large diagram and discussing the safety aspects.
Also the nature of a large rocket with red wire, and remote detonation
failsafes meant that the objective was to iron out common failures
not to rigorously detect all possible failures.
Consequently it was not designed to guarantee to covering all component failure modes,
and has no rigorous in-built safeguards to ensure coverage of all possible
system level outcomes.
\subsubsection{ FTA weaknesses }
\begin{itemize}
\item Possibility to miss component failure modes.
\item Possibility to miss environmental affects.
\item No possibility to model base component level double failure modes.
\end{itemize}
\subsection { FMEA }
\label{pfmea}
This is an early static analysis methodology, and concentrates
on SYSTEM level errors which have been investigated.
The investigation will typically point to a particular failure
of a component.
The methodology is now applied to find the significance of the failure.
It is based on a simple equation where $S$ ranks the severity (or cost \cite{bfmea}) of the identified SYSTEM failure,
$O$ its occurrence\footnote{The occurrence $O$ is the
probability of the failure happening.},
and $D$ giving the failures detectability\footnote{Detectability: often failures
may occur but not be noticed or cause an effect.
Consider an unused feature failing.}. Muliplying these
together,
gives a risk probability number (RPN), given by $RPN = S \times O \times D$.
This gives in effect
a prioritised `todo list', with higher $RPN$ values being the most urgent.
\subsection{Manufacturing Cost Reduction FMEA} \subsubsection{ FMEA weaknesses }
\begin{itemize}
\item Possibility to miss the effects of failure modes at SYSTEM level.
\item Possibility to miss environmental effects.
\item No possibility to model base component level double failure modes.
\end{itemize}
Second a methodology for reducing cost in manufacturing by taking fauls, their frequency \paragraph{Note.} FMEA is sometimes used in its literal sense, that is to say
and their cost, multiplying these together, and then coming up with a priority list Failure Mode Effects analysis, simply looking at a systems' internal failure
for fixing knmown faults. modes and determining what may happen as a result.
"The basics of FMEA by Robin E. McDermott et all" FMEA described in this section (\ref{pfmea}) is sometimes called `production FMEA'.
ISBN 0-527-76320-9.
\subsection{FMECA}
Failure mode, effects, and criticality analysis (FMECA) extends FMEA.
This is a bottom up methodology, which takes component failure modes
and traces them to the SYSTEM level failures.
%
Reliability data for components is used to predict the
failure statistics in the design stage.
An openly published source for the reliability of generic
electronic components was published by the DOD
in 1991 (MIL HDK 1991 \cite{mil1991}) and is a typical
source for MTFF data.
%
FMECA has a probability factor for a component error becoming % causing
a SYSTEM level error.
This is termed the $\beta$ factor.
%\footnote{for a given component failure mode there will be a $\beta$ value, the
%probability that the component failure mode will cause a given SYSTEM failure}.
%
This lacks precision, or in other words, determinability prediction accuracy \cite{fafmea},
as often the component failure mode cannot be proven to cause a SYSTEM level failure, but is
assigned a probability $\beta$ factor by the design engineer. The use of a $\beta$ factor
is often justified using Bayes theorem \cite{probstat}.
%Also, it can miss combinations of failure modes that will cause SYSTEM level errors.
%
The results of FMECA are similar to FMEA, in that component errors are
listed according to importance, based on
probability of occurrence and criticallity.
% to prevent the SYSTEM fault of given criticallity.
Again this essentially produces a prioritised `todo' list.
%%-WIKI- Failure mode, effects, and criticality analysis (FMECA) is an extension of failure mode and effects analysis (FMEA).
%%-WIKI- FMEA is a a bottom-up, inductive analytical method which may be performed at either the functional or
%%-WIKI- piece-part level. FMECA extends FMEA by including a criticality analysis, which is used to chart the
%%-WIKI- probability of failure modes against the severity of their consequences. The result highlights failure modes with relatively high probability
%%-WIKI- and severity of consequences, allowing remedial effort to be directed where it will produce the greatest value.
%%-WIKI- FMECA tends to be preferred over FMEA in space and North Atlantic Treaty Organization (NATO) military applications,
%%-WIKI- while various forms of FMEA predominate in other industries.
\subsubsection{ FMECA weaknesses }
\begin{itemize}
\item Possibility to miss the effects of failure modes at SYSTEM level.
\item Possibility to miss environmental affects.
\item No possibility to model base component level double failure modes.
\end{itemize}
\subsection { FMEDA or Statistical Analyis }
Failure Modes, Effects, and Diagnostic Analysis (FMEDA)
% This
is a process that takes all the components in a system,
and using the failure modes of those components, the investigating engineer
ties them to possible SYSTEM level events/failure modes.
%
This technique
evaluates a products statistical level of safety
taking into account its self-diagnostic ability.
The calculations and procedures for FMEDA are
described in EN61508 %Part 2 Appendix C
\cite{en61508}[Part 2 App C].
The following gives an outline of the procedure.
\subsubsection{Two statistical perspectives}
FMEDA is a statistical analysis methodology and is used from one of two perspectives,
Probability of Failure on Demand (PFD), and Probability of Failure
in continuous Operation, or Failure in Time (FIT).
\label{survey:fit}
\paragraph{Failure in Time (FIT).} Continuous operation is measured in failures per billion ($10^9$) hours of operation.
For a continuously running nuclear powerstation, industrial burner or aircraft engine
we would be interested in its operational FIT values.
\label{survey:pfd}
\paragraph{Probability of Failure on Demand (PFD).} For instance with an anti-lock system in
automobile braking, or other fail safe measure applied in an emergency, we would be interested in PFD.
That is to say the ratio of it failing
to succeeding to operate correctly on demand.
\subsubsection{The FMEDA Analysis Process}
\paragraph{Determine SYSTEM level failures from base components}
The first stage is to apply FMEA to the SYSTEM.
%
Each component is analysed in terms of how its failure
would affect the system.
Failure rates of individual components in the SYSTEM
are calculated based on component type and
environmental conditions. The SYSTEM errors are categorised as `safe' or `dangerous'.
%
%Statistical data exists for most component types \cite{mil1992}.
%
This phase is typically implemented on a spreadsheet
with rows representing each component. A typical component spreadsheet row would
comprise of
component type, placement,
part number, environmental stress factors, MTTF, safe/dangerous etc.
%will be a determination of whether the component failing will lead to a `safe'
%or `unsafe' condition.
\paragraph{Overall SYSTEM failure rate.}
The product failure rate is the sum of all component
failure rates. Typically the sum of all MTTF rates for all
components in an FMEDA spreadsheet.
%This is the sum of safe and unsafe
%failures.
\paragraph{Self Diagnostics.}
We next evaluate the SYSTEM's self-diagnostic ability.
%Each component’s failure modes and failure rate are now available.
Failure modes are now classified as safe or dangerous.
This is done by taking a component failure mode and determining
if the SYSTEM error it is tied to is dangerous or safe.
The decision for this may be
based on heuristics or field data.
EN61508 uses the $\lambda$ symbol to represent probabilities.
Because we have statistics for each component failure mode,
we can now now classify these in terms of safe and dangerous lambda values.
Detectable failure probabilities are labelled `$\lambda_D$' (for
dangerous) and `$\lambda_S$' (for safe) \cite{en61508}.
\paragraph{Determine Detectable and Undetectable Failures.}
Each safe and dangerous failure mode is now
classified as detectable or un-detectable.
EN61508 assumes that products have a high level of
self checking features.
%
This gives us four level failure mode classifications:
Safe-Detected (SD), Safe-Undetected (SU), Dangerous-Detected (DD) or Dangerous-Undetected (DU),
and the probablistic failure rate of each classification
is represented by lambda variables
(i.e. $\lambda_{SD}$, $\lambda_{SU}$, $\lambda_{DD}$, $\lambda_{DU}$).
Because it is recognised that some failure modes may not be discovered theoretically during the static
analysis, the
% admission of how daft it is to take a component failure mode on its own
% and guess how it will affect an ENTIRE complex SYSTEM
% Admission of failure of the process really !!!!
next step is to investigate using an actual working SYSTEM.
Failures are deliberately caused (by physical intervention), and any new SYSTEM level
failures are added to the model.
Heuristics and MTTF failure rates for the components
are used to calculate probabilities for these new failure modes
along with their safety and detectability classifications (i.e.
$\lambda_{SD}$, $\lambda_{SU}$, $\lambda_{DD}$, $\lambda_{DU}$).
These new failures are added to the model.
%SD, SU, DD, DU.
With these classifications, and statistics for each component
we can now calculate statistics for the diagnostic coverage (how good at `self checking' the system is)
and its safe failure fraction (how many of its failures are self detected or safe compared to
all failures possible).
The calculations for these are described below.
\paragraph{Diagnostic Coverage.}
The diagnostic coverage is simply the ratio
of the dangerous detected probabilities
against the probability of all dangerous failures,
and is normally expressed as a percentage. $\Sigma\lambda_{DD}$ represents
the percentage of dangerous detected base component failure modes, and
$\Sigma\lambda_D$ the total number of dangerous base component failure modes.
$$ DiagnosticCoverage = \Sigma\lambda_{DD} / \Sigma\lambda_D $$
The diagnostic coverage for safe failures, where $\Sigma\lambda_{SD}$ represents the percentage of
safe detected base component failure modes,
and $\Sigma\lambda_S$ the total number of safe base component failure modes,
is given as
$$ SF = \frac{\Sigma\lambda_{SD}}{\Sigma\lambda_S} $$
\paragraph{Safe Failure Fraction.}
A key concept in FMEDA is Safe Failure Fraction (SFF).
This is the ratio of safe and dangerous detected failures
against all safe and dangerous failure probabilities.
Again this is usually expressed as a percentage.
$$ SFF = \big( \Sigma\lambda_S + \Sigma\lambda_{DD} \big) / \big( \Sigma\lambda_S + \Sigma\lambda_D \big) $$
%This is the ratio of
%Step 4 Calculate SFF, SIL and PFD
%The SIL level of the product is finally determined from the Safe Failure Fraction (SFF) and the Probability of Failure on Demand (PFD). The following formulas are used.
%SFF = (lSD + lSU + lDD) / (lSD + lSU + lDD + lDU)
%PFD = (lDU)(Proof Test Interval)/2 + (lDD)(Down Time or Repair Time)
% Often a given component failure mode there will be a $\beta$ value, the
% probability that the component failure mode will cause a given SYSTEM failure.
%\paragraph{Risk Mitigation}
%
%The component may be have its risk factor
%reduced by the checking interval (or $\tau$ time between self checking procedures).
%
%Ultimately this technique calculates a risk factor for each component.
%The risk factors of all the components are summed and
%%give a value for the `safety level' for the equipment in a given environment.
\paragraph{Classification into Safety Integrity Levels (SIL).}
There are four SIL levels, from 1 to 4 with 4 being the highest safety level.
In addition to probablistic risk factors, the
diagnostic coverage and SFF
have threshold bands beoming stricter for each level.
Demanded software verification and specification techniques and constraints
(such as language subsets, s/w redundancy etc)
become stricter for each SIL level.
%%
%% Andrew asked me to expand on this here, but it would take at least two
%% pages. I think its more appropriate for the survey.tex chapter.
%%
Thus FMEDA uses statistical methods to determine
a safety level (SIL), typically used to meet an acceptable risk
value, specified for the environment the SYSTEM must work in.
EN61508 defines in general terms,
risk assessment and required SIL levels \cite{en61508} [5 Annex A].
%the probability of
%failures occurring, and provide an adaquate risk level.
%
%A component failure mode, given its MTTF
%the probability of detecting the fault and its safety relevant validation time $\tau$,
%contributes a simple risk factor that is summed
%in to give a final risk result.
%
Thus an FMEDA
model can be implemented on a spreadsheet, where each component
has a calculated risk, a fault detection time (if any), an estimated risk importance
and other factors such as de-rating and environmental stress.
With one component failure mode per row,
all the statistical factors for SIL rating can be produced\footnote{A SIL rating will apply
to an installed plant, i.e. a complete installed and working SYSTEM. SIL ratings for individual components or
sub-systems are meaningless, and the nearest equivalent would be the FIT/PFD and SFF and diagnostic coverage figures.}.
\subsubsection{FMEDA and failure outcome prediction accuracy.}
FMEDA suffers from the same problems of
lack of component failure mode outcome prediction accuracy, as FMEA in section \ref{pfmea}.
%
This is because the analyst has to decide how particular components failing will impact on the SYSTEM or top level.
This involves a `leap of faith'. For instance, a resistor failing in a sensor circuit
may be part of a critical monitoring function.
The analyst is now put in a position
where he probably should assign a dangerous failure classification to it.
%
There is no analysis
of how that resistor would/could affect the components close to it, but because the circuitry
is part of critical section it will most likely
be linked to a dangerous system level failure in an FMEDA study.
%
%%- IS THIS TRUE IS THERE A BETA FACTOR IN FMEDA????
%%-
%A $\beta$ factor, the heuristically defined probability
%of the failure causing the system fault may be applied.
%
%In FMEDA there is no detailed analysis of the failure mode behaviour
%of the component in its local environment
%Component failure modes are traceable directly to the SYSTEM level.
%it becomes more
%guess work than science.
%
With FMEDA, there is no rigorous cause and effect analysis for the failure modes
and how they interact on the micro scale (the components adjacent to them in terms of functionality).
Unintended side effects that lead to failure can be missed.
Also component failure modes that are not
dangerous, may be wrongly assigned as dangerous simply because they exist in a critical
section of the product.
% some critical component failure
%modes, but we can only guess, in most cases what the safety case outcome
%will be if it occurs.
This leads to the practise of having components within a SYSTEM partitioned into different
safety level zones as recomended in EN61508\cite{en61508}. This is a vague way of determining
safety, as it can miss unexpected effects due to `unexpected' component interaction.
The Statistical Analysis methodology is the core philosophy
of the Safety Integrity Levels (SIL) embodied in EN61508 \cite{en61508}
and its international analog standard IOC5108.
\subsubsection{ FMEDA weaknesses }
\begin{itemize}
\item Possibility to miss the effects of failure modes at SYSTEM level.
\item Statistical nature allows a proportion of undetected failures for given S.I.L. level.
\item Allows a small proportion of `undetectable' error conditions.
\item No possibility to model base component level double failure modes.
\end{itemize}
%AND then how we can solve all there problems
\subsection{Deterministic FMEA} \subsection{Deterministic FMEA}