after Friday meeting with Andrew Fish
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@ -47,8 +47,13 @@ failure rates)\cite{mil1991}. For instance, a simple resistor is generally consi
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to fail in two ways, it can go open circuit or it can short.
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Thus we can associate a set of faults to this component $ResistorFaultModes=\{OPEN, SHORT\}$.
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The UML diagram in figure
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\ref{fig:component} shows a component as a simple data
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structure with its failure modes.
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\ref{fig:component} shows a component as a data
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structure with its associated failure modes.
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From this diagram we see that each component must have at least one failure mode.
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Also to clearly show that the failure modes are unique events associated with one component,
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each failure mode is referenced back to only one component.
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% Perhaps talk here about the failure modes being shared, but by being referenced
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% by the component ?
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\begin{figure}[h]
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@ -59,14 +64,6 @@ structure with its failure modes.
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\label{fig:component}
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\end{figure}
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% \begin{figure}[h+]
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% \centering
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% \includegraphics[width=400pt,bb=0 0 433 68,keepaspectratio=true]{component_failure_modes_definition/component.jpg}
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% % component.jpg: 433x68 pixel, 72dpi, 15.28x2.40 cm, bb=0 0 433 68
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% \caption{A Component and its failure modes}
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% \label{fig:component}
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% \end{figure}
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A product naturally consists of many components and these are traditionally
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kept in a `parts list'. For safety critical product this is a usually formal document
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and is used by quality inspectors to ensure the correct parts are being fitted.
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@ -96,39 +93,77 @@ determine the likelihood of component failures
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causing specific system level errors (see Bayes theorem \ref{bayes}).
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Another top down technique is to apply cost benifit analysis
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to determine which faults are the highest priority to fix\cite{FMEA}.
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The aim of this study is to produce complete failure
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The aim of FMMD analysis is to produce complete failure
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models of safety critical systems from the bottom-up,
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starting, where possible with known component failure modes.
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In order to analyse from the bottom-up, we need to take
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small groups of components from the parts~list that naturally
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work together to perform a simple function.
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The components to for the functional groups are chosen by a human, the analyst.
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We can term this a `Functional~Group'. When we have a
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`Functional~Group' we can look at the failure modes of all the components
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in it and decide how these will affect the Group.
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Or in other words we can determine the failure modes of the functional
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group. These new failure modes are derived from the functional group, we can therefore call
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these `derived failure modes'.
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We now have something very useful, because
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we can now treat this functional group as a component with a known set of failure modes.
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This newly derived component can be used as a higher level
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building block for the system we are analysing.
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Derived components, can be used
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to form higher level functional groups.
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This process can continue until have build a hierarcy that converges to a failure model of the entire system.
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To differentiate the components derived from functional groups, we can
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add a new attribute to the class `Component', that of analysis
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level. The UML representation shows a `functional group' having a one to one relationship with a derived component.
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group. By working from the bottom up we can ensure that
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all component failure modes must be considered. A top down approach
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could miss individual failure modes of components.
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\subsection{From functional group to newly derived component}
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The process for taking a functional~group, considering
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all the failure modes of all the componentsin figure \ref{fig:cfg} in it,
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and analysing these is called `symptom abstraction' and
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is dealt with in detail in chapter \ref{symptom_abstraction}.
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In terms of our UML model the symptom abstraction process takes a functional~group,
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and creates a new derived component from it.
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To do this it first creates
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a new set of failure modes, representing the fault behaviour
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of the functional group. This is a human process and the analyst
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will consider all the failure modes of the components in the functional
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group.
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These new failure modes are derived from the functional group, we can therefore call
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these `derived~failure~modes'.
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It then creates a new derived~component object, and associates it to this new set of derived~failure~modes.
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We thus have a `new' component, or system building block, but with a known and traceable
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fault behaviour.
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The UML representation shows a `functional group' having a one to one relationship with a derived~component.
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We can represet this using an UML diagram in figure \ref{fig:cfg}
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\begin{figure}[h]
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\centering
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\includegraphics[width=400pt,bb=0 0 712 235,keepaspectratio=true]{component_failure_modes_definition/cfg.jpg}
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% cfg.jpg: 712x205 pixel, 72dpi, 25.12x7.23 cm, bb=0 0 712 205
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\caption{Components Derived from Functional Groups}
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\includegraphics[width=400pt,bb=0 0 712 286,keepaspectratio=true]{./cfg.jpg}
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% cfg.jpg: 712x286 pixel, 72dpi, 25.12x10.09 cm, bb=0 0 712 286
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\caption{UML Meta model for FMMD hierarchy}
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\label{fig:cfg}
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\end{figure}
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\clearpage
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%
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% \begin{figure}[h+]
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% \centering
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% \includegraphics[width=400pt,bb=0 0 712 235,keepaspectratio=true]{component_failure_modes_definition/cfg.jpg}
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% % cfg.jpg: 712x205 pixel, 72dpi, 25.12x7.23 cm, bb=0 0 712 205
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% \caption{Components Derived from Functional Groups}
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% \label{fig:cfg}
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% \end{figure}
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\subsection{Keeping track of the dereived components position in the hierarchy}
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The UML meta model in figure \ref{fig:cfg}, will build a hierarchy of
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objects, with derived~components forming functional~groups, and creating
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derived components higher up in the structure.
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The level variable in each Component,
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indicates the position in the hierarchy. Base or parts~list components
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have a `level' of 0. Derived~components take a level based on the highest level
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component used to build the functional group it was derived from plus 1.
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So a derived component built from base lavel or parts list components
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would have a level of 1.
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%\clearpage
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\section{Set Theory Description}
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$$ System \stackrel{has}{\longrightarrow} PartsList $$
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@ -362,17 +397,26 @@ therefore
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$$ FM(R) \in U $$
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We can make this a general case by taking a set $C$ (where $c1, c2 \in C$) representing a collection
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We can make this a general case by taking a set $F$ (where $f1, f2 \in F$) representing a collection
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of component failure modes.
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We can now state that
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We can define a boolean function {\ensuremath{\mathcal{ACTIVE()}}} that returns
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whether the fault mode is active (true) or dormant (false).
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We can say that if any pair of fault modes is active at the same time, then the failure mode set is not
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unitary state:
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we state this formally
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\begin{equation}
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\forall f_1,f_2 \in F \dot ( f_1 \neq f_2 \wedge \mathcal{ACTIVE}({f_1}) \wedge \mathcal{ACTIVE}({f_2}) ) \implies F \not\in U
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\end{equation}
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\begin{equation}
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c1 \cap c2 \neq \emptyset | c1 \neq c2 \wedge c1,c2 \in C \wedge C \not\in U
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\end{equation}
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%
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% \begin{equation}
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% c1 \cap c2 \neq \emptyset | c1 \neq c2 \wedge c1,c2 \in C \wedge C \not\in U
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% \end{equation}
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That is to say that it is impossible that any pair of failure modes can be active at the same time
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for the failure mode set $C$ to exists in the family of sets $U$
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for the failure mode set $F$ to exists in the family of sets $U$
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Note where that are more than two failure~modes,
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by banning pairs from being active at the same time
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@ -383,8 +427,8 @@ we have banned larger combinations as well.
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\section{Component Failure Modes and Statistical Sample Space}
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%\paragraph{NOT WRITTEN YET PLEASE IGNORE}
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A sample space is defined as the set of all possible outcomes.
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Here the outcomes we are interested in are the failure modes
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of the component.
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For a component this set of all possible outcomes are its normal correct
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operating state and all its failure modes.
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When dealing with failure modes, we are not interested in
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the state where the component is working perfectly or `OK' (i.e. operating with no error).
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We are interested only in ways in which it can fail.
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