Going thourgh data model UML to DAG
rough draft still
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@ -13,7 +13,7 @@ paper: paper.tex fmmd_data_model_paper.tex
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# Remove the need for referncing graphics in subdirectories
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# Remove the need for referncing graphics in subdirectories
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fmmd_data_model_paper.tex: fmmd_data_model.tex paper.tex
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fmmd_data_model_paper.tex: fmmd_data_model.tex paper.tex
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cat fmmd_data_model.tex | sed 's/fmmd_data_model\///' > fmmd_data_model_paper.tex
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cat fmmd_data_model.tex | sed 's/fmmd_data_model\///' > fmmd_data_model_paper.tex
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bib:
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bib:
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bibtex paper
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bibtex paper
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@ -8,10 +8,13 @@
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%% What I have done
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%% What I have done
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%%
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%%
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This paper presents a simple two stage Failure Mode Modular De-Composition (FMMD)
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This paper presents a simple two level Failure Mode Modular De-Composition (FMMD)
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model of a theoretical System.
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model of a theoretical System.
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The Analysis model is then represented as a Directed Acyclic Graph (DAG), of the {\fg}s
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Firstly a UML model is presented and the class relationships described.
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components and failure modes represented in it.
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Secondly the theoretical model is developed and analysed.
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This model is then represented as a Directed Acyclic Graph (DAG),
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showing the data relationships between the {\fg}s
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components and failure modes.
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% What I have found
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% What I have found
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%%
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%%
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@ -22,12 +25,12 @@ can also be automatically determined.
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%% Sell it
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%% Sell it
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%%
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%%
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By having a clear data model, we can not only produce results
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By having an FMMD data model, we can derive failure mode models
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for the traditional methodologies, we can trace common mode and
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for the traditional methodologies (such as FMEA, FMECA, FMEDA and FTA).
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component dependency failures as well.
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Also, with statistical data, we can use the minimal cut set results
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Also, with statistical data, we can use the minimal cut set results
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to determine the likelihood of particular system failures, even
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to determine the likelihood of particular system failures, even
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if they have multiple causes.
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if they have multiple causes.
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%
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} % abstract
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} % abstract
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} % ifthenelse
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} % ifthenelse
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{
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{
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@ -49,9 +52,8 @@ can also be automatically determined.
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%% Sell it
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%% Sell it
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%%
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%%
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By having a clear data model, we can not only produce results
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By having an FMMD data model, we can derive failure mode models
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for the traditional methodologies, we can trace common mode and
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for the traditional methodologies (such as FMEA, FMECA, FMEDA and FTA).
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component dependency failures as well.
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Also, with statistical data, we can use the minimal cut set results
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Also, with statistical data, we can use the minimal cut set results
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to determine the likelihood of particular system failures, even
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to determine the likelihood of particular system failures, even
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if they have multiple causes.
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if they have multiple causes.
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@ -62,18 +64,23 @@ if they have multiple causes.
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\section{From UML Model to Object Model}
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\section{From UML Model to Object Model}
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Let us consider a theoretical FMMD model. For the sake of simplicity
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Let us consider a theoretical FMMD model. For the sake of simplicity
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consider that all components and functional groups have only two failure modes that
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consider that all base~components have %only
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two failure modes that
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we will label $a$ and $b$.
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we will label $a$ and $b$.
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We can start with some base components, of types C and K say, $\{ C_1, C_2, C_3, K_4, C_5, C_6, K_7 \}$.
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We can start with some base components, of types C and K say, $\{ C_1, C_2, C_3, K_4, C_5, C_6, K_7 \}$.
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\input{./shortfm}
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\paragraph{Determing Failure Mode collections.}
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Thus applying the function $fm$ to any of the components
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Thus applying the function $fm$ to any of the components
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gives error modes identified by a or b.
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gives error modes identified by a or b.
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For the sake of example, let us say that each component has two failure
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As each component has two failure
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modes $a$ and $b$. So the function $fm$ applied to
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modes $a$ and $b$. So the function $fm$ applied to
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$C_1$ yields $C_{1 a}$ and $C_{1 b}$:
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$C_1$ yields $C_{1 a}$ and $C_{1 b}$:
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i.e. $fm(C_1) = \{ C_{1 a}, C_{1 b} \}$.
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i.e. $fm(C_1) = \{ C_{1 a}, C_{1 b} \}$.
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HOW UML OBJECT MODEL OF COMPONENT AND ITS ERROR MODES
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%HOW UML OBJECT MODEL OF COMPONENT AND ITS ERROR MODES
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\ifthenelse {\boolean{paper}}
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\ifthenelse {\boolean{paper}}
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{
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{
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@ -89,17 +96,17 @@ $$ FG^0_1 = \{C_1, C_2\},$$
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$$ FG^0_2 = \{C_1, C_3, K_4\},$$
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$$ FG^0_2 = \{C_1, C_3, K_4\},$$
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$$ FG^0_3 = \{C_5, C_6, K_7\}.$$
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$$ FG^0_3 = \{C_5, C_6, K_7\}.$$
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Note that in this model the base~component $C_1$ has been used in
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Note that in this model the base~component $C_1$ has been used in two separate functional groups.
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two separate functional groups. This could be a component that they
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This could be a component that they
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both commonly use. A real world example of a component included in
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both commonly use. A real world example of a component included in
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more than one {\fg} could
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more than one {\fg} could
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be a powersupply or DCDC\footnote{A DCDC (direct current to direct current)
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be a power-supply or DCDC\footnote{A DCDC (direct current to direct current)
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converter, is a common feature in modern PCBs, used to provide isolation
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converter, is a common feature in modern PCBs, used to provide isolation
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and/or voltage supplies at a different EMF from the source of power.}
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and/or voltage supplies at a different EMF from the source of power.}
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converter shared to power
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converter shared to power
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the functional groups $FG^0_1$ and $FG^1_1$.
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the functional groups $FG^0_1$ and $FG^1_1$.
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Also that the component type $K$ has been used by
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Also note that the component type $K$ has been used by
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two different functional groups.
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two different functional groups.
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For the sake of example let our temperature environment
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For the sake of example let our temperature environment
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@ -117,14 +124,15 @@ A processes of symptom extraction is now applied to the functional groups.
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Again for the sake of example, let us say that each functional
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Again for the sake of example, let us say that each functional
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group has one or two symptoms again subscripted by $a$ and $b$.
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group has one or two symptoms again subscripted by $a$ and $b$.
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Applying symptom abstraction to $FG^0_1$ i.e. $\bowtie fm ( FG^0_1 ) = \{ FG^0_{1 a}, FG^0_{1 b} \} $
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%Applying symptom abstraction to $FG^0_1$ i.e. $\bowtie fm ( FG^0_1 ) = \{ FG^0_{1 a}, FG^0_{1 b} \} $
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We can now create a new derived component, $DC^1_1$, whose failure
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%We can now create a new derived component, $DC^1_1$, whose failure
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modes are the symptoms of $FG^0_1 $ thus $ fm ( {DC}^1_1 ) = \{ FG^0_{1 a}, FG^0_{1 b} \} $.
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%modes are the symptoms of $FG^0_1 $ thus $ fm ( {DC}^1_1 ) = \{ FG^0_{1 a}, FG^0_{1 b} \} $.
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\paragraph{Building the Object Model}
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\paragraph{Building the Object Model}
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Using the UML model in figure \ref{fig:cfg2fmmd_data} we will apply FMMD analysis stages
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Using the UML model in figure \ref{fig:cfg2fmmd_data}, we apply FMMD analysis stages
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to build a hierarchy representing the whole system, begining with the $FG^0$ level functional groups.
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to build a hierarchy representing the whole system.
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We shall begin with the $FG^0$ level functional groups $ FG^0_1, FG^0_2 $ and $FG^0_3$ defined above.
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\begin{figure}[h]
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\begin{figure}[h]
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\centering
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\centering
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@ -147,22 +155,13 @@ to build a hierarchy representing the whole system, begining with the $FG^0$ lev
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Consider the SYSTEM environment with its temperature range of ${{0}\oc}$ to ${{125}\oc}$.
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Consider the SYSTEM environment with its temperature range of ${{0}\oc}$ to ${{125}\oc}$.
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We must check this against all components used.
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We must check this against all components used.
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For our example, we component `K' which has an extra
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For our example, we component `K' which has an extra
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failure mode for degraded performance `d'.
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failure mode for degraded performance `d'. Thus applying the function $fm$
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to component type `K' under these temperature range conditions
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give the foillowing failure modes, $fm{K} =\{ K_a, K_b, K_d \}$.
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Were our system specified for a ${{0}\oc}$ to ${{80}\oc}$ range
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\ifthenelse {\boolean{paper}}
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we could say $fm{K} =\{ K_a, K_b \}$.
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{
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We can definine a `failure modes' function $fm$ that has a functional group as its range
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and returns a set of failure modes as its domain.
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We now use this to determine the failure modes
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in our functional groups.
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}
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{
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Using the overloaded function $fm$ from chapter \ref{fmdef} we can determine the failure modes
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in our functional groups.
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}
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\paragraph{Get the failure modes from the functional groups.}
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Applying the function $fm$ to our functional groups, with the SYSTEM environmental
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Applying the function $fm$ to our functional groups, with the SYSTEM environmental
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constraint applied to component type `K', yields
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constraint applied to component type `K', yields
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@ -177,7 +176,9 @@ $$ fm(FG^0_3) = \{C_{5 a}, C_{5 b}, C_{6 a}, C_{6 b}, K_{7 a}, K_{7 b}, K_{7 d}\
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The next stage is to look at the failure modes from the perspective of
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The next stage is to look at the failure modes from the perspective of
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the functional groups, rather than the components.
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the functional groups, rather than the components.
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We can call these failures modes `symptoms'.
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We can call these failures modes `symptoms'.
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As this is a theoretical example, we shall have to skip this step.
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As this is a theoretical example, we shall have to skip this step\footnote{
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In a real analysis this would involve evaluating the effect of each components failure mode, (or combinations of)
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on the performance of the {\fg}.}.
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The next stage is to collect the common symptoms, or the symtoms that
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The next stage is to collect the common symptoms, or the symtoms that
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are the same {\em from the perspective of a user of the {\fg}}.
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are the same {\em from the perspective of a user of the {\fg}}.
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We can define this stage as the function $\bowtie$ which has a set of failure modes as
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We can define this stage as the function $\bowtie$ which has a set of failure modes as
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@ -229,7 +230,7 @@ UML OBJECT MODEL OF DERIVED COMPONENT TOO
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\pagebreak[4]
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\subsection{Using Derived Components in Functional Groups}
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\subsection{Using Derived Components in Functional Groups}
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\ifthenelse {\boolean{paper}}
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\ifthenelse {\boolean{paper}}
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{
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{
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\paragraph{Failure Mode function $fm$.}
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We can definine a `failure modes' function $fm$ that has a functional group as its range
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We can definine a `failure modes' function $fm$ that has a functional group as its range
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and returns a set of failure modes as its domain.
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and returns a set of failure modes as its domain.
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We now use this to determine the failure modes
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We now use this to determine the failure modes
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