Monday night second go at it
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@ -75,13 +75,14 @@ There are four methodologies in common use for failure mode modelling.
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These are FTA, FMEA, FMECA
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and FMEDA (a form of statistical assessment).
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These methodologies date from the 1940's onwards and have several draw backs and
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These methodologies date from the 1940's onwards, and were designed for
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different application areas and reasons; all have draw backs and
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advantages that are discussed in the next section.
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%In short
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%FTA, due to its top down nature, can overlook error conditions. FMEA and the Statistical Methods
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%lack precision in predicting failure modes at the SYSTEM level.
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\paragraph{FMMD outline.}
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The Failure Mode Modular De-composition
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(FMMD) aims to address the
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weaknesses in these methodoligies and to add
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@ -90,12 +91,15 @@ failure mode scenarios, and to allow modular re-use
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of analysis.
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%FMMD is an incremental bottom up FMEA process.
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%% TERRIBLE PARAGRAPH
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The FMMD
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methodology presented here provides a more detailed and analytical
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modelling system which will create a more complete and detailed hierarchical failure mode model from which
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methodology provides a detailed, hierarchical, incremental and analytical
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modelling system which will create a failure mode model from which
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the data models from FTA, FMEA, FMECA and FMEDA (the statistical approach) can be
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derived if required. An FMMD model is therefore a super set of all these models.
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It also applies rigorous checking in all the analysis stages
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derived if required.
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An FMMD model is effectively a super set of all the four traditional models.
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It also focuses on component interaction within the model.
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In addition it applies rigorous checking in all the analysis stages
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ensuring that all component failure modes must be considered in the model.
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%
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@ -109,21 +113,22 @@ paper
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{
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chapter
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}
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presents the design considerations that determined
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presents the design considerations that motivated and provided the specification for
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the FMMD methodology.
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It first briefly reviews the four traditional
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%
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It first reviews the four traditional
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static failure mode analysis methodologies and
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lists their known weaknesses. A wish list is then drawn up
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addressing these weaknesses and adding some extra requirements.
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Using this wish list the philosophy for the new methodology
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is built up.
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%
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FMMD works by working from the bottom up, taking small groups
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FMMD works from the bottom up, taking small groups
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of components, {\fgs}, and then analysing how they can fail.
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This analysis is performed using FMEA from a micro rather than a macro perspective.
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Thus instead of looking at component failure modes and determining how
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they {\em may} cause a failure at SYSTEM level, we are looking at how
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they {\em will} affect the {\fg}.
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they {\em will} affect the components local {\fg}.
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When we know the failure modes of a {\fg} we can treat it as a `black box'
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or {\dc}. With {\dc}s we can build {\fgs}
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at higher levels of analysis, until we have a complete
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@ -149,24 +154,36 @@ are held in a computer program, we can determine if the model is complete
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\subsection{General Comments on bottom-up and top down approaches}
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\paragraph{A general defeciency in top-down systems analysis}
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\paragraph{A general defeciency in top-down systems analysis.}
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With a top down approach the investigator has to determine
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a set of undesirable outcomes or accidents.
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a set of undesirable outcomes or `accidents'.
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As most accidents are unexpected and the causes unforseen \cite{safeware}
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it is fair to say that a top down approach is not guaranteed to
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predict all possible undesirable outcomes.
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It also can miss known component failure modes, by
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simply not de-composing down to the base component failure mode level of detail.
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simply not de-composing down to the base component failure level of detail.
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\paragraph{A general problem with bottom-up}
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With the bottom up techniques we have all the known component failure modes
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and the freedom to determine how each of these may affect the SYSTEM.
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We do have a real prolem though in determining how
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the failure mode of one component will affect another working component
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to cause an undesirable state. Because of the number of components
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our one failure mode may interact with is large,
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we cannot consider them all and human judgement is used to
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decide which interactions are important.
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%
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A problem with this is that a component typically
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interacts in a complex way with several other functionally
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adjacent components
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%
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To take a component failure mode and then attempt to tie that
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to a SYSTEM level outcome is very difficult.
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%
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The difficulty lies in
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%
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%Because of
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the number of components
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our one failure mode may interact with is large.
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%
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We cannot consider all the components in the SYSTEM
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when looking at a single failure mode,
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and human judgement must be used to
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decide which interactions could be important.
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Let N be the number of components in our system, and K be the average number of component failure modes
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(ways in which the component can fail). The total number of base component failure modes
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@ -197,12 +214,13 @@ the equation reads $(N-2) \times (N-1) \times N \times K \times E$.
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The bottom-up methodologies FMEA, FMECA and FMEDA take single failure modes and link them
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to SYSTEM level failure modes. Because of the astronomical number of possible interactions,
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some valid ones are in danger of being missed, we can term this analysis a `leap of faith' from the
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component failure mode to the SYSTEM level.
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some valid ones are in danger of being missed, we can term this analysis as a `leap~of~faith'
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(i.e. leaping from from the
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component failure mode to the SYSTEM level).
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\paragraph{Ideal static failure mode methodology}
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\paragraph{Ideal static failure mode methodology.}
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An ideal static failure mode methodology would build a failure mode model
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from which the traditional four models could be derived.
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It would address the short-comings in the other methodologies, and
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@ -331,14 +349,11 @@ The following gives an outline of the procedure.
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he Statistical Analysis method is used from two perspectives,
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Probability of Failure on Demand (PFD), and Probability of Failure
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in continuous Operation, Failure in Time (FIT).
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\paragraph{Failure in Time (FIT)}.
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Continuous operation is measured in failures per billion ($10^9$) hours of operation.
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\paragraph{Failure in Time (FIT).} Continuous operation is measured in failures per billion ($10^9$) hours of operation.
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For a continuously running nuclear powerstation
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we would be interested in its operational FIT values.
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\paragraph{Probability of Failure on Demand (PFD)}.
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For instance with the anti-lock system on a automobile braking
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\paragraph{Probability of Failure on Demand (PFD).} For instance with the anti-lock system on a automobile braking
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system, we would be interested in PFD.
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That is to say the ratio of it failing
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to succeeding on demand.
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@ -474,7 +489,7 @@ model can be implemented on a spreadsheet, where each component
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has a calculated risk, a fault detection time (if any), an estimated risk importance
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and other factors such as de-rating and environmental stress.
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With one component failure mode per row,
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all the statistical factors for SIL rating can be produced.
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all the statistical factors for SIL rating can be produced\footnote{A SIL rating will apply to an installed plant, i.e. A complete 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}.
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@ -602,7 +617,7 @@ this methodology must start at the bottom, with base component failure modes.
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In this way automated checking can be applied to all component failure modes
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to ensure none have been inadvertently excluded from the process.
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\paragraph{Problem with functional group hierarchy}
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\paragraph{Problem with functional group hierarchy.}
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A hierarchy of functional grouping, leading to a system model
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still leaves us with the problem of the number of component failure modes.
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The base components will typically have several failure modes each.
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