=Paper=
{{Paper
|id=Vol-1212/paper6
|storemode=property
|title=Failure Mode and Effect Analysis for Abductive Diagnosis
|pdfUrl=https://ceur-ws.org/Vol-1212/DARe-14-paper-6.pdf
|volume=Vol-1212
|dblpUrl=https://dblp.org/rec/conf/ecai/Wotawa14
}}
==Failure Mode and Effect Analysis for Abductive Diagnosis==
https://ceur-ws.org/Vol-1212/DARe-14-paper-6.pdf
Failure Mode and Effect Analysis for
Abductive Diagnosis
Franz Wotawa1
Techn. Univ. Graz, Institute for Software Technology,
Inffeldgasse 16b/2, 8010 Graz, Austria
wotawa@ist.tugraz.at
Abstract. Diagnosis, i.e., fault localization in case of observing an un-
expected behavior, is an important practical problem. During the past
decades researchers have suggested several approaches for using models
of the systems directly for identifying the root causes of failure. This
model-based diagnosis approaches are either based on retaining consis-
tency or on abduction. Despite their advantages both approaches are
only rarely used in practical applications. In this paper, we focus on
bringing abductive diagnosis closer to its application. In particular, we
describe how failure mode and effect analysis, a technique of growing
interest in applications, can be directly mapped to abductive diagnosis
models. We discuss the basic foundations, and also problems that occur
and how to deal with them. A direct conversion of FMEAs to abduc-
tive diagnosis problems would increase the use of abduction in practice
because of avoiding writing logical theories directly.
Keywords: Abductive diagnosis, model-based diagnosis, FMEA
1 Introduction
Diagnosis as the task for identifying root causes explaining observations has been
gaining lot of attention since the beginnings of Artificial Intelligence. The first di-
agnosis approaches like MYCIN [2] where based on expert systems, where knowl-
edge has to be coded as rules that allow to derive causes from the observations.
Later model-based approaches for diagnosis either using consistency-based ap-
proaches [7, 18] or abduction [4, 8, 5] has become very popular in research, which
holds especially for consistency-based diagnosis. There are many advantages of
model-based diagnosis. For example, the support of knowledge re-use or being
able to derive root causes from the model itself, which is called reasoning from
first principles. Despite the fact of all these advantages a broader application in
practice is still missing. There are many reasons for this including the lack of
available system models, the lack of modeling standards, and the intrinsic time
and space complexity of model-based diagnosis.
More recently, industry uses more and more often failure mode and effect
analysis (FMEA) [11, 17] as a framework for identifying critical faults that might
occur. There the main focus is on the consequences of faults for the system’s
behavior. FMEA are most often used for reliability analysis but also for other
purposes like measuring diagnostic coverage [9]. One reason for the increasing
use of FMEAs in practice is that there are standards like IEC61508, which
require a detailed safety assessment (see for example [3]). Such an assessment
for example is done on a regular basis within the automotive industry to ensure
safety regulations. However, there are also other engineering areas where FMEAs
are of growing importance, e.g., wind turbines [1].
Given the growing importance of FMEAs or similar analysis methods in in-
dustry and a growing demand for automated fault localization, the question is
whether FMEAs can be directly used for the purpose of fault localization. It is
worth mentioning that currently FMEAs are most often used for reliability anal-
ysis and not for fault localization. The main objective of this paper is to discuss
the use of FMEAs for automated fault localization, which of course requires a
FMEA to comprise essential information, i.e., at least the modes of replaceable
parts responsible for effects and a description of effects, which can be observed.
In particular, we show how adductive reasoning can be used for fault localiza-
tion. Hence, in all cases where FMEAs are available that fit our purpose, we are
able to use abductive diagnosis for practical applications, which widens the use
of abductive reasoning in practice.
This paper is organized as follows. We first discuss the FMEA. For this pur-
pose we introduce a small example that is going to be used in the rest of this
paper. Afterwards, we briefly recall the basic definitions of abductive diagno-
sis. Finally, we show how FMEAs are mapped to abductive diagnosis problems,
discuss some coding issues, and a property to hold in order to ensure effective
diagnosis in practice. Moreover, in case of not fulfilling the property, we state
several methods for solving the underlying problem. In the conclusion, we reca-
pitulate the content of this paper and state our future research directions.
2 Failure mode and effect analysis
In the following we briefly introduce the FMEA but focus only on relevant parts
we are going to use in this paper and thus ignoring all FMEA parts that belong
to reliability analysis, e.g., assigning a likelihood to certain faults, the costs of
the faults, and their severity. We focus on relevant parts only because the others
do not provide any information used in the context of adductive diagnosis. We
further assume that a FMEA describes the relationship between a fault and its
effects ignoring intermediate effects or system internal non-observable effects.
This is not a restriction because we are only interested in observable effects
that might lead to dangerous situations. In cases where internal or intermediate
effects are relevant they must have a corresponding observable effect, which has
to considered in the FMEA.
We first, discuss FMEAs in more detailed using a simplified example coming
from the wastewater treatment domain. In particular, we specify a FMEA for
a constructed wetland. In Figure 1 we depict the structure of a constructed
wetland. A constructed wetland is an artificial wetland used amongst others
Fig. 1. A constructed wetland
for wastewater treatment. The idea behind the constructed wetlands is to use
plants for the removal of nitrates and phosphate from polluted water. There are
some parameters like the inflow of water, the status of the membrane used to
prevent the water flowing out of the basin, and the outflow. All these parameters
influence the water level. Moreover, also the weather conditions like rain, or a
long period of heavy sunshine have an impact on the water level. On the other
hand, the water level is important for ensuring the plant to survive. Is there is not
enough water, the plant will finally die. Moreover, the concentration of nitrates
and phosphates also impacts the health status of the plants. A healthy plant
has a green color whereas a dying plant becomes brown. For more details about
constructed wetlands as well as a discussion on the use of abductive reasoning
versus decision trees we refer the interested reader to [20] and [19]. The latter
discusses a different example from the wastewater treatment domain.
In order to construct a FMEA we first have to identify the components
and potential fault modes. A constructed wetland comprises the components
in-valve, out-valve, membrane, and plants. In addition we have to consider also
external components, which also impacts the behavior. Such external compo-
nents represent parts of the environment of a system that impacts the behavior.
For the constructed wetland these components are the weather condition and
the nitrates and phosphate (NP) concentration of the water that flows into the
constructed wetland. The fault modes are wrong settings of the valve, a leak in
the membrane, a period of hot temperature and an unexpected NP concentra-
tion. It is worth noting that we are of course able to introduce more different
fault modes like a lower inflow a higher inflow in order to distinguish different
situations that are outside the operational settings of the constructed wetland.
However, for simplicity of our example we only consider a few of them.
So, how can we construct a FMEA? A FMEA is a table, where each row
describes the effects of a fault constituting because of a failure of a certain
component. Hence, for each component and fault mode we make one entry. For
example, from the verbal description of the constructed wetland, we see that
a leaking membrane impacts the water level negatively, which in turn causes
a plant to die because of lack of water. Hence, the color of the plants become
brown. We are able to make a similar entry for each possible component and
fault mode finally leading to the FMEA. For the constructed wetland the FMEA
is given in Table 1. It is worth noting that in a FMEA usually only single faults
are considered.
Table 1. A FMEA for a constructed wetland
Component Fault mode Effect
in-valve too low low inflow, wrong water level, brown plants
out-valve too high high outflow, wrong water level, brown plants
membrane leaking wrong water level, brown plants
weather too hot long sunshine period, wrong water level, brown plants
NP-conc. wrong brown plants
We now state a formal definition of a FMEA. We assume that a FMEA
is for systems comprising components COM P , that have corresponding fault
modes M ODES. Moreover, we assume the existence of propositions P ROP S
that belong to observations we obtained when running the system. If we observe
a certain value, the proposition is said to be true, and false otherwise. We now
define FMEAs as relation between components, a fault modes, and a set of effects
that has to be observed when the given component is in the given fault mode.
Note that in our constructed wetland example, there is a one to one relationship
between components and modes. This is usually not the case in practice where
a component like a logic gate has many fault modes. A fault model of a logic
gate may at least comprise the fault modes stuck at one and stuck at zero.
Definition 1 (FMEA). A FMEA is a set of tuples (C, M, E) where C ∈
COM P is a component, M ∈ M ODES is a fault mode, and E ⊆ P ROP S
a set of effects.
For our constructed wetland example, we have to map the verbal descrip-
tions given in Table 1 to elements of COM P , M ODES, and P ROP S. For
this purpose we set COM P = {in, out, membrane, weather, np}, M ODES =
{low, high, leaking, hot, wrong}, and P ROP S = {low in, high out, wrong level,
sunshine, brown} and obtain the following FMEA for the constructed wetland
example:
(in, low, {low in, wrong level, brown})
(out, high, {high out, wrong level, brown})
F M EACW = (membrane, leaking, {wrong level, brown})
(weather, hot, {sunshine, wrong level, brown})
(np, wrong, {brown})
In the next section, we briefly recapitulate the basic definitions of abductive
diagnosis.
3 Abductive diagnosis
In this section we discuss the basic definitions of abductive reasoning and abduc-
tive diagnosis in particular. For this purpose, we first define a knowledge base
comprising horn clause rules (from HC) over propositional variables P ROP S.
The restriction of logical formula to be horn is not a limitation in the context of
physical systems since those models usually code the knowledge from causes to
their effects. The used definitions are close the ones introduced by Friedrich at
al. [8] and others in the area of abductive diagnosis [6, 5].
Definition 2 (Knowledge base (KB)). A knowledge base (KB) is a tuple
(A, Hyp, T h) where A ⊆ P ROP S denotes the set of propositional variables,
Hyp ⊆ A the set of hypothesis, and T h ⊆ HC a set of horn clause sentences
over A.
In the definition of KB hypotheses corresponds directly to causes, such that
for every cause there is a propositional variable that allows to hypothesize about
the truth value of the cause. Hence, we use the terms hypotheses and causes in
an interchangeable way.
Example 1. For example, the tuple ({wrong level, mode(in, low)}, {mode(in, low)},
{mode(in, low) → wrong level}) forms a knowledge base. In the tuple the
set {wrong level, mode(in, low)} represents the used propositional variables,
{mode(in, low)} the hypotheses, and {mode(in, low) → wrong level} the set
of clauses.
Definition 3 (PHCAP). Given a knowledge base (A, Hyp, T h) and a set of
observations Obs ⊆ A, the tuple (A, Hyp, T h, Obs) forms a propositional horn
clause abduction problem (PHCAP).
Example 2 (cont.). Assume that we observe a wrong water level, i.e., Obs =
{wrong level} we obtain a PHCAP.
Given a PHCAP we define a solution like in [8] as follows:
Definition 4 (Diagnosis; Solution of a PHCAP). Given a PHCAP (A, Hyp,
T h, Obs). A set ∆ ⊆ Hyp is a solution if and only if ∆ ∪ T h |= Obs and
∆ ∪ T h 6|= ⊥. A solution ∆ is parsimonious or minimal if and only if no set
∆0 ⊂ ∆ is a solution.
A solution of a PHCAP is an explanation for the given observations. Instead
of solution we also say abductive diagnosis (or diagnosis for short). In Defini-
tion 4 diagnoses need not to be minimal or parsimonious. In most practical cases
only minimal diagnoses or minimal explanations for given effects are of interest.
Hence, from here on we assume that all diagnoses are minimal diagnoses if not
specified explicitly.
Example 3 (cont.). For our small example we obtain only one minimal diagnoses:
∆ = {mode(in, low)} for the observation wrong level.
It is well known that the problem of finding minimal diagnoses for a given
PHCAP is NP-complete. See for example Friedrich et al. [8] for a proof. More-
over, in the same paper the authors describe a general diagnosis and therapy
process that make use of a PHCAP directly in order to identify and correct the
detected faulty behavior. Unfortunately, we are not able to use Friedrich et al.’s
algorithm because we require exactly one diagnosis before executing a repair ac-
tion. Note that this holds in many cases where we want to diagnose engineered
systems that behave outside the expectations. Of course, a single diagnosis might
not always be required. Instead if the set of diagnosis is small a replacement of
all potential diagnosis candidates can be performed. However, this can only be
done if the set of diagnoses and their corresponding replacement costs are small
enough. Therefore, we suggest to compute all possible diagnoses and to reduce
this set by adding new observations that allow for discriminating diagnoses. This
process is done until one diagnosis (or a diagnoses set that is small enough) is
left and the corresponding treatment can be applied.
For adding new observations in order to decrease the number of diagnoses,
we introduce the notation of discriminating observations.
Definition 5 (Discriminating observation). Given a PHCAP (A, Hyp, T h,
Obs) and two diagnoses ∆1 and ∆2 . A new observation o ∈ A\Obs discriminates
two diagnoses if and only if ∆1 is a diagnosis for (A, Hyp, T h, Obs ∪ {o}) but
∆2 is not.
In order to compute discriminating additional observations it is possible
adopt the approach by De Kleer and Williams [14] who introduced an algorithm
for measurement selection. For more details about the use of measurement selec-
tion for abductive diagnosis and an algorithm we refer to [19]. The underlying
idea is to rank potential observations accordingly to their power of discrimina-
tion, e.g., preferring the observation that is able to divide the search space into
two halves.
We implemented an algorithm for solving the PHCAP as well as the finding
discriminating observations problem. This implementation is based on De Kleer’s
Assumption-based Truth Maintenance System (ATMS) [12]. See for example [13]
for an ATMS algorithm. An ATMS handles the consistency state of propositions
that are connected via horn-clause rules. Moreover, an ATMS makes use of as-
sumptions, which are assumed to be true unless a contradiction can be derived
from the respective assumption. In ATMS terminology assumptions and propo-
sitions are said to be nodes, rules are justifications, and the contradiction (⊥) is
represented by the special node NOGOOD. Besides the truth status an ATMS
also manages for each node all the different assumptions that are necessary for
making that node true. This set of assumptions is called label, and its elements
an environment. An ATMS environment in the context of non-monotonic logic
can be seen as a possible world that makes the corresponding node true.
An ATMS makes sure that for each node all the stored environments are con-
sistent and each of them allow for deriving the node. Moreover, the environments
are also minimal, and complete. The latter is important to ensure that there is
not a possible world missing. An ATMS can be easily used for abductive diag-
nosis. Given a PHCAP. Let us first assume that all elements of A are mapped
to nodes, all elements of Hyp are mapped to ATMS assumptions, and all rules
in T h are mapped to ATMS justifications. If we want to obtain an observation
Obs = {o}, then we only have to return the label l of the nodes that corresponds
to o. All the elements of l are consistent and minimal. Moreover, from l ∪ T h we
obtain o and there is no element missing in l. In general, the observations might
contain more elements, i.e., Obs = {o1 , . . . , ok }. In this case we generate a new
proposition σ and add o1 ∧ . . . ∧ ok → σ to the theory T h that is passed to the
ATMS. The label of the corresponding node of σ is an abductive diagnosis for
Obs.
It is worth noting that the ATMS has been used in consistency-based diag-
nosis. The General Diagnostic Engine (GDE) [14] makes use of the ATMS for
computing conflicts. A conflict in consistency-based diagnosis is a set of correct-
ness assumptions for components that lead together with the logical description
of a system and the given observations to a contradiction. When using an ATMS
all possible conflicts are stored in the NOGOOD node. Reiter [18] proved that
the hitting sets of minimal conflicts are the diagnoses. In his paper, Reiter also
provided an algorithm for computing the diagnoses from the conflicts. Greiner
et al. [10] provided an improved version of Reiter’s hitting set algorithm. There
have been many applications of the GDE or its variant for diagnosis includ-
ing [16, 15]. In contrast to adductive diagnosis used in this paper, the original
consistency-based diagnosis approach considers the correct behavior of a system
but not models of faulty behavior. Moreover, the GDE makes us of the informa-
tion stored in the NOGOOD node, whereas we are interested in the assumptions
that are stored in the node, which corresponds to σ.
In the next section, we discuss how to make use of a FMEA for obtaining
a corresponding KB further used to construct a PHCAP. We also discuss some
coding issues because of the restriction to horn-clause propositional logic.
4 Mapping FMEAs to abductive diagnosis
In this section we discuss the mapping of FMEA to a corresponding (abduc-
tive) KB, which can be used for diagnosis purposes. Afterwards we discuss some
properties of the knowledge base, which must hold in order to ensure the com-
putability of a single diagnosis in case enough information (i.e., observations) are
given. Furthermore, we show how FMEA can be revised in order to guarantee
the computation of single diagnosis.
Let us first start with the mapping of FMEAs to their corresponding knowl-
edge base KB. This mapping is rather straightforward because FMEA already
capture the cause-effect relationship in tabular form. Formally, we use a function
M : 2F M EA 7→ HC for mapping a particular FMEA to a horn clause theory. In
the following we define M in the following way iterating over all tuples stored in
FMEA:
Definition 6 (FMEA to HC; M). Given a FMEA, the function M is defined
as follows:
[
M(F M EA) =def M(t)
t∈F M EA
where
M(C, M, E) =def {mode(C, M ) → x |x ∈ E } .
The causes, i.e., the components and their modes, stored in an FMEA, are
mapped to proposition. Hence, we define the set of hypotheses as follows:
[
Hyp =def {mode(C, M )}.
(C,M,E)∈F M EA
What remains is the set of proposition. This set is equivalent to the union of
all effects stored in a FMEA, i.e.:
[
A =def E.
(C,M,E)∈F M EA
Example 4. When using the F M EACW of the constructed wetland given at the
end of Section 2 we obtain the following corresponding KBCW when applying
the M function and the other definitions for Hyp and A:
mode(in, low), mode(out, high), mode(membrane, leaking),
Hyp =
mode(weather, hot), mode(np, wrong)
A = {low in, high out, sunshine, wrong level, brown, }
mode(in, low) → low in, mode(in, low) → wrong level,
mode(in, low) → brown, mode(out, high) → high out,
mode(out, high) → wrong level, mode(out, high) → brown,
Th = mode(membrane, leaking) → wrong level,
mode(membrane, leaking) → brown, mode(weather, hot) → sunshine,
mode(weather, hot) → wrongl evel, mode(weather, hot) → brown,
mode(np, wrong) → brown
When using KB together with the observation that the plants are brown,
i.e., Obs = {brown}, all the elements of Hyp are also a (single) diagnosis. Hence,
someone is interested in reducing the number of diagnoses by adding new obser-
vations.
A reduction of diagnosis candidates can be done – as already discussed – using
additional observations. For example, when adding the observation low in to Obs
in Example 4, only the diagnosis {mode(in, low)} remains. In practice the ob-
servation low in would be obtained by inspecting the in-valve and comparing its
flow with the expected flow. But what happens if the inflow is correct? In this case
we have to add ¬low in to Obs and {mode(in, low)} would be removed from the
list of diagnosis candidates because {mode(in, low)} ∪ T h 6|= {brown, ¬low in}.
However, also the other diagnosis candidates would be removed, because non of
them lead to the derivation of ¬low in.
One way of overcoming this problem is to model also negated observations.
However, a straightforward implementation would not be possible if we still rely
on horn-clause logic. Hence, we would have to introduce special propositions,
e.g., not low in, representing the fact ¬low in. In addition we would have to
add inconsistency checking rules, stating that a proposition and its negation
cannot be true at the same time, e.g., low in ∧ not low in → ⊥. Such a solution
increases the size of the KB.
Another solution would be to state that a certain property does not longer
hold. For example, if low in has not been observed, we are able to simple state
that this property in the current world would lead to an inconsistency, e.g.,
low in → ⊥. In this way, we rule out all diagnoses that derives an observation
we are not able to establish in reality. In our system we follow this idea and
represent negated observations o as additional rules of the form o → ⊥ in the
KB’s theory T h.
What is missing is to give an answer to the question whether the used model
and in this case the underlying FMEA is good enough for diagnostic purposes.
By ’good enough’ we mean that the obtained results are correct and complete.
This is ensured using abductive diagnosis where only correct, i.e., consistent,
diagnoses are computed. Moreover, when searching for all diagnoses, we are sure
that completeness is obtained with respect to the underlying model. Hence, we
define ’good enough’ for diagnosis here as the ability of the KB to find one single
fault diagnosis if all necessary information is given. We call this property one
single fault diagnosis property and define it as follows:
Definition 7 (One Single Fault Diagnosis Property (OSFDP)). Given a
KB (A, Hyp, T h). KB fulfills the One Single Diagnosis Property (OSDP) if the
following hold:
∀m ∈ Hyp : ∃Obs ⊆ A : {m} is a diagnosis of (A, Hyp, T h, Obs) and
¬∃m0 ∈ Hyp : m0 6= m such that {m0 } is a diagnosis for the same PHCAP.
The OSFDP property allows us to ensure that with enough knowledge we
are able to distinguish all single-fault diagnosis. If a KB does not fulfill OSFDP,
then we cannot distinguish all diagnoses. Hence, in some cases we need to replace
more components. Checking for OSFDP can be done in a simple but effective
way. We only have to go through all hypotheses and compute all proposition
that follows from the current hypothesis and the theory T h. Afterwards we have
to check for two hypotheses whether their derived propositions are the same. If
yes, then the OSFDP can never be fulfilled. If we do not find such a pair, then
the OSFDP has to hold because we can distinguish the two hypotheses with the
non-intersecting parts of their propositions. In Algorithm 1 we summarize the
checking procedure.
Algorithm 1 CHECK OSFDP(A, Hyp, T h)
Input: A KB (A, Hyp, T h) comprising propositions A, hypothesis Hyp, and a
horn-clause propositional logic.
Output: True if the KB fulfills the OSFDP, and false otherwise.
1: for all h ∈ Hyp do
2: Let O ⊆ A be the largest set such that {h} ∪ T h |= O, if {h} ∪ T h |= ⊥, and ∅
otherwise.
3: Let δ(h) be O.
4: end for
5: for all h1 ∈ Hyp and δ(h1 ) 6= ∅ do
6: for all h2 ∈ Hyp and h1 6= h2 ∧ δ(h2 ) 6= ∅ do
7: if δ(h1 ) = δ(h2 ) then
8: return f alse
9: end if
10: end for
11: end for
12: return true
Algorithm 1 obviously terminates assuming Hyp to be finite (which is always
the case in practice). The algorithm’s time complexity is polynomial and in
particular of order O(|Hyp|2 ). The runtime is mainly determined by the nested
loops (line 5 to 10). Note also that the algorithm requires too many checks,
which might be avoided. However, the time complexity would still remain the
same considering the O(.) notation.
Example 5. The KB from Example 4 fulfills the OSFDP. Algorithm 1 would
return true.
It is worth noting that – because of the construction of M – the check for
OSFDP can also be easily checked on side of the FMEA. For this purpose only
the effects of the given causes have to be compared with each other. We will use
this observation for the further elaboration on the question of how to deal with
KBs not fulfilling OSFDP. In principle, we have three possibilities:
1. We ignore that OSFDP is not fulfilled. In this case we know that there are
diagnoses, which we cannot distinguish regardless of the given observations.
2. If we know that there are two hypotheses h1 and h2 contradicting the
OSFDP, then there is no way for distinguishing h1 from h2 . Hence, we can
treat both hypotheses the same. For this purpose, we introduce a new hy-
pothesis h0 and replace both h1 and h2 with h. We proceed with this replace-
ments until the OSFDP is fulfilled. This method summarizes all hypotheses
that cannot be distinguished and which should be treated as a single replace-
able unit.
3. It is worth noting that there is another reason why an OSFDP is not ful-
filled. There might be other observations of the system or internal values
not observable from outside that are currently not considered in the given
FMEA. By introducing these observables into the FMEA, we might be able
to distinguish hypotheses. Hence, the third way of dealing with the problem
is to ask the user for further observations to be used in the FMEA.
Hence, with the described methods the OSFDP can either be handled as it is,
or treated in a way that allows for coming up with a FMEA (or the corresponding
KB) where the OSFDP is fulfilled.
5 Conclusions
In the paper we argued that abductive reasoning and in particular abductive
diagnosis become more important for practical diagnostic applications. This is
due to the fact of the growing importance of FMEAs used in industry for ensuring
and also evaluating reliability and safety constraints. We discussed a method
for mapping FMEAs directly to knowledge bases that code the knowledge in
rules representing the cause-effect relationship directly. Furthermore, we also
discussed how this knowledge base can be used for diagnosis and also give some
information on how to deal with observations. In particular, we discussed the
problem of handling negated observations without changing the knowledge base
substantially. We do this by adding a rule stating that the observation would
lead to an inconsistency.
Finally, we introduced a property that has to hold for knowledge bases in
order to allow for computing exactly one single-fault diagnosis if there is enough
information provided. We also discussed what to do in case the knowledge base
does not fulfill the property. There are two active ways of dealing with the prob-
lem: (1) handling non-distinguishable single-fault diagnosis as one component,
and (2) asking the user for more observation and extensions for the FMEA.
The former method generates a super-component that can be seen as a replace-
able unit in case of failing. The latter might be interpreted in the way that the
modeling has to be improved.
Future research will include an implementation of the proposed techniques
and algorithms. Currently, the abductive diagnosis is implemented based on an
ATMS implementation. Moreover, we want to use this method for diagnosis of
technical systems. The first application will be in the context of wind-turbines
used to generate electricity, where the FMEA comes from our industrial partner.
Using the application domain we want to clarify whether abductive reasoning is
a good foundation for diagnosis or not. This includes computational aspects as
well as usability concerns. With the automated conversion of FMEAs to KBs,
we believe to have taken, an important step towards increased usability.
Acknowledgements. The research presented in this paper has received fund-
ing from the Austrian Research Promotion Agency (FFG) under grant 842407
(Applied MOdel-based Reasoning (AMOR)). Many thanks also go to the anony-
mous reviewers for their very valuable comments helping to improve this paper.
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