=Paper=
{{Paper
|id=Vol-1815/paper16
|storemode=property
|title=Diagnosing Root Causes and Generating Graphical Explanations by Integrating Temporal Causal Reasoning and CBR
|pdfUrl=https://ceur-ws.org/Vol-1815/paper16.pdf
|volume=Vol-1815
|authors=Hoda Nikpour,Agnar Aamodt,Pål Skalle
|dblpUrl=https://dblp.org/rec/conf/iccbr/NikpourAS16
}}
==Diagnosing Root Causes and Generating Graphical Explanations by Integrating Temporal Causal Reasoning and CBR==
https://ceur-ws.org/Vol-1815/paper16.pdf
162
Diagnosing Root Causes and Generating
Graphical Explanations by Integrating Temporal
Causal Reasoning and CBR
Hoda Nikpour, Agnar Aamodt, and Pål.Skalle
Nowegian University of Science and Technology, Department of Computer and
Information Science
Sem Sælandsvei 9 Gløshaugen, Trondheim, Norway
{Hoda.Nikpour,Agnar.Aamodt
}@idi.ntnu.no
Pal.Skalle@ntnu.no
https://www.ntnu.edu/idi
Abstract. This study proposes a methodology to diagnose the root
causes of failures in the domain of oil well drilling. The idea is to combine
a Bayesian network, which is generated based on an expert knowledge,
with situation-specific knowledge of past failure cases. A causal chain
is viewed as a temporal sequence. To test the model’s capability, six
failure cases from the study’s application domain (oil well drilling) are
considered and one of them has been picked up as the studying case. The
model is applied to diagnose the root causes of the chosen failure case.
A temporal reasoning approach has been employed to narrow down the
determination of the effective concepts, given the observations. The pre-
liminary results show some advantages of the new model in comparison
with the model that integrated a multi relational knowledge model with
case based reasoning.
Keywords: Bayesian Network, Case-Based Reasoning, Explanations,
Temporal Reasoning
1 Introduction
In complex technical domains with a high level of uncertainty, experts are dealing
with types of failures in which implementing ad hoc solutions frequently leads
to a reemergence of the problem. Oil well drilling is one such domain. Diagnos-
ing and handling these types of problems requires appropriate focus. A strong
interaction is required between the system and an expert who examines system
event logs and applies his knowledge of the system to identify the root causes
of a given failure [1, 2]. Therefore, in domains such as this, diagnosis of the root
causes and possible explanations for these causes is a critical issue.
During the last decades, AI experts have tried to create Machine Learning
methods that produce results which are interpretable to the human users [3].
People interpret events in their surrounding environment using a variety of ex-
planations. Therefore, providing diagnostic output in the form of explanations
is potentially one of the most important properties of intelligent systems [4].
Copyright © 2016 for this paper by its authors. Copying permitted for private and academic purposes.
In Proceedings of the ICCBR 2016 Workshops. Atlanta, Georgia, United States of America
163
Case-based reasoning is suitable for capturing and reusing human experi-
ences for complex problem solving, and has earlier shown its success also in the
oil drilling domain [5]. However, a pure CBR system suffers from the inability to
justify a solution - an explanation that goes beyond referring to the best match-
ing case or cases. Further, CBR represents in itself a knowledge-lean method
for case retrieval. A model of general domain knowledge would enable cases to
be matched based on semantic rather than purely syntactic criteria. Hence, a
general domain model combined with CBR will enable the system to generate
targeted explanations for the user as well as for its internal reasoning process.
Earlier work in our group has addressed this problem by combining CBR with
a semantic network of multi-relational domain knowledge [6]. The created sys-
tem’s architecture, and its implementation in the Java programing language are
called Creek and TrollCreek, respectively. A problem with that method was the
lack of a formal basis for the semantic network that was used, which made the
inference processes within the network difficult to develop and less powerful than
wanted. The need for a more formal treatment of uncertainty lead to some initial
investigations into how a Bayesian network model could be incorporated [7, 8].
The work reported here is the first attempt to seriously develop and test such a
combined model. Bayesian network has shown its feasibility to build probabilis-
tic models without introducing unrealistic assumptions of independencies [9].
The probability distribution provided by BN enables the conditioning over any
of the variables and supports any direction of reasoning [10]. Also, the Bayesian
networks framework includes an inference engine, which, given some evidence, is
capable of updating its beliefs [11]. The nature of Bayesian networks allows for
some explanations to be given regarding the reasoning process [9]. All this makes
BNs a proper candidate for causal reasoning in the diagnosis of root causes [10].
Some researches introduced temporal reasoning into Bayesian networks, which
is found useful for diagnosis applications [12]. Temporal reasoning can enhance
basic causal reasoning by focusing on the time aspect of diagnosing [13].
In our research we focus on identifying root causes of failures in the domain
of oil well drilling. We have considered two approaches for our study, BN-Creek1
and BN-Creek2. BN-Creek1 replaces the multi relational semantic network by a
Bayesian network as its knowledge model. The BN-Creek2, which we will focus
on in this paper, uses a Bayesian network as a knowledge model in addition
to a multi relational semantic network. In BN-Creek2 the general knowledge of
causal dependencies is combined with situation-specific knowledge of past failure
cases. Then, to make the result more accurate, some features of TrollCreek and
Temporal reasoning analysis are employed.
The remainder of the paper is organized as follows: Section 2 outlines related
work. Our proposed system is presented in Section 3. Section 4 presents an
illustrative example, and section 5 discusses and concludes the paper.
2 Related Work
Been et al., 2014 [3] studied a bridging of the gap between machine learning
methods and human’s decision-making strategy. They modeled the underlying
164
data, using a mixture model. They used case based classifiers and BN as two
interpretable models to identify the most representative cases and important
features. Bruland et al., 2010 [14] studied reasoning under uncertainty in the
forms of aleatory and epistemic uncertainty. The aleatory uncertainty works on
assigning a probability of a particular state given a known distribution and the
epistemic uncertainty refers to cognitive mechanisms of processing knowledge.
They advocated the use of Bayesian networks to model aleatory uncertainty
and case-based reasoning to handle epistemic uncertainty. They discussed two
types of architectures for combining CBR and BN. Houeland et al., [11] focused
their research on automatically detecting the robustness and performance of
systems which combine case-based reasoning and Bayesian network to solve new
problem queries, given the system’s current state of uncertainty. They presented
an automatic reasoning architecture that uses meta reasoning to achieve their
goal. Tran et al., 2008 [15], aimed to assist operators in finding solutions for
faults in large-scale communication systems. To determine the cases that share
common symptoms, they have used a distributed CBR system.
Aamodt et al., 2014 [16], focused on supporting the processes of retrieval and
reuse of past cases, computing similarities, generating indexes, etc. They pro-
posed a BN-powered sub-model as a calculation method that works in parallel
with general domain knowledge to satisfy their goal. Petersen et al., 2010 [4]
addressed weaknesses of Bayesian network with regard to structural and para-
metrical changes. They suggested adding case based reasoning functionality to
Bayesian networks to better observe changes in behavior. Lacave [9], in his pa-
per, analyzed what has been done to date and what challenges remain to be
done in the field of explanation in Bayesian networks. Dørum et al., 2002 [17],
focused on prediction problem within oil well drilling to avoid costly failures.
They introduced a method for reasoning with time-dependent situations in the
form of temporal intervals, within a knowledge intensive CBR framework. Their
system gives warnings to the user when an unwanted event may be approaching.
Casey is another system that combines CBR with a general domain knowl-
edge model, particularly a causal model. Hence, for the situations that CBR is
not able to retrieve a qualified case, CASEY uses the causal model as a sec-
ond attempt to solve the problem [18]. Long [13], utilized temporal reasoning
in Heart Disease Program and discussed about the domain’s existing issues and
solutions in integrating temporal reasoning with pseudo Bayesian probabilistic
reasoning.
Some of the aforementioned researches employ BN in different segments of
CBR. Our research, as the others, takes advantage of both BN and CBR’s fea-
tures but in a way that BN is used as a causal relation knowledge model asso-
ciated with cases. The temporal reasoning is employed to increase the accuracy
of the root causes diagnosing process.
3 Proposed Model Architecture
The final goals of this study are prediction of failure root causes and to generate
explanations given the observed symptoms or errors.
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The main structure of a Bayesian network has been designed in order to ex-
press the elements’ relations and calculate the updated beliefs based on the prior
probabilities assigned by a field expert. The domain concepts are presented by
nodes and their causal relations are shown by arrows. A parent causes a child,
and each node represents the current belief of the network given its parents.
Our approach in the first place, views the BN as a different type of, and a re-
placement for the knowledge model in TrollCreek (BN-Creek). Then, integrates
TrollCreek case retrieval results with the BN-Creek results to get benefit from
the other type of relations that been considered in TrollCreek. Fig. 1 depicts the
graphical structure for the proposed approach. The filled and not filled circles are
indicators of Bayesian network nodes and cases, respectively. TrollCreek and the
present approach are extracting the cases from the raw data, but the main differ-
ence between them is their knowledge models. TrollCreek uses a multi relational
semantic based knowledge model while the new approach uses a probabilistic
causal model as its knowledge model.
Fig. 1. The BNCreek knowledge structure
The field expert’s knowledge has been exploited to create the aforementioned
BN. The causal relations between the oil well drilling process’ concepts were
identified by the expert and were used as the prior probabilities of the BN.
The main task in this study is to answer the query of: ”What is the whole
probability distribution over variable X given evidence e?”. In other words, the
most plausible causes of the failure under study, given some observations, is
desired. In our approach, the mentioned query will be answered in the following
three steps.
– Step one: This step utilizes BN to calculate a temporal probability distribu-
tion of the new case.
By creating a new case, a copy of the domain’s prior Bayesian network is
assigned to it. The inference process is started, by applying each of the
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observed concepts as evidence in the network. Then the network’s beliefs
will be updated given those evidence and the result will be shown as the
posterior distribution (PD) of the network, corresponding to that specific
case.
– Step two: In this step, we utilize the CBR’s capability of employing the
past experiences, aimed to improve the BN’s accuracy in suggesting the root
causes.
TrollCreek is used to retrieve the most similar cases to the new one. The best-
matched case is considered and an impact factor is assigned to its recorded
PD, based on its similarity degree.
TrollCreek uses MAC-FAC method [18] to retrieve the cases. As the MAC
phase each of the findings from the testing case are compared to all the
findings from the retrieved case aimed to find similar findings as many as
possible. The Eq.1 illustrates the similarity assessment formula:
Pn Pm
i=1 j=1 sim(fi , fj ) ∗ relevancef actorfj
sim(CIN , CRE ) = Pm (1)
j=1 relevancef actorfj
In Eq.1 the CIN stands for the under study case and and CRE demonstrates
the retrieved case. n and fi , m and fj are the number of findings and the
finding’s number in the CIN and CRE , respectively. The sim(CIN , CRE ) is
equal to 1 if fi = fj , otherwise it’s value would be 0. The relevance factor is
a number that combines the predictive strength and importance of a feature
for a stored case and comes from the expert [6].
The FAC phase considers the paths in the semantic network that represent
relation sequences between un-identical features. Based on a method for
calculating the closeness between two features at each end of such a sequence,
the two features are given a local similarity score.
– Step three: This step integrates the probability distributions from the first
two steps and calculates the new case’s finalized probability distribution. In
other words, in this step we have added the CBR’s capability in employing
the past experiences, to improve the BN’s accuracy in suggesting the root
causes. The result of step three is the system’s outcome.
Eq.2 integrates the effect of the pure BN and CBR from the first two steps
and generates the finalized posterior distribution for the new case.
Pk
i=1 P Pji ∗ αi
P Pjf = Pk (2)
i=1 αi
In Eq.2 the PP stands for the posterior probabilities which are the elements
of the Posterior distribution. The 0 < α < 1 is the impact factor that is
larger for the cases with higher similarity. The ’k’ is the number of PDs that
are integrated together and would be higher than two in a situation that
the expert wants to involve the effect of less matched cases. ’j’ and ’i’ a’re
the indicators of a specific PP in a PD and the PD’s number, respectively.
Consequently, the P Pjf stands for the finalized posterior probability of the
PP number ’j’. The index ’f’ stands for finalized PP.
167
After completion of the third step, the finalized updated network’s beliefs
(PD) are achieved. Using the final PD, the strengths of the potential root causes
are listed and are given to the expert for assessment.
Before suggesting the list of candidate concepts to the expert, a narrowing
down process can do on the list, to improve accuracy, by applying temporal
analysis.
Temporal probabilistic reasoning analysis considers the aspect of time for
events. One of the main effects of this perspective is the consideration of older
observations as extraneous observations. Depending on the dynamic nature of
the system and considering a long enough time between the observation time
and the time point of interest, such observations can be ignored without any
loss in the accuracy of the conclusion [12].
In this work the list of concepts from the three aforementioned steps are
considered and the time sequence of their concepts are extracted from the raw
data. Then, the concepts that passed the expert threshold are removed from the
list. The next chapter illustrates how temporal reasoning is incorporated in this
research.
Based on the selected root causes, the explanation path between the given
evidence and the considered root cause will be presented.
Fig. 2. Some instantiated parts of BNCreek
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4 An illustrating example
This section provides an example to illustrate how BNCreek works. To test the
proposed method’s capability we exploited 6 drilling failure cases and considered
one of them as the studying case, namely case wellbore clean02.
The Bayesian network of the drilling process, which is created based on a field
expert’s knowledge is employed. Five different types of concepts are used in the
network. Symptom(s), Static Symptom(ss), Error(err) and Failure(f) which are
observable concepts and cannot be the root causes of a situation as they express
situations. The fifth category of concepts, internal parameters(i), are chosen
to be the potential root causes of the failure, as they represent unobservable
parameters. The internal parameters have been identified by the drilling experts,
for some other purposes but introducing the root causes. Therefore, not all of
them can be the root causes of a failure, necessarily. On the other hand, they are
the most proper candidates to satisfy the concept of root cause. Then considering
the main focus of the study, which is illustrating the capability of BN to serve as
a replacement for the multi-relational knowledge model, we have accepted the
internal parameters as the candidates of root causes in this study. Additional
efforts are being conducted to identify new type of concepts, which will serve as
stronger candidates for root causes.
Employing BNCreek: By employing BNCreek to diagnose the root causes
of the failure case wellbore clean02, the 3 aforementioned steps are implemented
as follows:
Step One: First of all, one copy of the drilling process’ Bayesian network is
prepared for the under-studying case, called the case wellbore clean02 BN. This
copy demonstrates the prior beliefs of the network based on the experts beliefs.
Besides, the observed Mechanical Restrictions of the case wellbore clean02 are
retrieved from the case description.
Fig.2 demonstrates a small part of the case wellbore clean02 BN after up-
dating the networks beliefs, given the above-mentioned evidence. The networks
updated beliefs are called the temporal posterior distribution of the case wellbore
clean02. The figure illustrates the observations of the present case which connect
the cases to the Bayesian network. Let us pick the internal parameter Wellbore
Enlarged (i) as a sample to illustrate the network’s Bayesian analysis to update
the beliefs in BNCreek model. The goal is to figure out the P(Wellbore Enlarged
(i) | evidence). To reach our goal we need to consider all concepts that are re-
lated to and affected on the Wellbore Enlarged (i) and calculate the posterior
probability of the considered concept by inference into the network.
As it is observable from Fig.2, the Wellbore Enlarged (i) is caused by Fm
Soft(s), Mud Water Activity High(ss) and Wellbore Wall Erosion(i), then it is
dependent on them. Moreover, each of these concepts is dependent on some other
concepts, e.g. the Wellbore Wall Erosion(i) is dependent on Side Force High(s)
and Build/Drop Section Inside Openhole(ss) and this sequence continues. On
the other hand, there are nine evidence that affect the beliefs of the network.
Consequently, considering the number of involved concepts and the enlargement
of the network, manual computation of the P(Wellbore Enlarged (i) | evidence) is
169
almost impossible. Therefore, we have utilized an automatic calculation employ-
ing Genie and Smile tool. Finally, considering all the dependent concepts, their
prior probabilities and the nine evidence the result of doing an exact inference
in the network illustrates the value of %51 as the temporal posterior probability
of the Wellbore Enlarged (i). The temporal posterior distribution for the rest of
the internal parameters are listed in the second column of Table 1.
Step Two: The similarity matching section of the CBR is employed here. The
case wellbore clean02 is tested with the CBR to find the most similar case from
the case base. By applying the method, the case wellbore clean01 is suggested
as the best match case. The total similarity matching degree between aforemen-
tioned cases has been calculated employing Eq.1 and the FAC phase. Finally,
the 23% of similarity is achieved. As the final part of step two, the case wellbore
clean01 recorded PD has been retrieved and its internal parameters have been
listed to be used in the next step, see the third column of Table 1.
Table1: list of possible root causes
Root Causes WelboreClean02 WelboreClean01 WelboreClean02
Final
Activity Of Tripping (i) 100 % 94% 61%
Activity Of Reaming (i) 85% 85% 52%
Mechanical Restriction (i) 83% 78% 50%
BHA Balled(i) 98% 12 % 50%
Time Long (i) 70% 40% 40%
Sliding Mode (i) 66 % 50% 39%
Wellbore Wall Erosion (i) 56% 54% 34%
Drill String Cyclic Load High(i) 54% 54% 33%
Wellbore Enlarged(i) 51 % 47% 30%
Cyclic Load High (i) 50% 50% 31%
Cuttings Concentration Low (i) 48% 46 % 29%
Shale Swelling Invisible (i) 50% 37% 29%
ECD Surge High (i) 43% 43% 26%
Fm Boundary (i) 30% 99% 26%
Accumulated Barite (i) 42% 35% 25%
Wellbore Ledge/Shoulder (i) 40% 40% 25%
Cuttings Concentration high (i) 42% 31% 25%
Well Complex (i) 40% 33% 23%
Cavings Produced (i) 39% 19% 22%
Mud LGSC High (i) 38 % 21% 21%
Shale Brittle(i) 32 % 32% 20%
Cuttings Bed Compact (i) 32% 25% 19%
Fm Gas Bearing Zone (i) 30 % 30% 18%
Cavings Blocky (i) 25% 43% 17%
Cement Sheath Quality Low (i) 28% 28% 17%
Mud Gas Content High (i) 28% 28% 17%
Bending Of BHA (i) 22% 22% 14%
Cavings On Shaker (i) 24% 12% 13%
Filter Cake Thick (i) 20 % 21% 12%
Cement Insufficiently Displaced (i) 20% 20% 12%
Cuttings Bed Erosion Low (i) 20% 15% 12%
Fm Fault Intersected (i) 18% 22% 11%
WellboreWall Restricted(i) 16% 27% 11%
Motor Erosion (i) 17 % 19% 10%
Cuttings On Shaker (i) 17 % 13% 10%
Csg Ann P High (i) 16% 14 % 10%
Fm Above Charged(i) 12 % 11 % 7%
Cuttings Bed Erosion High (i) 10% 2% 5%
Bit Vibration (i) 0% 16 % 2%
Wellbore Restricted(i) 0% 9% 1%
Step Three: The potential root causes of the case wellbore clean02 and case
wellbore clean01 are considered and the impact factor 1 and 0.23 are assigned
to them respectively. To illustrate more details, let us continue by the Wellbore
Enlarged (i) with the temporal PD value of 51% which was obtained from step
one. The recorded PD of the matched case shows 47% of possibility for wellbore
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Enlaged obtained from step two. Considering the similarity degree between the
two cases, the impact factor 23% has been assigned to the retrieved case and
100% to the inputed one. Consequently, by applying Eq.2, the final posterior
distribution of the wellbore Enlarged(i) has been achieved 30%. case wellbore
clean02 is generated and recorded as a part of case description. Table 1, column
4 demonstrates a list of the drilling domain’s final PD for case wellbore clean02.
The expert would assess this list.
Fig. 3. Sample paths between two evidence and a root cause candidate in BNCreek
Employing temporal reasoning: Fig.3 illustrates some elements of down-
hole drilling problems as a sequence of concepts which is a part of the drilling
failure’s Bayesian network. Following example shows how temporal reasoning
increases the accuracy of diagnosing the root causes of drilling failures.
Adding a time sequence to the causal relations will provide a threshold for
the impact level of the observation. Table 2 illustrates the hypothetical time
sequence which is considered in this study.
Table2: time sequence
Side Force High(s) 01:00 am
Wellbore Wall Erosion(i) 01:30 am
Wellbore Enlarged(i) 01:40 am
Accumulated Cuttings(err) 02:00 am
Mechanical Restriction(i) 02:15 am
Took Weight(s) 02:30 am
Due to the nature of the problem, assume that passing one hour is an enough
time to ignore the older observations without loss in the accuracy of the con-
clusion. Then, for diagnosing the root cause of the ‘Took Weight (s)’ the ‘Side
Force High(s)’ could be removed from the root causes candidates.
To use the temporal reasoning results we need to know the time sequence
of observations and the threshold of impact level of the observations. In the
explained example the considered time for the observations and the threshold
for impact level are hypothetical. The real time sequence of the cause-effects
should be obtained from the raw data during the failure case extraction process.
Moreover, the threshold of the impact level would be determined by the expert.
Based on the raw data, the expert should decide how long it takes till the effect
of an observation becomes small enough to be negligible.
171
5 Discussion and Conclusion
Two main advantages and one of the weaknesses of BNCreek are being addressed.
The first and the most important advantage of BNCreek in comparison with
TrollCreek is its global view to the network’s beliefs. BNCreek takes benefits
from the dynamic information flow between the concepts as it has employed
the Bayesian network. It means that the new observed information will affect
the beliefs of all the related concepts in the network which in this study leads
to the global and dynamic adjustment of the potential root causes given the
observations. While TrollCreek method has a local perspective. It means it uses
the static strength of the relations. The second advantage of developed system is
the ability of considering the effect of more than one evidence in the network at
the same time which leads to simulating the logical operators, i.e. ”And”, ”Or”
which is an important capability due to our studying domain’s demands. While
importing such operators to Trollcreek’s knowledge model needs a significant
changes at the model. On the other hand, the knowledge model that is employed
at TrollCreek covers other types of relations in addition to the causal ones, which
results in covering more important details in addition to the cause and effects.
As a conclusion BNCreek showed a more flexible and stronger capability to
make inferences and to make a diagnosis of the root causes in comparison with
TrollCreek method.
Our future study will focus on more integrating temporal reasoning with
inference in the BN and make an effort to include other types of relations, a
combination of the BN and knowledge model.
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