APPLYING FUSION TECHNIQUES TO GRAPHICAL METHODS
FOR KNOWLEDGE BASED PROCESSING OF PRODUCT USE
INFORMATION
Susanne Dienst, Fazel Ansari Ch., Alexander Holland and Madjid Fathi
University of Siegen, Hoelderlinstr. 3, 57068 Siegen, Germany
Institute of Knowledge Based Systems and Knowledge Management, Siegen, Germany
Keywords: Product Lifecycle Management, Product Use Information, Graphical Methods, Bayesian Networks,
Fusion Techniques.
Abstract: In this paper the processing and modelling of product use information raised by graphical methods on the
basis of a praxis and application scenario. Product Lifecycle Management (PLM) ensures a uniform data
basis for supporting numerous engineering and economic organisational processes along the entire product
life cycle – from the first product idea to disposal or recycling of the product respectively. The Product Use
Information (PUI) -e.g. condition monitoring data, failures or incidences of maintenance- of many instances
of one product type is generated in the product use phase. The processing and modelling of PUI raised by
graphical methods like Bayesian Networks. In accordance, the product use knowledge leads back to the
product development phase and is used for discovering room for product improvements of the next product
generation. Therefore the PUI of the different instances should be aggregated by applying fusion techniques
to deduce/achieve generalized product improvements for a product type. As a result this paper reveals a
novel approach of applying new feedback mechanism of PLM for product improvements.
1 INTRODUCTION
Today’s Product Lifecycle Management (PLM)
systems focus on supporting the early phases of the
product lifecycle (Holland et. al., 2008b).
Downstream phases, such as the product use phase,
are currently not, or only rudimentarily flanked and
supported. In (Holland et. al., 2008b) a concept for
integrating the product use phase into the PLM
concept is represented. It highlights the possibility of
incorporating the Product Use Information (PUI) of
product i, sensor data, environmental parameters,
failures and incidences of maintenance from the
product use phase into the development of following
product generations and propagates the expansion of
the conventional product type PLM with regard to
the management of product item data, as it occurs
within the product use phase (Holland et. al.,
2008b). The principal target of the project is to
deploy potentials of improvement for the next
product generation of production machines.
Because, basically, in productions machines
objective feedback should be led back (e.g. increase
battery long life time, decrease loudness of the drive
belt) from the product use phase into product
development phase. Objective feedback refers to the
information that should be possibly without
subjective meaning (e.g. customer interview). Thus
the focus of PUI lays on machine data which can be
captured and submitted completely and can be
remaining unchanged. The PUI in this paper is, also,
captured form production machines e.g. rotation
spindles. The advantage is, therefore, to transmit
data, e.g. sensor data from customer to
manufacturer. The praxis scenario is aimed to
process data with Bayesian Networks (BN) and lead
back to product development phase of the next
product generation (Holland et. al., 2009). Thereby
the knowledge is used to locate improvement
potentials for the next product generation e.g. raising
the quality of a component (motor) of a machine. In
this context, a learning algorithm is used to learn
BNs from PUI, as formal graphical language for
representation and communication of decision
scenarios requiring reasoning under uncertainty.
Principally, a BN is a probabilistic graphical model
136
Dienst S., Ansari F., Holland A. and Fathi M..
APPLYING FUSION TECHNIQUES TO GRAPHICAL METHODS FOR KNOWLEDGE BASED PROCESSING OF PRODUCT USE INFORMATION.
DOI: 10.5220/0003065301360142
In Proceedings of the International Conference on Knowledge Management and Information Sharing (KMIS-2010), pages 136-142
ISBN: 978-989-8425-30-0
Copyright
c
2010 SCITEPRESS (Science and Technology Publications, Lda.)
that represents a set of (random) variables and their
probabilistic dependencies. Moreover, BNs are
directed acyclic graphs whose nodes represent
variables, and whose arcs encode the conditional
dependencies among the variables (Salini et. al.,
2009). The probabilities at the nodes are computed
by the Bayesian rule and therefore inferences (as
different types of reasoning) are performed by What-
If analysis as a learning process (in this paper the
learning process is applied on unknown structure
and complete data set) i.e. in case of changing senor
data, the possible probability of a defect is
recognized as higher or lower. The outcome of such
analysis can provide opportunities to calculate the
probability based on certain evidences as described
in section 3.1. These opportunities are: (a) the
probability that a defect appears can become higher
or lower and (b) the maintenance will be advanced
in order to protect against a machine defect.
Figure 1: Leading back PUI into product development
with BNs.
In terms of PLM the awareness leads back to the
producers of the machine to integrate them into
product development and to improve the next
product generation. In this sense, the rotation
spindles, which are used at various places as the
feedback of the learnt BNs, can be different. Also
the results of only one product whereas general
results are used in order to attain an improvement of
the next product generation. In this sense, the
rotation spindles, which are used at various places as
the feedback of the learnt BNs, can be different.
Also the results of only one product whereas general
results are used in order to attain an improvement of
the next product generation. Therefore the BNs of
various products are aggregated to one new BN by
means of fusion techniques as shown in Figure 1.
Thereby acquiring sufficient products and also
enough product data are vitally important. Thus a
general possible BN to support the developer
improvements for the next product generation can be
learnt.
2 BAYESIAN NETWORKS
The use of graph-based or probabilistic models
based on directed acyclic graphs apply within the
field of artificial intelligence. Such models are
known as Bayesian Networks (BN) (Salini et. al.,
2009; Koski et. al., 2009).Their development was
motivated by the need to model the top-down
semantic and bottom-up perceptual combination of
evidence in reading. The capability for bi-directional
inferences, combined with a rigorous probabilistic
foundation, were the reason for the appearance of
BNs as a method of choice for reasoning under
uncertainty in artificial intelligence and expert
systems. A BN can be described as a graphical
model for probabilistic relationships among a set of
variables.
BNs model the quantitative strength of the
connections between variables allowing probabilistic
beliefs about them to be updated automatically as
new information becomes available. It is therefore a
graph in which the following holds:
• A directed acyclic graphs G = (V, E) whose nodes
V represent a set of discrete or continuous
variables. The variables can be described as
propositional variables of interest. Each variable
has a set of finite mutually exclusive states. Edges
represent conditional dependencies; and
unconnected nodes represent variables which are
conditionally independent of each other (Cowell
et. al., 2007).
• Condensed, a generic entry in the joint probability
distribution P is the probability of a conjunction
of particular assignments to each variable given
by the formula 1:
|
(1)
where pa(V
i
) is the set of parents of V (Jensen et. al.,
2007; Borgolt et. al., 2002).The learning
APPLYING FUSION TECHNIQUES TO GRAPHICAL METHODS FOR KNOWLEDGE BASED PROCESSING OF
PRODUCT USE INFORMATION
137
characteristics (e.g. structure learning) of the BNs
are explained in (Holland et. al. 2008a, Neapolitan,
2003). Equation 1 implies certain conditional
independent relationships that can be used efficiently
to guide a product or knowledge engineer in
constructing the network topology.
Figure 2: Different fusion techniques for BNs.
3 USING FUSION TECHNIQUES
In product using phase PUI is captured by rotation
spindles which are aggregated and led back into
product development. Figure 2 illustrates the use of
two different fusion techniques within the feedback
mechanism of PLM. These two principal approaches
are applied in order to aggregate the data sets by:
(1) Merging the data sets directly by using
sampling methods, or
(2) Learning BNs, firstly, from the data sets and
then merging probability distribution of BNs by
applying the Linear Opinion Pool
(LinOP)/Logarithmic Opinion Pool (LogOP)
algorithms.
Aggregation is generally defined as the use of
techniques that combine data from multiple sources
and gather information in order to achieve
inferences, which will be more efficient and
potentially more accurate than if they are achieved
by means of a single source (Klein, 2004). An
aggregated BN is a combination of data from two or
more BNs, where every BN is an individual data set.
Besides, mathematical fusion techniques range from
simple methods such as arithmetic or geometric
means of probabilities to procedures based on
axiomatic approach (Clemen et. al., 1999).
Moreover, sampling is the process of selecting units
e.g. product from a population of interest. Hence
related results, with respect to the population from
which they were chosen, will be fairly generalized.
Sampling is a method designed for aggregation of
data, and particularly in case of insufficiency of
data, samples will be generated based on the existing
data. The sampling methods are classified as: The
Estimated Posterior Importance Sampling algorithm
for Bayesian Networks algorithm (Yuan et. al.,
2004), the Adaptive Importance Sampling for
Bayesian Networks algorithm (Cheng et. al.,2000),
the probabilistic Logic Sampling algorithm
(Henrion, 1988), the Backward Sampling algorithm
(Fung et. al., 1994), and the Likelihood (Fung et. al.,
1994; GeNIe, 2009). Also sampling facilitates the
fusion techniques by: (a) learning from available
data of a BN taken as the optimal BN, (b)
synthesizing of each expert network to a case
database using a sampling technique, (c) aggregating
the expert case databases, and (d) learning the
aggregated BN structure based on the case database
determined in section 2 by using a structure learning
algorithm
. Using sampling methods avoids induced
noise by applying an aggregation operator for a
common unified probability distribution (Stone,
1961). The LinOP is just a weighted linear
combination of the experts’ knowledge and thus it is
easily understood and calculated as shown by
equation 2.
|
(2)
where k is the number of experts,
represents
i’s probability distribution for unknown ,
represents the combined probability distribution and
the weights
,…,
sum to one, with
,…,
0 and
∑
1 (Clemen et. al., 1999).
The other similar approach, LogOP, is to use
multiplicative averaging as shown by equation 3:
|
(3)
Likewise, definition of variables is the same as
definition of variables by LinOP algorithm [10]. As
depicted in Figure 2, by using LinOP/LogOP, two
versions of the same BN (BN
1
…BN
n
) with the same
graphical structure and different probability are
aggregated into a single BN.
In order to evaluate the results of aggregated BNs
and compare the fusions techniques with each other
a What-If-Analysis by setting evidences needs to be
applied. This is explained in section 3.1.
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3.1 What-If-Analysis through the use
of Evidences
A statement about the certainty of a state of an
attribute is called evidence (Russell et. al., 2009).
This state will then occur with a probability of 100%
and the directed edges determine the causal
dependencies and, also, the flow of information in
the network. This also means that setting evidences
to all nodes within a BN, which are connected to
each other, have an effect, and thus spread the
probabilities under the given evidence (Lunze,
1995).
On the basis of a BN a What-If analysis is
performed trough the use of evidences, to show how
changing the probability distribution of the nodes is.
Thus, in BNs, the dependencies between the
measured machine data and the individual
components of the spindle are recognized. It can
then derive e.g. rules, when the risk of failure of the
spindle is particularly high. At high risk customers,
normally, will prefer maintaining earlier, in order to
prevent an outage. On the other hand, this
information is used in the product development
phase from manufacturer, to get a higher operational
reliability by spindle by the next product generation.
This is exemplified in the following.
Figure 3: BN with evidence of node “running time”.
The
Figure 3 shows the evidence at the node
“running time”, this lies in an interval of [50,168]
hours per week (h/week). For the other nodes, the
average probability
of occurrence is current. Here
through setting evidence the probability for a “crack
of drive belt” is increased as 4.23% to 10.15%.
Consequently, high life of the spindle can be
increased by determining the probability of a tear of
the belt.
In addiction, the BN (Figure 4) shows the
combination of two evidences as (1) “last
maintenance” in an interval of [0, 10] h, and (2)
“running time” in an interval of [50, 168] h / week.
The combination setting evidences, in this example,
reveals that the relatively high probability can be
reduced for a crack at a high maturity through
regular maintenance. Also as illustrated in Figure 5,
the fact that the target node “crack of drive belt”
evidence has been set, and the probabilities have
changed at all nodes, are therefore assumed to be
relevant. It is interesting to observe how the
probabilities have changed to the node temperature
and rotational speed, also where no evidence were
placed at the nodes. It shows, for example, that the
probability has fallen from 60% to 3.44% that the
“ambient temperature” is in the interval [normal].
Therefore the probability of a high temperature
in the interval is increased to 76.99%. So it comes to
a significant redistribution of output probabilities.
These shifts are, however, only so much, if all three
evidences - and not just the evidence for the
demolition of the belt- will be set.
Figure 4: BN with two evidence nodes “running time” and
“last maintenance”.
Figure 5: Combination of two evidences with the setting
evidence by the child node.
4 KULL BACK-LEIBLER-
DIVERGENCE
To merge different BN e.g. BN
1
and BN
2
, it is
necessary to determine a measure for the
approximation quality. A suitable measure is
applying the Kullback-Leibler (KL)-Divergence
between BN structures like BN
1
and BN
2
. The KL-
Divergence expresses the difference or distance
APPLYING FUSION TECHNIQUES TO GRAPHICAL METHODS FOR KNOWLEDGE BASED PROCESSING OF
PRODUCT USE INFORMATION
139
between two probability distributions (Gaag et. al.,
2001). Given the probability distributions p and q
where
,
,…,
represents probability
distribution
of optimal
BN
and
,
,…
probability distribution
of
aggregated BN. Therefore
,
is defined
in equation 4 as (GeNIe2009):
,
log
(4)
The cross entropy between two probability
distributions measures in information theory the
average number of bits needed to identify an event
from a set of possibilities, if a coding scheme is used
based on a given probability distribution q, rather
than the true distribution p. The KL divergence
values are not negative with
,
0
if and
only if
, then the probability distribution
of aggregate BN is the same as optimal BN
(Kullback, 1959; Kuntze, 2007).
4.1 Evaluation with KL-Divergence
In the evaluation, it is important to compare the
results of various techniques which are described in
section 3. In the sampling methods only the data sets
are aggregated as visualized in Figure 6. In contrast,
LinOP/LogOP algorithms are based on aggregating
of BNs .Therefore in order to obtain an aggregated
BN and to assess and evaluate, there are always two
BNs used with the same number of generated test
data for aggregation in all techniques e.g. in Figure 7
the first column of the table shows the number of
samples as the integral of generated test data for
BN1 and BN2 while the 50% of test data belongs to
BN1 and the rest 50% to BN2. Finally these BNs are
merged to obtain the aggregated BN (see Figure 2).
In this context, the use of sampling algorithms is
generated out of optimum BN e.g. for the number of
samples for BN
1
and BN
2
. These are then
aggregated, and hence the aggregated BN is learned
in Waikato Environment for Knowledge Analysis
(WEKA). There are generated multiple aggregated
BN from a different number of samples to find out
how the KL-Divergence develops.
In Figure 6 each of the plotted curves shows one
identical small fluctuation in all five sampling
algorithms. Also the KL-Divergence decreases with
increasing number of samples, and approaches from
50,000 samples to the value 0. So the probability
distribution of the aggregated BN is close to that of
the optimal BN. The Logic and Likelihood Sampling
supplying the test performed on the average the best
results.
Figure 6: KL-Divergence by the different Sampling
methods.
From a number of 50,000 samples, the curves are
close to 0, and thus a sufficient number is given to
learn a general BN too. This means that the BN is
then aggregated as good as the optimal BN to derive
general statements or e.g. a What-If analysis be
performed to lead back PUI for the development of
next product generation. The improvement of the
KL-Divergence is so marginal of 50,000 samples to
100,000, that to use fusion techniques is no longer
reasonable.
To determine the KL-Divergence for
LinOP/LogOP, two BNs from test data should be
learned and therefore probability distribution of the
BNs to aggregate is obtained. These are then merged
with both the LinOP/LogOP algorithms to obtain the
aggregated BN. As Figure 7 shows, based on the
curves clearly the level of KL-Divergence of the two
methods is very close. Also this comparison shows
that the curve of the aggregated BN with increasing
number of data sets dramatically tends to 0. This
means that the BN, formed by the two fusion
algorithms, is more and more close to the optimal
BN. Due to the fact that the BN used to aggregate
are the same, the both curves are also similar. As the
example shows, both methods are equally well
suited for aggregating BNs, while LinOP tends
slightly better.
Furthermore, the Figure 8 shows comparison of
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140
Logic Sampling and LinOP/LogOP. Besides the
black curve represents the Logic Sampling method
and the grey curve depicts the LinOP/LogOP
algorithms. The curve of the Sampling method may
begin at a higher KL-Divergence, crosses the
LinOP/LogOP curve already between 1000 and
5000 data sets, and then runs below the
LinOP/LogOP curve. The two curves meet by a
number of 100,000 data sets and remain, from that
time, on a similar course. From the shape of the
curves can be concluded that sampling methods is
faster and more improvement achieved than in the
LinOP/LogOP algorithms.
Figure 7: KL-Divergence by LinOP /LogOP algorithms.
Figure 8: KL-Divergence of the Logic sampling and
LinOP/LogOP.
5 CONCLUSIONS/ OUTLOOK
In this paper the knowledge-based processing of PUI
of the PLM is explained and described particularly
with a graph-based model, to aggregate and lead
back this information to product development. From
the real product rotation spindle as praxis scenario,
sensor data and environmental parameters are
measured and stored as PUI. The processing of PUI
is carried out by the Bayesian Networks. In this
context, the PUI is collected from multiple instances
of a product type. The aim is to improve the quality
of the next product generation and not only a
product instance. Therefore the data must be
aggregated to deduce generalized information, thus,
it is indispensible to apply fusion algorithms. For
this purpose an extension of the BN is made, and an
aggregated BN is created. It is also possible to
merge PUI of the individual spindles directly, and
then learn from an aggregated BN, or each product
instance is learned by a BN and then the entire
instances are aggregated. Besides, using the KL-
Divergence for evaluating the various fusion
techniques is shown that both possibilities are likely
to create aggregated BNs. Also it is pointed out that
within small number of samples it is advantageous
to apply sampling. In this sense by using the
LinOP/LogOP algorithm the graphical structure of
the BN which should be aggregate always must be
the same. However for attaining an optimal BN, the
experimental results of the rotation spindle imply
that nearly 50,000 of data sets the WEKA threshold
value is achieved. For data sets that are less than
50,000 samples the graph of the learned BN is not as
the optimal BN and because of this the merged BN
cannot be optimal. From a number of greater than
100,000 samples the gain is so low that no further
aggregation with LinOP/LogOP is reasonable.
Within this process, some questions are open e.g.
which sources of information are available and how
they can be integrated? The existing data is mainly
sensor data which are measured in the environment
of the rotation spindle. Furthermore, the proper
description of a defect and frequency of replacing of
the rotation spindle components is not fulfilled. Also
the prospective research trend of applying
knowledge-based processing of PUI is to provide
lead back to product development. This is enabled
by applying quality management systems and
policies for modification of know-how through
processes, standardization of best practices within
production, and identifying customer requirements
and expectation by defining of proper
measures.
APPLYING FUSION TECHNIQUES TO GRAPHICAL METHODS FOR KNOWLEDGE BASED PROCESSING OF
PRODUCT USE INFORMATION
141
ACKNOWLEDGEMENTS
Within the project “PLM Management Extension
through Knowledge-Based Product Use Information
Feedback into Product Development“ (WiRPro),
KBS staff is currently working on a solution to
integrate information from the product use phase
into the product development phase. The authors of
this paper have made significant contributions to that
development.
We express our sincere thanks to the Deutsche
Forschungsgemeinschaft (DFG) for financing this
research and to our project partner of the University
of Bochum, Chair of Information Technology in
Mechanical Engineering (ITM).
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