Hydraulic Circuit Design and Dynamic Learning using Case-Based
Reasoning
C.M. Vong and P.K. Wong
Faculty of Science and Technology, University of Macau, P.O. Box 3001, Macau
Keywords: Hydraulic circuit design, Case-based reasoning (CBR)
Abstract
This paper describes the design and implementation of a hydraulic circuit design system using
case-based reasoning (CBR) paradigm from AI community. The application domain of hydraulic
circuit design and case-based reasoning are briefly reviewed. Then a proposed methodology in
computer-aided circuit design and dynamic learning with the use of CBR is described. Finally an
application example has been selected to illustrate the usefulness of applying CBR in hydraulic
circuit design with learning.
Although production rules are effective,
the acquisition and maintenance of
production rules are the problems facing
by not only the software engineers but
also the designers using the software.
Moreover, static learning 1 is another
issue of traditional rule-based systems.
To resolve the problems inherited from
conventional rule-based expert systems,
the AI community proposed another
reasoning paradigm called case-based
reasoning (CBR). CBR supports
dynamic learning in the way that new
knowledge can be appended to the
knowledge base without recompilation
of the system. This is one of main
advantages of CBR that maintenance of
knowledge takes much less time. This
paper studies the application of CBR in
hydraulic system design and a hydraulic
circuit design system has been
developed to verify this proposed
methodology.
1. Introduction
The use of computers in engineering
design has become a major trend in
industry. Today, many commercial
computer-aided design (CAD) software
are available to improve the design
process
in
many
engineering
applications. However, CAD software in
hydraulic system design is not as
prominent as in many other areas of
engineering design. This is mainly due
to the complexity of hydraulic analysis
and lack of agreement of the most
appropriate approach to the design
process. In recent years, more and more
CAD software in hydraulic system
design have been developed using
object-oriented technology [Wong &
Chan 1997] to reduce the complexity in
defining components used in a hydraulic
system, with the help of artificial
intelligence (AI). Most of the systems
[Burton et. al 1989] incorporate with an
expert system containing production
rules to guide the design and check the
consistency of the hydraulic system.
1
Whenever the rules are updated, the whole
system have to be recompiled.
1
is employed to find the nearest neighbor
for the current problem from the
reference cases, where wi is the
importance of dimension i, sim is the
similarity function for primitives, and ,
fiI and fiR are the values for feature fi in
the input and retrieved cases,
respectively. The sim function could be
freely designed by user but mostly this
function could employ the Euclidean
distance function and turns (1) to
2. Revision
2.1 Review of CBR
CBR is a methodology that allows to
find analogies between a current
working situation and past experiences
(reference cases). CBR makes direct use
of past experiences to solve a new
problem by recognizing its similarity
with a specific known problem and by
applying its solution to find a solution
for the current situation as shown in
Figure 1. CBR has been used to develop
many systems applied in a variety of
domains,
including
manufacturing,
design, law, medicine, and planning.
E( f , f ,W ) 
I
n
E( f , f
i 1
n
i 1
wi
n
i 1
wi
(2)
C
Yes
Weight
WA
1
WB
2
WC
1
With these parameters, the similarity
function is calculated as
E
1  12  2  0.2 2  1  12
 0.72
4
The retrieved case with the smallest
value E is considered as most similar to
the new case because the value E is now
indicating the distance (difference) of
the input case and the retrieved case. The
most similar retrieved case and the new
wi  sim ( fi I , fi R )

wi  ( f i I  f i R ) 2

Retrieved Case:
A
B
9
3.7
When user inputs a problem, the
problem is interpreted and converted as a
new case into the specific format of the
reasoning system. Then the converted
new case enters the stage of
RETRIEVAL where the new case is
matched against the previous cases in the
case library of the reasoning system. In
the retrieval stage of CBR usually a
simple similarity function

,W ) 
n
i 1
0 if f i I  f i R
fi I  fi R  
I
R
1 if f i  f i
To illustrate the use of this function,
consider the following example.
Input case:
A
B
C
10
3.5
No
1. retrieve –– retrieve similar past case
matched against current problem
2. reuse –– reuse to solve current
problem based on solution of past
case
3. revise –– revise the past solution if
any contradiction occurs when
applied to current problem
4. retain –– retain the final solution
along with the problem as a case if
the case is useful in the future
R

For symbolic features,
CBR is constituted with four RE’s
[Watson 1994]:
I
R
(1)
2
2.2 Review of Hydraulic Circuit
Design
case are both passed to next stage
REUSE where the solution part of the
retrieved case is applied to the new case,
with the guidance of adaptation
knowledge. The application of old
solution involves substitution of solution
features, structural modification of the
solution and/or derivational replay of
solution. These methods were discussed
in [Kolodner 1993]. After this stage,
now the new case is along with the
adapted solution based on the old one.
This adapted solution is considered as a
suggested solution which is still
incomplete because it is adapted
according to the requirement of new case
and this solution may have inconsistency
among its solution parts. To ensure the
adapted solution is consistent, it will be
passed to next stage REVISE, where the
adapted solution will be further adapted
based on the user feedback and
additional meta-adaptation knowledge,
which provides information of how to
reuse adaptation rules to perform
adaptation. This final adapted solution is
then returned as a confirmed solution to
the new case but this does not come to
an end. It was discussed that CBR learns
by accumulating new cases. However,
should any newly derived case be
accumulated in the case library? The
answer is no. CBR should only store the
cases that can contribute to future
reasoning of solutions, that could not be
done only by the cases in current case
library. In other words, if the cases in a
case library is capable enough to cover
the newly adapted solution, this new
solution should not be stored in order to
avoid redundancy and inconsistency.
Otherwise, it should be stored for future
use. This leads to the final stage of
CBR – RETAIN. The case considered as
being able to contribute in the future is
called “learned case” and stored in the
case library.
Mechanical energy is converted to
hydraulic energy in hydraulic circuits.
This energy is then transferred as
hydraulic energy, processed either in an
open loop or closed loop circuit, and
then converted back to mechanical
energy. Hence the main task of hydraulic
power system design is circuit design
and hydraulic power circuit design is one
kind of configuration design problems.
At present, the design of hydraulic
circuit is conducted by an interactive
trial and modification procedure. This
involves sketching the schematic of the
circuit, and calculation of various
component and circuit parameters, and
then evaluate performance of the
designed circuit. The above process is
repeated to refine the circuit design
according to the evaluation result until
the result is satisfactory.
2.3 General Procedure in design
of hydraulic power circuit
1. The circuit is designed according to
the information provided by the user
such as maximum thrust required,
speed of actuator, duty cycle,
function, etc.
2. The piston and the rod diameters
required for the actuator are
determined
according
to
the
maximum
operating
pressure,
maximum thrust required and stroke
length.
3. Select the piston and rod diameter
required and convert these into
standard size.
4. The system parameters such as
system pressure, hydraulic oil flow
rate, etc, are calculated.
3
5. The suitable pump or pump circuit
based on the system pressure and
flow rate is selected.
6. Other hydraulic components used in
the circuit are then selected.
the similarity of cases to the current one,
the cases are already ranked with
different similarity level. After that, the
engineer could adapt/modify the
retrieved case manually by the above
procedure or by applying the adaptation
rules supplied by CBR. Those adaptation
rules are specific production rules
captured from experts or from the
engineer’s own experience. Finally the
engineer can decide if the case is good
enough to store into the case library for
future reuse.
3. Use of CBR in hydraulic
circuit design
Based on the above procedure, engineers
perform a hydraulic circuit design.
However, as it was noticed that
hydraulic
engineers
are
usually
accustomed to modify an existing circuit
design into a new one for different
situation and use. It is because hydraulic
circuit design is usually similar even for
different
situation,
so
hydraulic
engineers have to manually look through
their, or their company’s, old circuit
designs stored in a database and then
retrieve a similar and suitable one and
perform modification. This process is
repetitive and tedious in the stage of
retrieving because engineers have to
review the circuit designs one by one.
However, the situation that retrieving an
old design circuit and adapt the retrieved
circuit design into a suitable one for
current situation is exactly the paradigm
supported by CBR. Since each circuit in
the database (case library) was stored
along with its functional and applicationspecific requirements of the outputs,
these parameters were used as the case
(circuit design) indexes. Whenever the
hydraulic engineer wants to retrieve an
old case from the case library, he just
needs to input the functional and
application-specific requirements and
CBR uses (1) to calculate the most
similar past case. If the engineer does
not satisfy and think that the retrieved
case is not suitable, then the next most
similar past case would be shown. This
could be done because when calculating
3.1 Dynamic learning in hydraulic
circuit design
Hydraulic circuit designs differ by not
only the circuit diagram but also the
functional attributes along with them.
For circuit adaptation, if some
components are replaced, then the
number of attributes will also be
changed by adding or deleting some of
them. CBR enables dynamic structural
modification of cases so that attributes
can be added or deleted accordingly. For
example, an engineer retrieves an old
case and perform modification on the
circuit design, then the number of
attributes would be changed according to
which components are revised by adding
predefined attributes for the components
or deleting unnecessary attributes in the
circuit.
4. System implementation
and application example
The hydraulic circuit design system has
been built with Microsoft Visual Basic
and
running
under
Microsoft
Windows95 platform. The system, as
implemented, is able to recommend most
4
similar circuit design based on the circuit
requirement specification. The working
environment and front-end user interface
of the circuit design system are shown in
Figure 2. In addition, Figure 2 also
shows a sample circuit design and the
modifiable attributes along with it. The
input specification for an example of a
100-ton horizontal hydraulic wooden
press is shown in table 1. Figure 5 shows
the circuit configuration and the main
component sizes recommended by the
system.
Whenever an old similar case is
retrieved, the case is adapted in order to
reuse it for current situation. However,
not every case is adaptable by the system
as the adaptation rule set of any system
is always incomplete. At this moment,
human user intervention is necessary to
compensate the inability of adaptation of
the system. Usually, human user needs
to adapt the case by himself using his
domain expertise and this is a good time
to capture the user’s domain knowledge
by recording what operations he perform
to adapt a case, along with indexing of
the problem case that the system could
not adapt automatically. In recording the
operations, the system can dynamically
learn from the user the adaptation
knowledge. The learnt knowledge is
encapsulated as a case called adaptation
case because it is used to guide
adaptation. Later, when similar problem
case arises, the system will become
capable to handle the adaptation by
referring to the adaptation case.
4.1 Dynamic Learning in details
The dynamic learning ability of the
system is illustrated in this part. As the
retrieval process is already discussed in
section 2.1, the detail will not be
discussed here. Before going on the
example, the attributes of the case
representation for a hydraulic circuit is
briefly introduced. The attributes
constituting a hydraulic circuit is listed
below as a sample case:
4.1.1 System Implementation
Drawing name
Max. Flow
Max. Pressure
Useable range of speeds
Useable range of pressures
Viscosity range
Max. noise level
Service life
Price
Pressure function
Orientation
Load Type
Environment
Symbol Synthesis
Connection point 1
Connection point 2
Var_1
33 L/min
630 Bar
2
1
1
3
2
3
Constant
Horizontal
Press
Noisy
Pump 1
(230, 100)
(350, 100)
Initially, the existing standard block
drawings of hydraulic sub-circuits were
constructed along with their respective
attributes such as the ones listed above.
In the training stage of the system, a list
of preview of existing drawings will be
shown. The user can select one of them
to perform training of the system in
order to teach the system to recognize
other variations of the sub-circuit. For
instance, consider again the above list as
an existing standard sub-circuit. If the
user changes the attribute “Max. Flow”
from 33 L/min to 350 L/min, the pump
component of the initial sub-circuit has
to be replaced with a new one which is
able to support flow rate of 350L/min.
where 1 = very good/very large
2 = good/large
3 = satisfactory
4 = poor
5
This is learnt by means of the production
rule
if “Max. Flow” <= 33L/min then
use old_block
else
use new_block
The system will keep all of these
production rules to adapt the future cases.
Whenever a sub-circuit has maximum
flow rate greater than 33L/min, it has to
select a certain pump component.
Because of the change of the pump
component, the attributes “Synthesis
Symbol”, “Connection Point 1” and
“Connection Point 2” are necessary to
change so that in the drawing, a more
powerful pump component is inserted.
The change of drawing could be done by
integrating the system with a CAD
system for hydraulic circuit developed
by the author [Wong et. al 1998]. By
integrating the CAD system, only the
block drawing filename and the
connection points are necessary to
modify the existing drawing. Another
example illustrates the structural changes
of a case. The user changes the “Pressure
function” from “Constant” to “High
Speed, Low Pressure”. With this change
of value, one more attribute “Speed
function” should be added to the case to
represent the speed variation. In addition,
the actuator in the drawing is replaced
with another one with tail. The
replacement is shown in Figure 3 and
Figure 4. Again this could be done by
the CAD system. After the learning, a
new drawing should be saved together
with the modified attributes. Later, user
can retrieve this newly generated subcircuit to constitute the solution for a
new problem.
maximum flow rate and inserting a new
attribute, etc., are saved in the form of
operations to represent adaptation
knowledge. The forms of operations are
specified below.
For substitution operation:
Operator Feature
Ratio/ Value
name
Value
“Operator” means one of the following:
 substitute
 add
 minus
 multiply
 divide
“Feature name” means the name of the
feature in the case
“Ratio/Value” are the operands used for
the operators
“Value” means the value to replace the
original if the operator is substitute.
For transformation operation:
Operator Feature Value Attribute
name
Type
“Operator” means Add or Delete
“Feature name” means the name of the
feature in the case
“Value” means the value to insert for the
feature. So it is optional for “Remove”
“Attribute Type” means the data type for
the attribute.
These operators are saved in another
database and ready for adaptation.
Important meta-adaptation knowledge
are also saved in another database that
consists of the information of past
successful trace of adaptation operators,
source (retrieved most similar) case and
the target (current problem) case. These
meta-adaptation knowledge is called
“adaptation case”. The general structure
of an adaptation case is listed below.
4.1.2 Representation of adaptation
operators
During the user intervention, the actions
of substituting a new block for
Source case
6
Target case
Operators
Index
work is much simpler than designing a
brand new circuit from scratch.
To match an adaptation case, the source
case and target case are both the input
parameters and the output for the
matching is the sequence adaptation
operators. One thing to notice is that this
is not an exact matching. Once again this
is only a similar matching using more or
less the same similarity metrics. Once
the operators are retrieved, they are
applied to source case and the real
solution can be found for the target case.
5. Conclusions
This work is an attempt of applying
CBR methodology in hydraulic circuit
design to dramatically reduce the
unnecessary
time
consumed
for
repeatedly designing similar hydraulic
circuit by reusing past cases. CBR is a
methodology in AI which can more
robustly capture the thinking of human
beings. The most important part of CBR
is to reuse past experience in current
problem so that identical parts of the
current problem can be directly reused
while only similar and missing parts of
the problem can be solved by analogy
using expert adaptation rules. An
intelligent hydraulic circuit design
system has been built using the
recommended methodology and it has
been verified to work successfully. The
recommended methodology will be
further verified by applying to other
hydraulic engineering domains, such as
moulding
machines
and
marine
applications.
4.2 Discussion of results
Owing to no standard solution in the
circuit design, so design evaluation will
be concentrated on the validity of the
design. The results generated from the
prototype circuit design system has been
verified to perform in accordance with
the stipulated specifications by the
experts from two hydraulic engineering
companies. The major contribution of
this circuit design system is the
reduction of design lead time for the
stage of similar circuit retrieval and postanalysis of attributes for circuit
components. Non-experts normally
spend one to two weeks to finish a
circuit design. By reusing past circuit
design with slight modification, only
several minutes are necessary. The major
difficulties in manual design is to find
appropriate components and connection
among components. It is because of the
lack of universal design theory, hence
finding and understanding the properties
of components in a circuit is a very timeconsuming task, even for experts. With
CBR, most part of past circuit design can
be reused and the time for re-considering
the selection of components satisfied by
the circuit requirement specification can
be saved. The only effort left for a
circuit design is how to adapt an existing
circuit into a new suitable one and this
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1
2
7
Burton R T , Sargent CM. The use of
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Chan K K. Implementation of an
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10 Wong P K, Tam L M and Tam SC.
Object-oriented CAD for hydraulic
circuits
of
pressing machines.
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Society.
Table 1 The input specifications of a 100-ton
horizontal hydraulic wooden press
Actuator group no.
Maximum loading (Ton)
Cylinder constraint (bore
diameter/ mm)
Cylinder constraint (rod
diameter/mm)
Stroke length(mm)
Max. stroke speed (mm/sec)
Max. speed for pressing
(mm/sec)
Mounting method
No. of variation stage of the
load pressure during pressing
(i.e. at high load condition)
Type of control in press
Action
Stage of the actuator speed
Acceptable noise level
Expected service life time
Machine operating hours/day
Prime mover rated speed
System operating pressure
1 (Ram cylinder for
pressing)
100
Nil
Nil
300
30
10
Front and rear flange
1
Position sensing
Two
60 db
5 years
12 hours/day
1450 rpm
210 bar
Reference cases
Problem
Similarity
New Problem
Figure 2. Working environment and front-end user
interface
Adaptation
Solution
New Solution
Figure 1.Case-based Reasoning Scheme
8
Changes of
substitution
of block
drawing
Changes of
structure ––
Speed
variation
Figure 3 Standard subcircuit
Figure 4 Subcircuit after learning
Figure 5 Solution for the wooden press recommended by the
system
9