A SURVEY OF CASE-BASED DIAGNOSTIC SYSTEMS FOR
MACHINES
Erik Olsson
Departmant of Computer Science and Electronics, Mälardalen University, Västerås, Sweden
Keywords: Case-Based Reasoning, Fault Diagnosis, Artif
icial Intelligence, Machine Learning, Neural Networks.
Abstract: Electrical and mechanical equipment such as gearboxes in an industrial robots or electronic circuits in an
industrial printer sometimes fail to operate as intended. The faulty component can be hard to locate and
replace and it might take a long time to get an enough experienced technician to the spot. In the meantime
thousands of dollars may be lost due to a delayed production. Systems based on case-based reasoning are
well suited to prevent this kind of hold in the production. Their ability to reason from past cases and to learn
from new ones is a powerful method to use when a failure in a machine occurs. This enables a less
experienced technician to use the proposed solution from the system and quickly repair the machine.
1 INTRODUCTION
This paper addresses case-based reasoning (CBR)
(Aamodt, Plaza. 1994) systems used for diagnosis of
machines. The paper is intended to give the reader a
survey of CBR systems in this area. The particular
systems in this survey were chosen because of their
well-documented CBR-part (Pous et. al. 2004) and
their application in the area of machine diagnosis.
All systems in this survey were created or reported
after about 1999 and are published in major
Proceedings and Journals such as the ECCBR and
ICCBR Proceedings and Journal of Intelligent and
Fuzzy Systems.
The paper is structured as follows: Section 2
gi
ves an overview of five CBR diagnostic systems
for machines. Section 3 discusses and compares
features of the systems. Section 4 gives a brief
conclusion of the systems.
2 THE SYSTEMS
This section describes five CBR systems for
diagnostics of machines. The first system is a
diagnostic system for locomotives. It collects fault
codes from locomotives and uses them for off-board
locomotive diagnosis. The second system diagnoses
electric circuits. It uses measurement data from the
circuit as features and matches them with similar
cases. The proposed solution is then adapted to the
new case. The third system monitors the health of
satellites by looking for anomalies in the down
linked data from the satellite. The fourth system
diagnoses industrial robots with the aid of acoustic
signals. The fifth system uses a combination of a
neural network and CBR to diagnose induction
motors.
2.1 ICARUS – A Diagnostic System
for Locomotives
Locomotives are large and complex machines that
are very difficult and expensive to repair. Due to
their complexity, they are often best served and
repaired by their manufacturer. The manufacturer
often have a long time service contract with their
customers and it is important for the manufacturer to
reduce the service costs as much as possible.
ICARUS (Varma, 1999) is a case-based
reasoning tool for off-board locomotive diagnosis.
Locomotives are equipped with many sensors that
can monitor their state and generate fault messages.
ICARUS is designed to handle the fault codes that
are generated by the locomotives.
Each fault code is saved in a fault database.
Connected to
each fault is a repair log taken from a
repair database. The fault log combined with the
repair log is a case in ICARUS.
Most repair logs contains a fault cluster. This
mean
s that many small faults occur before a repair is
performed. The cluster of faults is used as features
381
Olsson E. (2005).
A SURVEY OF CASE-BASED DIAGNOSTIC SYSTEMS FOR MACHINES.
In Proceedings of the Seventh International Conference on Enterprise Information Systems, pages 381-385
DOI: 10.5220/0002522003810385
Copyright
c
SciTePress
for case matching. Each cluster is assigned a weight
between 1 and 0. The value of the weight is set to
represent a clusters ability to isolate a specific repair
code. If a cluster is connected to only one repair
code it’s weight will be 1. If a cluster is connected to
evenly distributed repair codes in the case base it’s
weight will be lower. Clusters below a certain
weight threshold will be assigned zero weights.
The weights are used in the matching formula.
The degree of likeness between a new case and as
stored case is calculated as:
[
[][
]
]
× casenewweightsclustersofweights
casenewandstoredbetweencommoninweights
in clusters of case storedin
2
clusters
The repair code associated with the case with the
highest degree of likeness is the retrieved case.
The system vas validated with a case base
consisting of 50 repair codes. Each repair code was
associated with 3-70 cases. Each case was removed
from the case base and matched to all other cases in
the case base. If the repair code of the case was in
the top three nearest neighbouring cases, the match
was considered as a success. As a result the overall
accuracy of the system was 80%.
2.2 Diagnosis of Electronic Circuits
Diagnosis of electronic circuits is based on the
analysis of the circuit response to a certain input
stimuli. Input signals are generated and
measurements are acquired in certain nodes of the
circuit. A traditional way of doing this is to use fault
dictionaries. Fault dictionaries are based on selected
measurements on faulty systems. The comparison is
performed by a nearest neighbour calculation and
the closest case is taken as a diagnosis. The problem
with fault dictionaries occurs when a new fault is
found that cannot be matched with the ones already
stored in the dictionary. To deal with this a case-
based approach is suitable to be able to
automatically extend the dictionary with new faults
as they occur (Pous et. al. 2004).
The case consists of two parts. Part one is the
numeric part that contains the case identification
number and the measurements taken from the
circuit. The second part contains information about
the fault diagnosis.
Table 1: Case structure. The measurement part
Case id Measure1 Measure2 … MeasureN
Case i M1 M2 … MN
Table 2: Case Structure Fault Part
Class Comp. Deviation Hierarchy
Class i Comp. i X% L
i
M
i
The class corresponds to the class of component
that is diagnosed. The components are divided into
different classes if they have different accepted
deviations from their normal value. E. g. +/-10% can
be an accepted deviation for a class of components.
The component field contains the component
location. The deviation field contains the measured
deviation of the component. The hierarchy field
contains a description of witch level in the circuit
hierarchy the components is.
A normalized Euclidian distance function is used
to retrieve the cases from the case base and the k
nearest neighbours where k=3 is retrieved. The
solution is adapted to the new case by
transformational reuse (Aamodt, Plaza. 1994). A
learning algorithm is then applied to decide whether
the case should be saved as a new case in the case
base or not. E.g. if the diagnosis is correct there is no
need to retain the new case in the library. But if the
retrieved cases produce a misclassification of the
new case, the case might be added to the case base
according to the results of the learning algorithm.
The system has been tested with the DROP4
(Wilson, Martinez. 2000) and the All-KNN learning
algorithms. All cases are also equipped with weights
to improve the classification.
A measurement on a circuit is performed
resulting in the k=3 nearest neighbors in table 3.
Table 3: An example of a case retrieval
M1 M2 M3 Comp Devi
New Case 0.6 0.7 1.2 C
1
75
Neighbor1 0.6 0.7 1.1 C
1
23
Neighbor2 0.7 0.4 1.3 C
1
24
Neighbor3 0.7 0.4 1.3 C
2
11
Neighbour 1 and 2 has the same component as
the new case but the deviation is smaller in both
cases. Neighbour 3 has a different component. The
new case will be selected as a component C
1
because
of its similarity in the measurements. The deviation
is far from normal so the case will be introduces in
the case base.
The system has been tested on a filter circuit that
is commonly used as a benchmark for electronic
circuits. The filter consists of several capacitors and
resistors. The average result with the All-KNN
retain algorithm was 89% and the average result
with the DROP4 retain algorithm was 88%.
ICEIS 2005 - ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT SYSTEMS
382
2.3 Satellite Diagnosis
Satellites are monitored from the ground using down
linked data (telemetry). The case-based diagnosis
program can be resembled as an expert apprentice.
The program remembers the human experts actions
along with the context that is defined by the down
linked data. It then attempts to make it’s own
diagnosis when similar data appears in another
occasion (Penta, Khemani. 2004).
The features in the case are not state values taken
at a certain point of time. Because of the telemetry’s
streaming values the features are instead trends
extracted from the streaming data flow. The length
of the trend is different for different parameters. The
table below shows a sample case with two
parameters:
Table 4: Case structure of a satellite case (problem part)
Case
id
Length
of time
series
Sampling
rate
Lower
bound
Upper
bound
1234
2345
1000
2000
45
60
-3
0
10
10
A case is constructed from the streaming data at
a time called the case point. A case is constructed
looking back from the case point a certain length of
time. The attribute values are picked using a window
of the same length as the sampling rate. For each
window only one average value is saved as
representing that window. The length of the time
series corresponding to an attribute is l/s were l is
the length specified in the case schema and s is the
sampling rate.
The distance between two time series R, W is
calculated by dividing the time series into smaller
sequences R
i
, W
i
. An Euclidian distance calculation
between each R
i
, W
i
is performed and a global
distance d
g
is calculated from all the obtained
distances between the time series sequences. See (1).
),(
1
),(
1
ii
k
i
ig
WRd
k
WRd
=
= (1)
The system notifies the user if a new case is
considered interesting. The new case is considered
interesting it two ways.
1. A similarity threshold ε determines if the
new case should be considered as an
anomaly. If the similarity of all the
retrieved cases is below that threshold the
case is considered to be an anomaly and the
user is automatically notified.
2. If some of the retrieved cases are above the
first threshold. Another threshold
determines if the new case is similar
enough to some other case in the case base
that is previously diagnosed as an anomaly.
If so the system will notify the user of the
type of anomaly. In both situations the user
is able to give feedback to the system.
2.4 Diagnosis of Industrial Robots
Mechanical fault in industrial robots often show
their presence through abnormal acoustic signals.
At the factory end test of industrial robots a
correct classification of the robot is very critical. An
incorrect classification of a faulty robot may end up
in the factory delivering a faulty robot to the
customer.
The industrial robot diagnosis system uses case-
based reasoning and acoustic signals as a proposed
solution of recognizing auditable deviations in the
sound of an industrial robot (Olsson et. al. 2004).
The sound is recorded by a microphone and
compared with previously made recordings; similar
cases are retrieved and a diagnosis of the robot can
be made.
Features are extracted from the sound using
wavelet analysis (Bachman, 2000). A feature in the
case is a normalized peak value at a certain
frequency. The case contains peak values from many
frequencies. The case also contains fields for
information of the robot model and type of fault (if
any). There is also room to enter how the fault was
repaired. Table 5. displays a part of the case
structure.
Table 5: A part of the case structure for robot diagnosis.
Serial
Number
Type Fault
Diagnos
is and
Repair
Features
1-n
45634 4500 2
Cases are retrieved using a nearest neighbour
function that calculates the Euclidian distance
between the new case and the cases stored in the
case library. A list with the k nearest neighbours is
retrieved based on the distance calculations. The
system learns by adding new cases to the case base.
A technician enters the diagnosis and repair action
manually in each case.
The system has been evaluated on recordings
from axis 4 on an industrial robot. Sounds from 24
healthy robots and 6 faulty robots were colleted to
enable case-based classification of the condition of
the robots. The prototype system demonstrated quite
A SURVEY OF CASE-BASED DIAGNOSTIC SYSTEMS FOR MACHINES
383
good performance by making right judgments in
91% of all tests.
2.5 Induction Motor Fault Diagnosis
Induction motors are very common within industry
as prime movers in machines. Induction motors has
a simple construction and are very reliable. But
working in a tough environment driving heavy loads
can introduce various faults in the motors. A system
for fault diagnosis of induction motors is presented
here. The system has interesting features such as a
neural network combined with a case-based
reasoning system (Yang et. al. 2004).
A case consists of 6 categories of features and 20
variables. Among the variables are measurement
positions, rotating frequency components and
characteristic bearing frequencies. The case also
includes the type of machine to be measured, the
symptom, the corrective action etc.
The system uses an ART-Kohonen neural
network (Carpenter, Grossberg. 1988) (ART-KNN)
to guide the search for similar cases in the case base.
CBR is used to select the most similar match for
a given problem. The advantage with the ART-KNN
compared to other neural networks such as the
Kohonen Self Organizing Map (Kohonen, 1995) is
that it can learn new knowledge without losing old
knowledge. When a new case is presented to the
system the ART-KNN learns the new case in one of
two ways:
1. If the similarity of the new case compared
to the cases already learned by the network
is below a certain threshold; the similarity
coefficient. The network learns the case by
adding new nodes to its layers.
2. If the similarity of the case is above the
threshold, the network learns the case by
adjusting its old nodes to resemble the new
case.
Cases are then indexed in the case base by
clusters of features in the ART-KNN. The indexed
cases are then matched against the new case with a
standard similarity calculation.
The system has been tested with measurements
from an AC motor in a plant. The motor had a rotor
fault witch resulted in high levels of noise and
vibration. The system was trained with 60 cases
containing different motor defects such as bearing
faults, rotor damages and component looseness.
The system retrieved two previous cases from the
case base together with results from a modified
cosine matching function. The retrieved cases both
indicated a bearing fault. The average result of a test
of all cases in the case base was 96,88%.
3 DISCUSSION
When comparing different case-based reasoning
systems with each other one must focus on the
features that are shared by all case-based reasoners.
Below is a comparative discussion of five
common problems that has to be faced when
implementing a case-based reasoner and how they
are solved in each system. The problems are as
follows:
1. Feature extraction and case representation.
2. Case retrieval and indexing.
3. Case reuse.
4. Case revision and retain.
5. Case base maintenance.
1. ICARUS uses combinations of fault codes as
features because that is the way a locomotive signals
its faults. A repair action on a locomotive is also
very expensive, thus several faults must be
combined before a repair action can be executed.
Often machines cannot provide such fault codes.
Instead features such as filtered measurements from
different kinds of sensors are used. This is the
situation for the electronic circuit diagnosis system,
the induction motor diagnosis system, the satellite
diagnosis system and the industrial robot diagnosis
system. They all collect single measurements or time
series measurements, e.g. current, vibration, acoustic
signals, streaming telemetry data etc. The data
collecting sensors can be an integrated part of the
object or an external portable measurement device.
The basic case representation is similar for the
systems in this survey. The three basic components
of the case are the features, the problem description
and the repair action. Sometimes the repair action is
implicit in the fault description. As in the electronic
circuit diagnosis system, the repair action is equal as
to replacing the faulty component.
2. The case retrieval process most commonly uses
some kind of distance calculation combined with
weights to calculate a distance between the new and
stored cases. The k nearest neighbours to the new
case is then retrieved. This kind of retrieval is used
in all systems except the induction motor diagnosis
system and the satellite health diagnosis system. The
satellite health diagnosis system uses two similarity
thresholds; one for anomaly detection and one for
event detection. The induction motor diagnosis
system uses a neural network to first index relevant
cases in the case base. After that a straightforward k
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384
nearest neighbour distance calculation is performed
to calculate the distance between the indexed cases
and the new case.
3. All systems in this survey implements the
reuse phase by suggesting the diagnosis extracted
from the retrieved k nearest neighboring cases. The
satellite diagnosis system also has a threshold for
sorting out irrelevant cases not to be considered for
reuse. In addition to this form of reuse the circuit
diagnosis system uses adaptation (Aamondt, Plaza.
1994) by transforming the past solution of the k=3
nearest neighbors to an appropriate solution for the
new case. The new solution is then inserted into the
new case as the proposed solution.
4. The simplest form of retaining is to just add the
new case in the case base. The industrial robot
diagnosis system uses this kind of retaining (the
robot diagnosis case base is then manually
investigated by an experienced technician in order to
remove irrelevant cases and provide relevant cases
with more diagnostic information). To few removals
of cases can in time cause problems with an
overfilled case base making the system perform less
well. Most system implements some kind of user
interaction before a case is retained. This is
performed in the satellite diagnosis system and in
ICARUS by letting an experienced technician decide
whether the case is relevant or not. The retaining
process can be extended by calculating if the new
case has any ability to improve the future diagnosis
of the system. The simplest form is to look if a
similar case already exists in the case base. If it
does, there is no need to retain the case. The circuit
diagnostic system also incorporates a machine-
learning algorithm that calculates the ability of the
case to improve the performance of the system.
5. Most systems in this survey are only
prototypes and have not yet implemented any
automatic maintenance process of the case memory.
The circuit diagnosis system implements a
confidence factor (Aha et. al. 1991) to prevent bad
cases from spoiling the performance of the system.
The case base is maintained by removing cases
when the performance of the case drops below a
certain confidence index.
4 CONCLUSIONS AND FUTHER
WORK
This paper has briefly described five intelligent
machine diagnostic systems that use case-based
reasoning as their primary approach to problem
solving. Case-based reasoning is still new in the area
of fault diagnosis of machines and most systems in
this survey are still prototypes. Some parts of the
CBR process seem to be implemented to a higher
extent than others in the systems. E.g. feature
extraction and case retrieval seems to be fully
implemented but adaptation is not widely
implemented. Also, automatic maintenance of the
case memory seems not to be implemented in the
majority of the systems in this survey.
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