E-Journal of Advanced Maintenance Vol.6 (2014) 48-56
Japan Society of Maintenology

Integration Technique of Diagnostic Results in Hybrid-type Agent System
1,*

2

1

3

Akio GOFUKU , Kazuma TAKATA , Kenji TAKATORI , Makoto TAKAHASHI
1

Graduate School of Natural Science and Technology, Okayama University, 3-1-1 Tsushima-Naka,
Kita-ku, Okayama 700-8530, Japan
2
Faculty of Engineering, Okayama University, 3-1-1 Tsushima-Naka, Kita-ku, Okayama 700-8530,
Japan
3
Graduate School of Engineering, Tohoku University, 6-6 Aramaki-Aoba, Aoba-ku, Sendai, Miyagi
980-8579, Japan
ABSTRACT
A hybrid-type diagnostic system to give a final diagnostic result by integrating the results of sub-systems is
one of promising ways to develop a flexible diagnostic system. This paper describes a hybrid-type diagnostic
agent system for a fast breeder reactor “Monju”. This paper also proposes an integration technique of diagnostic
results by sub-systems using what are called confidence values and trust values. Two simulations under thecondition that a diagnostic sub-system diagnoses plant condition by a random process are conducted to examine
the applicability of the proposed integration technique. The results suggest the applicability of the proposed
technique and some applicable conditions for the diagnostic performances of sub-systems.
KEYWORDS

ARTICLE INFORMATION

Information integration, Diagnosis, Small disturbance, Monju, Hybrid-typeagent system

Article history:
Received 5 March 2014
Accepted 4 July 2014

1.

Introduction

A fast-breeder reactor has a special characteristic feature to use Sodium as a coolant of reactor.
The fast-breeder reactor “Monju” has three types of circulation flow loops: two Sodium loops and one
water-steam loop. Because the Sodium reacts intensively with water, there are several differences inoperation conditions of “Monju” from those of light water reactors. Therefore, the detection andidentification of small anomalies are important.
There are two approaches to advance the capability of a diagnostic system. The one is to improve
diagnostic techniques themselves. The other is to integrate the diagnostic results of some diagnostic
systems as skilled human operators detect and identify early an anomaly by flexibly using many kindsof symptoms of the anomaly.
A hybrid-type diagnostic system that a final diagnostic result is given by integrating the results of
sub-systems is one of promising ways to develop a diagnostic system because such systemcomposition has several advantageous features in adding and improving diagnostic functions in thesystem. However, the topic on how to integrate the diagnostic results from sub-systems is an
important and difficult problem to develop and apply the hybrid-type diagnostic system.
The authors studied a hybrid-type diagnostic agent system [1] to detect and identify early ananomaly that may happen in the fast-breeder reactor “Monju”. The system utilizes an agent system
configuration composed of four diagnostic software agents. They are (1) an estimation agent ofoverall heat transfer coefficient of evaporator and superheater, (2) a state identification agent based on
SVM (Support Vector Machine) [2], (3) an anomaly detection agent by WT (Wavelet Transformation)[3], and (4) a CBR (Case-Based Reasoning) agent [4] using several attributes in both time and frequency
domains.
This paper first outlines the hybrid-type diagnostic agent system for “Monju”, then proposes anintegration technique of diagnostic results given by sub-systems using what the authors callconfidence values and trust values. Because the determination of confidence value is left to each*

Corresponding E-mail: fukuchan@sys.okayama-u.ac.jp

ISSN-1883-9894/10 © 2010 – JSM and the authors. All rights reserved.

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A. Gofuku et al. / Integration Technique of Diagnostic Resultsin Hybrid-type Agent System

diagnostic sub-system in the technique, this paper investigates the applicability of the determinationof trust value by several case studies using a variety of characteristic features of diagnostic
sub-systems.

2.

Hybrid-type Diagnostic System

2.1 General Configuration and Topics of Hybrid-type Diagnostic System
A hybrid-type diagnostic system is generally composed of an integration agent to collectdiagnostic results and to integrate them at the top of the system and diagnostic agents in the lower ofthe integration agent to diagnose by their own principles and to send the diagnostic results to theintegration agent. The integration agent may store trend data of observed signals and serve them to thediagnostic agents.
The advantageous characteristics of agents are: (1) easy realization of fault tolerant system by
duplex systems, (2) easy system maintenance because of small program size of an agent comparedwith a total system including many functions, (3) easy upgrade of system function by adding only
necessary agents, (4) high reusability of an agent because of the generality of the single function agent,and (5) easy localization of system trouble because of high transparency of agent function.
Usually, the performance of a diagnostic sub-system depends on the following features. First,
each diagnostic sub-system has its own applicable range due to its diagnosis principles, sensor data
used in its diagnosis, and so on. Second, the accuracy of the diagnostic result of a sub-system depends
not only on its diagnostic principles but also on the setting of threshold values for diagnosis that areusually determined by solving the trade-off of erroneous alarm and miss-alarm. In the integration of
the diagnostic results of sub-systems, these features should be considered. However, handling of theinformation of sub-systems is difficult for the integration agent due to the variety of characteristics of
sub-systems. Moreover, it is usually hard for the integration agent to know how much a diagnostic
parameter of a sub-system exceeds the threshold value used in diagnosis. This information isimportant and should be considered for the integration because it influences the accuracy of integrated
diagnostic results.
2.2 Hybrid-type Diagnostic Agent System for “Monju”
The developed hybrid-type diagnostic agent system is shown in Fig. 1. It is composed of thediagnosis integration agent and four diagnostic agents. The principles and capabilities of thediagnostic agents are outlined in the following.

Fig. 1 Hybrid-type diagnostic agent system for “Monju”

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A. Gofuku et al. / Integration Technique of Diagnostic Resultsin Hybrid-type Agent System

The first diagnostic agent is an estimation system of overall heat transfer coefficient. In order tomonitor the operation conditions of evaporator and superheater, a diagnostic technique by estimating
the overall heat transfer coefficients using observed process signals is developed [5, 6]. The techniqueestimates the overall heat transfer coefficients that are important unobserved state variables forevaporator and superheater. Simplified models of the evaporator and superheater of “Monju” areconstructed by considering their structures, the flows of secondary sodium and water/steam, and small
number of process signals available to estimate the overall heat transfer coefficients. Based on thesimplified models, equations to calculate overall heat transfer coefficients of evaporator andsuperheater are derived. Because process signals fluctuate by noises, digital low-pass filters areformulated to suppress the fluctuation of estimated values by noises.
SVM [7] is a kind of machine learning technique and is widely applied to construct a state
identifier. SVM has a characteristic feature to derive nonlinear identification functions from training
data. It can update the identification functions when new training data are obtained. A state
identification agent is developer by applying SVM [1, 2].
WT [8] has a strong capability to detect the inclusion of a similar wave (short-term change
pattern) in a changing signal to a reference wave called a mother wavelet (MW). WT can analyze
time-changing data in both frequency and time domains. Therefore, WT is widely applied to detect a
sudden anomaly of a component with rotating parts such as pump, motor, and so on. In principle, thedetection performance will increase if a MW is similar to the wave to be detected. An anomaly
detection agent [3] is developed, where a MW designed from a characteristic wave included in a real
signal at an anomaly is used. To design a MW from a real signal, the agent applies a parasitic discrete
wavelet transform (P-DWT) [9] that has a large flexibility in the design of a MW and a high processing
speed.
A diagnostic technique applying CBR [10] is developed [1, 4]. The characteristic feature of thetechnique is to use multiple attributes of process signals for similarity evaluation to retrieve a similarcase stored in a case base. The plant condition is evaluated in the normal condition, if the attributes ofprocess signals are similar to those in the normal condition. If the plant condition is diagnosed to be
an anomalous one, the anomaly is identified by comparing the attributes of process signals to those of
the anomalous cases. If there is no similar case, the plant condition is diagnosed to be in a different
anomalous condition from those of past cases.

Fig. 2 Software configuration of hybrid-type diagnostic agent system
in “Monju” distributed diagnostic agent system

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A. Gofuku et al. / Integration Technique of Diagnostic Resultsin Hybrid-type Agent System

The diagnosis integration agent and four diagnostic agents are implemented as an agent system of“Monju” distributed diagnostic agent system [11]. Figure 2 shows the relationship among “Monju”
process data acquisition system MIDAS, some data servers, developed hybrid diagnostic agent, fourdeveloped diagnostic agents, and other monitoring and diagnostic agents that configure “Monju”
distributed diagnostic agent system. The hybrid diagnostic agent corresponds to the diagnosis
integration system and calls a diagnostic agent with necessary data to initiate the diagnostic process ofthe sub-agent. Each diagnostic agent returns its diagnostic result and confidence value for the result tothe hybrid diagnostic agent. The hybrid diagnostic agent integrates the results of diagnostic agentsusing the confidence values returned from diagnostic agents and trust values for diagnostic agents
predetermined by the hybrid diagnostic agent. The integration technique using confidence values andtrust values will be described in detail in the next section.

3.

Integration Technique of Diagnostic Results by Agents

3.1 Integration Framework of Diagnostic Results
By considering the topics to be considered in the integration of diagnostic results by sub-systems,
the authors propose an integration framework [1]. In the framework, each sub-system outputs its
diagnostic result and “confidence value” that is a descriptor of the certainty of the result. On the other
hand, the diagnostic integration agent uses trust values that are descriptors of trust to the results ofdiagnostic sub-systems by the diagnosis integration agent in order to give an integrated diagnostic
result by considering the diagnostic results reported by sub-systems.
The confidence value is given between 0.0 and 1.0. The confidence values of 1.0 and 0.0 meanthat the sub-system has absolute confidence and no confidence, respectively. However, in theframework, the determination of the confidence value is left to each sub-system because there aremany kinds of diagnostic techniques and it is difficult for the diagnosis integration agent to grasp thecharacteristic features of diagnostic sub-systems. The confidence value may be determined base on
the following evaluation value depending on the diagnosis principle of a sub-system:
1) an exceeding or underrating value for a threshold value when the sub-system uses a thresholdvalue,
2) a distance from a discrimination function when the sub-system makes a state classification by thefunction,
3) a coincidence value for a typical pattern when the sub-system checks some attributes with thoseof past cases.
Instead, this paper focuses on the technique how to determine the trust values and proposes afeasible technique for the determination. The trust value is given between 0.0 and 1.0. The trust valuesof 1.0 and 0.0 mean that the diagnosis integration agent absolutely trusts the result and ignores theresult of the corresponding diagnostic sub-system, respectively. In this study, a sub-system is
supposed to output its diagnostic result as a category of plant condition such as normal, anomaly 1,
anomaly 2, and so on with a confidence value. Thus, the trust value is predetermined for eachcategory of plant condition that a sub-system identifies.
For the integration of diagnostic results by diagnostic sub-systems, an evaluation value is defined
as:
(1)
where
,
, and
are evaluation value for plant condition , confidence value of diagnostic
sub-systemfor plant condition , and trust value for the diagnostic result of diagnostic
sub-system
in its diagnostic result of plant condition . The integrated diagnosis result is given to
be a plant condition whose evaluation value is highest.
It is considered as reasonable that the trust value is determined by the diagnostic performance of51

A. Gofuku et al. / Integration Technique of Diagnostic Resultsin Hybrid-type Agent Systema diagnostic sub-system in the past if the confidence value of the diagnostic sub-system is given in an
is calculated byunchangeable way. From this consideration, the trust value(2)

at diagnosis roundandare the confidence value of diagnostic sub-system
giving correct diagnostic result for plant condition
and the confidence value at diagnosis roundgiving wrong diagnostic result. The meaning of the trust value defined by Eq. (2) is the ratio between
the total confidence value of past diagnoses giving correct results by a diagnostic sub-system and thetotal confidence value of its past diagnoses. In an online system application, the trust value is easy towhere

update by keeping the values of the summations ofand.

3.2 Applicability Evaluation
3.2.1 Purposes of applicability evaluation
In order to examine the applicability of the technique to determine trust value of each diagnostic
sub-system, two evaluation simulations are conducted. In both simulations, diagnoses by sub-systems
are assumed to be random processes under predefined probabilities. The first simulation investigates
the relations among diagnostic performance of a sub-system, confidence values given by the
sub-system, and determined trust value by the proposed technique. The second simulation isconducted to investigate the combination patterns of sub-systems such that the proposed hybrid
diagnostic framework gives better diagnostic results than the cases of straightforward integration ofdiagnostic results.
3.2.2 Relations among diagnostic performance of sub-systems, confidence values, and trust values
The conditions of the first simulation are as follows. There are two sub-systems and four plantconditions. The happening probabilities of the plant conditions are 0.6, 0.2, 0.1 and 0.1. The
diagnostic performances of the two sub-systems are given as shown in Table 1.
When a sub-system gives its diagnostic result at random and the plant condition is also given at
random, the probability of correct identificationof sub-systemfor plant conditionisgiven by
(3)

where

and

are happening probability of plant condition

and identification probability of

sub-system
as plant condition
when the plant condition happened is plant condition
are given byrespectively. On the other hand, the theoretical values of trust values,(4)

where C ai andare averages of the confidence values

and, respectively when theconfidence values of a sub-system change depending on diagnosis rounds and the probability density
functions to determine the confidence values are symmetry around their averages. The theoreticalvalues of correct identification probabilities and trust values for the sub-systems of Table 1 arecalculated as shown in Table 2.

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A. Gofuku et al. / Integration Technique of Diagnostic Resultsin Hybrid-type Agent System

As seen from Table 1, the results of plant condition 4 by the sub-systems are always correct
although sub-system B has high probability of miss-identification to other plant conditions for plantcondition 4. This is because both sub-systems do not diagnose plant condition as plant condition 4 at aplant condition different from plant condition 4. Therefore, the trust values of both sub-systems forplant condition 4 are 1.000. As seen from Table 2, the trust values for sub-system A are the same as
the probabilities of correct identification for plant conditions. This is obvious from Eq. (4) because theconfidence value is the same independent on the identified plant condition. The trust values ofsub-system B are higher than the probabilities of correct identification except for plant condition 4.
This is because that sub-system B gives high confidence values for the plant conditions of highdiagnostic performance and middle and low confidence values for the plant conditions of low
diagnostic performance. The theoretical values of trust values for the random process case suggest theadequacy of the determination of trust values by Eq. (2).
Table 1 Diagnostic performances of sub-systems in simulation 1
Subsystem

Condition
1
2
3
4
1
2
3
4

A

B

Identification probability [%]
1
2
3
4
95
3
2
0
0
100
0
0
3
2
95
0
5
0
5
90
98
2
0
0
3
95
2
0
10
20
70
0
25
0
25
50

Confidence value for
identified condition
0.9
0.9
0.75
0.45

Table 2 Theoretical trust values of sub-systems in simulation 1
Subsystem
A

B

Condition
1
2
3
4
1
2
3
4

Probability of correct
identification [%]
98.6
90.9
84.8
100
93.5
85.6
70.7
100

Trust value
0.986
0.909
0.848
1.000
0.957
0.869
0.780
1.000

3.2.3 Combination patterns of sub-systems giving high diagnostic performance
In the second simulation, ten sub-systems whose diagnostic performances are specified as shownin Table 3 are considered, whereandare confidence values for identified condition. The
happening probabilities of plant conditions are the same as those of simulation 1: 0.6, 0.2, 0.1 and 0.1
for plant condition 1, 2, 3 and 4. In this simulation, the combination patterns of sub-systems that give
better diagnostic performance than the combination cases of straightforward integration of diagnostic
and. The confidence value
isresults are examined by changing the confidence values
is changed fromset to be 0.80 or 0.90 in the simulation. On the other hand, the confidence value
0.30 to 0.60 by 0.10.
As shown in Table 3, the sub-systems 1 to 3 exhibit high diagnostic performances. On the other
hand, the diagnostic accuracies of sub-systems 4 to 6 are low. The sub-systems 7 to 10 exhibit high or
low diagnostic performances depending on happening plant condition.
Two types of straightforward integration techniques are considered in the simulation. The one isthe decision by majority. The technique is often used at decision making in human society. The otheris to give an integrated diagnostic result by considering only confidence values of sub-systems. Thiscase corresponds to the one that all trust values in Eq. (1) are set to 1.0.

53

A. Gofuku et al. / Integration Technique of Diagnostic Resultsin Hybrid-type Agent System
Table 3 Diagnostic performances of sub-systems in simulation 2
Subsystem
1

2

3

4

5

6

7

8

9

10

Condition
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4

Identification probability [%]
1
2
3
4
98
1
1
0
2
95
2
1
1
1
97
1
1
1
3
95
98
1
1
0
2
95
2
1
1
1
97
1
1
1
3
95
98
1
1
0
2
95
2
1
1
1
97
1
1
1
3
95
60
20
10
10
30
50
10
10
20
20
55
5
30
10
10
50
60
20
10
10
30
50
10
10
20
20
55
5
30
10
10
50
60
20
10
10
30
50
10
10
20
20
55
5
30
10
10
50
98
1
1
0
20
55
20
5
2
2
95
1
30
10
10
50
98
1
1
0
20
55
20
5
2
2
95
1
30
10
10
50
98
1
1
0
20
55
20
5
2
2
95
1
30
10
10
50
98
1
1
0
20
55
20
5
2
2
95
1
30
10
10
50

Confidence value for
identified condition
CH
CH
CH
CH
CL
CL
CL
CL
CH
CL
CH
CL
CH
CH
CH
CH
CL
CL
CL
CL
CH
CL
CH
CL
CH
CH
CH
CH
CL
CL
CL
CL
CH
CL
CH
CL
CL
CH
CL
CH

As examples of simulation results, Table 4 shows the total number of combination cases ofsub-systems that give better diagnostic results by the integration technique than the compared
integration technique at the confidence valueof 0.80. The total combination cases of four
sub-systems are 715 because combination cases containing same sub-systems are permitted. As seenfrom the table, the proposed technique tends to give better integration results than the straightforward
for plant conditions. It is
integration techniques according to the increase of the confidence valueinteresting that the decision by majority gives slightly better integration results than the proposed.
technique at a low
In order to confirm the tendency of combination patterns that the proposed technique gives worse
integrated diagnostic results, Table 5 summarizes the worst five combination patterns in the case of. In the table, the combination of sub-system is shown as “[ ,,,]”, where,,andare identification numbers of sub-systems. The numbers in the second line ofeach cell are correct identification probability by the proposed technique and that by the compared54

A. Gofuku et al. / Integration Technique of Diagnostic Resultsin Hybrid-type Agent Systemstraightforward integration technique. The table suggests two tendencies. The one is the degradationof diagnostic performance by the proposed technique is not high even in the worst case. The other isthat the worse cases include the sub-systems that give low confidence values at high diagnostic
performance and/or give high confidence values at low diagnostic performance. Sub-systems 2 and 8give low confidence values at high diagnostic performance. Sub-systems 6 and 7 tend to give highconfidence values at low diagnostic performance. Sub-system 9 gives low confidence values for plantconditions 2 and 4 that are diagnosed at high correct identification probability and gives highconfidence values for plant condition 1 that is diagnosed at not-high correct identification probability.
The similar tendency is seen in the case ofas the case shown in Table 4. The
simulation results show the applicability of the proposed technique if each diagnostic sub-systemgives high confidence values for the conditions diagnosed at high diagnostic performance.
Table 4 Comparison results of diagnostic performances among three integration techniquesby changing lower confidence values for plant conditions
Comparison
case
1
2

Integration technique
Proposed technique
Decision by majority
Proposed technique
Considering only confidence value

CL (Lower confidence values for plantconditions)
0.30
0.40
0.50
0.60
345
417
517
604
370
298
198
111
542
629
594
640
173
86
121
75

Table 5 Worst five combinations of diagnostic agents giving lower performancesby the proposed technique
Comparison case

Order fromworst case

0.30
[2, 4, 4, 8]
0.82, 0.91

0.40
[2, 6, 6, 7]
0.86, 0.91

0.50
[6, 6, 6, 8]
0.77, 0.80

0.60
[6, 7, 7, 9]
0.88, 0.89

2

[2, 2, 6, 7]
0.89, 0.97

[3, 6, 6, 7]
0.86, 0.91

[6, 6, 6, 10]0.78, 0.80

[6, 8, 9, 9]
0.88, 0.89

3[2, 2, 4, 4]
0.87, 0.95

[2, 5, 6, 7]
0.87, 0.91

[4, 6, 6, 8]
0.84, 0.86

[6, 9, 9, 9]
0.88, 0.89

4

[2, 4, 4, 10]0.83, 0.91

[2, 6, 6, 10]0.87, 0.91

[6, 8, 8, 9]
0.87, 0.89

[3, 4, 10, 10]0.92, 0.93

5[2, 3, 6, 7]
0.90, 0.97

[6, 6, 6, 8]
0.76, 0.80

[4, 4, 4, 10]0.78, 0.80

[6, 6, 6, 10]0.79, 0.80

1[5, 6, 6, 6]
0.68, 0.71

[3, 5, 6, 7]
0.87, 0.90

[5, 6, 7, 9]
0.86, 0.89

[5, 6, 7, 9]
0.86, 0.89

2[3, 5, 6, 7]
0.85, 0.88

[5, 6, 7, 9]
0.85, 0.87

[5, 5, 9, 10]0.87, 0.89

[5, 5, 5, 7]
0.83, 0.84

3[5, 6, 9, 9]0.86, 0.88

[5, 5, 7, 9]
0.85, 0.87

[5, 5, 7, 9]
0.87, 0.89

[4, 5, 7, 7]
0.87, 0.88

4[5, 5, 5, 9]
0.84, 0.86

[3, 6, 6, 7]
0.86, 0.88

[3, 5, 6, 7]
0.91, 0.92

[5, 6, 7, 7]
0.87, 0.88

5[3, 4, 5, 7]
0.88, 0.90

[5, 6, 9, 9]0.87, 0.88

[2, 5, 5, 7]
0.91, 0.92

[5, 5, 7, 8]
0.87, 0.88

1

1 (Proposed
technique anddecision by majority)

2 (Proposed
technique andtechnique only
considering
confidence value)

4.

CL (Lower confidence values for plant conditions)

Conclusion

In order to increase the capability of a diagnostic system, an agent system configuration is apromising solution. This paper describes a hybrid-type diagnostic agent system for “Monju” andproposes a technique for an important topic of a diagnostic agent system on how to integrate the55

A. Gofuku et al. / Integration Technique of Diagnostic Resultsin Hybrid-type Agent System

diagnostic results of agents. The paper also discusses the applicability of the proposed integration
technique of diagnostic results reported from sub-systems by two simulations assuming that eachdiagnostic sub-system diagnoses plant condition by random processes.
The simulation results comparing two straightforward integration techniques suggest theapplicability of the proposed integration technique. The simulation study also suggests some
applicable conditions on the diagnostic performances of sub-systems for the proposed technique.
Future works for the proposed integration technique include an extension of the calculation oftrust values for extending the applicability and applicability evaluations under the conditions of real
diagnostic cases rather than random process cases. In addition, applications of the developed
diagnostic agent system not only to “Monju” but also to other industrial plants are also future works.

Acknowledgement
The present study includes the results of “R&D of an anomaly detection system by the advanced
hybrid processing of plant data of Monju” entrusted to Okayama University by the Ministry of
Education, Culture, Sports, Science and Technology of Japan (MEXT). The authors would like toexpress their thanks to MEXT.

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