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SIMPREBAL: An Expert System for Real-Time Fault Diagnosis of
Hydrogenerators Machinery
Edgar J. Amaya and Alberto J. Alvares
Department of Mechanical Engineering and Mechatronics
University of Brasilia
Campus Universitario Darcy Ribeiro
CEP 70910-900, Brasilia, DF, Brazil
eamaya@unb.br, alvares@alvarestech.com
Abstract
This paper proposes an expert system to aid plant maintainers and operators personnel for solving hydro-
electric equipments troubleshootings. The expert sys-
tem was implemented into intelligent maintenance sys-
tem called SIMPREBAL (Predictive Maintenance System
of Balbina). The SIMPREBAL knowledge base, the ar-
chitecture and the inference machine are presented in de-
tail. The knowledge base is based on experts empirical
knowledge, work orders, manuals, technical documents
and operation procedures. The predictive maintenance
system architecture is based on the OSA-CBM framework
that has seven layers. The software application has been
successfully implemented in client-server computational
framework. The data acquisition and intelligent process-
ing tasks were develop in the server side and the user in-
terface in the client side. The intelligent processing task is
an expert system that use JESS inference machine. During
two years, the SIMPREBAL has been used for monitoring
and diagnosing hydrogenerators machinery malfunctions.
The industrial application of the SIMPREBAL proved its
high reliability and accuracy. Finally, satisfactory fault
diagnostics have been verified using maintenance indi-
cators before and after the SIMPREBAL installation in
the hydroelectric power plant. These valuable results are
been used in the decision support layer to pre-schedule
maintenance work, reduce inventory costs for spare parts
and minimize the risk of catastrophic failure.
1. Introduction
The supervisory system of HPP (Hydroelectric Power
Plant) continuously monitor different features of several
equipments: bearing, heat exchanger, generators, motors,
pumps, turbines, etc. The equipment features are related
to a set of variables that define the current condition. The
evaluation of these variables gives some guidelines to op-
erators to detect abnormal situations in hydroelectric gen-
erator machinery. However, only a small set of variables
can be observed and analyzed, to give useful information
to the operators. On the other hand, automatic monitoringsystems are, in general, able to analyze all the input values
and generate alerts, alarms and trip signals. The monitor-
ing systems warn when the numerical value of a variable
is outside the range set by expert engineers.
Therefore, it is a great necessity for developing of per-
sonnel supporting tools based on information technology
(IT) for hydroelectric operations (i.e., repair and mainte-
nance, troubleshooting, emergency planning, etc.). The
additional software support especially towards mainte-
nance process of hydrogenerator machinery system ([6],
[9], [22] and [15]) is able to reduce operator’s workload,
fatigue, and cognitive errors. Furthermore, the develop-
ment of trouble diagnosis modules within system mainte-
nance software provides invaluable contributions for the
personnel to reduce the response time for failures.
Recommended maintenance actions is when very few
corrective maintenance actions are undertaken and when
as little preventive maintenance as possible is performed
[8]. Continuous maintenance would lead to decreased
availability and high direct and indirect maintenance costs
in terms of lost production, rework, scrap, labor, spare
parts, fines for late orders, and lost orders due to unsat-
isfied customers [19]. This demands great skills in plan-
ning proper Condition Based Maintenance (CBM). CBM
is explained as "maintenance actions based on actual con-dition (objective evidence of need) obtained from in-situ,
non-invasive tests, operating and condition measurement"
[18]. Based on RCM (Reliable Centered Maintenance)
and OSA-CBM (Open System Architecture for Condition
Based Maintenance) framework model, the SIMPREBAL
UML (Unified Modeling Language) was proposed [4].
The developed of the OSA-CBM layes applied to predic-
tive maintenace system is detailed [3].
The application of AI techniques, in particular ES (Ex-
pert Systems), represents a relatively new programming
approach for effective fault diagnosis and trouble shoot-
ing in machines of industrial plants. AI is being used in
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maintenance programs of industrial plants from common
malfunctions to rarely emergencies [5]. Usually, it is dif-
ficult for operators and maintainer to analyze immediately
the cause of abnormal situation and present a suggestion
for maintenance action. Therefore it is critically important
for a safe and steady operation of HPP to monitor health
equipments in real time, to diagnose faults, and to analyzetheir cause promptly. The model based diagnosis would
facilitate the analysis of abnormal situations. However,
such models are difficult to construct due to the complex-
ity of the equipments in hydroelectric generator systems.
Therefore the use of ES (Expert Systems) is a feasible
alternative enabling a time-efficient analysis of abnormal
situations.
Fault diagnosis that uses AI has been researched by [1],
[7], [20] and [23]. Reports on ES for fault diagnostics
have also been frequently published in the last decade by
[2], [16] and [12]. An expert diagnosis system are ca-
pable of utilizing human knowledge and tracing the com-
plex relations between various signals and possible resultsas experts do, successful diagnosis applications based on
knowledge processing have often been reported.
In this paper, is described the SIMPREBAL develop-
ment, implementation, functions and advantages of the
predictive maintenance system applied to hydroelectric
equipments. Moreover is developed and implemented
of knowledge-base approach system using expert system
rules for fault diagnosis applied to the HPP of Balbina.
Database Data Acquisition
Prognostic
Signal Processing
Decision Support
Diagnostic
Rules
Condition Monitor P r e s e n t a t i o n
Variables
Quality signal
Alert, Alarm and Trip
signal
Potencial or Functional
failures diagnosis
MTTF, MTTD, Reliability
and Availability
Work order suggestions
FMEA
Failure’s frequency
and duration
Failure’s diagnosis
OPC Server
Figure 1. SIMPREBAL architecture.
2. SIMPREBAL Architecture
The SIMPREBAL implementation has a necessity of
integrate a wide variety of software and hardware com-
ponents to develop a diagnosis system for the hydroelec-
tric equipments show in the Table 1. OSA-CBM simpli-
fies this process by specifying a standard architecture for
implementing CBM systems. This architecture has seven
layers as show in the Fig. 1: data acquisition (sensors and
databases), signal processing, condition monitoring, diag-
nostics, prognostics, decision support, and presentation.
The standard describes the flow information between theseven layers. The application of the OSA-CBM frame-
work is perform by [13], [14] and [3] as reference in their
publications.
3. Implementation of the Expert System
In the design of the SIMPREBAL knowledge base is
used the framework show in the Fig. 2, as detailed by [1]
to construct this architecture the following items need to
be considered carefully:
a) The knowledge base should be well-structured. It
makes the representation of domain knowledge easier and
convenient for management the knowledge base;b) The inference machine should able in Java environ-
ment in order to integrate the maintenance system devel-
oped in Java;
c) The system architecture should be developed based
open standard and client-server framework.
With these requirements in mind, the proposed system
is implemented as follows.
FMEA
OTI
MTI
AMP
WO
Domain
Experts
Knowledge
Engineer
Explanation
Instrument
Inference
Machine
Knowledge
Acquisition and
Management
Operators
Knowledge
Base
Knowledge
Verification
Database
Data
Acquisition
Figure 2. Expert system framework.
3.1. Knowledge acquisitionThe performance of the proposed system depends on
quantity and quality of knowledge contained in the KB.
The main knowledge source of the SIMPREBAL is expe-
rience of domain specialists. Knowledge rules in the pro-
posed system are obtained from the experienced experts
and operators of the hydroelectric plant. The knowledge-
base was built based on interviews with experienced main-
tenance engineers and technicians, work orders, manuals,
technical documentations and operations procedures.
The knowledge acquisition process includes extracting,
transforming and validating expertise from different in-
formation sources for developing a knowledge base [11].
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Knowledge acquisition has always been the bottleneck in
developing expert systems, and tends to be very long and
time consuming process [17].
3.2. Knowledge representation
An hydroelectric power plant is characterized by many
variables. However, the experience accumulated throughyears by domain experts allows for the representation of
behaviour of hydroelectric plants not only by the mathe-
matical models but also by a set of production rules. Dur-
ing the interview process, conversations were recorded in
detail and then converted in FMEA (Failure Mode and
Effect Analysis) worksheet (Tab. 2). The knowledge
consists of concepts, objects, relationships and inference
rules.
An expert knowledge represented by statements in a
natural language, by proposition or predicates. According
to [21] the problem-solving knowledge of an expert can
also be represented in terms of IF < Situation > THEN <
Action > rule. The general framework that is being used isthe rule-based ES. In such systems, expertise of an expert
are encoded in the form of inference rules of the form: IF
S1, S2, S3,..., Sn then A. Where Si is a situation and A
is the action for these situations. The set of rules is the
knowledge-base of the rule-based ES.
3.3. The inference machine
Another important component of a KBS is inference
engine that uses the given knowledge base to solve a prob-
lem. Besides of apply procedure tests and maintenance
manuals, the experts maintenance engineers use their in-
tuition or heuristics and understanding of how the systemworks to solve the problems. Based on years of experi-
ence, maintenance engineering develops an intuitive un-
derstanding of how the system will behave when a certain
subsystem fails.
During the preliminary system design process, several
system requirements were identified to achieve the objec-
tives of the system. Among them, programming language,
friendliness of the user interface, and ability to connect
with OPC (OLE - Object Linking and Embedding - for
Process Control) servers, database and ES shell were re-
garded as necessary for the success of the SIMPREBAL.
Such system requirements or specifications determined
the choice of the software and hardware platform used todevelop the project. The ES was developed using JESS
(Java Expert System Shell) as a rule engine. JESS uses
an enhanced version of the Rete algorithm to process the
ES rules. Rete is a very efficient mechanism for solving
the difficult many-to-many matching problem [10]. The
SIMPREBAL was developed in Java in client-server ar-
chitecture, integrated with OPC and databases servers.
3.4. Implementation
There are five HGU (Hydroelectric Generator Unit) in
the HPP of Balbina. However, all HGU are very similar,
if not identical. The knowledge engineering phase of this
research involved the identification of the different main
components and corresponding failure modes for the three
systems of the HGU (Tab. 1), electric generator, bearing
system and hydraulic turbine. These systems have equip-
ments associates, the instruments and the operation limits,
some of these instruments are described in the Table 1.
Through extensive research, relevant data were col-lected of all the possible failure modes (Tab. 2) that may
prevent the selected pieces of equipment from operating
properly. Such data were recorded on reliability cen-
tered maintenance analysis FMEA sheets. An example
of FMEA is illustrated in the Table 2. As noted in the Ta-
ble 2, the sheets contain information about the machine,
equipments and associated failure modes.
4. Application in hidrogenerators machinery
The first step in the development of the system was to
identify all the systems and equipments in each of the five
HGU. The list of the assets of one HGU is show in theTable 1. The assets are divided in three systems: elec-
tric generator, bearing system and hydraulic turbine. Each
system has incorporated foundation fieldbus transmitters
in their equipments in order to monitor the process vari-
ables.
The transmitters are connected to an low speed H1 net-
work of 31.25 Kbps. To communicate the information
from the H1 network to the HSE (High Speed Ethernet)
network of 100 Mbps is used a DFI (Distributed Field In-
terface) as a bridge. The instruments in each HGU are
organized by DFI devices, through of the DFIs, the in-
struments are capable to send their information to an OPC
server.
Figure 3. Operation zones.
4.1. System inputs
SIMPREBAL acquires information through the dataacquisition layer, online and historic variables from OPC
server and database respectively. Data from the OPC
server is collected using the JOPCClient driver that is
implemented in Java. The database is accessed using
JDBC (Java Database Connectivity) and is used to stor-
age faults/failures, variables related to faults/failures and
decisions or maintenance action recommendations. Also,
the database includes maintenance and operation person-
nel information. The system is foresee to communicate
with another databases to integrate in the future with ERP
(Enterprise Resource Planning) system, MES (Manufac-
turing Execution Systems) and others systems.
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Table 1. Systems and equipments.System Indicators Code Unit Normal Alert Alarm Trip
Coil stator Temperature 49G1A oC <85 100-130 130-155 >155
Core stator Temperature 49G2A oC <80 100-130
Electric Cold air Heat exchanger 26GAF1 oC <44 44-45 >45
generator Hot air temperature 26GAQ1 oC <65 70-75 75-85 >85
Blind bus system pressure 63PBB mbar <15 18-20 >20Coil excitation temperature 49TEA1 oC <100 105-110 110-130 >130
Metal Inferior Guide Temperature 38MK1 oC <60 70-75 75-85 >85
Oil Inferior Guide temperature 38MJ1 oC <55 60-70 >70
Oil tank pressure 63MS bar >0.35 0.35-0.25 0.25-0.06 <0.06
Metal superior guide temp. 38GMM1 oC <65 70-80 80-85 >85
Bearing Oil superior guide temp. 38GMO1 oC <60 63-70 70-75 >75
system Metal Inter. Guide temp. 38MG1 oC <60 70-75 75-85 >85
Metal Support Guide Temp. 38ME1 oC <75 80-85 85-90 >90
Oil Combined Guide temp. 38MI oC <55 65-75 >75
Oil flow 80GMO l/min >32 30-28 28-19 <19
Water flow 80GMA l/min >70 65-60 60-40 <40
Gasket water flow 80MP l/min <75 80-90 <90Hydraulic Gasket water pressure 63MQ bar <3 3.0-2.5 2.5-1 <1
Turbine Cooling water temperature 26AR oC <30 32-35 >35
Oil regulation temperature 26LK oC <45 46-48 48-55 >55
4.2. System processing
In this section is described the methods adopted in
the information process. The knowledge base storage in
rule files will be process in the signal processing, condi-
tion monitor and health assessment layers. These rules
were implemented using the CLIP language and pro-
cessed through Rete inference machine of the JESS.
Signal processing - In this layer the system verifythe connectivity of the SIMPREBAL with the DFI, OPC
server and database. The connectivity test with the DFI
and OPC server is done using the PING command, this
command is used to verify the IP (Internet Protocol) con-
nectivity, sending messages and waiting for the response
of the ICMP (Internet Control Message Protocol). The
variable value change is tested in periodical cycle, if
the variable values do not change means that the system
stopped. In this layer is processed information about the
OPC and fieldbus signal quality. The rules of this layer are
show in the Table 3. The rules detect the signal quality in
the OPC server and in the foundation fieldbus instrument.
Condition Monitor - This layer receives as information
the variable value. This value is compared with the val-
ues established previously. The rules showed in the Table
3 verify the relationship among variables values and ma-
chine fixed thresholds (Table 1). The output of this layer
is the equipment operation state. There are four thresholds
that characterize the condition monitor.
NORMAL: The values are inside of the normal equip-
ment operation.
ALERT: In this state, the monitored values show an
incipient equipment fault. This threshold was established
to find any alteration out of normal condition.
ALARM: This state indicates the risk of the equipment
monitored to achieve a failure stage. When is arrived to
this state is require to take preventive actions in order to
avoid unexpected stops.
TRIP: Values in this state are considered inacceptable
in the equipment operation. When this state is achieved,
as a security measure, the equipments are turned off.
Diagnostics - This layer uses a FMEA tool (Table 2)to find relations between the monitored variables and the
equipment faults. The operation and maintenance per-
sonnel contributed to indentify the maintenance problems
in the HPP. Also were used documents like TOI (Tech-
nical Operation Instructions), TMI (Technical Mainte-
nance Instructions) and MPA (Maintenance Planning Au-
tonomous). Other documents used are maintenance work
orders generated in the last years, in this case was ana-
lyzed in detail the failures occurrence and the maintenance
procedure realized for each failure.
All the information collected was used to develop a
complete FMEA (i.e. see the Table 2). The following
problems are identified: oil contamination, heat exchangeroverheats, oil leaks, coil overheats, mechanical looseness,
bearing problems, etc. The rules developed from FMEA
are about failure diagnostic of the condition monitor.
TTF1 TTFnTTF3TTF2
. . .
TTR1 TTR2
TTFp MTTF
TTRn-1 TTRn
Figure 4. Time to failure prediction.
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Table 3. Rules structureLayers Rules
- IF (quality == 3)
THEN (COM-GOOD)
Signal ELSE (COM-BAD)
Processing - IF (COM-GOOD and
(status == 2 or status == 3))THEN (signal-GOOD)
ELSE (signal-BAD)
- IF (signal-GOOD and value ≤ 105)
THEN (condition-NORMAL)
- IF (signal-GOOD and
value > 105 and value ≤ 130)
Condition THEN (condition-HIGH)
Monitor - IF (signal-GOOD and
value > 130 and value ≤ 155)
THEN (condition-ALARM)
- IF (signal-GOOD and value 155)
THEN (condition-TRIP)
- IF (condition-HIGH)
THEN (code-G149H and color-YELOW
and email-OPERATORS)
- IF (condition-ALARM)
Diagnostic THEN (code-G149A and color-RED and
email-ELECTRICIANS)
- IF (condition-TRIP)
THEN (code-G149T and color-RED and
email-ENGINEERS)
4.4. Results
Where there are specific maintainability requirements
or goals, which must be obtained by a system, then there
is a need to determine the system’s quantitative maintain-
ability characteristics. This could be represented in terms
of a percentage of success, MTTR (Mean Time To Repair)
and MTBF.
In the past the analysis was made by the operational
and maintenance personnel, but at now the SIMPREBAL
generate suggestion of decisions and the operator decide
if the suggestion will be adopted or not. The system was
installed in march 2008, considered an analysis period of
500 days. The ES detects failures presents in the KB and
an operator need to check if the ES detection is true. Theinformation introduced by the operator is used to calculate
the success indicator. The SIMPREBAL success to detect
fault and failure in the HPP is calculated through of the
Eq. (3). The trend of the percentage of success is shown
in the Fig. 6.
A disadvantage of ES is that fault and failure detection
is performed considering only the rules store in the KB.
New failure modes need to be store in the KB in order to
be detected by the SIMPREBAL. Predictive maintenance
system based on ES needs to be update as soon as new
failures appear. With this requirement the SIMPREBAL
can be more accurate detecting equipments failures.
Variable Inspection Window
Hierarchic Tree
Decision Support
Fault Diagnosis
Historic Tendency Chart
Figure 5. The SIMPREBAL user interface.
Jun08 18,7
Jul08 25,7
Aug08 35,9
Sep08 45,7
Oct08 43,6
Nov08 49,6
Dec08 55,6
Jan09 54
Feb09 56,7
Mar09 58,8
Apr09 61,5
May09 66,4
Jun09 53,4
Jul09 51,4
Aug09 61,3
Sep09 62,1
Oct09 65,4
22,3 24,518,7
25,7
35,9
45,7 43,649,6
55,6 5456,7 58,8 61,5
66,4
53,4 51,4
61,3 62,165,4
0
10
20
30
40
50
60
70
%
Figure 6. ES diagnosis success trend.
%Success =N◦ Failures detected
N◦ failures(3)
4495 83
680 650
0
200
400
600
800
2005 2006 2007 2008 2009
MTBF-Mean Time Between Failure
30
1518
8,4 6,5
0
10
20
30
40
2 005 2 006 2 007 2 008 2 009
MTTR-Mean Time To Repair
Figure 7. Key performance indicators.
The SIMPREBAL key performance indicators are
shown in the Fig. 7. The MTTR of the five HGU of the
HPP in the last years is calculated based on the Eq. (4).Furthermore, the MTBF indicator is calculated using the
Eq. (5). The MTTR decrease after the SIMPREBAL in-
stallation and the MTBF increase, the reason is that some
fault and failure can be detected more early. The mainte-
nance decision is taken quickly by the operators, reading
the ES suggestions offered in natural language.
MTTR =Total time of the component repair
N◦ of repairs(4)
MTBF =Operational period
N◦ failures(5)
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5 Conclusion
An expert system of real-time fault diagnosis for the
HPP of Balbina equipments has been developed in this pa-
per. Java environment, Apache, PHP and MySQL Server
have been used in the developing the proposed system.
SIMPREBAL has been successfully implemented usingthe OSA-CBM framework where is possible to develop in
modular way, processing distributed and easily scalable.
The benefits to the organizations include reduction in ma-
chine down time, reduction in skill level for maintenance
activities and speedy response.
This study dealt with the design and development of
a knowledge-based diagnosis system, the inference ma-
chine, and the knowledge maintenance that determine the
optimal structure of the system. The reliability of diagno-
sis is highly dependent on the accuracy information from
online and historic sources. The strategies are proven to
be effective in real applications. SIMPREBAL helps the
operators to eliminate potential faults of the equipments.If a fault symptom appears, the corresponding fault causes
can be identified by the proposed system and actions sug-
gested to the operators.
Based on the SIMPREBAL information generated is
analyzed the percentage of success, MTTR and MTBF as
key performance indicators. This indicators shows that
SIMPREBAL shows good performance in the percentage
of success, the MTTR was decreased and the MTBF as
increased after the IMS installation. However, at present
the system is not able to perform self-learning. There-
fore, in the future work, we are going to not only collect
more empirical knowledge from the experts, but also ap-
ply decision tree algorithm to learn rules from the histor-
ical data and develop the prognostic layer based on the
historical data of the faults/failures and its associates vari-
ables stored in the SIMPREBAL database, this layer will
be capable to calculate the RUL (Remaining Useful Life)
of the hydroelectric equipments.
6 Acknowledgment
We acknowledge the support of the Eletronorte and
Manaus Energia provided by the Research and Devel-
opment Program under contract number 4500052325,
project number 128 "Modernization of Processes Automa-tion Area of the Hydroelectric power stations of Balbina
and Samuel", that has as a technical responsible Prof.
Alberto Jose Alvares of the UnB, the engineer Antonio
Araujo from Eletronorte played a significant role in this
project.
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