Simprebal Real Time Monitoring

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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

[email protected], [email protected]

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

978-1-4244-6850-8/10/$26.00 ©2010 IEEE



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].

2



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.

3



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.

4



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)

6



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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