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LABORATORY OF THERMAL TURBOMACHINES NATIONAL TECHNICAL UNIVERSITY OF ATHENS

Combining Advanced Data Analysis Methods For The Constitution Of An Integrated Gas Turbine Condition Monitoring And Diagnostic System

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COMBINING ADVANCED DATA ANALYSIS METHODS

FOR THE CONSTITUTION OF AN INTEGRATED GAS TURBINE CONDITION MONITORING AND

DIAGNOSTIC SYSTEM

A.Tsalavoutas, N. Aretakis, A.Stamatis ,

K. Mathioudakis ,

Laboratory of Thermal Turbomachines National Technical University of Athens



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• Desirable features and basic functions of a System

• Features of individual system functions § Data acquisition and management § Thermodynamic analysis § EGT monitoring § Vibration monitoring

• Application test-case: Industrial gas turbine

• Summary – Conclusions



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Desirable Features of Monitoring System

• Automated, Integrated o Performing all actions from data collection to derivation of diagnostic

decisions.

• Provide information with high confidence o Derivation of the same conclusion by different methods is a very useful feature.

• User friendly o Used by non-specialized personnel, output clear enough to need very little or no

interpretation.

• Wide coverage of detectable faults o Allow additions of new faults, which have not been initially included

• Prognostic capabilities o Helps ensuring that spares are available and that outages be minimized

• “Robust” o Not susceptible to noise or faulty input information

• Employ as few instruments as possible

• Modular and flexible o Open architecture to be adapted to operator’s needs



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The Main Function to be Implemented in a Monitoring System

• Measurement data acquisition.

• Data evaluation: discard unreliable readings and possibly detect sensor faults.

• Data processing: to derive diagnostic information

• Diagnostic inference: what is the nature, the location and the severity of a malfunction present

• Data management: keep historical data records for long term monitoring, without too much storage



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Functional Diagram Of The Gas Turbine

Condition Monitoring System.

Measurements Data Base

On Line Acquisition

Measurements Validation

Thermodynamic Analysis

EGT Monitoring Vibration Analysis

Inference Engine Rules

Engine Condition Assessment



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Main Interface Of The Condition Monitoring System.



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Data Acquisition

Inspection of Measured Data

Example of on-line acquisition screen.



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Measurement Validation

Levels of measurements validation checks Ø Check instrument and physical ranges Ø Check cross-relation of quantities Ø Evaluation through thermodynamic analysis

Example: Compressor delivery pressure at different operating points.

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0Engine Load (MW)

8.0

8.5

9.0

9.5

10.0

10.5

11.0

11.5

12.0

CD

P (b

ar)

Baseline

Variation Limits

Faulty CDP

Accepted CDP



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Data Acquisition Module

Example: Inspection of measurement time evolution



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• Process measurement data for component condition assessment

Ø Derive component oriented information,

free from influence of operating point or ambient conditions Ø Easily interpretable, unified treatment

• Study different Operation Scenarios

Ø Simulation Key Concept: Modification Factors: refX

XMF =



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Example: Component Condition Indices.



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Example: Time evolution of compressor capacity.



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Example: Engine simulation screen.



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EGT Monitoring

Attention to Most Critical Parts: Hot End Quantities Observed • Global EGT Pattern Properties

Ø Spread

Ø Deviation from average

• Reduced Temperature Profiles



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EGT Monitoring

Example of a display screen.



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Vibration Analysis

Identify mainly mechanical alterations

Example display from Vibration analysis

(a) (b)

(c) (d)



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The Test Case of an Industrial Gas Turbine

Measurement Set-up Standard Engine Measurements

Ø Generator Load and Speed. Ø Vibration Overall Level. Ø Compressor Inlet Temperature. Ø Compressor Delivery Pressure and Temperature. Ø Cooling Air Temperature.

Ø Exhaust Gas Temperature (eight thermocouples).

Additional Measurements

Ø Ambient Pressure and Relative Humidity. Ø Compressor Inlet Total and Static Pressure. Ø Turbine Outlet Static Pressure. Ø Engine Outlet Static Pressure (backpressure).



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Time Evolution Of Inlet Filters Fouling Coefficient

0 10 20 30 40 50 60 70Days

60.0

65.0

70.0

75.0

80.0

DPf

il

Filter fouling coefficient:

insint

intamb

PPPP

DP,,

,

−−

=



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Evolution of Health Indices

‘Healthy’ operation.

Co mp resso r F low Cap aci ty Heal th In dex

-4

-3

-2

-1

0

1

2

3

4

Perc

enta

ge D

evia

tion

(%)

Compressor Efficiency Health Index

-4

-3

-2

-1

0

1

2

3

4

Perc

enta

ge D

evia

tion

(%)

T urbin e Flow C ap acity H ealth Index

-4

-3

-2

-1

0

1

2

3

4

Perc

enta

ge D

evia

tion

(%)

Turb ine Effic iency H e alth Ind ex

-4

-3

-2

-1

0

1

2

3

4

0 10 2 0 30 4 0 50 60 7 0Da ys

Perc

enta

ge D

evia

tion

(%)



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Detection of Sensor Error by Simple Limits Checking

Time evolution of exhaust gas temperature registered by thermocouple 6.

0 40 80 120 160 200 240Time (min.)

0

200

400

600

800

1000

1200

1400

EG

T, T

h. 6

(Cel

sius

)

Limits

Measurements



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Detection of Sensor Error by Checking Interrelations

Fluctuation of CDP measurements in the case of sensor fault.

20 22 24 26 28 30 32 34 36Load (MW)

9.5

10.0

10.5

11.0

11.5

CD

P (b

ar)

Baseline

Limits



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Detection of Sensor Error by Thermodynamic Analysis

Health indices evolution demonstrating CDP fault.

C o m p resso r F lo w C ap acity H ealth In d ex ( f1)

-6

-4

-2

0

2

4

6

Per

cent

age

Dev

iatio

n (%

)

T u rb in e Ef f icien cy H ealth In d ex ( f6)

-6

-4

-2

0

2

4

6

0 2 4 6 8 1 0 1 2 1 4 1 6 1 8 2 0H ours

Per

cent

age

Dev

iatio

n (%

)

T u rb in e F lo w C ap acity H ealth In d ex ( f 5)

-6

-4

-2

0

2

4

6

Per

cent

age

Dev

iatio

n (%

)

C o m p resso r Ef f icien cy H ealth In d ex ( f 2)

-6

-4

-2

0

2

4

6

Per

cent

age

Dev

iatio

n (%

)



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Fault Signature Of CDP Fault.

Produced through the simulation/adaptive mode

-6

-4

-2

0

2

4

6

f1 f2 f5 f6

Perc

enta

ged

Dev

iatio

n (%

)



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SUMMARY-CONCLUSION

• An integrated monitoring system that can meet current requirements has been developed.

• Implementation of the developed system to an operating gas turbine discussed.

• The effectiveness of the developed monitoring system was demonstrated by presenting a number of cases of successful fault identification.

• Deterioration of either simple-to-observe components, such as inlet filters or turbo machinery components, namely compressor and turbine, is detected and quantified.

• Sensor faults could be identified even if they are of such a magnitude that they cannot be detected by simple interrelations of parameters.

combining advanced data analysis methods for the ...€¦ · Ø turbine outlet static pressure. Ø...

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