COMBINING ADVANCED DATA ANALYSIS METHODS FOR THE CONSTITUTION OF AN INTEGRATED GAS TURBINE CONDITION MONITORING AND DIAGNOSTIC SYSTEM
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1 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 00-34:1/ 24
2 Combining Advanced Data Analysis Methods For The Constitution Of An Integrated Gas Turbine Condition Monitoring 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 00-34:2/ 24
3 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 00-34:3/ 24
4 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 00-34:4/ 24
5 Functional Diagram Of The Gas Turbine Condition Monitoring System. On Line Acquisition Measurements Validation Measurements Data Base Thermodynamic Analysis EGT Monitoring Vibration Analysis Inference Engine Rules Engine Condition Assessment 00-34:5/ 24
6 Main Interface Of The Condition Monitoring System :6/ 24
7 Data Acquisition Inspection of Measured Data Example of on-line acquisition screen :7/ 24
8 Measurement Validation Levels of measurements validation checks Check instrument and physical ranges Check cross-relation of quantities Evaluation through thermodynamic analysis Example: Compressor delivery pressure at different operating points Faulty CDP CDP (bar) Baseline Variation Limits Accepted CDP Engine Load (MW) 00-34:8/ 24
9 Data Acquisition Module Example: Inspection of measurement time evolution 00-34:9/ 24
10 Thermodynamic Analysis Process measurement data for component condition assessment Derive component oriented information, free from influence of operating point or ambient conditions Easily interpretable, unified treatment Study different Operation Scenarios Key Concept: Simulation Modification Factors: MF = X X ref 00-34:10/ 24
11 Thermodynamic Analysis Example: Component Condition Indices :11/ 24
12 Thermodynamic Analysis Example: Time evolution of compressor capacity :12/ 24
13 Thermodynamic Analysis Example: Engine simulation screen :13/ 24
14 EGT Monitoring Attention to Most Critical Parts: Hot End Quantities Observed Global EGT Pattern Properties Spread Deviation from average Reduced Temperature Profiles 00-34:14/ 24
15 EGT Monitoring Example of a display screen :15/ 24
16 Vibration Analysis Identify mainly mechanical alterations Example display from Vibration analysis (a) (b) (c) (d) 00-34:16/ 24
17 The Test Case of an Industrial Gas Turbine Measurement Set-up Standard Engine Measurements Generator Load and Speed. Vibration Overall Level. Compressor Inlet Temperature. Compressor Delivery Pressure and Temperature. Cooling Air Temperature. Exhaust Gas Temperature (eight thermocouples). Additional Measurements Ambient Pressure and Relative Humidity. Compressor Inlet Total and Static Pressure. Turbine Outlet Static Pressure. Engine Outlet Static Pressure (backpressure) :17/ 24
18 Time Evolution Of Inlet Filters Fouling Coefficient DPfil Days Filter fouling coefficient: DP = P P amb t, in P P t, in s, in 00-34:18/ 24
19 Evolution of Health Indices Healthy operation. 4 Compressor Flow Capacity Health Index Percentage Deviation (%) Percentage Deviation (%) Compressor Efficiency Health Index Percentage Deviation (%) Turbine Flow Capacity Health Index Percentage Deviation (%) Turbine Efficiency Health Index Days 00-34:19/ 24
20 Detection of Sensor Error by Simple Limits Checking Time evolution of exhaust gas temperature registered by thermocouple Limits Measurements 1000 EGT, Th. 6(Celsius) Time (min.) 00-34:20/ 24
21 Detection of Sensor Error by Checking Interrelations Fluctuation of CDP measurements in the case of sensor fault Limits CDP (bar) 10.5 Baseline Load (MW) 00-34:21/ 24
22 Detection of Sensor Error by Thermodynamic Analysis Health indices evolution demonstrating CDP fault. 6 Compressor Flow Capacity Health Index (f1) Percentage Dev iation (% ) Compressor Efficiency Health Index (f2) P ercentage Dev iation (% ) Turbine Flow Capacity Health Index (f5) Percentage Dev iation (% ) Turbine Efficiency Health Index (f6) Percentage Deviation (% ) Hours 00-34:22/ 24
23 Fault Signature Of CDP Fault. Produced through the simulation/adaptive mode 6 Percentaged Deviation (%) f1 f2 f5 f :23/ 24
24 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 :24/ 24
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