Corrected gas turbine data.pdf

ASME/IGTI TURBOEXPO Conference 2004 June 14-17, Vienna, Austria

GT – 2004 – 53760 MEASURED DATA CORRECTION FOR IMPROVED FOULING AND DEGRADATION ANALYSIS OF OFFSHORE GAS TURBINES Timot Veer, Klaus K. Haglerød*, Olav Bolland Norwegian University of Science and Technology Department of Energy and Process Engineering N-7491, Trondheim, Norway E-mail: [email protected], Tel.: +4773598462, Fax: +4773598390 *Norsk Hydro, Competence Center, Rotating Equipment, Porsgrunn, Norway

ABSTRACT The authors suggest a straightforward methodology to correct measurement data in order to facilitate condition assessment of gas turbines. After data being prepared as such, a considerable improvement in accuracy is obtained in regard to condition evaluation of the machine. Such methodology brings proven benefits when regarding the fouling problem as well as washing scheduling at sites where the fouling process is relatively slow, e.g. offshore applications. Analyses of other relatively slow performance loss processes, like degradation, are also targeted. The usefulness of the methodology is validated against field data by employing advanced software tools and reliability and availability as well as condition and lifing prediction. INTRODUCTION A large amount of data originating from an offshore operating gas turbine was analysed. This data is gathered with relatively high frequency (1/min) by the gas turbine’s own data acquisition system. Despite the relatively high accuracy, these momentary values present scatter that makes them very difficult to be directly used for performance loss evaluation purposes. [3,7,8,16] Nevertheless, even a longer term averaging or other arithmetic data processing did not help much due to the variation in operating conditions. These variations might have seasonal cycles, daily cycles or even down to Herz scale1. The amplitudes of these are in the higher single digit percentage points. The authors focus herein mainly on power output measurements. The usual thermodynamic corrections to ISO reference condition turns out to do little good.[7,9] In fact these corrections cover only the effect of thermodynamic properties change of the working fluid entering the inlet annulus of the machine[14]. Thus, these are unsatisfactory 1

Pulsations and other upstream disturbances.

for the machine as a whole. Besides, when attempting to monitor derived parameters in many cases, the mathematically amplified measurement inaccuracy2 is hiding the real phenomenon aimed to be analysed and/or facilitates formulation of inaccurate findings. Special concern is given to fouling and degradation in this paper. Mainly because lately, progressive performance deterioration processes are intensively disputed due to their importance for both the operator and designer.[2,3,6,8,11,12,16,22] Further on, for their relatively slow development we count on these effects as challenging references for the methodology that is presented in this paper. The main idea behind the correction mode suggested herein is to process the acquired data in order to eliminate the uncertainties and scatter of measurements. Both empirical as well as analytic considerations are employed. The data is then ready to be passed over to a monitoring routine or off-line diagnostic unit in order to proceed with condition assessment. The authors base their final findings on off-line post-run data analysis. Last but not least, better understanding of these processes can lead to development of the know-how necessary for condition and lifing prediction tools. The goal of the paper is to present: • Status of measurement data processing, • Problems encountered during use of this data in order to formulate operational findings, • An alternative to thermodynamic correction, • Results, discussion of most important features, • Additional findings. BACKGROUND Data found in the literature [7,16,18] as well as the measurements the authors had access to, show a very similar 2 The efficiency like parameters include – in fact – arithmetic operations with inputs, measurement results or thermodynamic properties presenting uncertainties each.

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Copyright © 2004 by ASME

pattern, see Figure 1. The instant values of measured power are shown versus time, in minutes. The power values are all relative values, fraction of nominal output.

⎛ 1.013bar ⎞ T1 & corr = m & ⋅ ⎜⎜ ⎟⎟ ⋅ ; m p 288 K 1 ⎝ ⎠

(2)

288K ; T1

(3)

N corr = N ⋅

288K ; (4) T1 Whereas, the “1” index refers to conditions at inlet annulus of the compressor. T5, corr = T5 ⋅

Figure 1., Typical power measurement – relative values One can easily notice the highly scattering picture also presents quite a stochastic feature. It should be mentioned that the results (in Fig. 1) originate from a generator-driving unit. The situation one has to face when analysing mechanical drive power plants is even more difficult.[19] What is actually the main problem? Typical questions, which the operator might face on a regular basis, would be: • Which is the operating point the machine is actually operating at? • How far off-design the machine operates? • What are the presumed causes for current derating? Additionally, other diagnostic preliminaries are implicitly derived from the questions above: how much is the effect of fouling or of degradation, how much the nominal (100%) output is, how much of the performance loss can be restored by washing, when to wash, etc… [16,20,22] In order to answer these, machine, site and operation specific findings can be successfully employed. Any predetermined correlation between any of the above mentioned operational parameters and time is proven as to be questionable.[3,19,18] More than that, it is often barely a projection of previously acquired information on very current case/data. This fact is however not of a great help to the operator.[3,16] It should be mentioned that the very same unit could most probably show different progressive performance loss development in time. This topic shall be later discussed. COMPENSATION TO ISO INLET CONDITIONS It is very well known that performance of a machine operating at a different location or different ambient conditions than the one described as standard ISO3 condition, is subject to compensation.[3,11,19] Typically power and mass flow, but additionally efficiency and speed are compensated by arithmetical formulas (1): ⎛ 1.013bar ⎞ T1 ⎟⎟ ⋅ ; (1) P corr = P ⋅ ⎜⎜ ⎝ p1 ⎠ 288K

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ISO 3977-1 is here referred at.

Figure 2., Thermodynamic correction factor versus time The factor P corr ⎛ 1.013bar ⎞ T1 ⎟⎟ ⋅ ; (5) = ⎜⎜ P p 288 K 1 ⎠ ⎝ – as in (1) – is represented above in Figure 2. After multiplication with the momentary value of the power (P) we shall obtain the corrected values (Pcorr) as shown in Figure 3.

Figure 3., power versus time after purely thermodynamic compensation No doubt, efficiency of the compressor plays a dominant role in performance derating of gas turbines. When modifying the compressor inlet conditions, the primal effect is the change in volume flow – mass flow correlation. Nevertheless, the turbine inlet conditions are functions of operating parameters (speed, VGV, TIT, age, etc…) and compressor inlet parameters. That is, turbine and compressor inlet conditions are not generally in direct correlation. Thus, for a proper usage of thermodynamic correction for the machine as whole, it is necessary that one determines accurately the turbine inlet temperature and

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pressure as well; independently from the compressor inlet conditions. The latter seems though, not to be a realistic possibility in real operations. This is why compensation of the gas turbine power based only on compressor inlet condition was proven not to be satisfactory. Obviously, the compensation helps in a very little extent to level measured values of power. The corrected values still show practically the same biasing effects of ambient conditions. It would still be rather difficult to make accurate findings on the questions as raised previously.

• Fuel change5, • Fouling of the compressor, rf • Degradation of the entire unit, rd. The factors above are considered as being most important and quantitatively dominant. We chose to introduce multiplicative factors for a coherent consideration of these influences. The amount of power the machinery is delivering at the specific moment can be written as follows: P(τ)= P0 ⋅ ∏ ri (τ) ;

EXTENDED COMPENSATION Introduction One must bear in mind that these compensation routines (1) only consider external effects. Therefore, one possibility to extend the previously shown thermodynamic compensation is required. A good starting point is to take those effects into consideration, which actually originate or just act internally. These are, so to say, intrinsic. On the other hand, we must also find the ways and means to consider effects that are momentarily not influenced by external factors. These are functions of the state the machines achieved through operation – even though perfectly failure free. That is, there are accounted effects of previous life of the machine: fouling processes, degradation processes within the machine and last but not least, operator dependant (load variation, fuel change, acceleration/deceleration). Thus, another compensation approach is proposed. Obviously a multitude of effects are to be considered, therefore a basically different approach, the multiple effect superposition, is chosen; see following section. We hereafter mainly focus on power value compensation. It is firstly presumed that a certain understanding of the phenomenon has been obtained in order to have a correlation between time and the progress of the derating4. Additionally, the availability of data – in order to tune the knowledge gained by the purely theoretical considerations – is of primary importance. The most important reason is so that a good many problems within operation can be outlined. Additionally, the power measurements usually represent a reliable starting point for preliminary condition assessment. Thus, obtaining reliably accurate corrected power values is an important input for reaching findings concerning efficiency, mass flow, temperature escalation in different points of the gas path. Preliminary considerations There are a great number of effects one could actually consider but the purpose of this work is not to exhaust these but rather to show the applicability of a methodology through a representative sample. Therefore, mapping of the most important effects is crucial. • Ambient temperature, rt • Ambient pressure, rp • Variable Guide Vanes (VGV) angle failure, rv • Load variation, rl 4 Derating is here considered the difference between momentary value of power and the expectable nominal one.

(6)

i

d.

Where: • P(τ): momentary power, [MW] • P0: reference power6, [MW] • ri(τ): momentary value of the multiplication factor i* *where, i represents for any of the following : t, p, v, l, f,

These factors, ri, are normally functions having different parameters7 as inputs and a dimensionless and close to unit number as output. In fact it is the fraction of power derating caused by the specific effect, or the following partial derivative: ∂P ri = ; (7) ∂I where I is the currently considered derating effect, external or internal parameter change, operation mode, fuel or whatever is chosen. E.g. rt would be partial derivative of output power to temperature at the inlet of compressor. Nevertheless, for prediction purposes it is a challenging possibility to have assigned a unique running parameter. In our investigations – aiming RAM performance prediction – calendar time is suggested as independent input variable; ri(I) ⇒ ri(I(τ)) is expressed. The multiplication factors, “ri” How do we obtain these? For determining the concrete functions of the “ri” factors thorough field data analysis and/or theoretic considerations are necessary. Basically, for determining the concrete coefficients in each of the functions corresponding to the factors mentioned above, an iterative approach is used. Initially, the form of the correction function has to be determined. It is possible to accomplish this step by analysing the literature and the theoretical considerations: polynomial form is chosen for temperature, pressure, VGV and load, while logarithmic function is optimal for fouling and degradation. As a next step, a preliminary set of coefficients is demanded for these functions. It is possible that these are produced based on previous experience and/or literature. Examples are shown in Figures 4-6. Several examples for correction factors are now presented in more detail, inlet temperature (rt) and inlet pressure (rp) and vgv failure correction (rv). The authors primarily present rt , rp and rv because building these up is 5

No separate factor. It is incorporated into the load factor. it is considered as a matter of agreement: could be the base line, the last crank wash, 1st of January, the last major overhaul, last maintenance shut-down, etc. 7 like ambient temperature, fired hours, ambient pressure, current VGV angle, desired load, etc… 6

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possible without it being necessary to process measurement data. Further on, one would start correcting the input data with these three anyway. Temperature correction

drop modification of an inlet system. The reason is that mass flow measurements are not generally in the industrial applications. The determination of mass flow by thermodynamic calculations can lead to results within certain precision but the pressure drop is not linearly dependant on mass flow. We suggest the correlation by calibration of the exhaust and inlet pressure drops. This methodology can lead to more accurate analysis. The arithmetic form of the correction factors is: → rp = - 0.00150 ⋅ p1 + 1

(9)

A good approximation is suggested in Figure 5., gas turbine power output modification due to small pressure drop variations ( 0

•

If Lgas = 0

25 43.5 25 rl = (Ldistillate − 10) ⋅ 64 rl = (Lgas − 30) ⋅

The notations are similar to those given previously. Π is the symbol for product. Ideally, if the compensation is correct, the P0 should be a constant value over the entire interval where no hardware change, corrective maintenance action, failure or breakdown occurs. Unfortunately, the transducer from VGV angle was wrong on the machine we analysed. This is why we firmly believe the scatter (>5-7%) could later be diminished if VGV angle correction could also be employed, Figure 8. In a similar manner, considering the effect of the shaft acceleration even more accurate results can be achieved.

(10a) (10b)

The rl functions can now be very easily processed by any digital or analogue data processing routine, as well as an online measurement or expertise system. One can continue and derive other functions by repeating the same routine. One typical example would be the

Figure 8., compensated values of power versus time

Considerable decrease of scatter is obtained by substituting the “instantaneous values” with their rolling average (12): τ

∑ P(τ)

P (τ) =

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:Due to drying and other processes 40-50 h after the start up is recommended, indeed.

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τ− N

N

(12)


the most suitable mode to express the relative effect of fouling. where: P (τ) is averaged power value corresponding the minute τ • N is size of the sample, that is number of consecutive measurement points considered : we employed 100 One can use a more appropriate averaging when performing off-line data analysis: •

τ+ N / 2

∑ P ( τ)

P ( τ) =

τ− N / 2

N

(13)

Figure 10., effect of the fouling versus time

The logarithmic character of the power derating is obvious. This feature of the fouling process was to a great extent known. Striking is the accuracy of determining the development of fouling in time. We suggest the following form of function as fitting best our expectations: rf = 1 − A ⋅ log(B ⋅ τC + 1) ;

Figure 9., rolling averaged values for power, both the non-compensated and compensated ones

A comment is needed with Figure 9.: detailed insight in data revealed, that around point 7200 fuel was changed. Load was diminished till idle, then was changed to liquid fuel. Shortly afterwards, point 8200 again fuel change occurred: liquid was changed to gas fuel. Nevertheless, this change in fuel is necessary to shift between two different gas supplies. This is shown by the slightly different LHV of the fuels; before and after point 7200-8200. Fouling of the machine

Both the fouling (and degradation) processes have in fact much slower development and clearly a non-dominant, secondary effect on the overall performance compared to effect the ambient condition change or load modification10. The first step in monitoring the time dependency of fouling is to correct the data omitting the fouling correction factor. In Figure 10., the result of such analysis is shown. The different curves correspond to different fouling cycles. E.g. 4000 means the time (equivalent op. hours.) at the corresponding fouling cycle, that is the last washing before the analysed fouling cycle. The values represented in the graph are therefore all relative to the power, right after the washing. As far as the clean state nominal output decreases in time relative to new and clean state, this is found to be

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The chosen examples of machines are operating in a very clean area, so this statement has special relevance.[1,21]

(14)

Where: A, B and C are constants. The more detailed analysis gave: A=0.006, B=0.8, C=1.3. So (14) becomes: rf = 1 − 0.006 ⋅ log(0.8 ⋅ τ1.3 + 1) ;

(15)

Another important observation in regard to fouling is the age dependency. From Figure 10., it is clear that these curves are of similar patterna but there is indeed a clear dependency of the effect of fouling over time is visible. That is, when the gas generator is new, the effect of the fouling is quicker and stronger than at a more advanced operation time. To conclude, not only was fouling easily, accurately and straight forward to determine, but also its age dependency was found and it also became possible to state its transposition in a closed empirical relation: rf = 1 − A ⋅ log(B ⋅ τC(τ _ GG ) + 1) ; (16) from above (16) results that C in fact a function of τ_GG is. τ_GG means elapsed fired hours since the last gas generator change, that is ‘its age’. The main reason for having a different relative fouling derating development for a new GG than an old one, is the following: the new compressor blade has an optimally chosen surface roughness. When particle ingestion takes place the main effects are relative annulus area modification, a slightly modified profile and considerable roughness increase due to both deposits and erosion.[13] When washing – even supposed 100% of the possible recoverable loss as recuperated – the machine would be brought back to a clean state. Nevertheless, when the subsequent fouling cycle starts, the cross section and profile modification will take in a

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nearly unchanged manner. The main difference will indeed be the roughness change. The initial and final roughness – that is, before and after washing – get closer and closer to each other. With other words, an old machine would also have a decreased cross section and modified profile when fouled, but the roughness (thus polytropic efficiency11) would quantitatively decrease less and less relative to reference level, that is last clean state. Of course the starting point of the fouling process (relative reference point) is different in absolute value. This is entering the group of non-recoverable loss phenomena and is generally called degradation. It should be mentioned that age dependency of fouling process is in full concordance with the operators’ experience.[13,16] Discussions with the operators revealed that they would perform washing each second month shortly after the GG change12 but later the crank washing is scheduled for each fifth or sixth month. Considering a permitted performance loss limit of 3%, our estimations confirmed these measures. Degradation Similarly to fouling, the degradation process analysis demands care and accurate methodology. The 1-5% degradation in a year would be from far obscured when load following operation or when the seasonal ambient condition variations are bringing ca. 10-35% modification in output, see Figure 1. Offshore, salt ingestion as well as the heavy metal content of fuel brings the most striking problems, but these are not as much performance but mainly life decreasing effects. This is why – from degradation point of view – offshore operation is a favorable (clean) site location. Once correction of measured data took place, the results can be read after each crank washing. In Figure 11., one can see the results of the analysis. It is preferable to dispose of new and clean state13 corrected value power, as well.

Figure 11., effect of degradation process versus time

Due to the authors’ lack of reference data the graph is not quantitatively accurate. A guess/estimation was made of the power in new and clean state then the other data was 11

whole.

12

Mainly responsible for the worse performance of the machine as a

Practically the first 1-1,5 years, 10000hours unfortunately we did not dispose of reliable and accurate data for this very machine right after GG change. 13

correlated to this value. That is, there is no guarantee for the full correctness of the vertical scale. This fact though does not influence in the least extent, the finding from a qualitative point of view. As in the fouling case, an analytic relationship was built. The starting form was to a great extent similar to (14). A and B were kept constant, and the value for C was found constant and C=1.55. The explicit form of the function is: rd = 1 − 0,006 ⋅ log(0,8 ⋅ τ1.55 + 1) ; (17) As reference both our own results as well as literature data is plotted, see Figure 11. The relative distance between the two datasets can also be due to the wrong reference value, i.e. the nominal load. On-line monitoring, diagnostics, R&A prediction The utility of proposed methodology was tested. The most important areas of employing this are suggested as follows. On-line monitoring is challenged. If accurately built up, the relations (8)-(17) serve as a reliable correction routine. By the fact that the proposed correction results in a diminished scatter of measured power values, the uncertainty of the measurement is decreased considerably. Based on accurate power output monitoring, important findings regarding performance, derating, health and failures can be obtained in real time. Diagnostics: Based on the findings related to derating of performance, operational and maintenance actions can more precisely be observed and/or scheduled. After pilot operation (especially for larger pools) correlation is possible to be mapped between derating and desired corrective maintenance actions. The main result is the possibility to predict corrective maintenance actions. Prediction: As a continuation and direct consequence, prediction of reliability, availability issues can be preliminary assessed too. The methodology described in the current work if implemented in advanced prediction tools [5, 15, 24, 25] results in an adequate tool for prediction performance, derating, degradation and outages as well as help planning and optimizing operation and maintenance policies. CONCLUSIONS A methodology for preparing gas turbine power measurement data was requested in order to facilitate assessment of slowly developing derating processes. The authors intended to focus on the effect of fouling and degradation upon power output. A critical analysis of the thermodynamic compensation revealed its relative minor effect on diminishing the scatter of the measured values. An alternative, extended methodology for compensation of measurement data, was suggested in the present work. Firstly the theoretical background and preliminary assumption as well as the typical problems are outlined. Next, an example for building such correction factor is illustrated. Load factor is chosen because in combination with the fuel Lower Heating Value (LHV) is a general and most dominant derating factor. Lastly test calculations and additional findings are presented. It is shown that with the help of such compensation the accuracy of determining the current value of the corrected power is considerably increased. All this employs a straightforward and easy to use set of analytic relations based on data processing as well as theoretic assumptions.

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Nevertheless, such data processing aimed to build correction factors is a preliminary job done once for each site/machinery/layout. We assume the dependency of the obtained coefficients on the layout, though analogies and qualitative similarities are present. After correcting the measured values, additional findings were outlined. Fouling and degradation are chosen for more in-depth analysis. Analytical dependency of fired hours and power derating due to these effects is given. Based on the findings, we highlight the importance of this methodology in order to perform on- or off-line monitoring, base line determination, derating estimation as well as corrective maintenance planning. Further important areas of applicability are the reliability and availability assessments and mainly prediction. FURTHER WORK Further work is required in order to refine some of the correction factors. It is a challenging task to validate the superposed effects against a wider range of operating conditions as well as machineries. For this, more machinery and more site dependant effects are to be taken into account. As a future aim, it is possible to try to generalise these correction factors and find correlations between ambiance/operating policy, machine and the coefficients of correction factors, respectively. ACKNOWLEDGMENTS Norsk Hydro, Norway as well as Statoil, Norway are deeply acknowledged for supporting this work. REFERENCES 1. Abdelrazik, Cheney, Compressor Cleaning Effectiveness for Marine Gas Turbines, ASME TurboExpo Orlando, 1991. 91-GT-11 2. Anuskiewicz R. J., An Operation and Maintenance Strategy to Maximize Performance of a CC Power Plant, ASME TurboExpo Proceedings, The Hague, 94-GT-121, 1994 3. Bakken, Skorping, Optimum Operation and Maintenance of Gas Turbines Offshore, ASME Turbo Expo, Birmingham, 1996. 96-GT-273 4. Batcho et al., Interpretation of Gas Turbine Response Due to Dust Ingestion, Transactions of ASME, Vol. 109, July 1987. 5. Bienvenuti, Innovative Gas Turbine Performance Diagnostics and Hot parts Life Assessment Techniques, 30th Turbomachinery Symposium, pp. 23.-31, 2001 6. Diakunchak, Performance Deterioration in Industrial Gas Turbines, ASME turbo Expo, Orlando, 1991, 91-GT-228 7. Flashberg, Haub, Measurement of Combustion Turbine Non-recoverable Degradation, ASME TurboExpo Koln, 1992, 92-GT-264 8. Haub, Hauhe, Field Evaluation of On-line Compressor Cleaning in Heavy Duty Industrial Gas Turbines, ASME Turbo Expo Bruxelles, 1990, 90-GT-107 9. Hoeft R., Heavy-Duty Gas Turbine Operating and Maintenance Considerations, GE Power Systems Reports, GER-3620G, GE Energy Services, Atlanta, GA, 2000 10. ISO 3977-9, International Standard, 1999 11. Kurz, Brun, Degradation in Gas Turbine Systems, Transactions of ASME, 2001. 12. Müller E., 10 Jahre Betriebserfahrungen mit einer Gas/Dampfturbinen-Heizkraftanlage, VGB 72. Heft 8, 1992

13. Perkavec, Wolf, Uberwachung der Leistungsparameter der Gasturbine in Abhangigkeit vom Verschmutzungsgrad, VGB Kraftwerkstechnik, vol. 72, Heft 6., 1992 14. Pinelli M., Gas Turbine Field Performance Determination: Sources of Uncertainties, Journal for Engineering for Gas Turbines and Power, vol. 124, January 2002. 15. Roemer M. J., Advanced Diagnostic and Prognostic Technologies for Gas Turbine Engine Risk Assessment, ASME TurboExpo 2000 Proceedings, 00-GT-30 16. Schepers, Hagermann, Optimierung der On-line- und Off-line-Wasche an einer 26MW-Gasturbine unter besonderer Berucksichtigung der Leistungssteigerung, VGB Kraftwerkstechnik, 99/3, 1999. 17. Stalder, Gas Turbine Compressor Washing State of the Art: Field Experiences, ASME Journal of Engineering for Gas Turbine and Power, 2001. 18. Stalder, Oosten, Compressor Washing Maintains Plant Performance and Reduces Cost of Energy Production, ASME Turbo Expo, 1994. 94-GT-436 19. Syverud et al., Gas Turbine Operation Offshore; On-line Compressor Wash at Peak Load, ASME turbo expo Atlanta, 2003, 03-GT-38071 20. Tarabrin et al., An Analysis of Axial Compressor Fouling and a Cleaning Method of their Blading, ASME Turbo Expo Birmingham, 1666. 96-GT-363 21. Tatge et al., Gas Turbine Inlet Air Filtration in Marine Environments, ASME Turbo Expo New Orleans, 1980 22. Thames et al., On-line Compressor Washing Practices and Benefits, ASME Turbo Expo Toronto, 1989, 89-GT91 Traupel, Thermische Turbomaschinen Vol I., Springer Verlag, Berlin, 3te Auflage, 1982 23. Veer, Bolland, Using Probabilistics and advanced Software Tools for Reliability and Availability Assessment and Lifing, ASME TurboExpo, Atlanta, 2003. 03-GT-38474 24. Veer, Ulvestad, Bolland, Frame, a Tool for Predicting Gas Turbine Condition as well as Reliability, Availability Performance, ASME TurboExpo, Wien, 2004. (under publication)

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