A STUDY ON INTELLIGENT PERFORMANCE DIAGNOSTICS OF A GAS TURBINE ENGINE USING NEURAL NETWORKS

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1 Proceedigs of the Iteratioal Gas Turbie Cogress 2003 Tokyo November 2-7, 2003 DRAFT IGTC2003Tokyo TS-002 A STUDY ON INTELLIGENT PERFORMANCE DIAGNOSTICS OF A GAS TURBINE ENGINE USING NEURAL NETWORKS Chag-duk Kog, Ja-youg Ki, Myoug-cheol Kag ad Seog-hee Kho Departmet of Aerospace Egieerig Chosu Uiversity #375, Seo-suk Dog, Dog-gu, Gwagju , Republic of KOREA Phoe: , FAX: , cdgog@mail.chosu.ac.kr ABSTRACT A itelliget performace diagostic computer program of a gas turbie usig the NN(Neural Network) was developed. Recetly o-coditio performace moitorig of major gas path compoets usig the GPA(Gas path Aalysis) method has bee performed i aalyzig of egie faults. However because the types ad severities of egie faults are various ad complex, it is ot easy that all fault coditios of the egie would be moitored oly by the GPA approach. Therefore i order to solve this problem, applicatio of usig the NNs for learig ad diagosis would be required. Amog them, a BPN (Back Propagatio Neural Network) with oe hidde layer, which ca use a updatig learig rate, was proposed for diagostics of PT6A-62 turboprop egie i this work. I this study, i order to facilitate applicatio of the NNs as well as to provide user-friedly circumstace a performace diagostic computer code usig MATLAB was ewly proposed. As a result, ot oly more precise ad prompt aalysis results could be obtaied due to use of the toolbox i MATLAB o diagosis ad umerical aalysis, but also the GUI (Graphical User Iterface) platform could be realized. The proposed egie diagostics system is able to trai the BPN with each fault patter ad the costruct the total traiig etwork by assemblig the traied BPNs. Database for etwork learig ad test was costructed usig a gas turbie performace simulatio program. I order to ivestigate reliability o costructio of database for diagostic results, a aalysis was performed with 5 combiatio cases of 40 fault patters. Fially a diagostic applicatio example for the PT6A-62 turboprop egie was performed usig the traied etwork with database, which represeted the best diagostic results amog test sets. 1. INTRODUCTION Egie coditio moitorig is a effective complex way to improve safety as well as reduce operatio ad maiteace costs of gas turbies. To keep track of the health of various compoets that make up a moder aircraft egie, a large umber of moitorig ad diagostic techiques have to be applied. Amog them, the GPA(Gas Path Aalysis) is a kid of fault diagostic techiques that ca be used to isolate ad quatify gas path faulted compoets of gas turbies. Some features of the GPAs are the Copyright 2003 by GTSJ Mauscript Received o March 31, 2003 capability to idetify the compoet resposible for the loss of performace, detect multiple faults ad quatify the deterioratio affectig idividual compoets. (Zedda & Sigh, 1998) Performace diagosis of major gas path compoets usig the GPA ca be carried out by idepedet parameters, (such as compoet efficiecies, mass flow parameters, etc.) ad the FCM(Fault Coefficiet Matrix). Because the FCM, which is a iverse matrix of the ICM(Ifluece Coefficiet Matrix) that is relatioship betwee measurable depedet parameters (such as pressure, temperature, fuel flow, etc.) ad idepedet parameters, is a o-square matrix, there might be some error to obtai the iverse matrix due to umerical treatmet. (Urba, 1972) Recetly AI(Artificial Itelligece) ad especially the NN(Neural Network) techiques have bee applied to gas turbie egie diagostics. The NNs have iheret features that make them particularly suited to diagostic tasks. (Lu et al., 2000, Su et al., 2000, Volpoi et al., 2000, Depold & Gass, 1999) Chages of measured parameters i egie gas path reflect the chage of compoet characteristics. If the iterrelatioship betwee them ca be built usig the NN, the differet types of faults ca be diagosed. Lots of research works have bee coducted o egie fault diagosis usig the NN, ad several NN approaches have bee developed. Amog them, the BPN (Back Propagatio Network) is widely used because of its simplicity ad already made algorithm. (Su et al., 2000, Zedda, 1998, Tag et al., 1998) The BPN was created by geeralizig the Widrow-Hoff learig rule to multiple-layer etworks ad oliear differetiable trasfer fuctios. Iput vectors ad the correspodig target vectors are used to trai to trai a etwork util it ca approximate a fuctio, associate iput vectors with specific output vectors, or classify iput vectors i a appropriate way as defied. Networks with biases, a sigmoid layer, ad a liear output layer are capable of approximatig ay fuctio with a fiite umber of discotiuities. Backpropagatio is a gradiet descet algorithm, as is the Widrow-Hoff learig rule, i which the etwork weights are moved alog the egative of the gradiet of the performace fuctio. (Hayki, 1994) I order to reduce some difficulties of curretly used BPNs for gas turbie egie diagostics, this work proposed a method, which is able to trai the BPN with each fault patter ad the costruct the total traiig etwork by assemblig the traied BPN. Fault ad test database to build the NNs were obtaied usig a gas turbie performace simulatio program. I order to use easily the proposed diagostics system, a GUI(Graphical User Iterface) program for costructig database, traiig, test ad applyig the NNs was developed. -1-

2. THE DIAGNOSTIC SYSTEM WITH GUI The proposed gas turbie egie diagostic system cosists of costructio of database for traiig the NN, learig the BPN, verificatio of the NN usig test data, ad

I the stage of traiig stage of the BPN, the traiig is performed usig iput data (such as traiig error goal, umber of euros i hidde layer, umber of maximum iteratios, etc.

3 Widow for BPN traiig I the stage of data base costructio, a performace simulatio program calculates base egie performace ad measurable parameters for various faults.

I the stage of test ad applicatio at the target egie, the types of faults ad their severities are quatitatively idicated usig the traied NN with iput data such as test ad measured data.

2 2. THE DIAGNOSTIC SYSTEM WITH GUI The proposed gas turbie egie diagostic system cosists of costructio of database for traiig the NN, learig the BPN, verificatio of the NN usig test data, ad applicatio for a target gas turbie egie. Figure 1 shows mai widow for the proposed diagostic system. I the stage of traiig stage of the BPN, the traiig is performed usig iput data (such as traiig error goal, umber of euros i hidde layer, umber of maximum iteratios, etc.), ad database obtaied i the previous stage. Figure 3 shows i/out widow for the stage of traiig. Fig. 1 Mai widow for diagostic system Fig. 3 Widow for BPN traiig I the stage of data base costructio, a performace simulatio program calculates base egie performace ad measurable parameters for various faults. Egie performace degradatio is saved at a file through ormalizatio usig the followig equatio. Figure 2 shows i/out widow for the stage of data base costructio. I the stage of test ad applicatio at the target egie, the types of faults ad their severities are quatitatively idicated usig the traied NN with iput data such as test ad measured data. Figure 4 shows i/out widow for the stage of test ad applicatio. Fig. 2 Widow for database acquisitio Z Z b Z 100 (1) Z Where Z b is the value at the established baselie coditio ad Z is the measured or calculated value respectively. b Fig. 4 Widow for NN test/diagosis 3. NEURAL NETWORK APPROACH TO GAS TURBINE PERFORMANCE DIAGNOSTICS A egie selected for diagostics system is the P&WC PT6A-62 free-turbie turboprop egie. The egie shaft horsepower is 857.9kW(1150 hp) at sea level, but flat rated at -2-

3 708.7kW(950 hp). Table. 1 shows the performace data at maximum take-off coditio, which were provided by the egie maufacturer. (Kog & Ki, 2001) Table. 1 Performace data by egie maufacturer Variable Values Atmospheric coditio Sea Level Static Stadard Coditio Mass flow rate (kg/s) 4.18 Fuel flow rate (kg/s) Shaft horse power (hp) 1150 S.F.C (kg/kw hr) Gas geerator rotatioal speed (100% RPM) Propeller rotatioal speed (100% RPM) 2000 Measurable parameters for this model were SHP(shaft horse power), MF(fuel flow), PT2(compressor exit pressure), TT2(compressor exit temperature), PT4(compressor turbie exit pressure), TT4(compressor turbie exit temperature), TT5(power turbie exit temperature). Flow capacities ad efficiecies of gas path compoets were used for detectio parameters for their performace degradatios. It meas that the six performace parameters, which are flow capacities ad efficiecies of compressor, compressor turbie ad power turbie, used for detectig sigle or multiple faults such as compressor foulig, turbie erosio, ad so o. 40 patters of traiig data ad 15 patters of test data were obtaied usig the performace simulatio program of the PT6A-62 turboprop egie. (Kog, 2001) Fault patters for traiig ad test have various cases of compressor foulig, turbie erosio, ad simultaeously combied faults of gas path compoets with the performace degradatio rage of 1% to 5%. Learig algorithm used the BPN with oe hidde layer. The hidde layer used the taget sigmoid trasfer fuctio, ad the output layer used the liear trasfer fuctio DATA SET CONSTRUCTION Data set for learig was composed of 40 patters of faults. Compressor foulig causes decrease of both flow capacity ad efficiecy, ad it was cosidered that the degradatio rate due to this fault has the maximum rage of 5%. However turbie erosio causes icrease of flow capacity ad decrease of efficiecy, ad it also was cosidered that the degradatio rate has the maximum rage of 5% like the compressor foulig case. Various cases for fault patters were assumed that there are sigle fault patter such as compressor foulig, compressor turbie erosio or power turbie erosio, ad multi patters with simultaeously combied faults. If variatio rates of compoet flow capacities ad efficiecies are iput at GUI widow, each compoet performace map is scaled by them. Therefore the egie performace ca be matched at a ew operatig poit with this scaled map. After measuremet parameters (such as SHP, MF, P2, T2, P4, T4 ad T5) of the degraded egie are compared with them of clea egie ad calculated usig equatio (1), they will be saved as a data file. With the same procedure as costructio of the learig data set, 15 patters of faults were costructed for test data set. Table 2 shows fault patters for learig data set. (Diakuchak, 1992) Where is compoet flow capacity, meas compoet efficiecy. Subscripts of CO, CT, PT preset compressor, compressor turbie, power turbie. Table. 2 Fault patters for learig data set CO CO CT CT PT PT NEURAL NETWORK TRAINING ALGORITHM Fault patters were traied usig the BPN algorithm. The BPN cosists of oe iput layer with 7 euros, oe hidde layer with oe euros ad oe output layer with 6 euros as show i Figure 5. Seve euros of the iput layer mea variatios of measuremet parameters such as SHP, MF, P2, T2, P4, T4 ad T5, ad 6 euros of the output layer preset degradatio rates of flow capacities ad efficiecies for compressor, compressor turbie ad power turbie. The taget sigmoid fuctio (2) was used as the trasfer fuctio of the hidde layer, ad the liear trasfer fuctio (3) was applied as the trasfer fuctio of the output layer. (Lee & Mu 1999) x x e e y (2) x x e e y x (3) -3-

4 Where e meas expoetial, y ad x are output ad iput values respectively. is tagetial parameter, ad it was set as 1 i this work. Fig. 5 Architecture of three layer BPN Figure 6 shows a tred that the etwork output is covergig to the target output for the 40 th fault patter. 4. NETWORK VALIDATION Test data set composed of 15 fault patters was used to validate the leared NN. Test set cosists of various types ad severities of compoet faults similar to data sets for learig. Table 3 presets the applied test data set for validatio. (Diakuchak, 1992) I order to ivestigate ifluece o costructio of database for diagostic results, a aalysis was performed with 5 combiatio cases, which were derived from 40 fault patters. Table 4 shows 5 combiatio cases for database ad their RMS errors. From diagostic results, it is oted that the case I with all 40 fault patters at Table 2 has much greater RMS errors rather tha the cases with partial fault patters. It meas that uecessary data may have iaccuracy diagostic results iversely. The case II is the results that trai the data set usig the fault patter umber 1 to 15 at Table 3. These are various types that sigle fault occurs at each compoet. It is foud that the case II have much less errors rather tha the case I icludig multi-fault types. The BPN eeds differetial operatio of the trasfer fuctio for effective calculatio i the forward pass. Therefore differetial equatios of taget sigmoid ad liear trasfer fuctios are respectively as follows. (Lee & Mu 1999) 2 1 [ f x ] y (4) y 1 (5) I the NN learig process, i order to icrease the learig speed ad maitai the stability the learig rate factor (LRF) icreases 10 % of the previous LRF if the errors betwee etwork ad goal outputs decrease, but the LRF decreases 50% of the previous LRF if the errors icrease. Moreover the weight factor was oly updated i case of decrease of the errors. The errors is defied as the followig RMS(Root Mea Square). RMS error i 1 y T Where T is target output, y is output value calculated by the NN, ad is the umber of output layer euros. I this work the target maximum RMS error was set at (6) Table. 3 Fault patters for NN validatio CO CO CT CT PT PT Table. 4 RMS errors for each learig database sets CASE I CASE II CASE III CASEIV CASE V Fig. 6 RMS errors versus umber of iteratios The case III is the result that trais the data set usig the fault patter umber 3, 4, 8, 9, 13 ad 14 with compoet performace degradatio rates of 3 ad 4%, ad the case IV is the result that lears the data set usig the fault patter umber 1, 5, 6, 10, 11 ad -4-

Fially, the case V costructs learig set usig the fault patter umber 1, 2, 3, 4 ad 5 (oly compressor foulig).

5 15 with compoet performace degradatio rates 1 ad 5%. As a result of compariso, it is show that have less RMS errors tha the case II, the case III ad the case IV with wide degradatio rates has the smallest RMS errors. Fially, the case V costructs learig set usig the fault patter umber 1, 2, 3, 4 ad 5 (oly compressor foulig). This case ca detect well the compressor foulig patter, but it has big RMS errors for other sigle ad multi compoet fault patters. I this test, i order to get better diagostic results, firstly it was cofirmed that uecessary database may give rise to reduce the diagostic reliability, iversely. Secodly, the case usig the learig database for sigle compoet fault patters has much higher reliability tha that for multi compoet fault patters, ad thirdly, the case usig wide rage of performace degradatio rates has much better tha that usig arrow rage of performace degradatio rates. Fially the leared case with a sigle compoet fault patter ca detect precisely the same compoet fault patter, but it ca detect well the other compoet fault patters. Figure 7 ad 8 show respectively applicatio results for diagostics of the PT6A-62 turboprop egie. Because diagostic results show reductio of both flow capacity ad efficiecy of compressor as show i Figure 7, it ca be foud that cause of fault is compressor foulig. 5. CONCLUSION REMARKS I this work, a performace diagostics system for a gas turbie was ewly proposed usig the NNs. The proposed diagostics system was composed with the basis of the GUI platform, which users ca use easily for costructio of data base, traiig, test ad eve applicatio of the NNs. For learig the NN, a BPN with oe hidde, oe iput ad oe output layer was used. The iput layer had seve euros of variatios of measuremet parameters such as SHP, MF, P2, T2, P4, T4 ad T5, ad the output layer used 6 euros of degradatio ratios of flow capacities ad efficiecies for compressor, compressor turbie ad power turbie. The taget sigmoid fuctio was used as the trasfer fuctio of the hidde layer, ad the liear trasfer fuctio was applied as the trasfer fuctio of the output layer. After obtaiig 55 fault patters usig the performace simulatio program of PT6A-62 turboprop egie, 40 patters were used for etwork traiig, ad 15 patters were applied for etwork validatio. I order to ivestigate reliability o costructio of database for diagostic results, a aalysis was performed with 5 combiatio cases of 40 fault patters. From aalysis results, i order to get better diagostic results, firstly it was cofirmed that uecessary database might give rise to reduce the diagostic reliability, iversely. Secodly, the case usig the learig database for sigle compoet fault patters had much higher reliability tha that for multi compoet fault patters, ad thirdly, the case usig wide rage of performace degradatio rates had much better tha that usig arrow rage of performace degradatio rates. Fially the leared case with a sigle compoet fault patter could detect precisely the same compoet fault patter, but it could detect well the other compoet fault patters. From applicatio results for diagostics of the PT6A-62 turboprop egie usig the leared etworks, it was cofirmed that the proposed diagostics systems could detect well the sigle fault types such as compressor foulig, compressor turbie erosio ad power turbie erosio as well as multi compoet combied fault types. Fig. 7 Applicatio results of the case IV etwork Because Figure 8 shows decrease of both flow capacity ad efficiecy at compressor as well as icrease of flow capacitiy ad decrease of efficiecy at compressor turbie, it presets that causes of combied faults are compressor foulig ad compressor turbie erosio. Fig. 8 Applicatio results of the case IV etwork ACKNOWLEDGEMENTS This study was supported by Factory Automatio Research Ceter for Parts of Vehicles(FACPOV) i Chosu Uiversity, Gwagju, Korea. FACPOV is desiged as a regioal research ceter of Korea Sciece ad Egieerig Foudatio(KOSEF) ad operated by Chosu Uiversity. REFERENCES Lu, P. J., Zhag, M. C., Hsu, T. C., ad Zhag, J., 2000, A Evaluatio of Egie Faults Diagostics usig Artificial Neural Networks, Proceedigs of ASME TURBO EXPO 2000, GT-0029 Su, B., Zhag, J., Zhag, S., 2000, A Ivestigatio of Artificial Neural Network (ANN) I Quatitative Fault Diagosis for Turbofa Egie, Proceedigs of ASME TURBO EXPO 2000, GT-0032 Volpoi, A. J., Depold, H., Gaguli, R., ad Daguag, C., 2000, The Use of Kalma Filter ad Neural Network Methodologies i Gas Turbie Performace Diagostics: A Comparative Study, Proceedigs of ASME TURBO EXPO 2000, 2000-GT-547 Depold, H. R., ad Gass, F. D., 1999, The Applicatio of Expert Systems ad Neural Networks to Gas Turbie Progostics ad Diagostics, Joural of Egieerig for Gas Turbies ad Power, Vol. 121, pp Zedda, M., ad Sigh, R., 1998, Fault Diagosis of a Turbofa Egie usig Neural Networks: A Quatitative Approach, America Istitute of Aeroautics ad Astroautics, AIAA

6 Tag, G., Yates, C. L., ad Che, D., 1998, Comparative Study of Two Neural Networks Applied to Jet Egie Fault Diagosis, America Istitute of Aeroautics ad Astroautics, AIAA Urba, L.A., 1972, "Gas Path Aalysis Applied to Turbie Egie Coditio Moitorig", J. of Aircraft, Vol. 10, No. 7, pp Diakuchak, I.S., 1992, "Performace Deterioratio i Idustrial Gas Turbies" Tras. ASME Joural of Egieerig for Gas Turbie ad Power, Vol. 114 : , 1992 Kog. C.D., Ki, J.Y., 2001, Performace Simulatio of Turboprop Egie for Basic Traier, ASME 00-GT-391 Heyki, S., 1994, Neural Networks A Comprehesive Foudatio, Macmilia Lee, H. Y., Mu, G. I., 1999, Fuzzy-Neuro usig MATLAB, A-Ji -6-

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