Aircraft Engine Gas Path Fault Diagnosis Based on Fuzzy Inference

Transcription

1 202 Internatonal Conference on Industral and Intellgent Inforaton (ICIII 202) IPCSIT vol.3 (202) (202) IACSIT Press, Sngapore Arcraft Engne Gas Path Fault Dagnoss Based on Fuzzy Inference Changzheng L, and Yong Le School of Power and Energy, Northwestern Polytechncal Unversty, X an, Chna Abstract. The workng condton of arcraft engnes s very coplcated and severe. Gas path faults such as degradaton of perforances, decreasng of work effcency are the ost coon fault patterns. In ths paper, a fuzzy nference syste s establshed. 9 easured paraeters of an arcraft engne are selected as features paraeters to detect 2 knds of gas path fault patterns. The data preprocessng ethods and the effect of easureent nose are also dscussed. Keywords: fuzzy nference syste, arcraft engne, gas path, fault dagnoss. Introducton Arcraft engne gas path faults not only reduce the econoy of arcraft, but also serous threat the flght safety. It s very portant to dagnose and nsulate faults. One characterstcs of the descrpton of faults s fuzzy. In ths way, the fuzzy nference can be ntroduced nto fault dagnoss syste. In Ref. [], fuzzy concepts were ntroduced nto fault dagnoss. The pressures, teperatures and speeds were taken as characterstcs paraeters of Trent-800 trple spools turbofan engne of Roll-Royce. The Matlab was used as the developent tool of dagnoss syste. In Ref [2-3], the fuzzy nference technology was ntroduced nto a gas turbne engne fault dagnoss syste. The speed of hgh pressure spool, the teperature of exhaust gas, and the fuel flow were taken as the characterstcs paraeters. The result showed that the fault was the degradaton of effcency of hghpressure turbne. In Ref [4], the fuzzy logc and the gas path paraeters were adopted to dagnose a sngle fault of cells of engnes. The effect of nose of easured paraeters s concerned. In ths paper, we develop a gas path fault dagnoss syste based on fuzzy nference, wth 9 easured paraeters and 2 knds of faults, whch s used n factory for repar test of engnes. 2. Fuzzy Inference Syste 2.. Structure of Fuzzy Inference Syste Generally, the structure of a fuzzy nference syste s shown as Fg. Fro Fg, t can be seen that a fuzzy nference syste can be dvded nto four parts naed as fuzzfer, fuzzy nferor, knowledge base and ant-fuzzfer[5, 6] Fuzzfer The role of the fuzzfer s appng a deterned pont of the nput space nto a fuzzy set. The procedure s that, frstly, a scale transforaton wll be taken on nputs to transfor the to ther own doan; then, transfor precse values to fuzzy values. In ths procedure, fuzzy sets and correspondng ebershp functons are used to descrbe precse values. Several ebershp functons are coonly used such as trangle functon, Gaussan dstrbuton functons, S-curve, etc. 73

2 Knowledge base Input Fuzzfer Fuzzy nference Ant-fuzzfer Output 2.3. Knowledge Base The knowledge base s generally constructed by the ebershp functons of lngustc varables and nference rule lbrary, whch are usually derved fro expert experences. The knowledge base s the core of the fuzzy nference syste. The an functons of rest parts of the syste are to explan and use these rules to solve specfc probles. Inference rules typcally have the for as followng, IF (eet a set of condtons) THEN (a set of conclusons can be ntroduced) In fuzzy nference systes, rules are ths knd of condtonal stateents. The prerequste condtons are condtons for specfc states. The conclusons are fault patterns Fuzzy Inference The an functon of fuzzy nference s to transfor IF-THEN rules n fuzzy rule lbrary to a specfc appng, whch eans appng fro nput fuzzy sets to output fuzzy sets. The procedure of fuzzy nference usually ncludes descrpton of IF-THEN rules, calculaton of conjunctons, and fuzzy logc operatons Ant-Fuzzfer The result of fuzzy nterference s the fuzzy quanttes. Whle for specfc probles of dagnoss, a specfc fault pattern s needed. So, an ant-fuzzfer s needed. Methods are usually used by ant-fuzzfers are the centrod of area, the bsector of area and the average axu ebershp degree ethod. 3. Gas Path Fault Dagnoss Fg. : The Structure of Fuzzy Inference Syste Studes of gas turbne data have shown two an features of the health sgnal ) ost ajor probles n the engne are caused by a sngle fault whch s preceded by a sharp trend shft [7] and 2) long-ter deteroraton n the engne causes a low-order polynoal varaton n the easureents wth te, wth a lnear polynoal beng a very good approxaton [8]. 3.. Choosng Baselne Values Arcraft engne s a coplex syste. Dfferent values of condton paraeters can be got wth dfferent atosphere condton. Even wth the standard atosphere condton, dfferent values can be got when the engne run n dfferent condtons. So, baselne values should be chose, resduals generated by coparng easureents wth the baselne values. Generally speakng, the perforance of engnes s defned by NH, NL and TET of several certan thrust stages. That eans when the thrust of engne s n the gve secton, and the NH, NL and TET are n certan sectons, the engne s n noral perforance. We choose the baselne values of corrected easureents got near or n a condton n whch the engne often runnng. Supposng XG 0 s the thrust n ths condton, NH, NL, M, P 2, T 2, P 3, T 3, FF and TET cobne to be the baselne vector V 0. The eanngs of paraeters are lsted n Table. The baselne vector can be calculated by progras for a new type of engne or can be statstcs wth anufactured engnes. As for an ndvdual engne, the corrected easureents of acceptance test can be used baselne values Generatng Resduals Two knds of resduals should be got for gas path dagnostc. One s that resduals ( V, =,, N) whch stand for standard falure patterns. That eans f the easured resduals s sae or slar wth the resduals, 74

3 the fault ust be happened. Ths knd of resduals can be calculated wth engne odels or be gotten by statstcal ethod. In our research, engne X s studed. The fault patterns of perforance deteroraton of coponents are 2 knds. There are LP (low-pressure) copressor capacty, HP (hgh-pressure) copressor capacty, LP turbne capacty, HP turbne capacty, LP copressor effcency, HP copressor effcency, LP turbne effcency, HP turbne effcency, can loss, bypass duck loss and so on (Lsted n Table 2). We have calculated the resdual vectors of each fault patterns. Table. Meanngs of Measured Paraeters No. Sybol Nae of Paraeter M Ar flow of fan 2 P 2 Outlet total pressure of fan 3 T 2 Outlet teperature of fan 4 NL Speed of low-pressure spool 5 NH Speed of hgh-pressure spool 6 P 3 Outlet total pressure of hgh-pressure copressor 7 T 3 Outlet teperature of hgh-pressure copressor 8 FF Fuel flow 9 TET Outlet teperature of xer Table 2 Coponent Perforance Faults No. Nae of Fault No. Nae of Fault LP copressor capacty 2 HP 7 th stage to bypass duct 2 HP copressor capacty 3 HP 7 th stage overboard 3 HP turbne capacty 4 HP turbne coolng ar 4 LP turbne capacty 5 LP turbne coolng ar 5 LP copressor effcency 6 Bypass duct loss 6 HP copressor effcency 7 Jet ppe loss 7 HP turbne effcency 8 Can loss 8 LP turbne effcency 9 Turbne ext xer area 9 LP bleed overboard 20 Bypass duct xer area 0 HP bleed overboard 2 Fnal nozzle area HP bleed to bypass duct Another knd of resduals ( V ) s generated by coparng the easureents of an ndvdual engne wth ts baselne values, whch can be used for fault detecton and dagnoss of the engne Fault Dagnoss As shown n Fg. 2, noralzed vectors of fault patterns are drawn on a two-densonal plan. The horzontal axs s for the 9 easured paraeters. The vertcal axs s for the values of coponents of V. Dfferent shapes of ponts stand for dfferent fault patterns. It can be seen that for dfferent fault patterns, the noralzed vectors are not exactly equal to each other. Let V stands for the noralzed vector of easured paraeters. There are several rules for the fuzzy nference syste. Rule(): IF V s V, THEN F s F. (=~2) () The vectors wrte n coponent odel wll be: Rule(): IF v s v AND v 2 s v 2 AND AND v 9 s v 9, THEN THEN F s F. (=~2) (2) Here, v j (=, 2,, 2; j=, 2,, 9), a real nuber, s the j th coponent of noralzed vector V of ' fault pattern F. In fuzzy nference syste, Gauss Fuzzer s adopted to ap v j nto a fuzzy set V j. For exaple, the frst coponent of V of fault pattern F, v = , t can be fuzzed wth Equ. 3 and shown n Fg. 2. x ( ) 0.05 μ v ( x) = e (3) 75

4 Fg. 2: Fuzzy set of vectors of fault patterns Here, the Sugeno style fuzzy nference syste s adopted. The nputs of syste are 9 coponents of characterstc vector. One output uses constant ebershp functons to stand for noral state and 2 fault patterns. Wth Matlab, the knowledge base s constructed wth 2 rules. The fuzzy nference syste s shown n Fg. 3. Fg. 3: The fuzzy nference syste for fault dagnoss Sulate data are generated to exane the fault dagnoss ablty of the syste. The data are generated n ths way: take the standard values at XG 0 as a reference pont; the range of perforance degeneraton of each coponent s 0.~5%; for each coponents, a vector s selected wth nterval of 0.%. For 2 knds of faults and noral state, 00( 00= ) vectors are generated. Vectors wth easureent nose are generated n ths way: frstly, generate vectors wthout nose as entoned above. Then, add Gaussan nose N(0, σ ) (=, 2,, 9)to each coponent of vectors. Here, σ s a thrd of the easureent errors. All 00 vectors wthout nose can be ndentfed correctly. The dentfcaton rate s 00%. 899 vectors of 00 vectors wth nose can be dentfed correctly. The dentfcaton rate s 8.7%. The relatonshp between correct dentfcaton rate and falure severty s shown n Fg. 4. Fg. 4: Identfcaton rate of fault dagnoss 3.4. Effect of Nose It can be seen fro Fg.4 that for vectors wth nose, the correct rate of fault dentfcaton ncreases wth the severty of falure. In engneerng, nose of easureent s usually assued as noral dstrbuton 76

5 N(0, σ ). In certan sense, the values of nose are fxed relatvely. So, when the severty of falure s sall, the sgnal to nose rate s lower. It causes the reducton of the ablty of dentfcaton. 4. Suares In the repar process of arcraft engne, t should be found out whch degeneraton caused the devaton of easured values to standard values. In ths paper there are 2 knds of fault patterns of coponents perforance degeneraton. 9 easured paraeters are selected as feature sgnals. A fuzzy nference syste s establshed to ndentfy the fault pattern. Exaples show that all falures can be dentfed f the data wthout nose. Serous degeneraton s easer to dentfed f the data wth easureent nose. It s sae to ntuton 5. References [] A. Prya, and R. Sngh. Gas Turbne Engne Fault Dagnostcs Usng Fuzzy Concepts. AIAA st Intellgent Syste Techncal Conference.AIAA : -20. [2] D. Gaye, S. Menon, C. Ball, and et al. Fault Detecton and Dagnoss n Turbne Engnes Usng Fuzzy Logc. Fuzzy Inforaton Processng Socety 22 nd Internatonal Conference of the North Aercan, 2003: [3] D. Gaye, S. Menon, C. Ball, and et al. Fault Detecton and Dagnoss n Turbne Engnes Usng Fuzzy Logc. IEEE Internatonal Conference on Syste, Man and Cybernetcs. 2003, (4): [4] S. Ogaj, L. Marna, S. Sapath, and et al. Gas-turbne Fault Dagnostcs: a Fuzzy-logc Approach. Elsevler Appled Energy, 2005, (82): [5] Y. Zhao, X.H. Qu, and R. L. Self-Adjustng Control Syste of Teperature Based on Fuzzy Algorth. J.Tanjn Unversty, 20, (): [6] C. Deng, J. Wu, and Q. Lu. Poston Precson Copensaton Method Based on Fuzzy Control. Coputer Integrated Manufacturng Systes, 200, (2): [7] H. Depold, and F. D. Gass. The Applcaton of Expert Systes and Neural Networks to Gas Turbne Prognostcs and Dagnoss, ASME J. Eng. Gas Turbnes Power, 999, 2(4): [8] K. Mathoudaks, P. Kaboukos, and A. Staass. Turbofan Perforance Deteroraton Trackngusng Nonlnear Models and Optzaton Technques. ASME J. Turboach, 2002, 24(4): [9] noy. Klagz, A. Baran, Z. Yldz, and et al. A Fuzzy Dagnoss and Advce Syste for Optzaton of Essons and Fuel Consupton. Elsevler Expert Syste wth Applcaton, 2005, (28):