Health Monitoring of an Auxiliary Power Unit Using a Classification Tree

Transcription

1 Health Monitorg of an Auxiliary ower Unit Usg a Classification Tree Wlamir O. L. Vianna 1, João.. Gomes 1, Roberto K. H. Galvão 2, and Takashi Yoneyama 2 and Jackson. Matsuura 2 1 EMBRAER, São Jose dos Campos, São aulo, , Brazil wlamir.vianna@embraer.com.br joao.pordeus@embraer.com.br 2 ITA Instituto Tecnológico de Aeronáutica, São José dos Campos, São aulo, ,Brazil kawakami@ita.br takashi@ita.br jackson@ita.br ABSTRACT The objective of this work is to present a method to monitor the health of Auxiliary ower Units (AU) usg a Dynamic Computational Model, Gas ath Analysis and Classification and Regression Trees (CART). The ma data used to tra the CART consists of measurements of the exhaust gas temperature, the bleed pressure and the fuel flow. The proposed method was tested usg actual AU data collected from a prototype aircraft. The method succeeded classifyg several relevant fault conditions. The few misclassification errors were found to be due to the sufficiency of the formation content of the measurement data. * 1. INTRODUCTION Increased aircraft availability is one of the most desirable fleet characteristics to an airler. Delays due to unanticipated system components failures cause prohibitive expenses, especially when failures occur on sites without proper matenance staff and equipments. In recent years researches have focused on providg new technologies which could prevent some failures or notify matenance staff advance when any component is about to fail. Health Monitorg (HM) provides this knowledge by estimatg the current health state of components. This may guide the matenance activities and spare parts logistics to properly remove or fix the component at the most suitable time and place. * Vianna, W. O. L. et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction any medium, provided the origal author and source are credited. Sce the Auxiliary ower Unit (AU) represents a significant matenance cost to an airler, several HM studies have been conducted on this component (Vieira et al, 2009; Urban, 1967; Jones, 2007). Many of them exploit similar approaches to methods devoted to the ma enges, due to the similarities physical behavior. Methods based on thermodynamic models, or gas path analysis, may provide more precise formation as compared to data-driven methods. However, the use of model-based techniques still presents challenges when dealg with a large and heterogeneous fleet. This paper aims to provide a HM solution based on a classification and regression tree (CART) employg data obtaed from a mathematical model of an AU derived from thermodynamic prciples. The proposed method is validated with AU field data. The work is organized as follows. Section 2 contas a brief description of the system under analysis. Section 3 presents the methodology adopted. Section 4 contas the model description used on the implementation. Section 5 presents the implementation steps and the results of the method applied on the AU performance data. The last section presents the conclusion of the study and some remarks. 2. SYSTEM DESCRITION An AU is a gas turbe device on a vehicle with the purpose of providg power to other systems apart from enges. This power can either be of pneumatic nature, extracted from a bleed system, or of electrical type, extracted from the generator. AUs are commonly found on large aircraft, as well as some large land vehicles. Its primary purpose is to provide bleed to start the ma enges. It is also used to run accessories such

2 as air conditiong and electric pumps. It is usually located at the tail end of the aircraft as represented Figure Typical AU EGT profile 450 Exhaust Gas Temperature transient (A/C start) AU steady state with out bleed AU steady state with bleed Enge start AU Start Figure 1: AU at the tail end of an aircraft. A typical gas turbe AU contas a compressor, a burner and a turbe as every conventional gas turbe. It also has a bleed system that controls the amount of extracted pneumatic power, a fuel system, a gearbox and a generator. rotective components such as antisurge, and guide vane may also be present. The logics and control are executed by the Full Authority Digital Enge Control (FADEC). A simplified AU representation is illustrated Figure Samples Figure 3: Typical EGT profile durg the operation of an AU. Durg the AU start an EGT peak is observed and can be used as a health monitorg feature (SAE, 2006). After the speed has reached its operational value the EGT stabilizes until the air conditiong system turns on. This produces an crease EGT, which then reaches another steady-state value. When the enge starts, usually all other pneumatic sources are turned off so the AU can provide the required bleed. x 10 4 Generator Gearbox Air Inlet Injection Burner Turbe Figure 2: Simplified AU representation. In order to provide proper formation to the FADEC, the system must conta sensors for several variables, such as speed, exhaust gas temperature (EGT) and bleed pressure. A fuel flow meter may also be valuable but it is not an essential sensor. The EGT is a useful parameter for health monitorg and can dicate several failures such as core degradation and let blockage (SAE, 2006). A typical EGT profile durg AU operation is dicated Figure HEALTH MONITORING METHODOLOGY Gas path analysis is a methodology for monitorg gas turbes proposed by (Urban, 1967), which has been used several studies for the purpose of health performance analysis (Saravanamuttoo et al, 1986), (Li, 2003). With the scope of AU monitorg, one of the ma challenges consists of discrimatg among possible failure modes affectg different components. In this context, promisg results have been obtaed with the use of classification methods (Vieira et al, 2009), (Sabyasachi, 2008). Classification and Regression Trees (CART) are a popular set of classification methods that have as one of its key characteristics the easess of terpretation of the results. This feature facilitates the validation of the results or the adjustment of the classification rules on the basis of the knowledge of a system specialist. CART uses a learng sample of historical data with assigned classes for buildg a decision tree, which expresses a set of classification rules terms of a sequence of questions. The use of CART volves three stages (Timofeev, 2004): 1. Construction of the maximum tree 2. Selection of an appropriate tree size 3. Classification of new data usg the resultg tree The classification tree uses some rules to split the learng sample to smaller parts, thus creatg the nodes and the tree itself. Such rules are called splittg 2

3 rules. Some examples are the Gi splittg rule and the Twog splittg rule. The first one is the most broadly used (Timofeev, 2004) and uses the followg impurity function: i t) = p( k t) ( (1) k l where k and l are class dexes, t is the node under consideration and p(k t) is the conditional probability of class k provided node t. At each node the CART solves a maximization problem of the form: arg max R x j x j, j= 1,... M [ i( t ) i( t ) i( t )] p l l r r (2) where l and r are probabilities of the left and right node respectively. Usg the Gi impurity function, the followg maximization problem must be solved to isolate the larger class from other data arg max K 2 K 2 K 2 [ p ( k t p ) + l p ( k tl ) + r p ( k tr )] k = 1 k = 1 k = (3) The health monitorg algorithm proposed the present work employs CART for failure classification based on residuals from faulty and healthy data steady state condition. The first implementation step was choosg the failure modes, variables used for model seeded fault, list of sensors and operational data snapshots for analysis. Eight types of faults were considered: 1. Increase shaft torque extraction; 2. Increase bleed; 3. Reduction compressor efficiency; 4. Reduction turbe efficiency; 5. Speed sensor bias; 6. EGT sensor bias; 7. Reduction combustor efficiency; 8. Decrease fuel flow. The measured variables were assumed to be fuel flow, EGT and bleed pressure. data and faulty data were generated usg the mathematical model of an AU derived from thermodynamic prciples. The residuals used as puts for CART were calculated as shown Figure 4. R 1 x j x j, j= 1,... M Ambient/Operational conditions Fault arameters Base Le Model Model with Seeded Fault ressure Residual Figure 4: Calculation of the residuals employed the proposed HM methodology. 4. MODEL DESCRITION The thermodynamic model used this work is represented schematically Figure 5. Ambient ressure Ambient Temperature Flow Generator Torque Controller Shaft Burner Turbe Figure 5: Block diagram of the AU model. Flow ressure Flow Residual The model contas four puts and three outputs, which represent the AU sensors. Three of the blocks model the thermodynamic behavior: the compressor, burner and turbe. The puts to the compressor block consist of ambient pressure and temperature, as well as shaft speed. The outputs are compressor torque, air flow, compressor pressure and temperature. The compressor behavior is based on a map which relates pressure ratio, airflow, speed and temperature as illustrated Figure 6. EGT EGT Residual 3

4 air flow and Air Ratio (FAR). The put-output characteristic of this component is represented as: h out W = air. h W + LHVW. air + W fuel fuel (9) where h and h out are respectively the burner let and outlet enthalpies, W fuel is fuel flow, W air is the burner exhaust air flow and LHV is the fuel heatg value. The turbe is represented by a map as illustrated Figure 7. Figure 6: map. The pressure ratio (R) is defed as: R = out (4) where out is the outlet pressure and is the let pressure. The corrected air flow is defed as: W c = W θ δ (5) where W is the absolute air flow and δ and θ are given by: δ = θ = ref T T ref (6) (7) where ref is the standard day pressure, T ref is the standard day temperature and T is the let temperature. The corrected speed (N c ) is defed as: N c = N θ (8) where N is the absolute shaft speed. The puts to the burner block are air flow, compressor pressure and temperature, as well as fuel flow. The outputs are burner pressure and temperature, Figure 7: Turbe map. Apart from the thermodynamic blocks, two other components are modeled Fig. 5. The first one is the controller, which reproduces one of the ma features of the FADEC, namely the control of shaft speed by manipulation of fuel flow. Here, a ID controller is used. The other block represents the energy balance of the shaft speed, which can be described by the followg equation: = τ = I. N& N I τ (10) where I is the moment of ertia and Στ is the sum of compressor, turbe and generator torques. The latter represents the torque extracted from the AU to supply electrical components such as electrical pumps and lights. 5. RESULTS For the construction of the itial classification tree, a set of 180 data vectors was generated. This dataset generation consisted of 20 simulations of AUs 4

5 startups for each of the failure modes and other 20 simulations for the AU operatg without faults. Different loads, simulatg pumps, enges and air cycle mache were used. The data vectors collected comprised residual values of EGT, fuel flow and bleed pressure. The resultg classification tree is presented Figure 8. Turbe Eff. Loss Eff. Loss Turbe Eff. Loss EGT Sensor Figure 8: Initial classification tree. Analyzg the itial classification tree it is possible to notice the great number of nodes possibly resultg overfittg of the trag data. This problem was solved by prung some nodes of the tree usg expert knowledge provided by an AU system specialist. Some of the failure modes were very similar and it would be a better strategy to group them to a reduced number of nodes. The resultg tree is presented Figure 9. Turbe Efficiency Loss Efficiency Loss Efficiency Loss EGT Sensor System Ok torque extraction Ok torque extraction Speed Sensor Speed Sensor Figure 9: Classification tree resultg from the prung procedure. It is possible to observe Figure 9 that some of the failure modes were grouped to the same node, dicatg that they could not be separated based on the sensors that were used. Although the itial objective was to classify all failure modes, some ambiguities could not be resolved. However, the level of isolation provided by the proposed tree was considered adequate, as it helps to reduce significantly the troubleshoot time on an event of failure. The proposed method was tested usg actual data collected the field. The dataset consisted of 18 data vectors comprisg 6 healthy states and 12 failure events. The data were collected with the enge, the electric pump and the air cycle mache either on or off state, as shown Table 1. Table 1: Field data. ACM ump Enge off off off off off on off off on on on off on on on off off on off off off on on off off off off 50% Inlet Blockage off off off 50% Inlet Blockage on on off 50% Inlet Blockage off off off 75% Inlet Blockage off off off 75% Inlet Blockage on on off 75% Inlet Blockage off off off Filter Blockage off off off Filter Blockage on on off Filter Blockage off off off Although the "Inlet Blockage" was neither modeled nor used for the trag of the classification tree, the effects due to this type of failure are very similar to those of an EGT sensor bias. Therefore, it is expected that these particular conditions should be classified as EGT sensor bias failures. The results for the classification are presented Table 2. Table 2: Classification results for the field data. Ground Truth Classification Eff. loss 50% Inlet Blockage EGT Sensor 50% Inlet Blockage EGT Sensor 50% Inlet Blockage 75% Inlet Blockage 75% Inlet Blockage 75% Inlet Blockage EGT Sensor Filter Blockage Filter Blockage Filter Blockage 5

6 Observg the results presented Table 2, one can notice that the classification algorithm was able to classify correctly all healthy states, that is, no healthy system was classified as faulty. On the other hand, the algorithm was not able to classify correctly all failure events. In order to identify possible improvements on the method proposed, all classification errors were analyzed observg the raw data. Lookg at the data from 50% let blockage and fuel filter blockage faults, both classified as no failures, no significant difference the parameters were observed, as compared to a situation without fault. The conclusion is that the "Inlet Blockage" and the " Filter Blockage" were not sufficient to cause any modification on the monitored variables. One factor that could contribute to these errors is the difference the behavior of the AU hot and cold starts. This effect was not modeled the present work. Lack of precise calibration and modelg data, specifically compressor and turbe maps and lack of precise bleed flow test data lead to errors on beg classified as compressor efficiency loss and the 75% Inlet Blockage classified as. 6. CONCLUSION This paper presented an AU health monitorg method usg a Dynamic Model and a Classification and Regression Tree (CART). The CART was used to classify AU failure modes based on measurements of the exhaust gas temperature, the bleed pressure and the fuel flow. After designg the CART, the method was tested usg real AU data. Although the method was not capable to classify correctly all failure modes, it showed promisg results. ACKNOWLEDGMENT The authors acknowledge the support of CNq (research fellowships) and FAES (grant 06/ ). REFERENCES Chen, W. (1991). Nonlear Analysis of Electronic rognostics. h. D. Thesis. The Technical University of Napoli. Jones S. M. (2007). An Introduction to Thermodynamic erformance Analysis of Aircraft Gas Turbe Enge Cycles Usg the Numerical ropulsion System Simulation Code. NASA Glenn Research Center, Cleveland, Ohio NASA/TM Li Y. G. (2003) A gas Turbe Approach with Transient Measurements roceedgs of the Institution of Mechanical Engeers, art A: Journal of ower and Energy, Volume 217, Number 2, pp Sabyasachi B., Farner S., Schimert J. and Weland (2008) A Data Driven Method for redictg Enge Wear from Exhaust Gas Temperature roceedgs of International Conference on rognostics and Health Management. SAE. (2006). E-32 AIR A guide to AU Health Management Saravanamuttoo H.I.H. and Maclsaac B.D. (1982). Thermodynamic Models for ipele Gas Turbe Diagnostics, Journal of Engeerg for ower, Vol. 105, pp , October 1982 Timofeev R. (2004), Classification and Regression Trees (CART) Theory and Applications, Master Thesis - CASE - Center of Applied Statistics and Economics Humboldt University, Berl. Urban L. A.(1967). Gas Turbe Enge arameter Interrelationship, HSD UTC, Wdsor Locks, Ct., 1 st edition. Vieira, F. M. and Bizarria C. O. (2009).Health Monitorg usg Support Vector Classification on an Auxiliary ower Unit, roceedgs of IEEE Aerospace Conference, Big Sky, MO. Wlamir Olivares Loesch Vianna holds a bachelor s degree on Mechanical Engeerg (2005) from Universidade de São aulo (US), Brazil, and Master Degree on Aeronautical Engeerg (2007) from Instituto Tecnológico de Aeronáutica (ITA), Brazil. He is with Empresa Brasileira de Aeronáutica S.A (EMBRAER), São José dos Campos, S, Brazil, sce He works as a Development Engeer of a R&T group at EMBRAER focused on HM technology applications aeronautical systems João aulo ordeus Gomes holds a bachelor s degree on Electrical Engeerg (2004) from Universidade Federal do Ceará (UFC), Brazil, and Master Degree on Aeronautical Engeerg (2006) from Instituto Tecnológico de Aeronáutica (ITA), Brazil. He is currently pursug his h.d. from ITA. He is with Empresa Brasileira de Aeronáutica S.A (EMBRAER), São José dos Campos, S, Brazil, sce He works as a Development Engeer of a R&T group at EMBRAER focused on HM technology applications aeronautical systems Roberto Kawakami Harrop Galvão is an Associate rofessor of Systems and Control at the Electronic Engeerg Department of ITA. He holds a bachelor s degree Electronic Engeerg (Summa cum Laude, 1995) from Instituto Tecnológico de Aeronáutica (ITA), Brazil. He also obtaed the master s (1997) and 6

7 doctorate (1999) degrees Systems and Control from the same stitution. He is a Senior Member of the IEEE and an Associate Member of the Brazilian Academy of Sciences. He has published more than 150 papers peer-reviewed journals and conference proceedgs. His ma areas of terest are fault diagnosis and prognosis, wavelet theory and applications, and model predictive control. Takashi Yoneyama is a rofessor of Control Theory with the Electronic Engeerg Department of ITA. He received the bachelor s degree electronic engeerg from Instituto Tecnológico de Aeronáutica (ITA), Brazil, the M.D. degree medice from Universidade de Taubaté, Brazil, and the h.d. degree electrical engeerg from the University of London, U.K. (1983). He has more than 250 published papers, has written four books, and has supervised more than 50 theses. His research is concerned maly with stochastic optimal control theory. He served as the resident of the Brazilian Automatics Society the period Jackson aul Matsuura is an Associate rofessor of Systems and Control at the Electronic Engeerg Department of Instituto Tecnológico de Aeronáutica (ITA). He holds a bachelor s degree (1995) Computer Engeerg from ITA, Brazil. He also obtaed the master s (2003) and doctorate (2006) degrees Systems and Control from the same stitution. His ma areas of terest are fault tolerant control, robotics and augmented reality. He won the RoboChamps World Fals