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Neural Network Model of Airbus A340 Flight Dynamics for Reliability Analyses

Seref Demirci* and Rahmi Aykan † Turkish Airlines Inc., Yesilkoy, Istanbul, Turkey, 34850

An airline can evaluate the data obtained from Flight Data Recorder (FDR) of its own aircraft for flight performance, aircraft reliability and maintenance program improvement. The degradation measurement of the dynamics of aircraft or its components such as engine and flight control can be monitored over time, and these measurements can be used for their reliability and hence predictive maintenance of the component. We can define component failure in terms of a certain level of degradation, and estimate the reliability of that particular component based on its degradation measures. This work aims to the determination of the neural network model of aircraft flight dynamics from FDR. Then it presents an approach the determination of the degradation of the reliability of components by using FDR data. The data from various flights of Turkish Airlines A340 fleet are downloaded. Some portions of these data are used for training and validation of the obtained neural network model, and some of them are used for the intended analyses. The improvement of flight performance and reliability is analyzed via neural network model. The advantages and accuracy of the method are discussed. Implementation of this reliability monitoring analysis reduces the overall cost by obtaining the optimal component replacement and maintenance strategies.

Nomenclature M = window length N1 = fan speed N2 = core speed NW = neural network weights’ vector µ = training speed parameter

Abbreviations

ACARS = aircraft communications addressing and reporting system AGS = Aircraft Ground Systems COMPASS = Condition Monitoring Performance Analysis Software System ECM = Engine Condition Monitoring EGT = exhaust gas temperature FF = fuel flow GEM = Ground Engine Monitoring NN = neural network OATL = outside air temperature limit SAGE = System for the Analysis of Gas Turbine Engines

* Manager, Reliability, Ataturk Airport, Gate B and Prospect AIAA Member. † Engineer, Design and Development, Ataturk Airport, Gate B and Prospect AIAA Member.

AIAA Modeling and Simulation Technologies Conference and Exhibit15 - 18 August 2005, San Francisco, California

AIAA 2005-6111

Copyright © 2005 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

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I. Introduction OWER plant is the most critical and expensive component on aircraft that effects the airworthiness and safety. The aim of the power plant reliability is to keep engines on wing longer as much as possible and reduce

overhaul costs. In order to maintain this reliability level, engine performance is monitored continuously when cruising in air. On the other hand, from 1 January 2005 flight data analysis in airways has been mandated by civil aviation authorities. It has been resulted in development of several softwares such as Aircraft Ground Systems (AGS) of SAGEM, COMPASS, SAGE, Ground Engine Monitoring (GEM) and ADEPT. Due to availability of the vast useful data in Turkish Airlines, AGS is preferred for this study.

Engine Condition Monitoring programs (ECM) programs were originally developed by Pratt and Whitney.1 Engine Condition Monitoring is an important aspect of safe engine operation and effective engine operation. An effective monitoring assists to managing and forecasting engine maintenance. Engine condition monitoring can be used as a tool to track and restore engine performance, improve problem diagnosis, suggest solutions, promote better aircraft operation, minimize in-flight failures, and reduce costs of engine maintenance. The aims of the ECM are to assess the engine performance and health, to provide a "quick look" engine/instrumentation fault detection, to prevent unexpected engine problem such as in flight shut down and aborted take-offs to reduce unscheduled maintenance to monitor guarantees and to reduce the overhaul costs.

At take off, engine parameters typically monitored are exhaust gas temperature (EGT), outside air temperature limit (OATL), and fan speed (N1). In cruise, EGT, fuel flow (FF), fan speed (N1), core speed (N2), fan vibration, core vibration, oil pressure, and oil temperature. For the take off and cruise performance, the above parameters are monitored. The basic pre-set alert checks in cruise are performed according to sudden increase in EGT, N2 or fuel flow deviation from baseline, measured oil pressure outside of limits, excessive oil pressure deviation from reference, sudden increase in oil pressure deviation, VSV deviation fall outside of limits, N2 trend rises too far above initialization value, sudden shift in VSV deviation. At takeoff, they are performed according to OATL or EGT margin falls below limit, sudden decreases in OATL or EGT margin. 2 In this study, N1, N2, EGT and FF are selected for output as shown in Fig. 1.

The input to the trend programs are other engine parameters, bleed conditions and flight parameters. These parameters are all measured simultaneously under stable conditions. The most consistent trending parameters are recorded with the auto throttle turned off. That is the recommended procedures. For aircraft eqquipped with automatic data recording the auto throttle is normally not turned off. Consequently this will contribute to variation in trend parameters. Output from trend programs are ∆EGT, ∆FF, ∆N2 relative to a baseline engine characteristic. 3

These in-flight parameters are referred to as engine health readings taken at the data points under the cruising condition wherein the mach, altitude, and outside air temperature are held steady long enough to take a snapshot of fight data. The snapshot is automatically taken by an aircraft communications addressing and reporting system (ACARS) installed onboard if ACARS is available. The other methods used in receiving engine snapshot parameters are log pages which are entered manually and PCMCIA cards and diskets loaded DFDR data automatically in specified intervals drom aircraft etc. The baseline values are supplied by the engine manufacturers, which have been calibrated to with various fleet-engine configurations in the preflight testing process. The deviation between snapshots and baseline values demonstrates a trend curve that characterizes the engine health under the cruising condition. Thus the ECM system is able to trace the trend curves of FF, EGT, N1, and N2 to monitor the engine condition, in which a watch-list program has been developed to calculate the correlation coeficients2 of 20 consecutive data from the sequential flights in order to detect the engine faults. If diagnosed to be malfunctioning, the engines will be taken off the plane for maintenance. 1

P

Figure 1. Engine performance parameters to be tracked.

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Engine deterioration is indicated by an increase in both ∆EGT and ∆FF. An increase in ∆EGT and ∆FF is not always deterioration as there are other factors which can cause these parameters to increase.3 Trend monitoring showed N2 decreases indicates HPT deterioration. Increase in FF shows efficiency deterioration. In order to diminish this deterioration, engine FF is increased. In the result of increase of FF, EGT increases.3

Mindful of the heavy costs associated with unscheduled downtime, many airlines are seeking to boost their diagnostic and predictive capabilities in the area of engine condition monitoring. In response, certain key providers are leveraging the latest technologies to facilitate earlier detection and diagnosis of anomalies. Interestingly, the key to much of this functionality, according to Nicholls, is the pioneering use of artificial neural networks for such applications. Artificial neural networks (more commonly referred to as simply “neural networks”) can be ‘trained’ with data from multiple sensors. This training allows normal and fault conditions to be recognized. Moreover, engine health monitoring applications are ideally suited to neural network solutions. Such solutions can be protective and/or predictive in nature, both of which can be provided by novelty detection-based neural network solutions. For example, monitoring a gas turbine may well use vibration as the primary data but this would be fused with secondary data such as tachometer, temperature, pressure, load, environment, and other performance parameters. In this way neural networks will provide for the detection of fault conditions much earlier than single sensor systems. Whilst neural networks may take time to model, once they have been modeled, most of the really hard work has been accomplished, so subsequently, the real-time on-wing analysis does not consume a great deal of processing power for the online analysis once you have created the model .4

Link C. Jaw summarizes the findings of a survey of recent advancements in aircraft Engine Health Management (EHM) technologies. The traditional Engine Health Management approach uses fleet statistical data and signal processing techniques to detect and isolate faults. Modern EHM approaches enhance the traditional approach with physics-based models, individual engine performance tracking, predictive algorithms, and decision support capabilities. A modern approach typically measures key operating variables, compares model-estimated values with the measured values, and applies various algorithms and reasoning logic to make health management decisions; therefore, modern EHM capabilities often include model-based diagnostics (or prognostics) and model-based reasoning. 5

In this work, a neural network (NN) model is proposed for reliability analysis of CFM56 engines of Turkish Airlines A340 aircraft fleet.

II. Data Processing The necessary data for NN modeling is obtained from A340 flights from Istanbul to New York. The processed

data of AGS are selected. These downloaded data are normalized for NN process as making them zero mean and unit standard deviation. Normalization is a process of scaling the data to improve the accuracy of the numeric computations. One way to normalize data set is to center them at zero mean and scale it to unit standard deviation. This approach gains us to easily capture abnormal or faulty data.

Data of five flights from engine start to engine stop are taken. Each flight is used for a special purpose. One is for training, one is for validation, one is for testing and one is for application. In this paper, application illustrations are shown in the last chapter.

III. NN Model for Engine Reliability Analysis NNs have increasingly been shown as viable tools for mapping nonlinear systems and for the purpose of

parameter identification. It is very efficient method in the analysis of nonlinear and complex models if enough data are available for its training phase. This study aims to establish a NN model of aircraft engines for performance and reliability analysis. By monitoring the flight data, changes in the engine parameters are found, and a fault signal can be built up according to change level.

In this study, the proposed NN structure has 37 inputs covering flight parameters such as altitude, Mach number, total air temperature, weight and four outputs as engine parameters N1, N2, EGT and FF. One hidden layer having tangent activation function is proposed. In the output layer, linear activation function is used.

For training method the Levenberg-Marquardt backpropagation algorithm (LMBA) is used to maintain second-order training speed without having to compute the Hessian matrix, which includes the second derivatives of the network output errors (e) per network weights and biases (NW). Error represents the difference between network output and actual or simulated value, i.e. desired value. NN model of the aircraft can be calculated as below:

NWk+1 = NWk - [JTJ+µI]-1JTe (1)

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where J is the Jacobian matrix that contains first derivatives of the network errors with respect to the weights and biases. The Jacobian matrix can be computed through a standard backpropagation technique. µ is the parameter of LMBA to make the network faster and more accurate every step forward. If µ is zero, the method becomes the basic Newton’s optimization method. When µ is large, this becomes gradient descent with a small step size. Newton’s

method is quicker and more accurate near an error minimum. Therefore, the aim in LMBA is to shift towards Newton’s method as quickly as possible.

IV. Simulation Results As explained above, N1,

N2, EGT and FF are the most important engine parameters for engine reliability analyses. Therefore, these parameters are selected for illustration. In the figures, actual values and the outputs of trained NN model of these parameters are indicated as solid and dashed lines, respectively. In the figure legends, “target” means measurement. During training process these values are taken as targets. The error history is also given in the lower section of the each figure.

Note that predicted data by NN are smoother than measured data. Unreasonable data due to some measurement errors may be cancelled by NN process.

In the several downloaded data from Turkish Airlines A340 fleet no abnormal or faulty data has been found. If any fault had occurred, NN output and measurement would have gone far from each other

Figure 2. N1 history during cruise

Figure 3. N2 history during cruise

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References 1Yang, L. F. and Ioachim, I.,

“Adaptive Estimation of Aircraft Flight Parameters for Engine Health Monitoring System” Journal of Aircraft, May-June 2002, Vol.39, No.3, pp. 404-411.

2GE Engine Monitoring Using SAGE System

3CFM International Commercial Engine Service Memorandum, CESM no. 014, Rev1. March 3/99

4Aircraft Technology Engineering & Maintenance”, Paris 2005 Special

5Jaw, L. C., “Recent Advancements in Aircraft Engine Health Management (EHM) Technologies and Recommendations for the Next Step,” Scientific Monitoring, Inc., Scottsdale, Arizona, U.S.A.

Figure 4. EGT history during cruise

Figure 5. FF history during cruise