 EXHAUST GAS TEMPERATURE
PREDICTION USING MACHINE
LEARNING MODELS IN JET ENGINE
• In recent years, with the growing generation of large amounts of data in modern aircraft
(e.g., an Airbus A350 generates 50 times more data than the A320), many improved
applications have been developed as we pass from snapshot to continuous data collection.
• Continuous Engine Operating Data (CEOD) are collected and recorded at high frequencies
in modern aircraft types, a development that can improve the predictive capabilities for
engine operators.
• The temperature of the exhaust gases of an engine, known as the exhaust gas temperature
(EGT), has evolved to become the standard industrial indicator of the health of an aircraft
engine.
• In this Study We will Determine the engine performance using the Condition Based
maintenance(CBM) and Generalized Additive method(GAM).
• The prediction model is employed on the N-CMAPSS sub-datasets DS01. The N-CMAPSS(New
Commercial Modular Aero-Propulsion System Simulation) database, developed with the use
of the MATLAB's Simulink environment at NASA's Glenn Research Center, is a public domain
tool for the simulation of realistic large commercial turbofan engine data.
 Condition Based Maintenance
• Data-driven condition-based maintenance and predictive maintenance strategies aim to
reduce maintenance costs, maximize availability, and contribute to sustainable Maintenance
operations.
• Here We have shown the five steps of CBM approach:
1) In Jet Engine Hardware (Sensors) which Collects Data Can be Thermocouple , Pressure
Sensors , Speed Sensors.
2) Data acquisition In useful By the sensors. Data can be Temperature , Speed , Flow Rate ,etc.
3) Data Storage in Efficient Transfer Process.
4) Data Analytics is used for Diagnostics and Prognostics . Prognostics is used to Predict the
future health performance and useful life of a system ( Here Jet Engine ).
5) Decision support is used to determine actions based on the provided information (EX
Temperature , Altitude , Air Velocity).
 In Decision Support RCA means Root Cause Analysis . It Detects The Fault and gives correct
information based on real Data.
 We will use Generalized additive model to predict the EGT (Exhaust Gas Temperature) of the
jet engine. First we will show generalized additive model principle.
 Generalized Additive Model (GAM)
 It is a type of Regression Algorithm.
 GAM is known to provide information of each Individual feature. The GAM is a machine
learning method that employs linear models.
 It sums up the arbitrary functions for each feature. These functions allow nonlinear
relationships and are called splines. This type of model therefore allows a great deal of
flexibility and a greater possibility of explanation than other methods.
 The general GAM expression can be defined as follows:
g(E(X/Y)) = 𝛽0 + s1(𝑥1) + s2(𝑥2) +...+ s𝑝(𝑥𝑝)
 where g is a link function , S is the smooth function
 Y is the variable to be predicted,
 E(Y|X) is the expected value of the predictor variables Xj.
 We will understand GAM with showing the required parameters to predict Exhaust Gas
Temperature.
 This study focuses on the engine performance parameters available in this database and
summarized in Table.
 E(T1|predictor) = S0 + S1(x1) + ……+ Sn(xn)
Target Parameter
Smooth function
Here , Hyper parameter (λ) is another
term which decides the smoothness of
a spline with giving it a single value.
Intercept
 Here x1 , x2 …are predictor variables .
 Each smooth function f shows how a single variable affects the output. Smooth Function may
contain linear or non linear data . It is based on the relation between parameters in the
dataset.
 Intercept shows the initial condition.
• Generalized In GAM means it deals with linear as well as non linear data to link functions.
• Additive means it sums up the all the functions of each predictor assembly.
• Model means it is a mathematical model.
 The value of hyperparameter here is 0.6 .We will take 10 readings for each unit.
 As a first step, a correlation matrix is put together that reveals the correlation between all the
different available engine performance parameters Based on Correlation Method.
Table. N-CMAPSS engine performance parameters
• In order to assess the performance of the model, the root mean square error (RMSE) is
calculated and presented as :
• Here, yi = T1j are the real values of LPT outlet
temperature and y^i = T1^j are the predicted values.
• Lower value of RMSE indicates the model is more
accurate.
• It gives the idea of how much the predicted value is
changing from the actual value.
• R2 (Coefficient of determination) :
• Here, The same variables are used as those above
with the addition of T1mean, which represents the
arithmetic mean of the real EGT values.
• Higher R2 value means the model is more accurate.
 Here we have a DS08 dataset which is a standard dataset to predict EGT Temperature. we
will now calculate RMSE Training and Test ,R2 Training and test from per unit for DS08.
Table. RMSE and R2 per unit for DS01a dataset
 Here we have the maximum value of RMSE training and Test is 1.698 and 1.956 respectively
which is a satisfactory Error.
Figure. Performance parameters correlation matrix
 There are some units that were less accurate (e.g., unit 13), with an RMSE of 12.78 ◦R, which
translated into approximately 7◦C, an acceptable value for marginal cases. The
corresponding R2 in this case was 0.95.
 The above plot is T50 (EGT) Prediction Graph of unit 13 . Here , A unit consists one engine.
 In the graph actual value is very closer to predicted value. This indicates the capability of
GAM to find the predicted value is very accurate.
 The data were divided into a training set and a test set at proportions of 70% and 30%,
respectively.
 This doesn’t mean the dataset only contains 15 engines — it means the researchers used a
subset of 15 engine units (out of the 100 available) for model development, due to
computational limits or for demonstration. The size of dataset is 35.6 Million .
 The decision to develop an EGT prediction framework stems from the fact that the exhaust
gas temperature has always been the main gas turbine health-monitoring metric, and most
of the crucial operational decisions are being made on the basis of the measured EGT.
 Results and Discussions
 The model achieved R² values > 0.99 and low RMSE (≈ 1–7 °R) for both Training and Testing
under multiple deterioration conditions.
 GAMs is explored because of their ability to provide information of each individual feature.
If you want to predict the parameter which have a relation with other variables (may be
positively or negatively) the GAM will arrange the variables in the form of functions based
on the relation with target parameter.
 The decision to develop an EGT prediction framework stems from the fact that the exhaust
gas temperature has always been the main gas turbine health-monitoring metric, and most
of the crucial operational decisions are being made on the basis of the measured EGT.
 NASA, C-MAPSS Aircraft Engine Simulator Data.
https://data.nasa.gov/dataset/C-MAPSS-Aircraft-EngineSimulator-Data/xaut-bemq
Thank you