Jeffrey B. Armstrong

ASRC Aerospace Corporation, Cleveland, Ohio

Donald L. Simon

Glenn Research Center, Cleveland, Ohio

Implementation of an Integrated On-Board 

Aircraft Engine Diagnostic Architecture

NASA/TM—2012-217279

April 2012

AIAA–2011–5859
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Jeffrey B. Armstrong

ASRC Aerospace Corporation, Cleveland, Ohio

Donald L. Simon

Glenn Research Center, Cleveland, Ohio

Implementation of an Integrated On-Board 

Aircraft Engine Diagnostic Architecture

NASA/TM—2012-217279

April 2012

AIAA–2011–5859

National Aeronautics and

Space Administration

Glenn Research Center

Cleveland, Ohio 44135

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Level of Review: This material has been technically reviewed by technical management. 
NASA/TM—2012-217279 1

Implementation of an Integrated On-Board 

Aircraft Engine Diagnostic Architecture

Jeffrey B. Armstrong

ASRC Aerospace Corporation

Cleveland, Ohio 44135

Donald L. Simon

National Aeronautics and Space Administration

Glenn Research Center

Cleveland, Ohio 44135

Abstract

An on-board diagnostic architecture for aircraft turbofan engine performance trending, parameter 

estimation, and gas-path fault detection and isolation has been developed and evaluated in a simulation 

environment. The architecture incorporates two independent models: a real-time self-tuning performance 

model providing parameter estimates and a performance baseline model for diagnostic purposes reflecting

long-term engine degradation trends. This architecture was evaluated using flight profiles generated from 

a nonlinear model with realistic fleet engine health degradation distributions and sensor noise. The 

architecture was found to produce acceptable estimates of engine health and unmeasured parameters, 

and the integrated diagnostic algorithms were able to perform correct fault isolation in approximately 

70 percent of the tested cases.

Nomenclature

C-MAPSS Commercial Modular Aero Propulsion System Simulation

FDI Fault Detection and Isolation

Fn Net thrust

H Fault influence coefficient matrix

h Health parameter vector

HPC High Pressure Compressor

HPT High Pressure Turbine

LPC Low Pressure Compressor

LPT Low Pressure Turbine

m Numerical representation of a gas-path fault magnitude

Nf Fan rotational speed and related sensor

Nc Core rotational speed and related sensor

P2 Pressure at engine inlet

P24 Low Pressure Compressor exit pressure

p Percentage contribution to the sum-squared of residuals

PLA Power lever angle

PBM Performance Baseline Model

Ps30 Static pressure at High Pressure Compressor exit

q Engine tuning parameter vector

R Sensor measurement covariance matrix

ROC Receiver Operating Characteristic

RTAPM Real-Time Adaptive Performance Model

SmLPC Low pressure compressor stall margin

SQL Structured Query Language
NASA/TM—2012-217279 2

T2 Temperature at engine inlet

T24 Low Pressure Compressor exit temperature

T40 High Pressure Turbine inlet temperature

T48 Low Pressure Turbine inlet temperature

T50 Exhaust gas temperature

V* Transformation matrix mapping health parameters into engine tuning parameters

VBV Variable Bleed Valve

VSV Variable Stator Vanes

Wf Fuel flow

w Weighting parameter

y Sensed engine parameter vector

z Unmeasured engine parameter vector

γ Flow capacity

η Efficiency

Introduction

Modern aircraft gas turbine engines are complex systems where reliability and efficiency are of 

particular interest to operators. To manage a collection of such assets, the accurate estimation of current 

engine performance and the fast diagnosis and correction of problems are necessary to maintain safety 

and reduce operating costs. Gas-path health management refers to the application of these concepts to an 

engine’s flow path, including turbomachinery modules, control system sensors, and related actuators. 

A commercial turbofan engine will exhibit gradual degradation over its operating life. 

Turbomachinery module efficiencies will slowly degrade as parts wear from regular use. The estimation 

of engine performance can help operators determine flight hours remaining until regular maintenance is 

necessary and manage the replacement of these modules as performance approaches unacceptable levels. 

Furthermore, like all complex systems, turbofans can experience a variety of faults, including 

turbomachinery performance shifts, sensor measurement biases, and actuator malfunctions. These faults 

may occur suddenly or intermittently, or they may gradually develop as a slow drift from nominal 

operation. To ensure efficient and safe operation, gas-path performance trending and fault diagnostics are 

essential.

The conventional strategy for gas-path diagnostics and performance trending relies on simple onboard diagnostics and ground-based performance trending and fault diagnostics. Conventional on-board 

diagnostics include range checks, rate-of-change limits, and built-in-test logic to ensure proper operation 

of the engine control systems. Ground stations rely on snapshot sensed data measured during flight to 

trend engine performance and diagnose possible faults. A reliance on this strategy may require multiple 

flights before detecting and isolating gas-path faults, and intermittent faults are unlikely to be detected at 

all unless advanced on-board diagnostics are available to diagnose the problem (Ref. 1).

Enhanced on-board diagnostics offer advantages over the conventional ground-based architecture. 

With advancements in computational power available in avionics systems, model-based on-board 

diagnostics are possible, and the benefits of such systems can be exploited. Specifically, an on-board 

solution potentially has access to streaming sensed values from engine controllers; in contrast, the 

conventional ground-based snapshot approach utilizes far less data to track engine degradation trends and 

diagnose faults. The availability of real-time data can decrease fault detection and isolation latency 

significantly, and intermittent engine faults have a greater probability of detection. When used in 

conjunction with conventional ground-based approaches and existing on-board built-in-tests, fault 

detection and isolation accuracy is expected to improve significantly. Additionally, a model-based 

solution offers the ability to estimate unmeasured engine parameters, which can potentially be employed 

by control systems at both the engine and aircraft levels.

This paper provides details of an implemented integrated architecture for on-board gas-path 

diagnostics. This model-based diagnostic architecture design is first reviewed in detail, including the real-
NASA/TM—2012-217279 3

time filter applied to track performance deterioration, the diagnostic model, and the performance trending 

storage algorithm. Two gas-path fault detection techniques and two fault isolation algorithms 

implemented in conjunction with this architecture are also explained. The architecture is then exercised 

against a nonlinear engine model simulating a 90,000 lbf class turbofan engine. The diagnostic 

architecture’s accuracy in estimating engine health parameters and other unmeasured engine outputs, or 

auxiliary parameters, is evaluated. Finally, based on a database of engines containing simulated gas-path 

faults, the fault detection and isolation algorithms are also evaluated.

Implemented Architecture

An integrated architecture for trend monitoring and gas-path diagnostics was proposed to exploit onboard real-time sensor data (Ref. 2). To validate the design proposal, an implementation of the integrated 

architecture has been developed for testing within simulation environments. The design incorporates two 

independent simulations, each serving specific purposes. An overview of the architecture is illustrated in

Figure 1. A real-time adaptive performance model (RTAPM) implements a Kalman filter, providing 

streaming estimates of unmeasured outputs while tracking engine performance. An independent 

performance baseline model (PBM) utilizes periodically updated estimates of engine condition in 

association with a piecewise linear engine model to produce estimated sensor measurements for fault 

diagnostic purposes. Each model’s design and purpose is described in detail below.

Real-Time Adaptive Performance Model

The RTAPM is a self-tuning linear state-space model providing streaming engine health and 

unmeasured parameter estimates based on current operating conditions, engine commands, and sensor 

readings. Engine tuning in this implementation has been designed to capture gradual engine deterioration. 

This estimation of engine performance is captured via a piecewise linear Kalman filter designed 

specifically for this architecture.

Figure 1.—Enhanced On-Board Trend Monitoring and Gas-Path Diagnostic 

Architecture. The proposed architecture enhances conventional fault 

detection and isolation logic with the addition of a Real-Time Adaptive 

Performance Model and a Performance Baseline Model, outlined in red.

Control

Logic

Sensor

Measurements

Actuator

Commands

(Enhanced) Fault Detection

& Isolation Logic

Engine health reports, diagnostic information, snapshot data

Real-Time Adaptive

Performance Model

Performance 

Baseline Model

Tuners (Conventional) Fault Detection

& Isolation Logic

Transmission to

ground station

+

-
NASA/TM—2012-217279 4

The Kalman filter itself has been generated from a piecewise linear approximation of a nonlinear 

engine model. The piecewise linear model, including all state-space system matrices and the resultant 

piecewise linear Kalman filter are scheduled on three engine operating conditions: pressure altitude, Mach 

number, and corrected fan speed or power lever angle (PLA), which acts as a power reference parameter. 

The use of a three-dimensional scheduling algorithm was found to provide the best estimation accuracy 

versus the nonlinear model in this study. Depending on accuracy requirements, the number of scheduling 

dimensions can be configured appropriately.

Selection of Engine Model Tuning Parameters

The RTAPM Kalman filter is constructed to estimate engine dynamic states plus additional tuning 

parameters designed to capture gradual engine performance deterioration effects. Health parameters, 

modeled as efficiencies and flow capacities associated with each major rotating engine module in this 

study, are used as a measure of engine degradation. However, health parameters are not directly 

measurable. Additionally, for this study as well as most other aircraft engine applications, health 

parameters outnumber available engine sensed measurements, which leads to an underdetermined 

estimation problem. A common practice in the design of adaptive engine models is to define the tuning 

parameters to be a subset of the available engine health parameters. While this will enable Kalman filterbased estimation, it can result in “smearing” the effects of un-estimated health parameters onto those that 

are estimated, and in turn introduce error in the accuracy of overall model-based performance estimation 

applications (Ref. 3).

Instead of following the conventional approach of selecting a subset of health parameters to serve as 

the model tuner vector, this study applies a tuner vector, q, defined as a linear combination of all health 

parameters, h, constructed as:

q V h* = (1)

In the above equation, V* is a transformation matrix computed during the design phase via an 

optimization procedure designed to minimize the Kalman filter theoretical mean squared estimation error 

in the unmeasured parameters of interest. The size of the transformation matrix is dictated by the number 

of health parameters describing the engine degradation and the number of sensors available to the Kalman 

filter. Optimization involves exercising a search procedure that seeks to simultaneously minimize 

unmeasured parameter estimation errors at nine engine operating points (altitude, Mach number, and 

power lever angle), as illustrated in Figure 2. The points are chosen to be representative of common 

commercial flight profile operating conditions with additional weighting placed on cruise conditions; 

however, Monte-Carlo analysis suggests that, for this engine model, the estimation error is fairly 

consistent at operating points not included in the optimization (Ref. 4). Nine operating points were 

determined through testing to be sufficient for this particular application.

Kalman Filter Construction

Based on the results of the optimal tuner selection study, a piecewise linear Kalman filter dataset is 

constructed at multiple points throughout the flight envelope. Using the tuner vector arrived at through the 

optimization of the V* transformation matrix, a piecewise linear Kalman filter database is generated from 

a piecewise linear engine model. The resulting Kalman gains may be interpolated based on operating 

point similar to the simple piecewise linear model’s state-space matrices. 
NASA/TM—2012-217279 5

Figure 2.—Selected Operating Points for Engine Tuner Optimization. A 

collection of common operating points are selected to be representative of a 

commercial flight profile.

Performance Baseline Model

The PBM provides estimates of sensed engine values based on periodically updated engine 

performance estimates, a power reference parameter, and engine commands. The difference in true engine 

sensor values and the PBM’s sensor estimates is available for use by diagnostic algorithms. The model 

applies a piecewise linear engine model to capture dynamics coupled with periodically updated engine 

tuner parameters to incorporate the effects of engine performance degradation; performance estimates are 

calculated externally, passed periodically as inputs to the PBM. By only using periodically updated tuner 

estimates, the PBM avoids the possibility of engine tuners absorbing the effects of gas-path faults, which 

may occur if instantaneous Kalman filter tuner estimates are used.

The PBM’s piecewise linear dataset has been generated via a linearization of a nonlinear engine model at 

multiple operating points, identical to the original data employed to generate the RTAPM’s Kalman filter. 

Similar to the Kalman filter present in the RTAPM, the PBM is scheduled on three parameters: altitude, 

Mach number, and corrected fan speed, which acts as this implementation’s power reference parameter.

This diagnostic architecture uses a power reference parameter as an explicit input into the PBM. The 

power reference parameter input protects the PBM against divergence of the model’s internal states. In 

this particular example, sensed fan speed is employed as an input rather than using fan speed as an

internal model state. By treating fan speed as an input, the number of internal states within the embedded 

piecewise linear model is decreased by one.

The PBM does not employ instantaneous tuner estimates, and the PBM sensor estimates will differ 

from actual sensed engine values. Because engine degradation is assumed to be gradual, the deterioration 

effects due to normal engine operation are captured and accounted for in the PBM using periodic tuner 

updates. As gas-path engine faults are expected to manifest themselves as sudden performance shifts, the 

real-time tuner estimates generated by the RTAPM’s Kalman filter will immediately absorb sudden 

performance shifts. Because the tuners employed by the PBM are updated only periodically, a gas-path 

fault that can be manifested as sudden performance shifts will not be reflected in the tuner inputs to the 

PBM, leading to a measurable divergence of actual sensor values from respective PBM estimates.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

x 104

Mach Number

Altitude (ft)

Tuner Optimization Points

← PLA=80

← PLA=80

← PLA=80

← PLA=60,80

PLA=60,80 →

PLA=60,80 →
NASA/TM—2012-217279 6

Periodic Tuner Update

The PBM is provided with tuner update vectors calculated from real-time data from earlier flights. 

The periodic tuner update procedure can be separated into two distinct tasks: storage and retrieval. 

Additionally, the update system has been designed to support a “cellular” approach to storage and 

retrieval. The flight envelope is partitioned along three dimensions (altitude, Mach number, and power 

level) into a grid of cells; the grid density along any dimension is a design parameter that can be adjusted 

depending on accuracy requirements and storage constraints. As the engine transitions through different 

cells within the flight envelope, the periodic update storage and retrieval routines can both apply and 

retrieve tuner data specific to that portion of the flight envelope. 

In this architecture, the tuner data that is provided to the PBM is taken from data collected from the 

self-tuning RTAPM at least one flight prior to the current. Utilizing the older tuner data where gas-path 

faults are not expected to be present allows the PBM to operate without any absorption of fault signatures 

by the real-time tuners generated in the RTAPM. 

Storage

Real-time tuners calculated by the RTAPM are continuously streamed to a storage module. The 

storage module also accepts operating point information to allow for cellular storage during the flight. An 

incoming tuner vector is added to a running sum of tuner vectors for the flight envelope cell in which the 

engine is currently operating and a counter is incremented. At the conclusion of a flight, the number of 

samples collected for each cell is examined. An average tuner vector is computed for cells where a 

sufficient number of samples were stored to consider the data significant. This average tuning vector for 

any given cell is then committed to a backend storage system for later retrieval by the PBM. Some 

consideration is given to existing data in the long-term storage backend. Namely, the incoming data is 

linearly weighted against the existing data based on the age at the time of commit. The weighting of data 

based on age is a tunable design parameter.

Retrieval

The retrieval module provides engine tuning vectors from a backend storage module. Retrieval is 

achieved by requesting a tuner vector based on current operating conditions. Two methods of calculating 

the tuning vector for use by the PBM are implemented. The first employs a three-dimensional lookup 

table, where the tuning vector provided to the PBM is simply retrieved from the cell in which the engine 

is currently operating. This simple technique does not perform any smoothing during cell transitions.

The second technique utilizes a three-dimensional interpolation of tuning vectors based on current 

operating conditions. Based on pressure altitude, Mach number, and power setting, a weighted average of 

the tuning vectors from bounding cell centers is computed. This process, however, is complicated by the 

fact that data in select cells may be substantially older than data present in other cells. To overcome the 

data age differences, the weighting of each cell’s tuner data is computed from a combination of both 

distance from schedule points and age relative to the current flight profile. Given n points on which to 

base engine tuner parameters, a best-guess estimate of the current tuner vector would be calculated as:

∑

∑

=

= = n

i age i dist i

n

i age i dist i i

w w

w w q

q

1 , ,

1 , , ˆ (2)

The age-related weighting, wage, is computed from the tuner vector’s age in long-term storage, while the 

distance weighting, wdist, is based purely on scheduling axes and operating point. An equivalent age 

estimate is computed using these same weighting parameters. The technique attempts to produce a 
NASA/TM—2012-217279 7

compromise between applicability of surrounding cells based on operating point and the age of the stored 

data.

Tuner Vector Storage and Management

Two backend database solutions for long-term storage of tuner vectors are provided. The first solution 

uses true database to store tuner vectors and their relative ages for each cell in a SQLite database (Ref. 5).

The SQLite backend is desirable as it is able to store substantial information concerning all modifications 

to the database, permitting specific profiles to be backed out in case a fault is detected. Also, the SQLite 

backend has the benefit of being in a standardized format, allowing a host of other programs to explore 

and modify the stored data. A minor drawback of the database backend is a slight speed penalty due to a 

substantial number of Structured Query Language (SQL) statements executed at the initiation and 

conclusion of each flight. However, in a realistic implementation for on-board usage, the SQLite solution 

is desirable for the benefits listed above.

The second backend, referred to as the custom backend, relies on a simple binary file storing the most 

current tuner estimates for each cell. The main benefit of this backend is the speed benefit of loading 

binary data directly into storage arrays in memory. However, this backend does not provide a 

standardized solution for exploring and modifying stored data. Also, the ability to back out any given 

tuner vector due to the possible presence of a fault in a flight profile is limited to the previous flight alone. 

While this backend solution is optimal for performing Monte-Carlo analyses in a simulation environment, 

the drawbacks with regards to examining and modifying data may preclude this backend from actual onboard implementation.

Diagnostic Algorithms

The estimated sensor outputs of the PBM are designed to be used by a diagnostic algorithm for the 

diagnosis of gas-path faults. Gradual engine deterioration is accounted for by the periodic tuner update 

system and the PBM; therefore, engine sensor estimates generated by the PBM will account for engine 

degradation. Because the output from the PBM represents a fault-free estimate of engine measurements, 

the residual between the actual engine sensors and the PBM’s estimates can be used for the diagnosis of 

faults.

The diagnostic algorithms evaluated in this study partition the diagnostic process into two steps: 1) 

detecting the occurrence of an engine fault, and 2) isolating the root cause of any detected faults. Prior to 

processing, sensor residuals are normalized by either full-power sea-level values, in the cases of rotational 

speeds, combustor inlet pressure, and exhaust temperature, or standard day sea-level conditions, in the 

cases of inlet and interstage compressor conditions. This implementation considers two detection 

algorithms designed to analyze these sensor residuals. The first calculates a sum-squared of sensor 

residuals, which is compared to a fixed threshold. The second technique involves isolating the maximum 

sensor residual for comparison against a threshold. Both detection algorithms require a persistent 

threshold violation to raise a warning of possible fault. After a fault is detected, an isolation algorithm is 

activated, continually evaluating streaming data for the remainder of the flight profile.

Two single-fault isolation algorithms have been designed for integration with this architecture. The 

isolation algorithms are designed to classify faults of 16 possible types, outlined in Table 1. Fault sources 

include turbomachinery module faults, actuator biases, and sensor drifts and biases. Each algorithm is 

described in detail below. Once a fault is detected, the selected isolation algorithm is executed every 

5 seconds for the remainder of the profile, and the algorithm is provided with time-averaged sensor 

residuals. When the flight profile ends, the most frequently isolated fault source is reported. The two 

isolation algorithms, an inverse least-squares solution and a neural network technique, are explained in 

detail below.
NASA/TM—2012-217279 8

TABLE 1.—CLASSIFIABLE FAULT SOURCES

[Isolation algorithms are designed to 

detect these gas-path faults.]

Sensors Actuators Modules

Nf Wf Fan

Nc VSV LPC

P2 VBV HPC

P24 ----- HPT

Ps30 ----- LPT

T2 ----- -----

T24 ----- -----

T48 ----- -----

Inverse Least-Squares Isolation Algorithm

The inverse least-squares isolation algorithm attempts to locate a fault source by estimating fault 

scales necessary for all possible fault scenarios and selecting the most likely fault source. The algorithm 

relies on a database of precalculated influence coefficient matrices. These matrices are computed by 

applying faults to a nonlinear engine model, computing the resulting sensor residuals based on a 

piecewise linear model, and computing each individual influence coefficient term as:

j

i

i j

m

y H ∆ , =

(3)

In the above equation, the Δyi represents the difference between nonlinear sensed engine 

measurements and the piecewise linear model’s estimates of engine measurements given appropriate 

system inputs and mj represents a numerical measure of an applied fault magnitude. Complete influence 

coefficient matrices have been constructed at multiple operating points to improve estimation accuracy 

based on changes to engine response in different flight regimes.

Based on operating conditions, a single influence coefficient matrix, H, is selected for use. Using the

sensor residual values, fault magnitude estimates are generated for each fault type:

m (H R H ) H R y T

j j

T

j = j − ∆ − − 1 1 1 ˆ (4)

In the above equation, Hj represents one column of the influence coefficient matrix corresponding to fault 

type j. Once an estimate of fault magnitude, mj ˆ , is computed for each possible single-source fault, the 

estimated sensor residuals can be computed based on the estimated fault scale:

( ) j j ∆yˆ j = H mˆ (5)

A sum-squared error between the estimated residuals as calculated in Equation (5) and the actual residuals 

is computed for each fault hypothesis. The fault source resulting in the lowest sum squared error is 

selected as the most likely source of the fault.

Neural Network Isolation Algorithm

An alternative isolation algorithm has been designed using a probabilistic neural network as a fault 

classification system. This classifier uses a database of neural networks to select the most likely source of 

a given engine fault signature. The networks are trained by applying faults to an ideal nonlinear engine 

model and computing residuals relative to an ideal piecewise linear model. Each of the squares of the 

normalized sensor residuals is in turn normalized by the sum squared of all sensor residuals, effectively 
NASA/TM—2012-217279 9

creating a vector of percentage contribution of each sensor to the normalized sum squared of residuals. 

Each percentage is signed to be consistent with the sign of the residual from which it was derived: 

( )

∑=

∆

∆ = ∆ ny

j

j

i

i i

y

y

p sign y

1

2

2

(6)

The signed percentages have been found to improve classification results by effectively removing the 

variation in residual scaling due to fault magnitude differences. Each neural network is trained with five 

datasets per fault per operating point. 

The neural networks analyze the provided running average of the signed percent-of-sum-squared

residuals. The probabilistic neural network will return a fault identifier of the most likely candidate fault 

source based on training data. While additional training data can improve results, the marginal utility of 

adding training data has been found to be below the marginal cost measured in terms of storage and 

computing time required. This technique, in contrast with the inverse least-squares technique, does not 

provide an estimate of fault magnitude.

Results

The implemented architecture was evaluated against the NASA-developed Commercial Modular 

Aero Propulsion System Simulation (C-MAPSS) nonlinear model (Ref. 6). The C-MAPSS engine 

represents a two-spool high-bypass turbofan engine with five rotating turbomachinery modules. 

Integrated with the engine model is a controller based on corrected fan speed. The nonlinear model has 

been enhanced with realistic engine noise on sensor lines which was qualitatively tuned against actual 

flight data, and the sensor noise is present in the controller sensed feedback parameters. Additionally, the 

simulation models gradual engine health degradation via two health modifiers per module, an efficiency 

and a flow capacity. To simulate realistic engine degradation trends, a randomized statistical distribution 

based on actual engine fleet data is employed (Ref. 7).

Based on the described engine configuration, the diagnostic architecture was configured for 

compatibility with C-MAPSS. To construct the piecewise linear Kalman filter, the sensor measurement 

covariance matrix of a “fleet-average” C-MAPSS engine has been empirically estimated. Additionally, 

the formulation used to estimate the state covariance includes the covariance of health; the health 

modifier covariance has been empirically computed from the statistical data used to randomize engine 

health (Ref. 4).

The analyses for evaluating the diagnostic architecture involve simulations of realistic commercial 

flight profiles. A collection of randomized takeoff-climb-cruise flight profiles, an example of which is 

outlined in Figure 3, was generated using C-MAPSS. The architecture’s ability to accurately estimate 

engine health and unmeasured parameters is tested against a collection of nominal flight profiles 

consisting of 500 engines with brief 10-flight histories. The engines have turbomachinery module health 

degradation randomly applied based on fleet statistics (Ref. 1). The diagnostic architecture’s ability to 

estimate engine health and unmeasured engine parameters are quantified based on the nonlinear model 

output. 

Evaluation of fault detection and isolation is achieved using a similar collection of commercial flight 

profiles. A database of 300 engines, each with 20 takeoff-climb-cruise flight profiles, has been generated. 

The initial ten flights are considered training flights where faults are known to be absent. Gas-path faults 

have been applied randomly to the latter ten profiles of each engine to evaluate fault detection and

isolation capabilities of the architecture. Faults include turbomachinery efficiency and flow capacity 

biases, actuator biases, and sensor signal biases, all applied suddenly during a single flight. These faults
NASA/TM—2012-217279 10

Figure 3.—Example Commercial Flight Profile. Randomized takeoffclimb-cruise profiles were generated for testing the diagnostic 

architecture. All flight segment lengths and scales were subject to 

randomization.

do not persist to the following flight as the goal of this study is to accurately detect and isolate faults 

during the flight on which they occur. In total, 1231 random faults have been applied to 3000 possible 

flight profiles. The scaling and distribution of the applied random fault magnitudes is consistent with 

ground-based diagnostic algorithm evaluation studies (Ref. 1).

Health Parameter Estimation

The tuners produced by the RTAPM can be transformed to generate real-time engine health estimates. 

This study assumes that the engine health degrades slowly over time and engine health remains constant 

during a single flight profile. Therefore, a running average is used to estimate engine health at the 

conclusion of a flight profile. Additionally, the C-MAPSS nonlinear engine model uses a single set of 

health parameters regardless of operating point; the averaged health vectors transformed from the Kalman 

filter’s tuning parameter output can be compared directly against the nonlinear model’s input health 

parameters.

To evaluate the architecture’s performance trending capability, the final health parameter estimate 

reported by the architecture is collected from a significant sample of simulated engine histories. Each 

final health estimate represents the architecture’s trending of performance degradation over a brief history 

of ten takeoff-climb-cruise profiles. A residual value is computed based on the difference between the 

estimate for each engine calculated by the architecture and the simulated engine’s known health. The 

statistics of these residuals resulting from the evaluation of 500 engines are shown in Table 2. This 

implementation was designed using fleet average performance deterioration, which features 

approximately 3 percent efficiency and flow capacity losses on the high pressure compressor, often the 

most rapidly degrading component. The distribution of fleet engine health is presented in Reference 7. On 

average, the health estimates across all parameters are within 1 percent of the actual engine health.

Although the Kalman filter has been optimized for the estimation of unmeasured output parameters, the 

0 500 1000 1500 2000

0

20

40

Altitude (K ft)

Example Profile

0 500 1000 1500 2000

0

0.5

1

Mach #

0 500 1000 1500 2000

40

60

80

100

PLA (deg)

Time (s)
NASA/TM—2012-217279 11

health estimation accuracy is reasonable. Additionally, a bias is expected due to the underdetermined 

nature of this formulation (Ref. 3).

TABLE 2.—STATISTICS OF ESTIMATION ACCURACY OF TURBOMACHINERY HEALTH MODIFIER (%)

Parameters ΔηFan ΔγFan ΔηLPC ΔγLPC ΔηHPC ΔγHPC ΔηHPT ΔγHPT ΔηLPT ΔγLPT

Δ Mean 0.15 0.15 –0.21 0.43 –0.04 –0.23 –0.31 –0.08 –0.35 –0.18

Δ Std. Dev. 0.36 0.24 0.61 0.28 0.29 0.45 0.26 0.31 0.35 0.15

Auxiliary Parameter Estimation

For this study, four auxiliary, unmeasured parameters were chosen for examination: the high pressure 

turbine inlet temperature (T40), the low pressure turbine exit temperature (T50), net thrust (Fn), and low 

pressure compressor stall margin (SmLPC). The difference between actual and estimated parameters is

computed for all time points across 5000 takeoff-climb-cruise flight profiles, similar to the sample in 

Figure 3. Three regime residual values are generated by averaging the difference at takeoff, at cruise, and 

across the entire flight profile for each individual profile. Table 3 contains the mean and standard 

deviation of the three residual values for all 5000 flight profiles tested. Within three standard deviations, 

the burner exit temperature and exhaust gas temperature estimates all fall within 15 °R of the actual 

values, approximately 0.7 and 1.5 percent of cruise values respectively. Thrust estimates vary more 

during takeoff, exhibiting a variation measuring near 2 percent of full thrust to three standard deviations.

During cruise, the thrust disparity is somewhat lower, and the average error measured to three standard 

deviations is approximately 1.5 percent of average cruise thrust. The Low Pressure Compressor stall 

margin in the source flight profiles is maintained near 10 percent on average, and the architecture’s 

estimation error is maintained under 1 percent on average.

TABLE 3.—AVERAGE AUXILIARY PARAMETER ESTIMATION ERRORS

Parameter T40 

(°R)

T50

(°R)

Thrust 

(lbf)

SmLPC

(%)

Takeoff

Δ Mean –0.66 1.25 63.49 0.56

Δ Std. Dev. 4.34 4.39 221.60 0.50

Cruise

Δ Mean 0.47 1.05 –10.10 0.46

Δ Std. Dev. 4.06 3.28 29.69 0.48

Overall

Δ Mean 0.40 0.74 12.34 0.49

Δ Std. Dev. 3.20 2.86 63.96 0.47

Gas-Path Fault Diagnostics

The engine database, comprised of 300 engines with 10 training flight profiles and 10 testing flight 

profiles each, contains 1231 gas-path faults. The distribution of fault types is uniformly random. The 

faults, all modeled as sudden changes, include turbomachinery module performance faults, actuator 

biases, and sensor biases. The diagnostic algorithms are provided with a residual vector at every time 

point representing the difference between the PBM sensor estimates and the actual engine sensor 

measurements. In this particular implementation six sensors are available, but sensed fan speed is 

employed by the PBM as a model input, decreasing the available sensor suite to five.

Detection Algorithms

The two detection algorithms, sum-squared residuals and maximum residual, were exercised against 

the database as described. Figure 4 shows the receiver-operating characteristic (ROC) curves for both 

detection algorithms (Ref. 8). The ROC curve allows for the comparison of the true and false positive 

fault detection rates of the two techniques to be compared independent of tunable threshold parameters. 
NASA/TM—2012-217279 12

The maximum residual threshold technique in general performs better than a sum-squared residual 

technique in this particular application. The area under the ROC curve for the maximum residual 

threshold algorithm is 0.96, in contrast to the residual sum-squared algorithm’s area under the curve of 

0.88. The detection rates all refer to detecting a fault during processing of the same flight profile in which 

the gas-path fault occurred. 

The ROC analysis presented shows that, independent of threshold settings, the maximum residual 

detection algorithm performs better than the residual sum-squared algorithm. While maintaining a false 

positive rate of 1 percent, the maximum residual algorithm is able to achieve a true positive rate of 

81 percent. Figure 5 shows the detection rates using the maximum residual algorithm as a function of the 

fault type. The algorithm performs well when detecting turbomachinery component faults. In contrast, 

Variable Bleed Valve faults often remain undetected due to the relatively small impact of such faults on 

gas-path sensor measurements.

Figure 4.—Comparison of Detection Techniques. The 

ROC curves show the relative detection performance 

independent of threshold tuning.

Figure 5.—Detection Rates for the Maximum Residual Algorithm. The detection rate for the given algorithm is 

dependent on both the fault type and the magnitudes of gas-path faults. 

0 0.2 0.4 0.6 0.8 1

0

0.2

0.4

0.6

0.8

1

False Positive Rate

True Positive Rate

Max Residual

Sum-Squared Residuals
NASA/TM—2012-217279 13

Isolation Algorithms

The two isolation algorithms, inverse least-squares and neural networks, were evaluated using the 

same database. To quantify the ability to correctly classify gas-path faults, confusion matrices were 

constructed based on the results from processing the database of 300 engine histories. Only the fault cases 

correctly detected using the maximum residual detection algorithm with a threshold established to 

maintain a 1 percent false positive rate are considered. Table 4 and Table 5 present the fault isolation 

results for both isolation techniques. The values within the confusion matrices represent the fraction of

correctly detected faults classified as the noted type; the fractions in each row should therefore sum to 1.0. 

Values residing on the diagonal represent correct classifications.

TABLE 4.—CONFUSION MATRIX FOR INVERSE LEAST-SQUARES CLASSIFIER

TABLE 5.—CONFUSION MATRIX FOR NEURAL NETWORK CLASSIFIER

Nf Nc P2 P24 Ps30 T2 T24 T48 Wf VBV VSV Fan LPC HPC HPT LPT

Nf 0. 95 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 04 0. 00 0. 00 0. 01 0. 00 0. 00 0. 00

Nc 0. 00 0. 70 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 06 0. 00 0. 25 0. 00 0. 00 0. 00 0. 00 0. 00

P2 0. 06 0. 00 0. 76 0. 04 0. 00 0. 00 0. 00 0. 00 0. 03 0. 00 0. 00 0. 10 0. 00 0. 00 0. 00 0. 00

P24 0. 00 0. 00 0. 03 0. 97 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00

Ps30 0. 00 0. 00 0. 00 0. 00 0. 98 0. 00 0. 00 0. 00 0. 02 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00

T2 0. 00 0. 00 0. 00 0. 00 0. 02 0. 98 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00

T24 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 96 0. 00 0. 02 0. 00 0. 00 0. 00 0. 00 0. 00 0. 02 0. 00

T48 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 99 0. 01 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00

Wf 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 1. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00

VBV 1. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00

VSV 0. 00 0. 21 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 06 0. 00 0. 73 0. 00 0. 00 0. 00 0. 00 0. 00

Fan 0. 02 0. 00 0. 05 0. 02 0. 00 0. 00 0. 00 0. 00 0. 05 0. 00 0. 00 0. 85 0. 03 0. 00 0. 00 0. 00

LPC 0. 09 0. 00 0. 04 0. 01 0. 00 0. 00 0. 00 0. 00 0. 03 0. 03 0. 00 0. 16 0. 65 0. 00 0. 00 0. 00

HPC 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 1. 00 0. 00 0. 00

HPT 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 1. 00 0. 00

LPT 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 1. 00

Predicted Condition

True Condition

Nf Nc P2 P24 Ps30 T2 T24 T48 Wf VBV VSV Fan LPC HPC HPT LPT

Nf 0. 87 0. 00 0. 01 0. 00 0. 00 0. 00 0. 00 0. 00 0. 01 0. 06 0. 00 0. 02 0. 02 0. 00 0. 00 0. 00

Nc 0. 00 0. 43 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 08 0. 00 0. 49 0. 00 0. 00 0. 00 0. 00 0. 00

P2 0. 00 0. 00 0. 96 0. 00 0. 00 0. 00 0. 00 0. 00 0. 01 0. 00 0. 00 0. 03 0. 00 0. 00 0. 00 0. 00

P24 0. 01 0. 00 0. 01 0. 96 0. 00 0. 00 0. 00 0. 00 0. 00 0. 01 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00

Ps30 0. 00 0. 00 0. 00 0. 00 0. 96 0. 02 0. 00 0. 00 0. 02 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00

T2 0. 00 0. 00 0. 00 0. 00 0. 00 0. 95 0. 00 0. 00 0. 03 0. 00 0. 00 0. 00 0. 00 0. 00 0. 02 0. 00

T24 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 94 0. 00 0. 04 0. 00 0. 00 0. 00 0. 00 0. 00 0. 02 0. 00

T48 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 1. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00

Wf 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 1. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00

VBV 0. 00 0. 00 0. 50 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 50 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00

VSV 0. 00 0. 15 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 83 0. 00 0. 00 0. 00 0. 00 0. 02

Fan 0. 00 0. 00 0. 20 0. 00 0. 00 0. 00 0. 00 0. 00 0. 05 0. 00 0. 00 0. 64 0. 12 0. 00 0. 00 0. 00

LPC 0. 06 0. 00 0. 16 0. 00 0. 00 0. 00 0. 00 0. 00 0. 03 0. 03 0. 00 0. 34 0. 38 0. 00 0. 00 0. 00

HPC 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 01 0. 00 0. 00 0. 00 0. 00 0. 98 0. 01 0. 00

HPT 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 01 0. 00 0. 00 0. 00 0. 00 0. 00 0. 99 0. 00

LPT 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 1. 00

True Condition

Predicted Condition
NASA/TM—2012-217279 14

Both classifiers exhibit similar performance processing the given database of fault-containing flights. 

Two major confusion points stand out in both classifiers. Core speed (Nc) sensor faults and Variable 

Stator Vane (VSV) actuator faults are often confused by the algorithms. Additionally, Fan and Low 

Pressure Compressor (LPC) module performance faults are misclassified regularly. Confusion related to 

the Variable Bleed Valve faults may be misleading such fault cases are rarely detected given the VBV 

fault magnitudes considered in this study. Because both algorithms operate using significantly different 

classification strategies, these misclassifications common to both classification algorithms suggest that 

the signatures of the faults in this model do not allow for reliable discrimination. 

The overall correct classification rates of detected faults for the inverse least-squares and neural 

network classifiers are 73 and 70 percent, respectively. The inverse least-squares technique performs 

marginally better than the neural network classifier. Specifically, the neural network classifier exhibits 

significant confusion with Fan and LPC turbomachinery performance faults, incorrectly classifying them 

as station 2 pressure (P2) sensor faults. 

Conclusion

A proposed on-board diagnostic architecture has been designed to improve parameter estimation 

accuracy and fault detection and isolation by exploiting the availability of streaming engine sensor data. A 

real-time adaptive performance model based on a self-tuning Kalman filter provides streaming estimates 

of unmeasured engine and health parameters, while a separate performance baseline model utilizes recent 

past performance data to produce expected estimates of engine sensor measurements for diagnostic 

purposes. The implementation was evaluated against the Commercial Modular Aero Propulsion System 

Simulation, C-MAPSS. This nonlinear turbofan aerothermal model was updated with realistic sensor

noise, and simulated engine fleets with brief histories and varying degradation levels were produced for 

processing by the diagnostic architecture. This diagnostic architecture was able to provide reasonable

estimates of health and unmeasured engine parameters. Additionally, two detection algorithms were 

evaluated using the architecture. Receiver-operating characteristic curves showed detection using a 

maximum residual threshold technique to be superior to a sum-squared of sensor residuals technique. The 

maximum residual detection algorithm was combined with two fault classification algorithms, an inverse 

least-squares classifier and a neural network classifier, both exhibiting approximately 70 percent correct 

classification accuracy on detected faults. All detection and isolation results consider only conclusions 

arrived at during the processing of the fault-containing flight profile. The on-board diagnostic architecture 

has shown promise when evaluated using data generated by a nonlinear engine model. Access to 

streaming flight data allows for accurate fault detection rates while effectively decreasing detection 

latency to a single flight, in contrast to ground-based snapshot solutions, which may require multiple 

flights for fault detection. Although not tested, the availability of streaming data can allow for the capture 

and diagnosis of intermittent engine fault symptoms. Additional testing against actual engine flight data is 

also needed to validate the reliability and robustness of the design.

This study has discussed the architecture in an on-board application, but the design does not preclude 

its use in ground-based solutions. Given advances in data storage capacity and transfer bandwidth, full 

flight data may soon be available for download from aircraft for ground-based analysis. The enhanced 

architecture as described can also be employed to analyze retrieved flight data in a ground-based 

computing system for diagnostic purposes, effectively retaining the single flight latency and high 

accuracy in fault detection and isolation without the need to implement the architecture in an on-board 

system. While ground processing would eliminate the availability of streaming, unmeasured engine 

parameters during flight, the performance degradation trending and diagnostics would still be available to 

operators.
NASA/TM—2012-217279 15

References

1. Simon, D. L., Bird, J., Davison, C., Volponi, A., Iverson, E. R., “Benchmarking Gas Path Diagnostic 

Methods: A Public Approach,” NASA/TM—2008-215271, 2008.

2. Simon, D. L., “An Integrated Architecture for Onboard Aircraft Engine Performance Trend 

Monitoring and Gas Path Fault Diagnostics,” Proceedings of the 2010 JANNAF Joint Subcomittee 

Meeting, 2010.

3. Simon, D. L., Garg, S., ‘Optimal Tuner Selection for Kalman Filter-Based Aircraft Engine 

Performance Estimation,” Journal of Engineering for Gas Turbines and Power, vol. 132, March 2010.

4. Simon, D. L., Armstrong, J. B., Garg, S., “Application of an Optimal Tuner Selection Approach for 

On-Board Self-Tuning Engine Models,” Proceedings of the ASME Turbo Expo 2011, GT2011–

46408, 2011.

5. Kreibich, J. A. Using SQLite. Sebastopol, CA: O'Reilly Media, 2010.

6. Frederick, D. K., DeCastro, J. A., Litt, J. S., “User’s Guide for the Commercial Modular AeroPropulsion System Simulation (C-MAPSS),” NASA/TM–2007-215026,2007.

7. Sallee, G. P., “Performance Deterioration Based on Existing (Historical) Data – JT9D Jet Engine 

Diagnostics Program,” NASA Contractor Report CR–135448, Pratt & Whitney Aircraft Group 

Report PWA–5512–21, 1978.

8. Fawcett, T. “An Introduction to ROC Analysis,” Pattern Recognition Letters, v. 27, p. 861–874, 

2006.
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14. ABSTRACT

An on-board diagnostic architecture for aircraft turbofan engine performance trending, parameter estimation, and gas-path fault detection 

and isolation has been developed and evaluated in a simulation environment. The architecture incorporates two independent models: a realtime self-tuning performance model providing parameter estimates and a performance baseline model for diagnostic purposes reflecting 

long-term engine degradation trends. This architecture was evaluated using flight profiles generated from a nonlinear model with realistic 

fleet engine health degradation distributions and sensor noise. The architecture was found to produce acceptable estimates of engine health 

and unmeasured parameters, and the integrated diagnostic algorithms were able to perform correct fault isolation in approximately 70 

percent of the tested cases. 

15. SUBJECT TERMS

Systems health monitoring; Gas turbine engines; Fault detection; Kalman filters 

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