Donald L. Simon

Glenn Research Center, Cleveland, Ohio

An Integrated Architecture for On-Board Aircraft 

Engine Performance Trend Monitoring and

Gas Path Fault Diagnostics

NASA/TM—2010-216358

May 2010
NASA STI Program . . . in Profi le

Since its founding, NASA has been dedicated to the 

advancement of aeronautics and space science. The 

NASA Scientifi c and Technical Information (STI) 

program plays a key part in helping NASA maintain 

this important role.

The NASA STI Program operates under the auspices 

of the Agency Chief Information Offi cer. It collects, 

organizes, provides for archiving, and disseminates 

NASA’s STI. The NASA STI program provides access 

to the NASA Aeronautics and Space Database and 

its public interface, the NASA Technical Reports 

Server, thus providing one of the largest collections 

of aeronautical and space science STI in the world. 

Results are published in both non-NASA channels 

and by NASA in the NASA STI Report Series, which 

includes the following report types:

• TECHNICAL PUBLICATION. Reports of 

completed research or a major signifi cant phase 

of research that present the results of NASA 

programs and include extensive data or theoretical 

analysis. Includes compilations of signifi cant 

scientifi c and technical data and information 

deemed to be of continuing reference value. 

NASA counterpart of peer-reviewed formal 

professional papers but has less stringent 

limitations on manuscript length and extent of 

graphic presentations.

• TECHNICAL MEMORANDUM. Scientifi c 

and technical fi ndings that are preliminary or 

of specialized interest, e.g., quick release 

reports, working papers, and bibliographies that 

contain minimal annotation. Does not contain 

extensive analysis.

• CONTRACTOR REPORT. Scientifi c and 

technical fi ndings by NASA-sponsored 

contractors and grantees.

• CONFERENCE PUBLICATION. Collected 

papers from scientifi c and technical 

conferences, symposia, seminars, or other 

meetings sponsored or cosponsored by NASA.

• SPECIAL PUBLICATION. Scientifi c, 

technical, or historical information from 

NASA programs, projects, and missions, often 

concerned with subjects having substantial 

public interest.

• TECHNICAL TRANSLATION. Englishlanguage translations of foreign scientifi c and 

technical material pertinent to NASA’s mission.

Specialized services also include creating custom 

thesauri, building customized databases, organizing 

and publishing research results.

For more information about the NASA STI 

program, see the following:

• Access the NASA STI program home page at 

http://www.sti.nasa.gov

• E-mail your question via the Internet to help@

sti.nasa.gov

• Fax your question to the NASA STI Help Desk 

at 443–757–5803

• Telephone the NASA STI Help Desk at

 443–757–5802

• Write to:

 NASA Center for AeroSpace Information (CASI)

 7115 Standard Drive

 Hanover, MD 21076–1320
Donald L. Simon

Glenn Research Center, Cleveland, Ohio

An Integrated Architecture for On-Board Aircraft 

Engine Performance Trend Monitoring and

Gas Path Fault Diagnostics

NASA/TM—2010-216358

May 2010

National Aeronautics and

Space Administration

Glenn Research Center

Cleveland, Ohio 44135

Prepared for the

57th Joint Army-Navy-NASA-Air Force (JANNAF) Propulsion Meeting

sponsored by the JANNAF Interagency Propulsion Committee

Colorado Springs, Colorado, May 3–7, 2010
Available from

NASA Center for Aerospace Information

7115 Standard Drive

Hanover, MD 21076–1320

National Technical Information Service

5301 Shawnee Road

Alexandria, VA 22312

Available electronically at http://gltrs.grc.nasa.gov

Level of Review: This material has been technically reviewed by technical management. 
NASA/TM—2010-216358 1 

An Integrated Architecture for On-Board Aircraft Engine 

Performance Trend Monitoring and 

Gas Path Fault Diagnostics 

Donald L. Simon 

National Aeronautics and Space Administration 

Glenn Research Center 

Cleveland, Ohio 44135 

Abstract 

Aircraft engine performance trend monitoring and gas path fault diagnostics are closely related 

technologies that assist operators in managing the health of their gas turbine engine assets. Trend 

monitoring is the process of monitoring the gradual performance change that an aircraft engine will 

naturally incur over time due to turbomachinery deterioration, while gas path diagnostics is the process of 

detecting and isolating the occurrence of any faults impacting engine flow-path performance. Today, 

performance trend monitoring and gas path fault diagnostic functions are performed by a combination of 

on-board and off-board strategies. On-board engine control computers contain logic that monitors for 

anomalous engine operation in real-time. Off-board ground stations are used to conduct fleet-wide engine 

trend monitoring and fault diagnostics based on data collected from each engine each flight. Continuing 

advances in avionics are enabling the migration of portions of the ground-based functionality on-board, 

giving rise to more sophisticated on-board engine health management capabilities. This paper reviews 

the conventional engine performance trend monitoring and gas path fault diagnostic architecture 

commonly applied today, and presents a proposed enhanced on-board architecture for future 

applications. The enhanced architecture gains real-time access to an expanded quantity of engine 

parameters, and provides advanced on-board model-based estimation capabilities. The benefits of the 

enhanced architecture include the real-time continuous monitoring of engine health, the early diagnosis of 

fault conditions, and the estimation of unmeasured engine performance parameters. A future vision to 

advance the enhanced architecture is also presented and discussed. 

Nomenclature 

H Module performance deterioration influence coefficient matrix 

Nf Fan speed 

P Health parameter covariance matrix 

PLA Power lever angle 

R Sensor measurement covariance matrix 

V* Health parameter vector to tuner vector linear transformation matrix 

h Health parameter vector 

u Actuator commands 

v Sensor measurement noise 

x State vector 

y Measured output vector 

z Unmeasured output vector 

Δ Denotes change from baseline 

ΔΔ Denotes gradient in change from baseline 

Superscripts, subscripts, and diacritical marks 

† Pseudo-inverse (superscript) 

T Transpose (superscript) 

cmd Commanded value (subscript) 

i Sensor residual index (subscript) 

^ Estimated value (diacritical mark) 
NASA/TM—2010-216358 2 

Introduction 

Aircraft engines are highly complex systems consisting of static and rotating components, along with 

associated subsystems, controls, and accessories. They are required to provide reliable power 

generation over thousands of flight cycles while being subjected to a broad range of operating loads and 

conditions, including extreme temperature environments. Over repeated flight cycles the life of many 

engine parts will be consumed, and engine malfunctions may occur. Engine Health Management (EHM) 

systems play a critical role in assisting aircraft operators in managing the safety, reliability, availability, 

and affordability of their gas turbine engine assets (Refs. 1 to 4). The functionality provided by an EHM 

system includes deterioration trend monitoring, life usage tracking, diagnosis of engine faults, and 

providing recommended inspection and maintenance actions. The individual subsystems that comprise 

an EHM system vary by application, but generally consist of lubrication system health management, 

structural vibration monitoring, structural life usage tracking, and gas path health management. This 

paper will specifically focus on the gas path health management aspects of an EHM system. 

The goal of propulsion gas path health management (GPHM) is to reliably assess and manage the 

health of gas turbine engine flow-path components and controls accessories (Refs. 5 and 6). It is 

performed by relating observed changes in sensed engine variables to internal performance-related 

changes within the gas turbine engine cycle. Monitoring and processing engine sensed measurements 

enables the detection and isolation of problems, ultimately enabling corrective action to be taken (Ref. 7). 

Examples of the types of events that can be addressed by GPHM include gradual component 

deterioration, sensor faults, actuator faults, and turbomachinery damage. Because GPHM provides a 

system level assessment of engine health, it is a cornerstone technology of an overall engine health 

management system. 

GPHM primarily relies upon the gas path sensors installed on the engine for control purposes. 

Historically, GPHM came into prevalence concurrent with the introduction of digital engine controls and 

avionics. Prior to this, gas path performance trending was conducted manually based on cockpit gauge 

readings hand-recorded by pilots. The introduction of digital avionics and controls revolutionized this 

process by providing access to additional sensed measurements, along with automated data acquisition 

and processing capabilities. This enabled the inclusion of on-board engine diagnostic functionality, as well 

as ground-based fleet-wide EHM functionality in computer ground stations, which processes engine data 

acquired in-flight. Operators now rely on this combined on-board and off-board functionality to manage 

the health of their engine assets. 

Continuing advancements in avionics are enabling more sophisticated on-board GPHM functionality. 

Towards that vision, this paper presents a proposed enhanced GPHM architecture that incorporates onboard model-based diagnostic and performance estimation functionality. The enhanced architecture gains 

real-time access to an expanded quantity of engine parameters and provides real-time continuous 

monitoring of engine health, the early diagnosis of fault conditions, and the estimation of unmeasured 

engine performance parameters that can be directly applied for health management and controls 

applications. These benefits, along with identified technology challenges, are discussed in this paper. 

Additionally, the research and development being conducted by NASA to make the enhanced GPHM 

architecture a reality is presented.

The remaining sections of this paper are organized as follows. First, the conventional aircraft engine 

GPHM architecture commonly applied today is presented and discussed in more detail. This is provided 

to describe the current state-of-the-art and to serve as an introduction to the engine performance trend 

monitoring and diagnostics process. Next, the proposed enhanced GPHM architecture is presented, 

along with ongoing NASA research and development efforts focused on maturing that architecture. The 

paper concludes with a discussion of practical issues for implementing the technology, remaining 

technology challenges, and a summary. 

Conventional GPHM Architecture 

A representative example of a conventional GPHM architecture is shown in Figure 1. As shown, this 

architecture includes both on-board and ground-based functionality. On-board fault detection and 

isolation (FDI) logic continuously monitors control sensors and actuators. Additionally, automated data 

acquisition logic is applied to collect in-flight engine measurements. These engine measurements, along 
NASA/TM—2010-216358 3 

Control

Logic

Gas Path Sensor

Measurements

Actuator

Commands

On-Board System

FADEC

Ground-Based System

Ground

Station

Fleet-wide Trend 

Monitoring & Diagnostics

Transmission of “Snapshot” 

reports and engine fault codes

“Snapshot” data

acquisition Engine

fault codes

Fault Detection and

Isolation Logic

Figure 1.—Conventional gas path health management architecture. 

with any identified engine fault conditions identified by the on-board FDI logic, are transmitted off-board to 

fleet-wide ground stations that apply additional monitoring functionality. The elements of this architecture 

are discussed in more detail in the following subsections. 

Gas Path Sensor Measurements 

Most of the sensors used to conduct GPHM are installed on the engine for control purposes. The fact 

that these sensors also enable GPHM is an added benefit. While some engine designs do include 

additional sensors specifically for gas path monitoring purposes, their inclusion comes at an expense. 

Additional sensors add weight, cost, and complexity, and reduce system reliability. Therefore, any 

additional sensors must provide sufficient benefit to “buy their way on-board.” The types of gas path 

sensed measurements available typically include rotor speeds, pressures, temperatures, and actuator 

positions. The number of sensors available to conduct GPHM will vary by application, but sensor suites 

ranging from four to eight sensed measurements are common. 

On-Board Fault Detection and Isolation Logic 

Conventional on-board GPHM capabilities include FDI logic residing in the engine control computer. 

The FDI logic operates at an update rate consistent with the engine control computer (approximately 

50 Hz). Conventional on-board FDI logic includes checks on the range and rate of change of sensed 

measurements and built-in-test (BIT) logic to monitor sensor and actuator electronics. Digital engine 

control systems commonly employ a dual-redundant architecture design to provide increased reliability. 

This includes physically redundant control sensors with cross-checks between each of the physically 

redundant sensor channels. In the event of a disagreement, simple analytical models are used as a third 

vote to help isolate the faulty sensor channel. If a fault is detected by the on-board FDI logic, a fault code 

that uniquely identifies the fault type is generated. The fault mitigation response then taken by the system 

is dependent on the fault type. For minor faults that do not compromise the operation of the system, the 

fault code is simply archived for post-flight indication to maintenance and operations personnel. The 

operator must comply with dispatchability regulations that dictate the number of future flights, if any, 

during which the vehicle may continue to operate prior to addressing the fault code through corrective 
NASA/TM—2010-216358 4 

maintenance. Critical faults, which require an immediate response, are annunciated through cockpit alerts 

and warnings. Some automated accommodation is also incorporated within the control system. For 

example, if a failure is detected in a sensor channel, the system can automatically switch to the physically 

redundant backup channel. The control system may also include revisionary backup modes that can be 

entered upon the detection of certain fault types. This allows the engine to continue operating, albeit in a 

reduced performance mode, in the presence of faults. 

On-Board Snapshot Data Acquisition and Data Transfer to Ground Station 

During flight, the on-board data acquisition system collects and archives engine flight data for 

subsequent download to a ground station computer. The quantity of data archived varies by application, 

but is usually limited to “snapshot” engine reports, or measurements, taken at a limited number of engine 

operating points each flight (e.g., takeoff, climb and cruise). The vehicle to ground station data transfer 

may occur wirelessly (either in-flight, or while on the ground at an airline gate), or through the manual 

transfer of mass storage media. Transmission bandwidth limitations, transmission costs, and data archival 

costs currently restrict the amount of data collected and downloaded from each engine each flight. 

However, as data transmission and archival capabilities continue to advance these restrictions are 

expected to lessen. 

Ground Station Fleet-Wide Trend Monitoring and Diagnostics 

Off-board GPHM functions are performed within EHM computer systems, commonly referred to as 

ground stations. These ground stations provide fleet-wide engine health management functionality, 

including performance trend monitoring and gas path diagnostics (Ref. 8). This is accomplished by 

processing the snapshot engine data acquired in-flight. Performance trending enables operators to 

monitor the gradual performance deterioration that each engine will naturally undergo over its lifetime of 

use (Refs. 9 and 10). This deterioration is caused by factors such as fouling, corrosion, and erosion of 

turbomachinery, increased clearances, and seal leakage. Operators utilize performance deterioration 

trend information to assist them in making maintenance and overhaul decisions regarding their engine 

assets. The gas path diagnostic functionality conducted within the ground station is complementary to 

that conducted by the on-board FDI logic. In general, the ground station is capable of detecting smaller 

incipient faults, including those faults that evolve over several flights, while the on-board system provides 

the real-time diagnosis of larger magnitude faults. Reference 2 provides a top-level description of the 

ground-based fleet-wide trend monitoring and diagnostic process. A representation of this process is 

shown in Figure 2. 

Engine

measurement

data

1. Compare 

to reference 

model

Reference 

(nominal) 

model

3. Fault 

isolation

4. Module performance

deterioration trend monitoring

5. 

Reconcile 

& report 

results

y

ΔΔy +

-

Δy Yes

No

Engine

operating 

conditions

Calculate

gradient

Threshold 

exceeded?

Record 

no 

fault

2. Anomaly detection

Figure 2.—Ground station performance trend monitoring and gas path diagnostic process. 
NASA/TM—2010-216358 5 

The individual steps of this process are as follows: 

Step 1—Compare to reference model: Incoming data is provided in the form of a snapshot measurement 

vector, y. Typically, this data has been corrected, or normalized, to a standard operating condition to help 

reduce the effects of operating condition variance from flight to flight (Ref. 11). The incoming data is 

compared against a reference model (run at the same operating condition and power setting as the 

measured engine data). Typically, this model is a fleet average engine model representing average 

engine performance across a fleet of aircraft. The resulting residuals between the measurement data and 

the reference model are referred to as delta-measurements, or Δy’s. 

Step 2—Anomaly detection: Anomaly detection logic is applied to detect any unanticipated rapid shifts in 

the observed Δy measurements. This logic provides the capability to discriminate between gradual 

performance changes due to deterioration, which is not a fault, and rapid performance shifts that may be 

indicative of a fault (Ref. 2). An illustration of gradual versus rapid performance shifts is shown in Figure 3. 

Here time history data is shown for a single engine sensor residual, Δyi

. To improve the trend monitoring 

process it is typical to include some form of smoothing or filtering of the data to reduce the effects of 

measurement noise (Refs. 12 and 13). The blue points in the figure represent the Δyi

 residual time history, 

and the solid red line represents its moving average. Anomaly detection is performed by referencing the 

engine’s current Δy against an archived pre-trend shift Δy vector from the same engine. The change, or 

gradient, in this vector forms a ΔΔy, or delta-delta measurement vector. If the ΔΔy vector exceeds a 

defined threshold, a rapid trend shift event is detected and the logic attempts to isolate the cause (step 3). 

Otherwise the logic proceeds in performing a module deterioration assessment (step 4). 

A noteworthy aspect of this process that warrants emphasis is the coupling between gradient 

calculation and anomaly detection functionality. The distribution in engine-to-engine performance variation 

across a fleet of engines can be significant. Due to build tolerances and the range of deterioration levels 

engines will incur over their lifetime of use, no two engines exhibit identical performance. If detection 

thresholds were applied to Δy (instead of ΔΔy) they would have to be set suitably large to maintain an 

acceptable false alarm rate, that in turn would limit the system’s ability to detect smaller magnitude faults. 

By leveraging recent past engine performance information, the detection approach can be tailored to the 

current performance level of the engine. This allows the application of tighter anomaly detection 

thresholds and, consequently, the detection of smaller magnitude incipient faults. This is a key difference 

between the on-board versus ground-based FDI logic applied in the conventional GPHM architecture. The 

on-board FDI logic is in general a static design, whereas the ground-based FDI logic adapts to the current 

performance level of the engine. Overall, the on-board and off-board logic are complementary. The onboard FDI logic detects larger magnitude abrupt faults that require more immediate attention, whereas the 

ground-based FDI logic enables the early detection of smaller magnitude incipient faults before they 

escalate to more severe consequences. 

Gradual 

deterioration

Gradual 

deterioration

Rapid shift 

(potentially due to 

a fault event)

Figure 3.—Gradual versus rapid performance shifts. 
NASA/TM—2010-216358 6 

Step 3—Fault isolation: Upon anomaly detection, the ΔΔy vector will reflect the signature of the event. 

The ΔΔy vector is then processed to isolate the root cause of the trend shift. A variety of methods have 

been applied for gas path fault isolation (Ref. 14). Most of these methods are based upon the single fault 

assumption. The simultaneous occurrence of multiple faults is usually not considered, which is 

reasonable considering the high reliability of modern aircraft gas turbine engines. A single fault isolator 

will individually evaluate how closely each candidate fault case matches the observed ΔΔy measurement 

vector. It is common to incorporate additional system knowledge to help improve the accuracy of the 

diagnostic assessment. This includes component fault rates, fleet-wide trends, engine operating cycle 

counts, and recent maintenance actions. Upon completion, the single fault isolator will produce a 

diagnostic assessment isolating the cause for the observed trend shift. This diagnostic assessment may 

be a ranked list of plausible faults (including confidence levels of each), or isolation to an ambiguity group 

(a group of faults known to produce similar ΔΔy signatures). The diagnostic assessment may also include 

the capability to classify non-fault events known to cause rapid performance trend shifts such as 

compressor water washes. 

Step 4—Module performance deterioration trend monitoring: If a rapid trend shift event is not detected, 

the logic proceeds in performing a module deterioration assessment. This assessment is commonly 

referred to as gas path analysis. The objective of this assessment is to estimate and trend the engine’s 

level of performance deterioration over its lifetime of use. Several parameters are trended including fuel 

consumption rate, turbine temperature margin, and module health parameters such as efficiencies and 

flow capacities. The health parameters are not sensed directly, but are instead estimated using modelbased estimation techniques. To illustrate this, consider the following linear steady-state representation of 

the Δy measurement process 

y   Hh v (1) 

where Δh is a vector of unmeasured module health parameters that impact engine performance, H is an 

influence coefficient matrix relating health parameters to Δy measurements, and v represents random 

measurement error. The matrix H is typically derived by linearizing a physics-based aerothermal engine 

model, or cycle deck. The estimation objective is to estimate Δh given Δy. This typically poses an 

underdetermined estimation problem as there are usually fewer sensors than health parameters. A 

common estimation approach is to apply least squares maximum a posteriori (MAP) estimation (Refs. 5 

and 15) given as 

  1 ˆ 11 1 T T h P HR H HR y        (2) 

where P is the covariance matrix of the health parameters, and R is the covariance matrix of the 

measurement vector. By applying this process, operators can estimate and monitor ˆ h levels and trends 

to assist them in making engine maintenance and overhaul decisions. 

Step 5—Reconcile and report results: The final step in the process is to reconcile and report the outputs 

from the fault isolator and the module deterioration assessment. Reconciliation is done to increase 

confidence in the provided diagnostic assessment and to resolve any conflicts prior to relaying the 

information to the end user. 

Enhanced GPHM Architecture 

The previously described conventional GPHM architecture has several notable shortcomings. First, it 

conducts ground-based FDI based on the post-flight processing of a small number of engine snapshot 

measurements collected each flight. Often, this processing does not happen until several days after the 

flight has occurred causing significant diagnostics latency. Additionally, engine performance deterioration 

estimates produced within the conventional architecture are only available in ground-station. They are not 

available for on-board controls and health management applications, and thus are not utilized to their full 

potential. The enhanced GPHM architecture design addresses these limitations by migrating portions of 
NASA/TM—2010-216358 7 

the conventional ground-based functionality on-board. This is done to enable the real-time continuous 

monitoring of engine health, the earlier diagnosis of faults, and the estimation of unmeasured engine 

performance and health parameters that can be directly applied for on-board controls and health 

management applications. 

The enhanced architecture, like the previously presented conventional architecture, contains both onboard and ground-based functionality. In fact, many of the components that make up the two 

architectures are identical. The primary difference is the inclusion of real-time on-board model-based 

GPHM functionality within the enhanced architecture. The inclusion of on-board models is an emerging 

practice in the aircraft engine industry. These models have the ability to self-tune to match the current 

performance level of the engine as it degrades over time (Refs. 16 to 18). Current applications of onboard self-tuning engine models are largely for sensor validation. The future vision is to expand the utility 

of such models for use in engine diagnostics and to provide more accurate estimation of unmeasured 

engine parameters to support advanced model-based controls and life usage monitoring applications. 

The proposed enhanced GPHM architecture that leverages on-board model-based technology is shown 

in Figure 4. The enhanced functionality included in the architecture is outlined by dashed boxes. 

Enhanced On-board Model-Based GPHM Architecture 

A more detailed view of the on-board portion of the enhanced GPHM architecture is shown in 

Figure 5. It includes many of the same components as the conventional architecture including control 

logic, conventional FDI logic, and snapshot data acquisition capabilities. Added enhancements include 

two on-board real-time aerothermal engine models, enhanced fault detection and isolation logic, and realtime performance estimation capabilities. Pivotal to the operation of this architecture are the two engine 

models that operate in parallel. The first model, referred to as the real-time adaptive performance model 

(RTAPM), continuously self-tunes internal tuning parameters that allow it to match the current sensed 

performance level of the physical engine. The tuning parameters are proxies for the engine’s health, or 

level of deterioration. Through this tuning parameter adjustment, the model is able to track engine 

performance while the engine degrades with use. The second model, referred to as the performance 

baseline model (PBM), serves as a baseline of recent past engine performance. Residuals between the 

engine and PBM outputs are monitored for anomaly detection purposes. The PBM also adapts to account 

for engine performance deterioration by accepting tuning parameter inputs calculated within the RTAPM. 

However, updates of the tuning parameters used in the PBM only occur on a periodic basis (e.g., after 

each flight). This periodic update is necessary to ensure that the PBM doesn’t immediately absorb the 

effect of any rapid trend shifts, thus compromising anomaly detection capability. The on-board elements 

of the enhanced GPHM architecture are discussed in more detail in the subsections that follow. 

Control Logic 

The control logic included in the enhanced architecture is identical to the control logic included in the 

conventional architecture. Figure 5 provides an example in the form of a fan speed (Nf) controller, but the 

overall architecture is applicable for any controller design. In this example, power level angle (PLA) 

commands are converted to a commanded fan speed (Nfcmd). The error between actual and commanded 

fan speed is fed into the control logic that in turn generates a set of actuator commands, u, provided as 

engine inputs. Also shown in the control logic block is the conventional FDI logic. This is the same onboard FDI logic included in the conventional GPHM architecture. It includes BIT and cross-channel check 

logic applied for sensor and actuator validation purposes. This proven technology has been flight certified 

and plays a critical role in ensuring the safe, reliable operation of today’s aircraft propulsion systems. 

Instead of completely replacing this technology with enhanced FDI logic, it is maintained and relied upon 

to make flight critical real-time FDI assessments. Conversely, the enhanced FDI logic, to be described 

later, will enable the diagnosis of smaller magnitude faults to be mitigated through post-flight 

maintenance. 
NASA/TM—2010-216358 8 

Control

Logic

Sensor

Measurements

Actuator

Commands

On-Board System

FADEC

(Enhanced) Fault Detection

& Isolation Logic

Ground-Based System

Ground

Station

Fleet-wide Trend 

Monitoring & Diagnostics

Transmission of “Snapshot” reports 

and engine fault codes

“Snapshot” data

acquisition

Engine

fault codes

Real-time adaptive

performance model

Performance 

Baseline model

Model tuning

parameters

+ ‐ Denotes

enhanced 

functionality

(Conventional) Fault Detection

& Isolation Logic

Figure 4.—Proposed enhanced gas path health management architecture. 

Real-time Performance Estimates

Performance

Baseline Model (PBM)

Real-time

Adaptive

Performance

Model 

(RTAPM)

Controller

Nf schedule

PLA

Kalman 

filter

Engine

Deterioration trending

(full envelope)

y

u Nf Nf cmd

Real-time

model

u

u

Periodic

update y

+

+

+

-

-

-

ΔΔy

Anomaly

detection

Fault 

isolation

Real-time 

model

Reconcile & 

report results

Nf

Real-time performance estimates

• y: measured parameters

• z: unmeasured parameters (e.g., 

thrust, stall margin, metal temps)

(Conventional) 

FDI Logic

Transmission to ground-station

• Fault codes

• Snapshot parameters ( y and ) 

• Module performance assessments ( )

y

to

transformation

Control Logic

yˆ ˆ , z

qˆ qˆ ˆ

h ˆ

h

yˆ

qˆ

yˆ

xˆ

qˆ

(Enhanced) Fault Detection 

and Isolation Logic

ˆ

h

zˆ Denotes 

enhanced 

functionality

Figure 5.—Proposed enhanced on-board model-based GPHM architecture. 
NASA/TM—2010-216358 9 

Real-Time Adaptive Performance Model (RTAPM) 

The purpose of the RTAPM is to provide a continuous assessment of engine performance. It consists 

of a real-time physics-based aerothermal engine model and an associated tracking filter that 

automatically tunes the model to track the performance of the physical engine based on sensed feedback 

measurements. Kalman filters are typically applied to implement the tracking filter. The tracking filter 

produces real-time estimates of the engine’s state variables, xˆ , and tuning parameters, qˆ . The tuning 

parameters reflect the effects of performance deterioration on engine outputs. The xˆ and qˆ variables 

along with actuator commands, u, are provided as inputs to the real-time model, allowing the modelproduced estimates to “track” the outputs of the engine. This includes estimates of measured engine 

outputs, yˆ , and unmeasured, or auxiliary, engine outputs denoted as zˆ. The auxiliary outputs are user 

specified and may include model calculated parameters such as thrust and stall margin. In the upcoming 

real-time performance estimates sub-section, a methodology to help improve the estimation accuracy of 

the model will be discussed. 

Performance Baseline Model (PBM) 

The PBM provides a baseline of recent past engine performance used for trend monitoring purposes. 

It incorporates a version of the same real-time model used in implementing the RTAPM. The PBM 

accepts continuous inputs consisting of actuator commands, u, and a trim parameter representative of 

engine power such as fan speed. The PBM is trimmed to the power reference parameter to prevent the 

model from inadvertently diverging from the engine. In addition to the continuous inputs, the model also 

automatically receives periodically updated tuning parameter estimates, qˆ . This periodic update of the 

tuning parameters allows the PBM to adapt to normal performance deterioration that occurs gradually 

over time, but prevents the model from immediately adapting to rapid performance shifts caused by faults. 

The concept of periodically updating the tuning parameters used within an on-board model applied for 

diagnostics purposes was first introduced in Reference 19. The frequency of the periodic tuning 

parameter update is user-specified, but is typically on the order of once per flight. 

To illustrate the benefit of updating the PBM tuning parameters on a periodic basis, a simulated fault 

example is given. Here, the dynamic response of the PBM is compared to that of the RTAPM. First, 

consider the results shown in Figure 6 which are based on the RTAPM. Shown in this figure are time 

history plots of: (a) the sensed and model produced estimate of a single sensed output (top plot); (b) the 

corresponding residual between these two parameters (middle plot); and (c) the Kalman filter estimated 

tuning parameters (bottom plot). In this example the engine experiences a high pressure compressor fault 

at time = 10 sec. Since the Kalman filter provides continuous adjustments to the tuning parameters the 

model estimated output is able to closely track the sensed engine output and the post-fault residuals 

between the model and engine outputs are minimal. Because of this, the RTAPM alone is not ideal for 

anomaly detection purposes. Conversely, consider Figure 7 which shows the PBM response to the same 

fault event. Here the PBM tuning parameters are held constant (bottom plot). In this example the tuner 

values have been established based on the mean pre-fault Kalman filter estimates shown in Figure 6. 

When the fault occurs, the change in the residual between the PBM output and the engine output is 

clearly discernable (middle plot). The vector of residuals generated by the PBM is information that can be 

directly monitored by on-board logic for anomaly detection and fault isolation purposes. 

(Enhanced) Fault Detection and Isolation Logic 

By design, the on-board enhanced FDI logic closely emulates the FDI logic applied in the ground 

station (see Fig. 2). The residuals between the PBM outputs and the sensed engine outputs are 

analogous to the ΔΔy vector used in the ground station diagnostic process. They reflect shifts in current 

engine performance relative to recent past engine performance, and can be monitored directly to detect 

performance trend shift events. If a performance shift is detected, these same residuals can be processed 

within a single fault isolation process to determine the most likely root cause for the event. Two key 

differences between the on-board and ground-based solutions are processing latency and available data 

quantity. The ground-based approach performs post-flight processing of a small number of engine 

snapshot measurements collected each flight while the on-board approach performs real-time processing 

of engine data collected at a much higher sample rate. Additionally, the on-board approach will have 
NASA/TM—2010-216358 10 

Fault occurs (time = 10 s)

Minimal residuals

between sensed and 

estimated outputs

Kalman filter tuning

parameters adapt 

due to fault effect 

, ˆ i i y y

ˆ i i y y 

qˆ

Figure 6.—Real-time adaptive performance model (RTAPM) response to an 

abrupt fault. 

Fault occurs (time = 10 s)

Noticeable shift in

residuals between

sensed and

estimated outputs

Diagnostic model

tuning parameters

held constant

ˆ i i y y 

, ˆ i i y y

qˆ

Figure 7.—Performance baseline model (PBM) response to an abrupt fault. 

access to significantly more engine parameters (i.e., all engine control computer parameters) than what 

can practically be downloaded to a ground station due to data telemetry and archival restrictions. These 

advantages enable the on-board FDI solution to produce diagnostic assessments with improved 

accuracy, and reduced diagnostic latency. 

Real-Time Performance Estimates 

An additional benefit provided by the RTAPM is the generation of real-time estimates of measured 

and unmeasured engine outputs. This information can be used for a variety of on-board applications 

including sensor validation, model-based controls, deterioration trend monitoring, and component life 

usage calculations. The accuracy of these estimates is dependent on a number of factors including the 

accuracy of the physics-based model, the available sensor suite (type and accuracy), and the ability of 

the tracking filter to capture engine performance deterioration effects. 
NASA/TM—2010-216358 11 

As previously mentioned, engine performance deterioration is generally described in terms of an 

unmeasurable vector of health parameters, h. Previously, Equation (2) introduced the least squares 

maximum a posteriori (MAP) estimation approach commonly applied in ground stations for module 

deterioration assessment purposes. The MAP estimator includes a priori information regarding health 

parameter covariance, P. This enables the estimator to produce estimates of all health parameters, albeit 

biased estimates, in the underdetermined case where there are fewer sensors than health parameters. 

However, the MAP estimator, unlike a Kalman filter, is not a recursive estimator and does not take 

advantage of past measurements to enhance its estimate at the current time step. Furthermore, the MAP 

estimator only considers the static steady-state relationship between system states and measured 

outputs—it does not consider system dynamics. While the MAP estimator is ideal for processing steadystate snapshot engine measurements in ground stations, it is not suitable for the on-board processing of 

real-time dynamic engine measurements. Through Kalman filter-based estimation techniques, h can be 

estimated on-board in real-time, given that there are at least as many sensors as parameters to be 

estimated. However, in an aircraft engine the number of sensors available is typically less than the 

number of health parameters, presenting an underdetermined estimation problem. A common approach 

to address this shortcoming is to estimate a subset of the health parameters, referred to as model tuning 

parameters, q. The q vector serves as a proxy for capturing full-order engine deterioration effects as 

reflected by the h vector. Applying a reduced-order q vector will enable the Kalman filter to tune the onboard model to track measured (sensed) engine outputs. However, the Kalman filter produced estimate of 

unmeasured engine outputs may be biased due to the fact that the impact of the entire vector of health 

parameters will not be accurately represented within the model (Ref. 20). 

In a departure from the conventional technique of selecting a subset of h to serve as q, an innovative 

methodology was developed that constructs a tuning parameter vector that is a linear combination of all 

health parameters and of appropriate dimension to enable Kalman filter estimation (Refs. 20 and 21). The 

relationship between h and q is defined by a linear transformation matrix, V*

, as shown in Equation (3). 

* q Vh  (3) 

The selection of V*

 is performed such that the Kalman filter’s theoretical mean squared estimation error in 

the parameters of interest is minimized. For a complete derivation see Reference 20. Given the qˆ

estimates produced by the Kalman filter, an approximation of the health parameter vector, ˆ

h, can be 

obtained as 

ˆ *† h Vq  (4) 

where *† V is the pseudo-inverse of * V . These health parameter estimates are used on-board to help 

reconcile diagnosed fault conditions. They can also serve to augment or verify the health parameters 

estimated and trended within the ground station applying MAP estimation. Since these on-board 

estimates are updated continuously, they will be able to reflect engine performance trend changes earlier 

than the ground-based module deterioration assessment conducted using snapshot engine 

measurements alone. 

Figure 8 provides an illustration of the improvement in estimation accuracy that can be gained by 

applying the enhanced tuner selection approach (bottom plot) versus the conventional approach of 

defining tuning parameters as a subset of health parameters (top plot). In each plot the red line denotes 

actual net thrust and the cyan line denotes the Kalman filter estimate of thrust. Applying the enhanced 

tuner selection approach helps to significantly improve estimation accuracy by reducing the bias and the 

variance in the estimate. 
NASA/TM—2010-216358 12 

Figure 8.—Thrust estimation accuracy comparison 

of conventional versus enhanced estimation 

approaches. 

On-Board Snapshot Data Acquisition and Data Transfer to Ground Station 

The enhanced GPHM architecture, like the conventional architecture, contains both on-board and 

ground-based functionality. As such, the ability to perform on-board data acquisition and data transfer to 

the ground station is required. As with the conventional architecture, the data transfer can occur either in 

flight, or on the ground at an airline gate. The acquired and transferred data will consist of sensed 

measurements and engine fault codes. Additionally, the auxiliary parameter estimates, health parameter 

estimates, and information on any faults detected by the enhanced on-board FDI logic will also be 

acquired and transferred to the ground station. 

Ground Station Fleet-Wide Trend Monitoring and Diagnostics 

Although the proposed enhanced architecture holds promise for improving GPHM capabilities, it does 

not eliminate the need for EHM ground stations or the functionality that they provide. Manual human 

interrogation of the diagnostic assessments produced by the GPHM system will always be necessary to 

ensure that the recommended mitigation actions (e.g., inspections, repairs, overhauls, etc.) are indeed 

warranted. 

Furthermore, the on-board and ground-based elements of the architecture are viewed as providing 

synergistic diagnostic functionality. On-board functionality provides continuous processing resulting in 

reduced diagnostic latency compared to ground-based processing of snapshot measurements. On-board 

continuous processing is also expected to be better suited for detecting intermittent fault behavior, which 

is difficult to detect relying on snapshot measurements alone. Conversely, the conventional ground-based 

approach is better suited for the detection of faults that evolve over multiple flight cycles. The periodic 

updating of PBM tuning parameters applied on-board will eventually absorb the effect of any performance 

changes. If an update of the tuning parameters occurs before a fault evolves to a detectable magnitude, 

the fault may not be detected. In combination, the on-board and ground-based functionality provide 

operators information that can be combined to gain increased confidence in GPHM assessments. A 

notional illustration of the fault detection capability provided by on-board versus ground-based FDI logic is 

shown in Figure 9. The subplots on the left are representative of the once per flight ΔΔy snapshot engine 

measurements available in a ground-station, while the subplots on the right represent available on-board 

ΔΔy engine measurements sampled at a higher frequency (1000 samples per flight in this example). This 

figure plots nominal (green points) and faulty (red points) ΔΔy residual engine data observed in two 

sensors under the following four fault scenarios: a) large magnitude abrupt fault; b) small magnitude 

abrupt fault; c) intermittent fault; and d) evolving fault. The first three scenarios show 20 flights of nominal 

 
NASA/TM—2010-216358 13 

pre-fault data and one flight of faulty data. The fourth scenario, evolving fault, shows 20 flights of pre-fault 

data and 10 flights of faulty data to illustrate how an evolving fault signature can be absorbed within the 

PBM update. Each scenario is further discussed below: 

a) Large magnitude abrupt fault: In this scenario the faulty versus nominal ΔΔy points are clearly 

discernable in both the ground-based and on-board FDI systems. Both systems are expected to 

perform well in detecting these types of faults within one flight. 

b) Small magnitude abrupt fault: Small magnitude faults are much more difficult to detect based 

upon a single measurement sample. A fault of the magnitude shown would need to persist for 

several flights in order for the ground-based FDI approach to detect it while maintaining an 

acceptable false alarm rate. Conversely, the fault is more clearly noticeable in the on-board 

system due to the increased number of samples available. 

c) Intermittent fault: Here the intermittent nature of the fault will cause the system to randomly 

alternate between exhibiting nominal and anomalous behavior. The ground-based FDI system will 

only be able to detect these types of faults if the snapshot data acquisition occurs while the 

system is exhibiting the fault. Conversely, the on-board FDI system is much more likely to detect 

these types of faults due to its higher sample rate. 

d) Evolving fault: This scenario reflects a fault that grows in magnitude over 10 flights. The groundbased FDI approach reflects ΔΔy residuals generated by referring current engine measurements 

against those collected prior to fault initiation. After several flights the fault becomes apparent 

within the ground-based FDI approach. Conversely, the fault is not apparent within the on-board 

FDI system due to the PBM update, once per flight in this example, which continuously absorbs 

the fault signature as it evolves over time. 

a) Large magnitude abrupt fault

b) Small magnitude abrupt fault

c) Intermittent fault

d) Evolving fault

Conventional 

ground-based 

FDI

Enhanced

on-board

FDI

Figure 9.—Comparison of ground-based and on-board fault detection capabilities under different fault scenarios. 
NASA/TM—2010-216358 14 

Future Work and Technology Challenges 

There are several technology challenges that need to be addressed to bring the proposed enhanced 

GPHM architecture to fruition. These challenges and the plans to address them include: 

 On-board full operating envelope deterioration trending: The tuning parameter inputs provided as 

inputs to the PBM are not expected to be uniform over the entire engine operating envelope. 

Therefore, a full-envelope approach for tuning parameter trending and archiving is necessary. This 

potentially could be accomplished by partitioning the operating envelope into regimes, and updating 

the average tuning parameter values within each regime over time. As the engine transitions 

throughout its operating envelope, the PBM would transition to the corresponding tuning parameter 

vector. Such an approach was taken in Reference 17, which describes the development of a hybrid 

(empirical and analytical) self-tuning on-board engine model. The empirical element of the model was 

trained and implemented by partitioning the model into different “cells” spanning the engine operating 

envelope. In addition to full-envelope trending, some means of full-envelope “resetting” of the tuning 

parameters is also necessary. Otherwise maintenance events that result in a performance trend shift 

(e.g., compressor water washes, replacing biased sensors, etc.) could potentially cause a false alarm. 

 Adaptive anomaly detection thresholds: The on-board anomaly detection logic monitors ΔΔy residuals 

between the PBM and engine outputs, and declares an anomaly if these residuals exceed defined 

thresholds. The applied threshold levels will have a profound effect on the system’s false alarm rate, 

and can render the system useless if not set properly. Scenarios that have the potential to cause large 

residuals include engine transients, noisy sensor measurements, operating in fringe operating regimes 

where the tuning parameters do not accurately reflect current engine performance, and long delays 

between the periodic PBM tuner updates. These factors should be thoroughly considered when 

defining the thresholds. An approach that may be beneficial is the application of adaptive thresholds 

(Refs. 22 and 23). The concept is that the detection thresholds are relaxed within operating scenarios 

known to produce large residuals, and tightened in other scenarios where the residuals are expected 

to be lower. 

 Quantify the diagnostic benefits of the enhanced architecture: The overall cost versus benefit of the 

enhanced architecture needs to be demonstrated. While the enhanced architecture is expected to 

yield diagnostic improvements, it will add cost and complexity to the overall system design. NASA 

plans to perform a benchmarking exercise to quantify and compare the expected diagnostic 

performance of the on-board system relative to a conventional ground station gas path diagnostic 

approach. Additionally, NASA is creating and distributing publicly available gas path diagnostic 

benchmarking problems to the EHM community. These standard benchmark problems will allow 

others to apply and evaluate their GPHM approaches as well (Refs. 24 and 25). 

 Software certification: The inclusion of additional on-board model-based software poses certification 

challenges beyond those typically faced by conventional designs. Of primary concern is ensuring that 

the software does not cause unintended consequences. To address this concern a functional hazard 

risk assessment must be conducted to identify all possible software failures, assess the consequences 

of each failure, and ensure that each failure will be appropriately mitigated if it occurs. Integral to this 

assessment is defining how the software impacts the system. Software used for control, real-time fault 

accommodation, or cockpit indications will require a higher level of certification than software used to 

generate post-flight maintenance or inspection advisories. These are factors that should be considered 

early in the design process. 

Long Term Vision 

A key justification for the inclusion of more sophisticated GPHM functionality on-board is the limited 

access to continuous full-flight engine sensor measurements off-board. Today, the cost of transmitting 

and archiving full-flight engine data is cost prohibitive. In the future, as data transmission and data 

archival costs are lessened, this will become less of a hurdle and ground station access to full-flight 

measurement data may become available. If this occurs, the question of where the engine GPHM 

diagnostic functionality resides should be reconsidered. If the same data is available on-board and off-
NASA/TM—2010-216358 15 

board, then the on-board system may only require the FDI functionality required to address faults that 

require an immediate on-board mitigation response. 

Nevertheless, on-board self-tuning models are expected to continue to play a major role in future 

aircraft engine designs. Over time, as on-board model-based gas path diagnostics grows in use and 

credibility, the technology is expected to be applied for on-board fault mitigation functions. Additionally, 

perhaps the biggest technology benefit offered by the on-board models is the real-time performance 

estimation capabilities that they provide. It is not possible to directly measure engine parameters such as 

thrust or stall margin in flight. Today, engine controllers are designed to indirectly control these 

parameters based upon other sensor measurements. While functional, this current approach does not 

provide direct control of the main engine parameters of interest. Accurate on-board model-based 

estimates of thrust and stall-margin could revolutionize engine controller designs. This model-based 

control technology pull is only expected to increase in the future. 

Summary 

This paper reviewed the conventional aircraft engine gas path health management (GPHM) 

architecture commonly applied today, and also proposed an enhanced architecture that incorporates onboard model-based functionality. The inclusion of enhanced on-board GPHM functionality is becoming 

possible due to the increasing processing capabilities of modern avionics computers. Both the 

conventional and the enhanced architectures include on-board and ground-based functionality. By 

design, the two architectures follow a similar diagnostic process consisting of performance trend 

monitoring, anomaly detection, and fault isolation. A key difference is the fact that on-board systems are 

able to conduct the continuous real-time monitoring of engine sensor measurements, while ground-based 

systems are primarily relegated to the post-flight processing of snapshot engine measurements collected 

at a small number of operating points each flight. The enhanced model-based architecture allows onboard fault detection and isolation (FDI) logic to be tailored to the performance level of each individual 

engine, similar to the ground-based FDI logic. This promises to enable the earlier detection of gas path 

faults. In addition, the on-board model allows the real-time estimation of unmeasured engine parameters. 

This could prove to be highly enabling for model-based controls, module performance deterioration 

tracking, enhanced cockpit displays and annunciations, and usage-based lifing calculations. NASA is 

considering future work to quantify the diagnostic benefits provided by the enhanced architecture 

described in this report. This includes quantifying the reduction in diagnostic latency and the reduction in 

detectable fault magnitudes offered by the on-board approach. 

References 

1. Jaw, L.C., (2005), “Recent Advances in Aircraft Engine Health Management (EHM) Technologies and 

Recommendations for the Next Step,” ASME Turbo Expo Conference, paper GT2005-68625, 

June 6–9, Reno, NV. 

2. Volponi, A., Wood, B., (2005), “Engine Health Management for Aircraft Propulsion Systems,” The 

Forum on Integrated System Health Engineering and Management (ISHEM) in Aerospace, 

November 7–10, Napa, CA. 

3. Jaw, L.C., Mattingly, J.D., (2009), “Aircraft Engine Controls: Design, System Analysis, and Health 

Monitoring,” American Institute of Aeronautics and Astronautics, Reston, VA. 

4. Tumer, I.Y., and Bajwa, A., (1999), “A Survey of Aircraft Engine Health Monitoring Systems,” 35th 

AIAA Joint Propulsion Conference and Exhibit, paper AIAA–99–2528, June 20–23, Los Angeles, CA. 

5. Volponi, A.J., et al., (2003), “Gas Turbine Condition Monitoring and Fault Diagnostics,” Von Karman 

Institute for Fluid Dynamics, Lecture Series 2003-01. 

6. Doel, D.L., (1994), “TEMPER – A Gas Path Analysis Tool for Commercial Jet Engines,” Journal of 

Engineering for Gas Turbines and Power, Vol. 116, No. 1, pp. 82–89. 

7. Urban, L.A., (1974), “Parameter Selection for Multiple Fault Diagnostics of Gas Turbine Engines,” 

ASME Gas Turbine Conference and Products Show, paper 74–GT–62, March 30-April 4, Zurich, 

Switzerland. 

8. Society of Automotive Engineers E-32, (2005), “A Guide to the Development of a Ground Station for 

Engine Condition Monitoring,” SAE Aerospace Information Report 4175A. 
NASA/TM—2010-216358 16 

9. Sallee, G.P., (1978) “Performance Deterioration Based on Existing (Historical) Data – JT9D Jet 

Engine Diagnostics Program,” NASA Contractor Report No. CR—135448, United Technologies 

Corporation, Pratt & Whitney Aircraft Group Report PWA-5512-21. 

10. Wulf, R.H., (1980), “Engine Diagnostics Program CF6-50 Engine Performance Deterioration,” NASA 

Contractor Report No. CR—159867. 

11. Volponi, A.J., (1999), “Gas Turbine Parameter Corrections,” Journal of Engineering for Gas Turbines 

and Power, Vol. 121, No. 4, pp. 613–621. 

12. DePold, H.R., Gass, F.D., (1999), “The Application of Expert Systems and Neural Networks to Gas 

Turbine Prognostics and Diagnostics,” Journal of Engineering for Gas Turbines and Power, Vol. 121, 

No. 4, pp. 607–612. 

13. Ganguli, R., (2002), “Data Rectification and Detection of Trend Shifts in Jet Engine Gas Path 

Measurements Using Median Filters and Fuzzy Logic,” ASME Journal of Engineering for Gas 

Turbines and Power, Vol. 124, No. 4, pp. 809–816. 

14. Y.G. Li, (2002), “Performance-Analysis-Based Gas Turbine Diagnostics: A Review,” Proceedings of 

the Institution of Mechanical Engineers, Part A: J. Power and Energy, Vol. 216, pp. 363–377. 

15. Doel, D., (1994), “An Assessment of Weighted-Least-Squares-Based Gas Path Analysis,’’ ASME 

Journal of Engineering for Gas Turbines and Power, Vol. 116, No. 2, pp. 366–373. 

16. Luppold, R.H. Roman, J.R., Gallops, G.W., Kerr, L.J., (1989), “Estimating In-Flight Engine 

Performance Variations Using Kalman Filter Concepts,” AIAA 25th Joint Propulsion Conference, 

paper AIAA–89–2584, July 10–14, Monterey, CA. 

17. Volponi, A., (2008), “Enhanced Self-Tuning On-board Real-Time Model (eSTORM) for Aircraft Engine 

Performance Health Tracking,” NASA Contractor Report No. CR—2008-215272. 

18. Kumar, A., Viassolo, D., (2008), “Model-Based Fault Tolerant Control,” NASA/CR—2008-215273. 

19. Kobayashi, T, and Simon, D.L., (2007), “Integration of On-Line and Off-Line Diagnostic Algorithms 

for Aircraft Engine Health Management,” ASME Turbo Expo Conference, paper GT2007-27518, 

May 14–17, Montreal, Canada. 

20. Simon, D.L., Garg, S., (2009), “Optimal Tuner Selection for Kalman-Filter Based Aircraft Engine 

Performance Estimation,” Journal of Engineering for Gas Turbines and Power, Vol. 132, No. 3, 

/031601-1. 

21. Litt, J.S., (2008), “An Optimal Orthogonal Decomposition Method for Kalman Filter-Based Turbofan 

Engine Thrust Estimation,” Journal of Engineering for Gas Turbines and Power, Vol. 130, No. 1, 

/011601-1. 

22. Chen, J., and Patton, R.J., (1999), “Robust Model-Based Fault Diagnosis for Dynamic Systems,” 

Kluwer Academic Publishers. London, England. 

23. Yedavalli, R.K., Li, W., (2007), “Aircraft Engine Fault Detection Using Dynamic/Adaptive Threshold 

Approach,” ASME Turbo Expo, paper GT2007-28180, May 14–17, Montreal, Canada. 

24. Simon, D.L., Bird, J., Davison, C., Volponi, A., Iverson, R.E., (2008), “Benchmarking Gas Path 

Diagnostic Methods: A Public Approach,” ASME Turbo Expo Conference, paper GT2008-51360, 

June 9–13, Berlin, Germany. 

25. Simon, D.L., (2010), “Propulsion Diagnostic Method Evaluation Strategy (ProDiMES) User’s Guide,” 

NASA/TM—2010-215840. 
NASA/TM—2010-216358 17 

Appendix—Glossary 

BIT Built-in-test 

EHM Engine health management 

FDI Fault detection and isolation 

GPHM Gas path health management 

Hz Hertz 

MAP Maximum a posteriori 

PBM Performance baseline model 

RTAPM Real-time adaptive performance model 

s Seconds 
REPORT DOCUMENTATION PAGE Form Approved 

OMB No. 0704-0188 

The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the 

data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this 

burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. 

Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB 

control number. 

PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 

1. REPORT DATE (DD-MM-YYYY)

05-01-2010 

2. REPORT TYPE

Technical Memorandum 

3. DATES COVERED (From - To)

4. TITLE AND SUBTITLE

An Integrated Architecture for On-Board Aircraft Engine Performance Trend Monitoring and 

Gas Path Fault Diagnostics 

5a. CONTRACT NUMBER

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

6. AUTHOR(S)

Simon, Donald, L. 

5d. PROJECT NUMBER

5e. TASK NUMBER

5f. WORK UNIT NUMBER

WBS 645846.02.07.03.12 

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

National Aeronautics and Space Administration 

John H. Glenn Research Center at Lewis Field 

Cleveland, Ohio 44135-3191 

8. PERFORMING ORGANIZATION

 REPORT NUMBER

E-17294 

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

National Aeronautics and Space Administration 

Washington, DC 20546-0001 

10. SPONSORING/MONITOR'S

 ACRONYM(S)

NASA 

11. SPONSORING/MONITORING

 REPORT NUMBER

NASA/TM-2010-216358 

12. DISTRIBUTION/AVAILABILITY STATEMENT

Unclassified-Unlimited 

Subject Category: 07 

Available electronically at http://gltrs.grc.nasa.gov 

This publication is available from the NASA Center for AeroSpace Information, 443-757-5802

13. SUPPLEMENTARY NOTES

14. ABSTRACT

Aircraft engine performance trend monitoring and gas path fault diagnostics are closely related technologies that assist operators in 

managing the health of their gas turbine engine assets. Trend monitoring is the process of monitoring the gradual performance change that 

an aircraft engine will naturally incur over time due to turbomachinery deterioration, while gas path diagnostics is the process of detecting 

and isolating the occurrence of any faults impacting engine flow-path performance. Today, performance trend monitoring and gas path fault 

diagnostic functions are performed by a combination of on-board and off-board strategies. On-board engine control computers contain logic 

that monitors for anomalous engine operation in real-time. Off-board ground stations are used to conduct fleet-wide engine trend monitoring 

and fault diagnostics based on data collected from each engine each flight. Continuing advances in avionics are enabling the migration of 

portions of the ground-based functionality on-board, giving rise to more sophisticated on-board engine health management capabilities. This 

paper reviews the conventional engine performance trend monitoring and gas path fault diagnostic architecture commonly applied today, 

and presents a proposed enhanced on-board architecture for future applications. The enhanced architecture gains real-time access to an 

expanded quantity of engine parameters, and provides advanced on-board model-based estimation capabilities. The benefits of the enhanced 

architecture include the real-time continuous monitoring of engine health, the early diagnosis of fault conditions, and the estimation of 

unmeasured engine performance parameters. A future vision to advance the enhanced architecture is also presented and discussed.

15. SUBJECT TERMS

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

16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF

 ABSTRACT

UU 

18. NUMBER

 OF 

 PAGES

23 

19a. NAME OF RESPONSIBLE PERSON

STI Help Desk (email:help@sti.nasa.gov) 

a. REPORT

U 

b. ABSTRACT

U 

c. THIS 

PAGE

U 

19b. TELEPHONE NUMBER (include area code)

443-757-5802 

Standard Form 298 (Rev. 8-98)

Prescribed by ANSI Std. Z39-18