# **Engine Health Management for Aircraft Propulsion Systems**

> Al Volponi Bruce Wood

Senior Fellow Technology Manager

> Pratt & Whitney Pratt & Whitney

### ABSTRACT

In its broadest context, an Engine Health Management System (EHMS) deals
with the monitoring, detection, isolation, predictive trending and
accommodation of engine degradation, faults, and failures. Its approach
draws from methodologies and processes employing both physics-based and
empirical techniques, derived from a wide range of engine system
disciplines including Materials, Structures and Controls. Minimizing
total cost of (engine) ownership, and increasing asset availability /
and mission readiness and while maintaining or even increasing safety
are just some of the key drivers for the implementation of an Engine
Health Management System (EHMS). Relative weighting of these factors
vary in importance depending on the end-user's operating and business
environment, as well as application-specific needs. Although they share
a common vein, it is not unusual for Commercial Aircraft EHM systems to
direct a different emphasis than Military Aircraft EHM systems. As a
result, there is no specific one-size-fits-all approach for an EHMS, but
rather an array of capabilities that can be assembled to address the
specific needs of an end-user. The range of capabilities employed in
these systems include not only those that are engine specific, but also
draw from mathematical, statistical and artificial intelligent methods
that are more generic in nature and have potential cross-cutting
application to other propulsion and space vehicle health tracking.

This paper provides an overview of current and evolving Engine Health
Management System capabilities available for the spectrum of possible
aircraft applications. Many of the methods and issues examined are
generic in nature and have applicability to a wider range of engine
systems such as marine and power generation gas turbines as well as
rocket and hypersonic propulsion systems. For the sake of brevity,
however, we will only address aircraft propulsion system related items.
While the bulk of the material presented draws heavily from Pratt &
Whitney experience, it should not be considered unique to a specific
engine manufacturer or engine product.

### INTRODUCTION

Gas turbine engine health management is as old as the jet engine itself.
From its beginnings as simple monitoring practices performed by a line
mechanic, advances in engine complexity and economic pressures has
pushed the boundaries of monitoring to impact flight operation, safety,
and maintenance activities to an ever-increasing degree. Over three
decades ago, the acronym EHM would have been recognized as meaning
Engine Health Monitoring, instead of Management, the former referring to
passive observations and the latter an active pursuit, with dependencies
on the former. Engine Health Monitoring began in earnest with the advent
of commercial high bypass turbofan engines in the 1970s. These modular
machines provided an economic impetus for monitoring and the development
of analysis methods to track the performance health down to the module
level, in an attempt to impact maintenance work scope and hence reduce
overall maintenance costs. With the advent of the Full Authority Digital
Engine Control (FADEC) in the early 1980s, flyable instrumentation
intended for control functions was now available for diagnostic
purposes.

Slowly, as both monitoring and analysis methods matured, the scheduled
engine removals based on number of flights or engine operating time have
begun to give way to autonomic on-condition maintenance practices. This
has opened the door on the possibility of not only improving flight
safety, but also simultaneously having a positive impact on shop
logistics, asset availability through the implementation of engine fleet
management systems. Engineers are capable of designing very safe gas
turbine engines, but there is always a trade in increased weight, cost
or reduced performance in order to achieve the extremely high levels of
safety. One means to mitigate the penalties associated with increasing
levels of engine safety are the introduction of EHM capabilities that
continuously monitor the condition or health of the engine. The trades
between introduction of a new sensor and creating a robust engine
structure are often very favorable to the former solution.

As engine safety is now reaching unprecedented levels, pure economics is
becoming more of the prime driver for implementation of EHM systems in
commercial, military and power systems sectors. Commercial airlines seek
to reduce the overall cost of ownership and increase flight safety by
reducing the potential for in-flight shutdowns (IFSD), unplanned engine
removals (UER) and delays and cancellations (D&C) through managed
maintenance, by striving to eliminate unexpected events. These factors
are important to the military as well, where the war fighter's readiness
and availability to launch and successfully complete a mission on a
moments notice is of ultimate importance. Down time for power system gas
turbines carries direct economic penalties. The bottom line is that
judicious application EHM Systems to the commercial, military and power
engine sectors can provide benefits to each in similar, although not
identical means. Based on the weighting of the importance of each of the
above-mentioned metrics, the optimum EHM solution may vary between
sectors, and between applications within the sector. A one-size-fits-all
solution is not likely to exist and EHM systems must be tailored to
address the specific application at hand.

Experience has shown that EHM needs vary from engine-to-engine, even
customer-to-customer using the same engines. It is possible to design an
EHM system that provides near 100% fault isolation and prognosticates
engine service requirements weeks into the future that meets the basic
requirements of one customer, which does not have a viable business case
for another customer. EHM systems are intended to improve the business
propositions of engine applications by providing: 1) marketable
capabilities that make the product more attractive to the end customers,
2) a means to reduce Life Cycle Costs for the end user, 3) a means of
financial exposure reduction for a Fleet Management Services provider,
etc.

Engine Health Management can best be visualized as portfolio of
capabilities from which building blocks can be drawn to create
customized architectures that best meet individual user needs. Both
Engine-Hosted and Ground-Based elements are viable tactics, not as
competing EHM approaches, but rather complementary features of an
overall integrated system. Engine-Hosted elements generate data from
on-board sensors, and perform basic fault isolation and prediction,
supporting on-wing maintenance, while the Ground-Based elements support
long-term degradation trending, providing planning information that can
be used by aircraft fleet managers. These will be explored in greater
detail in the sequel.

**[Ground-Based Engine Health Management]{.underline}**

Among the EHM systems, ground-based diagnostic systems were the first to
evolve. Starting in the test cells, these systems were very specialized
and limited in their scope. These systems were largely comprised of
independent programs for vibration monitoring and analysis of high
bandwidth (2k Hz) accelerometer data and low bandwidth (5-20Hz) time
averaged steady-state gas path data and was typically performed during
outbound post overhaul acceptance test runs and occasionally for inbound
Test-As-Received (TAR) engine runs. One of the analysis areas to benefit
from the added instrumentation typical in the test cells was Gas Path
Analysis (GPA), also referred to as Module Performance Analysis (MPA).
The literature abounds with descriptions of this type of diagnostic
procedure, as it has been an area of active research over the past three
decades by practitioners in industry (engine manufacturers, small
businesses), academia, and the government (NASA, DOD, USAF, Navy, etc).
Since there is adequate information in the public domain describing the
many varied approaches (physics-based and empirical) a detailed
discussion of this, albeit important area of engine diagnostics, will
not be provided. The interested reader is referred to the general
literature (see references \[1-20\]). However, since it is one of the
more mature technologies and serves as a foundation methodology for
general engine diagnostics (both ground-based and engine hosted) a
cursory overview at this important area will be provided.

*Module Performance Analysis*

Most diagnostic methods rely on discernable changes in observable
parameters in order to detect physical faults. As a generality, physical
faults consist of combinations of problems such as foreign object damage
(FOD), blade erosion and corrosion, worn seals, excess clearances or
plugged nozzles, which result in *changes* in the thermodynamic
performance of the engine as measured by adiabatic efficiencies,
compressor flow capacities and effective nozzle areas, etc. These
thermodynamic changes, in turn, produce *changes* in observable engine
parameters measured along the engine gas path, such as rotor speeds,
temperatures, pressures, fuel flow, power output and the like. The
fundamental task of Module Performance Analysis (MPA) is to infer (from
these observable shifts) the level of deterioration in the various
modules of the engine, thus allowing correction of the underlying
physical faults through proper maintenance action (Figure 1).

![](media/image1.png){width="5.999305555555556in"
height="1.4270833333333333in"}**Figure 1: MPA Principle**

This type of diagnostic procedure can be referred to as *relative*
engine performance diagnostics, i.e. it assesses *changes* in engine
performance *not absolute* performance levels. This makes it possible to
track performance of a particular engine against itself (say from when
the engine was installed on an aircraft) or how a particular engine has
changed relative to some production level or fleet average. Whatever the
base reference point, valuable information can be obtained which can
directly impact the maintenance schedule, logistics and cost. This form
of gas turbine performance diagnostics is a fairly mature methodology,
which has been in practice in both commercial and military engine
diagnostic programs for almost three decades. References to this
approach first appeared in the literature by one of the early
investigators and pioneer, in this field, Louis A. Urban \[1-2\]. As a
result of its longevity, the method has been referred to by several
names, Gas Path Analysis (GPA) and Module Performance Analysis (MPA),
being two of the most popular.

To infer changes in these performance characteristics it is necessary to
observe discernible changes (from reference) in various engine
parameters taken along the engine's gas path and have a mathematical
model interrelating the two sets of variables. The fundamental tenet
underlying this approach is that physical faults occurring in the engine
(such as blade erosion, corrosion, tip clearance, fouling, etc.) induce
a change in component performance (as modeled by efficiencies, flow
capacities, etc.), which in turn produce observable changes in
measurable parameters (such as temperatures, pressures, speeds, etc.).
Through inverse relationships, it should be possible to estimate the
component shifts responsible for the measurement shifts observed which
in turn provides information needed to address the underlying physical
fault(s).

The general approach taken is through the use of a linearized model
approximation evaluated at a selected engine operating point. This
provides a matrix relationship between changes in engine component
performance (independent parameters) and the attendant changes in the
typically measured engine parameters. The model relationship may then be
succinctly represented as

> $Z = \text{Hx} + \theta$ ()

where *Z* is a vector of measured parameter deltas, *x* is a vector of
fault deltas, *H* is a matrix of fault *influence coefficients* and *θ*
is a random vector representing the uncertainties inherent in the
measurement process. In addition to the precision of the individual
sensors, it has been customary to address the potential for sensor bias
and drift. Consequently, the fault vector given in the model above is
often configured to contain components directly related to sensor error
in addition to engine fault deltas and the *H* matrix is augmented to
account for these additional fault parameters \[5\]. The MPA problem
then reduces to one of estimating the fault vector *x*, i.e.

> $\hat{x} = f\ (\ Z\ )$ (2)

While this might seem like a simple task, there are many difficulties
encountered, not the least of which is that, in practice, the number of
unknowns (size of *x*) is far greater than the number of equations (size
of *Z*), as well as *observability* issues between certain faults
amongst the measured quantities, poor signal to noise ratio, engine
non-linearity effects and a host of others \[6-7\]. For this reason the
estimation problem has been the focus of considerable research over the
last several decades with methodologies incorporating a wide range of
techniques from Kalman Filters \[6-12, 21-28\], Neural Networks
\[25,29-31\], Fuzzy Logic\[32\], Bayesian Belief Networks\[33-34\],
Genetic Algorithms\[35-37\], and combinations of the above \[35,38,39\].

*Engine Health Tracking*

As mentioned in the previous discussion, the matter of determining
performance shifts poses certain mathematical difficulties. Indeed, with
an undetermined system (number of unknowns outweighing the number of
equations), there cannot exist a mathematically unique solution. The
pursuit for greater estimation accuracy can only be achieved through the
incorporation of more information regarding the performance state of the
engine. Information abounds, however, most of it is *heuristic* in
nature and cannot be applied directly in a quantitative fashion without
considerable effort. A few examples of this type information might be

-   Over time, component performance degrades, not improves, i.e. ∆
    efficiencies and flow capacities tend negative, not positive.

-   Maintenance actions influence performance (engine and sensor) in a
    variety of ways (positive and negative).

-   The existence of similar gas path shifts in aircraft *companion*
    engine parameters might provide corroborative evidence for *common*
    instrumentation problems (e.g. altitude, Mach, TAT, etc.).

-   Non gas path information, such as engine vibration, inlet and
    exhaust debris monitoring sensors, etc might allow a different
    perspective on the type of underlying faults which should be
    assessed.

-   Measurement non-repeatability may differ (for a particular engine)
    from the assumed (fixed) variances for that engine model
    installation.

This list is by no means exhaustive. Once again the literature \[40-44\]
offers some guidance on potential improvement through the inclusion of
additional health feature information, some quantitative and some
heuristic. This topic of information fusion will be discussed in more
detail in the *engine-hosted* engine management section. In a
*ground-based* system, data availability (as to type and quantity, i.e.
bandwidth) imposes certain restrictions that are not necessarily present
for *engine-hosted* systems. Conversely, tracking engine health on a
*fleet basis* is only possible with *ground-based* systems. Having an
appropriate data infrastructure in place for capturing in-flight engine
data with seamless transfer to the ground-hosted systems is a critical
element for engine health management to be successful. This must be
accompanied by appropriate links to maintenance information, life cycle
information, supply chain information and the like as well as a suitable
interface for the end user to access and utilize the information. The
data IT aspects are enormous and are far beyond the scope of the present
discussion (and expertise of the authors). Some associated information
on some of these topics is available in the following references
\[45,46\].

This section will be concluded with a brief discussion of one aspect of
the general information-leveraging problem. The information comes by way
of inspecting a time series of points rather than just a single point in
isolation. It is concerned with the difference between gradual
deterioration and a rapid deterioration, in the *temporal* sense. Engine
performance changes can manifest themselves in one of two ways: a)
gradual (*long*-term) deterioration or b) rapid (*short*-term)
deterioration. These may effect component (module) performance changes
that can be characterized in terms of estimated parameters such as
efficiencies, flow capacities, and effective nozzle areas discussed
previously or can be changes in performance of engine sub-systems such
as bleeds, cooling flows, variable geometry mechanisms etc. To properly
address these different types of degradation, different algorithms with
different assumptions come in to play. It is assumed that *all* of the
engine components (whose shifts in performance are being estimated) are
deteriorating *slowly* whereas rapid trend shifts (if observed) are most
probably due to a *single* entity (or perhaps two) going awry. This
*concession* is a form of heuristic information based on experience. The
methodology defined below attempts to leverage this information allowing
for both of these processes to operate in concert with one another,
automatically, and without corruptive interaction. It might be
considered an example of algorithm fusion driven by observational
information.

Without much loss of generality, two different fault mechanisms can be
considered, *gradual* deterioration and *rapid* deterioration as
illustrated in Figure 2, below.

![](media/image2.jpeg){width="4.25in" height="2.625in"}

**Figure 2: Effect of gradual versus rapid deterioration on an observed
measurement**

Pictured in this plot is a particular gas path parameter ∆ (say Exhaust
Gas Temperature ∆). As the engine degrades naturally over time, the ∆
increases slowly as indicated by the averaged trend line. The scatter
observed is due to the measurement non-repeatability and processing
errors. Each of these points, along with similar ∆s in other gas path
parameters, would be taken collectively (at each time point) and
analyzed to estimate the underlying Module Performance deterioration, as
discussed in the preceding paragraphs. In the middle of the plot, the
gradual trend is observed to change and there is a rapid shift to a new
level, followed by a continuation of the slow trend. Clearly, something
has occurred to force this rapid trend shift, and this is where a
*single fault* assumption could be leveraged. Keeping in mind that data
is flowing into the system over time and that each data point is
analyzed individually as it is received, there is a need for a process
to check for outlier data versus true trend shifts, a detection
mechanism to recognize the shift and calculate the level offset (∆∆) and
a (single fault) isolation process to identify the fault and quantify
its severity. Figure 3 depicts a top-level view of the process.

![](media/image3.wmf){width="6.5in" height="2.125in"}

**Figure 3: Simplified MPA Process Overview**

Fundamental to this process is an automatic means to detect the presence
of a trend shift, quantify the level, identify the root cause and
establish (temporally) when the event has ceased and natural (slow)
trending has resumed. Figure 4 depicts the situation. Methods to analyze
this kind of behavior and integrate it with the normal MPA process vary
from user to user. Some suggestions (and comparisons) can be found in
\[8,47-49\].

![](media/image4.jpeg){width="4.375in" height="2.375in"}

**Figure 4: Calculating the ∆∆ shift for single fault isolation**

**[Engine-Hosted Health Management]{.underline}**

*Purposes of Engine-Hosted EHM: Detection and Isolation*

The primary purposes of the Engine-Hosted EHM systems are to enhance
engine safety against damage by providing early real-time detection of
conditions that might progress to critical system failures, and to aid
in the reduction of maintenance support costs and aborted missions by
automatically "reasoning" on data to minimize fault isolation time of a
failing component to a Line Replaceable Unit. An Autonomic Logistics
approach is being introduced for the military Joint Strike Fighter (JSF)
(see \[50\]). is an example of an application of on-board Diagnostics
and Prognostic Health Management (D/PHM) capabilities to eliminate
scheduled engine inspections and rely on on-condition health
assessments.

*Functions of EHM*

In general, it is the function of the Engine-Hosted EHM system to
acquire data, monitor and assess current engine condition, as well as
predict future engine state. Subtle changes in combinations of some
measured parameters can indicate early symptoms of a progression towards
a failure condition. These changes in a parameter's characteristics are
often difficult to detect by simply observing limit exceedances, since
the parameter values typically remain within the normal operating range.
Trend analysis of parameter values, taken over time at specific
operating conditions, enables detection of shifts, abnormal rate of
change, and abnormal scatter in the data. Measures of gas path
performance, lubrication and fuel system operation, and rotating
function are typically used to perform these trend analyses.

*Sensors*

"Sensed" measurements are required to provide data for comprehensive
monitoring. The number of sensors depends on the complexity of the
engine to be monitored and the level of coverage desired. These can
range from engine control sensors doubling in an EHM function, to a
sophisticated suite of dedicated EHM sensors. Sample rates can vary from
as low as once per flight to hundreds of thousands of times per second
(see \[51\]).

*Engine Gas Path*

At a minimum, the sensors that are used to provide closed-loop control
of the engine serve in a dual role as a basic suite of EHM sensors. This
suite usually consists of some mix of gas path pressures, temperatures,
and spool speeds as appropriate to the engine configuration, which can
typically range from as few as four to upwards of twelve or more
sensors.

*Operating Conditions*

Additional measures such as inlet temperature and pressure, vehicle
speed (Mach number), and altitude define the flight condition at which
the engine is operating. While they do not directly address the health
of the engine itself, these measures are useful in the "normalization"
of the main gas path parameters to a set of "standard" operating
conditions, significantly reducing the amount of computations that must
be used to interpret the gas path performance data at all flight
conditions.

*Actuation*

A basic suite of EHM sensors will also contain measures of the actuation
system such as mechanical displacement or rotation, index of rotation
position, fuel flow, fuel system pressures and temperatures, bleed air
valve position, etc.. Some systems also include a "wrap-back" of the
analog commands to the actuators to verify the integrity of the wiring
system. Often the actuation element of the EHM constitutes a good
portion of what is typically referred to as a basic Fault Detection
system.

*Mechanical Components*

In recent years, basic EHM systems are being expanded to include
measures of the condition of the Mechanical Subsystems, which include
components such as bearings, gearboxes, power extractions and the
lubrication system that supports it. This expansion was driven both by
the significant percentage of engine problems associated with these
components, and the relative ease with which new sensors could be
incorporated into the engine design.

*Vibration*

Bearing degradation, ultimately leading to failures, results from
unexpectedly rapid and often asymmetrical wear of the rotating parts
(e.g. balls, rollers, etc.) and the surfaces against which they move
(i.e. races). Likewise, gear degradation appears as uneven wear on the
surface of the gear teeth, often resulting in audible "chattering". The
most common means of sensing bearing and gear degradation is via
vibration monitoring. Typically this is accomplished by mounting single
or multi-axis accelerometers in the vicinity of the bearings and on the
gearbox. Mounting the sensor closer to the source of vibration is
preferred since it provides a "more distinct" signal, but often this is
not possible to temperature constraints on the sensor. Often the
accelerometers are simply mounted on the engine case.

The frequency range for the accelerometers depends on a number of
factors. One consideration is the attributes of the signal to be sensed,
which is generally a function of engine speed. Another is the operating
environment; the background noise which may obscure the signal. A third
consideration is how the signal will be used; whether it will be used to
provide a "high vibes" indication or used to perform real-time on-board
analysis of the signal to aid in fault isolation.

*Lubrication System*

Sensors generally used to monitor the Lubrication System include
measures of oil quantity, temperature, delivery pressure, debris and
degradation. Measure of specific components, such as the pressure drop
across an oil filter may provide indications of eminent bypass condition
and the need for filter replacement. Oil Debris Monitoring has been
found to be a highly effective means of early bearing degradation
detection; by monitoring the liberated metal particles in the oil it is
possible to identify and trend degradation well in advance of indication
of irregular rubbing provided by a vibration sensor, especially when
complications of filtering out system noise are considered.

*Turbo-machinery*

EHM Systems are now being enhanced to add the capability to monitor
components. Blade damage features of interest are bending, chipping,
etc. due to Foreign or Domestic Object Damage (FOD/DOD), high cycle
fatigue (HCF), creep, etc. Even more difficult to monitor is disk
damage, such as crack progression. monitoring has been long in finding
its way into EHM Systems due to the difficulty of installing sensors in
appropriate locations to monitor high-speed rotating parts in an
extremely high temperature environment.

Some form of vibration monitoring is typically performed on most
engines. This is usually on the low spool to measure Fan and Low
Pressure Turbine (LPT) vibration, but may include high spool vibration
probes as well as specific bearing and gearbox vibration measurements.

Basic monitoring has been accomplished via vibration monitoring, serving
in a dual role as a means of bearing monitoring. The premise is that
blade and or disk damage will result in a rotor shaft unbalance that is
detectable via accelerometers. This may or may not be true, depending on
the magnitude of the damage; it will not add in the detection of HCF or
creep. Higher frequency accelerometers may be required to monitor blade
damage.

*Direct Blade Measures*

New sensors are being developed to enhance turbo-machinery-monitoring
capabilities. Of particular interest are blade passage sensors that
project electromagnetic energy from their engine case mounting
locations. Reflections of the energy that occurs as the blades move past
the sensor are detected. Inconsistencies in the sensed "time of arrival"
are indicative of blade or disk damage. The time delay associated with
the signal reflection is also indicative of the clearance between the
blade and engine case. Deterioration of the blade tip, which widens of
the clearance "gap", correlates directly to the loss of component
efficiency.

Precise measures of the peak level and variations in operating
temperatures of the hardware are a key factor in assessing its
condition, especially with regards to creep. Taking measurements in the
hottest sections of the engine using conventional "direct" means are not
possible, due to the difficulties of placing sensors in the extremely
high temperature and rotational speed environment. Indirect means such
as inferring temperature from real-time analysis of electromagnetic
spectrum are now becoming feasible.

Debris monitors are another means being used sensing turbo-machinery
health. Rather than directly measure blades and disks health,
debris-monitoring devices attempt to quantify the number and mass of
particles ingested into or expelled by the turbo-machinery. The
magnitude of particles is correlated to damage by analytical and
empirical methods.

*Future*

Sensor technologies under development now will enable future EHM Systems
to provide a more comprehensive assessment of engine conditions. Fuel
and oil leak detection, on-board combustion products analysis and
self-monitoring wiring systems are but a few of the new capabilities on
the near horizon.

*[COMPUTING HOST]{.underline}*

*Engine Diagnostic Unit*

Sensors generate condition information that is either directly
digitized, or pre-processed in analog form before digitization. In
either case, modern EHM systems deal in digital data. Simple EHM systems
will merely capture the raw data as it is generated in real-time and
record it some storage media such as a digital tape, disk or memory
stick for on-ground processing. More sophisticated EHM Systems will
employ either a standalone Engine Diagnostic Unit (EDU) or some portion
of a Full Authority Digital Engine Control (FADEC) to process at least
significant portions of the date in real-time on-board.

*Processing*

Depending on the sensor suite utilized, the amount of on-board
processing can be significant. High sample rate sensors can require
implementation of Digital Signal Processors (DSPs) dedicated to a single
sensor in the EDU. The level of sophistication of the algorithms used to
convert raw sensor data into engine condition information sets the
requirements for the Central Processor Unit, which could call for
significant throughput.

*Memory*

Memory storage capability is as an important consideration as processing
speed. The EDU records in its memory a time-tagged chronology of
failures, as well as flight and engine operating conditions at the time
of the failure. For EHM systems that do not take advantage of on-board
algorithms to pre-process data, the requirements for data storage can be
enormous. Low sample rate gas path monitoring systems generally have
data storage requirements in the kilobyte range per flight hour. But if
raw data is to be recorded for high sample rate sensors such as certain
accelerometers, data storage requirements can easily move into the
gigabyte per hour range. In these cases, on-board data compression,
using analytical methods or by brute force (e.g. MPEG techniques), are
particularly valuable.

*Communication*

The EDU/FADEC also serves as the communication interface for the EHM
system. EHM data may be used in the FADEC for optimal control of the
engine. But it is often communicated to the pilot to annunciate
warnings, cautions, & advisories and to aid in decision making in the
presence of fault condition. It is ultimately communicated off board the
aircraft to assist the maintainer in troubleshooting problems and for
the Fleet Manager to aid in maintenance planning. The EDU may move EHM
data off the aircraft through the vehicle communication system (ARINC,
AFDX, 1553, Firewire, etc.), or it may offer a direct link such as a
USB-type port or even a wireless link. Consideration must be made as to
the amount of and the means by which data will be moved off the vehicle;
attempting to move huge sums of data through slow communication links is
not practical.

[SOFTWARE]{.underline}

A wide range of software may be hosted in an engine-mounted Engine
Diagnostic Unit (EDU). It could be as simple as routines that
periodically write data to memory or as sophisticated as a system of
algorithms that autonomously detect, isolate and report changes in
engine condition.

*FADEC Codes*

A basic EHM system will leverage the Fault Detection functions that are
typically performed within the FADEC. An electronic engine control
performs a number of performance tests on sensor signal condition and
fidelity. Cross-channel checks in a multi-channel FADEC can aid in
determining when engine sensor is drifting, periodically or continuously
exceeding range limits, or in fact, failing. Checks on bleed valves,
active clearance control, and variable geometry can provide independent
information regarding engine health and the health of various engine
subsystems. FADEC fault detection algorithms typically generate "codes",
that indicate the presences of one (or more) of a list of pre-identified
failure conditions. These "codes" are typically recorded in order of
occurrence and may have a time-stamp associated with them to aid the
maintainer in troubleshooting.

*Anomaly Detection*

In contrast, engine-operating conditions may be observed that are not
expected, for which there is no "Fault Code". These are said to be
"anomalous". Software algorithms that detect the presence of anomalies
are commonly called Anomaly Detectors (see \[64-72\]). These algorithms
typically work by comparing the relationships observed in
engine-generated parameters against some standard of expected
relationships. The persistence of significant variations in measured
versus expected relationships would be considered an "Anomaly".

There are a number of methods for establishing the expected "normal".
One means is to determine if the relationship between parameters
violates the basic rules of physics. Another means collects data over
some period of time to train an empirical mode of the engine (or engine
subsystem). A third method uses an on-board analytical model(s) of the
engine.

Empirical models developed from a statistically significant sample of
nominal engine operation data can be used to form the basis for an
anomaly detector. These types of models typically take the form of
Artificial Neural Networks (ANN) and are trained to output normal engine
operation measurement estimates. When compared to actual measurements
they provide a basis for making a statistical determination as to
whether or not the observations at hand conform to what is considered
"normal" operation. Although the ANN may take many forms, the Radial
Basis Function (RBF) ANN, has been used successfully in several
applications. During the training process for these types of ANNs, the
training data is self-organized into a group of classes wherein each
class is modeled by an n-dimensional Gaussian function referred to as a
radial basis function. These functions capture the statistical
properties and dimensional inter-relationships between the input and
output engine data parameters. The structure an RBF ANN is depicted in
Figure 5.

![](media/image5.wmf){width="2.0631944444444446in"
height="1.3611111111111112in"}

**Figure 5: Radial Basis Function ANN**

The practical implementation of an Anomaly Detector (AD) often involves
partitioning the modeling problem into smaller constituents to avoid the
difficulties associated with constructing an ANN large enough to cover
the entire flight regime and engine operating conditions. A Gas Path AD
described in \[66\] is configured as a set of several RBF ANNs, each
representing a particular flight regime or operational characteristic to
enhance the accuracy of the overall detector. For example there is an
RBF ANN for steady state operation with and without stability bleed
off-take, acceleration and deceleration. Simple regime recognition logic
controls the selection of the appropriate RBF ANN. Pre-processing of
engine parameters in terms of standard day corrections and range
normalization are made prior to input into the ANN. The primary output
of the system is a (fuzzy-like) detection variable that takes on the
values between 0 (anomalous data) and 1 (normal data). A threshold and
median filtering is applied to the output to produce a discrete binary
parameter to serve as a detection flag. A representation of this model
is depicted in Figure 6, below.

![](media/image6.wmf){width="2.8756944444444446in"
height="2.917361111111111in"}

**Figure 6: Anomaly Detection Scheme**

In addition to the binary AD output parameter, individual signal
distance measures are available which quantify each parameter's
contribution to the data's classification as normal or anomalous.
Collectively, these provide an empirical *signature* for anomalous data
and can be helpful in performing fault isolation once detection has been
made. More information on methods and application of ADs can be found in
the following references \[64-72\].

*Information Fusion*

As has been shown, engine condition information is generated by a
variety of on-board sources. Some of this information comes directly
from continuous sensor measures; some may be generated from on-board
component models, while other information comes from data bases stored
in the Diagnostic Unit (such as maintenance history). An ultimate goal
of an EHM system is to maximize the amount of meaningful information
that can be extracted from disparate data sources to obtain
comprehensive diagnostic and prognostic knowledge regarding the health
of the engine. "Fusion" techniques integrate of data/information from
multiple sources, to achieve improved accuracy; confirmation of
observations and more specific inferences than can be obtained from the
use of a single information source alone. The basic tenet underlying the
data/information fusion concept is to leverage all available information
to enhance diagnostic visibility, increase diagnostic reliability and
reduce the number of diagnostic false alarms. In-depth discussions on
particular approaches and methodologies can be found in \[55-64\].

*Fault Isolation*

It is most often desirable to have knowledge of more than just the
presence of a fault condition, but to actually isolate the fault to its
root cause. In legacy EHM systems, Fault Isolation has been limited to
simple cases in which a single measure can be interpreted as a specific
fault. But as part of the drive to provide more comprehensive engine
coverage, more sophisticated methods of fault isolation are desirable.
Often some form of "Reasoner" is used to provide the Fault Isolation
capability. Examples are a case-based Reasoner that utilizes trees of
past experience to reason its way from observed condition to the source
of variance from "normal". Other methods use detailed "failure
progression" models of the engine as an organized technique for
resolving anomalous conditions into faults. Autonomous isolation faults
can be used to specify real-time accommodation of faults by the FADEC,
as well as assist the maintainer in performing required repairs.

[ON-BOARD MODELS]{.underline}

Real-time executing models are an integral part of modern Engine-Hosted
EHM systems. The models use engine-operating conditions to generate
expected values for many of the engine parameters. Models can merely be
tables representing the "states" of the engine parameters at a number of
critical operating points, using piecewise linear interpolation to
estimate values between these key conditions. Or the model can be as
complex as a real-time implementation of a non-linear aero-thermal
representation of the engine. More sophisticated versions of the models
tune themselves to account for engine component deterioration and
component damage.

*On-Board Gas Path Analysis*

An alternative (or complement) to ground-based Module Performance
tracking systems is the use of on-board engine models and attendant
logic to perform gas path analysis in real-time during flight. These
systems (see references \[52-54,72\]) typically consist of simplified
on-board engine models with Kalman Filter observers to perform the
module performance deterioration estimation. A practical consideration
for implementing a real-time on-board Module Performance tracking system
is the development of a high fidelity engine model capable of providing
a reference level from which performance changes can be tracked.
Real-time engine models made their advent with the State Variable Model
(SVM) in the mid-'80s which provided a piecewise linear model that
granted a reasonable representation of the engine during steady state
operation and mild transients. Increased processor speeds over the next
decade allowed more complex models to be considered, that were
combinations of linear and non-linear physics based components. While
the latter may provide greater fidelity over transient operation and
flight envelope excursions, it bears the limitation of potential model
obsolescence as performance improvements in the form of hardware
modifications, bleed and stator vane schedules alterations, cooling flow
adjustments, and the like are made during an engine's life cycle. Over
time, these models may deviate enough from the actual engine being
monitored that the module performance estimations are inaccurate and
misleading. To mitigate these effects, an alternate approach to engine
modeling can be taken, utilizing a hybrid engine model architecture that
incorporates both physics-based and empirical components. This
methodology provides a means to *tune* the engine model to a particular
configuration as the engine development matures and furthermore, aligns
the model to the particular engine being monitored to insure accurate
performance tracking while not compromising real-time operation. This
approach effectively removes the uncertainty introduced with
engine-to-engine variability since each monitored engine would, in
effect, have its own custom (*hybrid*) model representation. A typical
configuration for a system of this type is depicted in Figure 7.

![](media/image7.wmf){width="6.323611111111111in"
height="4.021527777777778in"}

**Figure 7: Adaptive Hybrid Engine Model**

In this configuration, The "ANN" (Artificial Neural Network) represents
the empirically derived model of the *difference* between the simple
physics-based engine model (SVM) and the actual monitored engine. The
combination of the two represents a *hybrid* engine model that is
*tuned* to the specific engine being monitored. The real-time
determination of this empirical model is the key technical challenge in
realizing this emerging technology and is an area of current research.
Information on this type of methodology and its impact on improving
Module Performance deterioration tracking can be found in \[73-75\].

**[Component Life Usage Estimation]{.underline}**

Many engine components (Life Limited Parts) require inspection after
some regular intervals, and must be replaced well before all usable life
has been expended. Component Lifing algorithms that accurately track
component usage can allow Life Limited Parts to remain on wing until
their life is more nearly consumed. Safety is actually increased by
accurately knowing life consumption on an engine-by-engine basis, while
at the same time reducing support costs. The Component Lifing System can
be implemented either as part of an Engine-Hosted or Ground-Based EHM
system, with the Ground-Based implementation supported by on-engine
generated, compressed and downloaded data.

######## Traditional Component Lifing Methods

Analysis techniques commonly used to determine accumulated life consumed
are either engine operating time or total accumulated cycles, which are
correlated to low cycle fatigue for each life limited component.
Computation of these cycles assumes that each engine on each aircraft
will be used in a manner consistent with "expected" flight profiles.
Maintenance of life-limited parts is scheduled based on a very
conservative assumption of engine usage in order to maintain required
safety levels. In reality, engine usage often falls short of the
standard mission profiles. Yet in order to ensure safety, parts are
replaced "early", per the conservative schedule. As a result, due to a
lack of actual usage knowledge on an engine-by-engine basis, most
components on most engines are replaced with significant life and, hence
value remaining in the part.

######## Advanced Component Life Usage Tracking

By implementing methods to accurately track actual component life usage,
there is potential to greatly reduce the width of the usage uncertainty
distribution, allowing the scheduled maintenance point to moved much
closer to the center of the distribution. This greatly reduces the
number of parts that are scrapped with life remaining, while maintaining
or even increasing safety levels.

Advanced life estimation techniques use validated models derived from
component design tools to allow more accurate tracking of component life
used and estimation of life remaining. The algorithms accumulate usage
by tracking time-variant changes in parameters such as pressures,
temperatures and speeds to drive the "lifing algorithms", that are in
essence real-time models of critical component failure mechanisms. Based
on how a family of engines is predicted to be used for a particular
vehicle application, and based on how they are actually being used, the
life usage estimation can be transformed into a life remaining
estimation. Life Remaining for each modeled component accumulated and is
made available to the engine fleet management system, allowing the
scheduling of necessary overhaul based on actual usage.

**[DESIGN OF AN ENGINE HEALTH MANAGEMENT SYSTEM]{.underline}**

In the prior sections of this chapter, the function of many elements of
Engine Health Management has been described. But for any given
application, how does one select what elements are appropriate and "buy
their way" into the system configuration?

Generally there are two primary drivers for the inclusion of a Health
Management capability into an engine design: Safety and Reduction of
Life Cycle Cost. Providing the means to detect conditions indicative of
component failure well in advance of the actual event, the EHM system
can alert the engine operator with sufficient forewarning to allow the
risk to be mitigated. Similarly, the same EHM system can generate
information that allows maintenance practices to be transformed from
economically inefficient "scheduled" to more effective "on condition:
methods. But these benefits come at a cost; providing EHM system
hardware, software and infrastructure can be expensive.

Safety and Reduction of Life Cycle Cost are typically not standalone
drivers, but rather tend to be coupled. While it is necessary that any
engine system be "safe", "safe" is a relative term. An EHM System
designer must answer the questions, "What levels of safety are
acceptable for a given application?" and "How much safety is
affordable?" In a theoretical sense, it is desirable that any given
engine system be 100% safe and reliable. In practical application, 100%
reliability cannot be achieved. The designer must trade safety versus
cost. For example, is a system providing 99.99999% reliability truly
desirable if the cost of that system is two orders of magnitude more
than one providing 99.9999% reliability? Similarly, driving towards a
goal of "no delays and cancellations due to engine problems" is
certainly a desirable goal, but not if the implementation of the means
to achieve that goal is more costly than the engine itself.

The design challenge is then how to select a suite of capabilities that
affordably provides the desired benefits. An appropriate EHM
architecture is a function of the business model(s) selected for each
engine offering and the costs to implement them. Is the customer
interested in purchasing an engine that is safer than the one that the
competition is using? How much is a delay or cancellation worth the
operator? The design of an appropriate EHM is further complicated by the
fact that there are really several customer needs to be considered,
including the engine manufacturer, vehicle manufacturer, vehicle owner,
vehicle operator and maintenance services/part supplier.

The following section provides some insight into the factors that are
should be considered in the design of an EHM system. It does not attempt
to turn EHM System design into a regimented process, but rather seeks to
provide some general guidance and rationale.

[Safety]{.underline}

Engine Health Management systems in of themselves cannot make an engine
safer, but their judicious application enables those operating and
maintaining the engine to do so with higher margins of safety. Very
rarely do engine components fail without any warning. Changes in
performance, unusual vibration modes, the presence of material particles
in either the primary gas path or associated subsystems all provide
early indicators of what, if let unchecked, could propagate into
failures. The engine generally provides indications of its health; the
question is whether or not anyone is "listening".

As has been shown in the prior sections, a plethora of sensors and
analytical techniques are available for determining engine condition.
The challenge is to select those EHM elements that are capable of
"listening" to what the engine is saying; providing a means early of
detection of a problem prior to failure. A means of accomplishing this
is use the Failure Modes Effects and Criticality Analysis (FMECA)
document that is required of all engines as a guide of what needs to be
detected. This document generally lists failure modes of concern, the
generally accepted means of "observing" them, and the criticality should
such a failure occur. The priority and importance delineated by the
FMECA should provide guidance as to EHM element selection. Reference
\[76\] provides as a good summary of the trade considerations related to
Safety for design of a Rocket Engine Health Management System (EHMS).

A second EHM System Design consideration related to Safety is "perceived
safety". Clearly, no Engine Manufacturer wants to make a product that is
unsafe, nor does an Operator wants to use one that is unsafe. But, as
indicated earlier, safe is a relative term. If either the Operator, or
End Customer, believes an engine and associated vehicle to be unsafe,
the product will lose value relative to its competition. An EHM system
can in part address these concerns by offering a means to continuously
monitor engine condition for safety, a advantage over engines that rely
on periodic check-ups to assess engine health. But note that this factor
has the potential to cut both ways. If the customer perceives that an
engine requires an EHM system to ensure safety, the advantage could
become a detriment. It is important to coordinate with those people
involved with the marketing and sales aspect of the engine business, as
well as the engineering community, in the design of an EHM system.

[Life Cycle Cost]{.underline}

Health Management systems can enable significant reductions of
life-cycle costs associated with owning, operating and supporting a
fleet of engines. It is important to recognize that for most commercial
and military applications, EHM provides benefits to multiple customers.
This is especially important in building a business case for inclusion
of Engine Health Management capabilities in an engine configuration,
since introduction of a single capability may result in multiple
simultaneous payoffs. The cost benefits that an EHM system provides can
become a "tradable" commodity; the value that it provides can be
assigned by the engine manufacturer to either itself or to a customer
for appropriate compensation.

*Engine Manufacturer*

The Engine Manufacturer can obtain benefit from an EHM system by either
being able to produce and sell its product at a lower Initial Cost, or
by making their product more attractive by selling the ability to derive
additional value through the use of the EHM features of their engine
product. Typically engines are developed in cooperation with the Vehicle
Manufacturers; both of the benefits described above can be extended to
the integrated vehicle/engine system.

*Impact on Initial Cost Through Reduced Margins*

Engine Components can be designed with reduced design margins, assuming
that an appropriate Health Management system will actively monitor the
component use to ensure safety. This could enable weight or material
cost reductions in the both the component and associated structures
either supporting or potentially affected by the component. A new
centerline engine could take advantage of this \"Health
Management-Enabled Design\" to make significant reductions initial
production cost and weight.

*Engine Operator - Impact on Operational Costs Through Improved
Performance*

The Engine Operator obtains benefit by taking advantage of the ability
of the EHM system to reduce both operational and maintenance costs.
Significant reductions in operation costs via improvements in engine
performance can be realized through the implementation of an EHM System
that provides information as to the current condition of the engine,
allowing the engine control to understand how to provide near-optimal
engine performance over the entire engine life cycle. One example is
reduction in specific fuel consumption that can be achieved by improved
blade tip sealing in the High Compressor and Turbine. Due to various
wear mechanisms, the clearance between blade tips and seals erodes over
the life of the engine, with a corresponding loss of efficiency. Through
engine monitoring techniques, active clearance control systems can
recover performance lost due to deterioration.

*Engine Operator - Impact on Operational Costs Through Reduced
Maintenance Costs*

The benefits most often associated with Engine Health Management are
those associated with reduction of life-cycle maintenance costs. These
include both the direct cost of replacement of life-limited parts and
maintenance labor costs associated with flight-line repairs and shop
overhauls. The benefit for the Life-Limited Part cost is derived from
accurate tracking of part usage, so that parts are not prematurely
removed while still having significant usable life remaining. A benefit
for improvements in Maintenance Labor Costs is directly realized by,
among other means, reducing troubleshooting time on the flight line
through automated fault resolution. Significantly greater Maintenance
Cost benefits are possible if "indirect" factors are considered,
especially those associated with conversion of unscheduled to scheduled
maintenance. A number of these indirect factors that should be
considered are listed below:

-   Turnaround Supporting Time Limited Dispatch

-   Unscheduled Maintenance / Increase Mean Time Between Failures

-   Number of Non-Recoverable In-Flight Shutdowns

-   Number of Unplanned Engine Removals

-   Number of Rejected Take-Offs / Mission Aborts / Cancellations

-   Number of Flight Diversions / Schedule Interrupts / Delays

-   Dispatch Interruption Rate via In-Flight Fault Isolation

-   Number of Periodic Inspections (i.e., Eliminate Inspections)

*Fleet Management Provider*

In today's cost-conscious Aviation environment, Fleet Management Plans
(FMP), provided by companies other than those actually operating the
engines, are becoming more economically attractive. They allow the
engine Operator to establish via contract a fixed-cost for maintenance,
transferring risks to the FMP provider. EHM Systems offer the
opportunity for the FMP provider to better manage their risks by
providing a comprehensive understanding of the current condition of the
engine, but more importantly, by providing a means for accurately
predicting future condition of the engine at any given point it time.

Most of the engines Life-Cycle Costs are associated with the continual
refurbishment/replacement of consumable or Life-Limited Parts (LLPs).
These are typically rotating parts (blades, disks, shafts, hubs), which
have a defined Low Cycle Fatigue life and must be removed from the
engine and scrapped when life is reached. In addition to being able to
keep these LLPs on wing for longer intervals by more accurate tracking
methods described above, the same analytical capabilities can enable
better spares production planning by anticipating tracking part
consumption and accurately and trending predicting when spare parts will
be required.

The key is to be mindful that there is no "right" answer as to what
elements should be included in an EHM System. Engineering, business and
strategic factors determine what suite of EHM capabilities will be
provided for a given engine for a given business opportunity; and these
may not be identical for the same engine applied to a different business
arrangement. The best recommendation is to collect requirements from the
broad range of functional organizations that have some need for EHM
capability, identify the "must haves", and then look for those
capabilities that are crosscutting in terms of meeting requirements.
Other requirements will have buy their way into the configuration on a
priority basis. Note that once an EHM System is designed and implemented
into a product, benefit is often derived in ways that were never
anticipated in the original design. Whatever value an EHM System has
when it goes ito service, it will provide additional value as it is
used.

**[CONCLUSION]{.underline}**

While there is no specific one-size-fits-all approach for an EHMS, this
paper has attempted to provide an overview of the array of current and
evolving Engine Health Management (EHM) capabilities that can be
assembled into system to address the specific needs of an end-user, for
any number of possible applications. On-board Hosted as well as
Ground-Based EHM elements have been presented, along with information
regarding the interrelationship between the two. Capabilities discussed
have ranged from on-board sensors and failure detection methods to
ground-based performance and life trending algorithms. Finally, some
considerations for design of Engine Health Management Systems, both for
Increased Safety and Reductions in Life-Cycle Cost have been provided,
along with some notional benefits, and some general guidance as to how
the individual elements could coalesce into an integrated system. It is
hoped that this document will not be the final word in Engine Health
Management, but rather the first word; that the information provided
herein and the extensive Reference list will serve as a valuable
starting point for design of future EHM Systems.

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