**ROTORCRAFT HEALTH MANAGEMENT ISSUES AND CHALLENGES**

James Zakrajsek,^1^ Paula Dempsey,^2^ Edward Huff,^3^ Harry Decker,^4^

Michael Augustin,^5^ Robab Safa-Bakhsh,^6^ Alan Duke,^7^ Piet
Ephraim,^8^ and Paul Grabill^9^

**ABSTRACT**

This paper presents an overview of health management issues and
challenges that are specific to rotorcraft. Rotorcraft form a unique
subset of air vehicles in that their propulsion system is used not only
for propulsion, but also serves as the primary source of lift and
maneuvering of the vehicle. No other air vehicle relies on the
propulsion system to provide these functions through a transmission
system with single critical load paths without duplication or
redundancy. As such, health management of the power train is a critical
and unique part of any rotorcraft health management system. This paper
focuses specifically on the issues and challenges related to the dynamic
mechanical components in the main power train. This includes the
transmission and main rotor mechanisms. This paper will review standard
practices used for rotorcraft health management, lessons learned from
fielded trials, and future challenges.

**INTRODUCTION**

Helicopter transmission integrity is important to helicopter safety
because helicopters depend on the power train for propulsion, lift, and
flight maneuvering. A study of 1168 helicopter accidents from 1990 to
1996 found that after human-factors related causes of accidents, the
next most frequent causes of accidents were due to various system and
structural failures (Aviation Safety 1998). In 1999, of the world total
of 192 turbine helicopter accidents, 28 were directly due to mechanical
failures with the most common in the drive train of the propulsion
system (Learmount 2000). In order to reduce helicopter accidents, this
study recommended the design of Health and Usage Monitoring Systems
(HUMS) capable of predicting impending equipment failure for
on-condition maintenance, and more advanced systems capable of warning
pilots of imminent equipment failure. In order to make these
predictions, the system must provide health monitoring of the
transmission components and must also demonstrate a high level of
reliability to minimize false alarms.

Liu and Pines (2005) recently studied U.S. civil rotorcraft accidents
caused by vehicle failure or malfunction during the period from 1998 to
2004. It was a continuation of a study performed by NASA that covered
the period from 1963 to 1997. Results showed that the ratio of the
number of accidents caused by vehicle factors to the total number of
accidents during the period of 1998 to 2004 had been reduced by more
than one half in comparison to the earlier accident

![](media/image1.png){width="5.603472222222222in"
height="4.0368055555555555in"}

data. Pilot error continues to be the major cause of all rotorcraft
accidents, but the results also indicate that failure or malfunction of
the propulsion system remains the primary reason for vehicle factor
related accidents.

Rotorcraft health monitoring systems, while historically focused on
safety, can also provide economic benefits for rotorcraft operators.
Figure 1 shows the potential benefit of diagnostics and predictive
maintenance of critical mechanical systems. Such benefits were
demonstrated on a flight trial program at Petroleum Helicopters in the
form of reduced insurance cost through enhanced safety, lower operating
costs, increased aircraft availability and improved operating efficiency
(Cronkhite 1998). The service life of critical components was extended
if the actual usage, measured by load exposure via usage monitoring, was
low compared to the predicted usage, and, therefore, reduced operating
costs by 10 percent. Health monitoring also provided a safety benefit
when actual usage was more severe than predicted, as illustrated in
Figure 1.

One major independent study has concluded that: "HUMS was probably the
most significant isolated safety improvement of the last decade,"
(Hokstad et al., 1999). Although commercially available HUMS are
providing significant safety benefits when installed on rotorcraft, the
fault detection success rate of today's helicopter health monitoring
systems through vibration analysis is still in need of improvements.
HUMS experience documented by the CAA (UK Civil Aviation Authority) in
1997, and informally updated in 2002, shows a success rate of 70 percent
in detecting defects (McColl, 1997).

This paper will provide an overview of today's rotorcraft health
monitoring technologies for detecting anomalies in dynamic mechanical
systems. Standard practices in acquiring and processing vibration data
will be discussed first, to provide the reader with general knowledge of
how useful information is extracted. Then, lessons learned from systems
currently installed on rotorcraft will be discussed. This will include
the difficulties encountered when trying to optimize the detection of
damaged components with minimum false alarms, and difficulties in
assessing vibration diagnostic performance. Future challenges to
overcome in developing more advanced HUMS will then be outlined. These
include prognostic capabilities for HUMS that not only indicate that
damage has occurred, but also assess the magnitude of damage and the
remaining useful life of critical components. These will allow
helicopter operators to obtain maintenance credits, increase the time
between expensive overhauls, and facilitate using HUMS as a practical
tool for on-condition maintenance.

**STANDARD PRACTICES**

Over the past several decades, a number of diagnostic techniques have
been developed to detect damage and abnormal conditions of the dynamic
mechanical components in rotorcraft propulsion systems. A majority of
the technology developed focuses on the gears, bearings and driveshafts
of the main transmission system. Other areas addressed include main
rotor balance indication and correction, and, more recently, the planet
carrier of the final reduction stage in the main transmission. Brief
overviews of these technologies are given below.

**Vibration-Based Methods**

Vibration-based methods are the most common diagnostic tool used in HUMS
for distinguishing the nature of damage in helicopter transmissions.
Using vibration data collected from gearbox accelerometers, algorithms
are developed to detect when gear and bearing damage has occurred.
Damage in gears and bearings produce changes in the vibration signatures
of the helicopter. Over the past 25 years, numerous vibration-based
algorithms for mechanical component damage detection in transmissions
have been developed. The traditional methods of vibration based gear
feature detection and extraction methods in rotating equipment,
discussed in detail by Zakrajsek (1989), are typically based on some
statistical measurement of vibration energy. The primary differences are
based on which of the characteristic frequencies are included, excluded,
or used as a reference. There are six distinct elements of analyzing
vibration signals for transmission health monitoring: 1) time series
signal acquisition, 2) signal separation, 3) synchronous averaging, 4)
feature detection and extraction, 5) interpretation of results, and 6)
prognosis (Decker, 2002).

**Time Series Signal Acquisition Techniques**

Time series signal acquisition is typically done on the ground with the
main rotor engaged, or while airborne. In a series of flight studies,
Huff, Mosher, Tumer, et al. (2000 to 2004) reported extensively on
steady-state maneuvering influences on signal stationarity in two
helicopter models (i.e., an AH-1 Cobra, and OH-58 Kiowa). Stationarity
refers to the extent to which the statistical properties of the
vibration data remain invariant over time. They found that the forward
climb regime produced the most stationary vibration signal in a
rotorcraft. They also reported several methods for data collection
(Barszcz et al., 2004), including the use of multi-axis accelerometers,
and potential real-time screening of time series data using state-space
methods (Mosher et al., 2004).

**Complex Signal Separation**

Signal separation is often necessary in order to isolate a time series
that can be associated with a specific gear or bearing component. Due to
the fact that the transmission's gears are phase-locked, this can often
be accomplished by appropriate signal averaging as discussed below.
However, in complex mechanisms, such as the planetary gear assembly,
this requires specialized algorithms, due to the fact that the vibration
signal from a damaged planet gear is easily masked by the other
non-damaged planet gears meshing at the same frequency. Mosher (2005)
recently reported a new method that shows promise for overcoming this
barrier.

**Time Synchronous Averaging**

Synchronous averaging refers to techniques for extracting periodic
waveforms from additive noise by averaging vibration signals over
several revolutions of the shaft (Stewart, 1977 and Decker and
Zakrajsek, 1999). This can be done in either the time or frequency
domains. However, the typical signal time synchronous average is
obtained by taking the average of the signal in the time domain with
each record starting at the same point in the cycle as derived from the
once per revolution signal. The desired signal, which is synchronous
with the shaft speed, will intensify relative to the non-periodic
signals. This time synchronous average signal is used as a basis for
gear vibration-based feature extraction methods. Hochmann (2005) shows
that the rule of thumb in estimating amount of attenuation in
synchronous averaging (reciprocal of square root of N, where N = number
of averages) although representative for non-coherent components of a
signal, is not representative of the coherent, non-synchronous part of
the signal. Hochmann developed a method for computing the attenuation of
coherent components of the signal for one-dimensional data. Several
statistical and filtering operations can be used on the time synchronous
averaged signal (Dempsey, 2000 and Mosher et al., 2002).

**Feature Detection and Extraction**

Feature detection and extraction refers to using a signal processing
technique to extract useful information from the vibration signal that
can indicate damage and differentiate between damage to different
components. As discussed by Larder (1997), no one signal processing
technique is effective for all types of defect signatures. Spectrum
analysis techniques in the frequency domain can work well if a defect
can be identified at low frequencies, but may not work well if the
defect shows up in the mid and high frequency bands. To quote from Pipe
(2003), "Key to the performance of a HUM system is that appropriate
vibration analysis techniques are employed for the range of fault
detection capability claimed."

**Planet Carrier Monitoring**

A flight critical component of many rotorcraft is the planet carrier in
the final reduction stage of the transmission. In a planetary
transmission system, the planet gears rotate about a shaft fixed to a
single structural piece called the planet carrier. The planetary carrier
transmits torque directly to the main rotor. Unlike other aircraft
propulsion systems, there is no inherent redundancy for the planet
carrier, and in-flight failure has the potential for total loss of
vehicle and crew. Not only would the vehicle experience loss of power,
it would also lose maneuvering and auto rotation ability. Several cases
of cracked planetary carriers were found on US Army UH-60A Black Hawk
helicopters in 2002, causing a fleet grounding of all non-mission
critical flights (Champagne et al., 2004). The possible consequences
that could have resulted if this fault were not identified led the Army
to investigate tools to detect planetary carrier cracks well in advance
of catastrophic failure. Garga et al. (2005) developed a technique to
detect the planetary carrier crack based on the change in vibration
pattern of the planetary carrier as a result of the reduced stiffness of
the carrier post with the crack. This method applies an energy ratio
technique to time synchronized data tracked on the rotational speed of
the planet carrier. The method was applied to aircraft vibration data
and test cell data with undamaged and damaged planetary carriers.
Results showed that the method was most effective when applied to the
higher planetary frequencies and at high torque levels. Increasing the
torque level results in an increasing change in stiffness and vibration
of the cracked planetary carrier, and, as a result, is much easier to
differentiate from a nominal signal.

**Transmission Bearing Health Monitoring**

The average rotorcraft power train contains over 70 rolling element
bearings. Although bearings usually have a high tolerance for operating
while damaged, detection of the damage is in many cases difficult due to
the masking of the bearing vibration by other components in the system.
In addition, wide variability of vibration data from similar bearings
across a fleet of aircraft adds to the complexity of detecting specific
damage in its early stages. Collateral damage caused by debris from a
degraded bearing can result in system failure even before the terminal
failure of the bearing itself. Randall (2001) provides a summary of
diagnostics developed for bearings in helicopter gearboxes. Chin et al.
(2005) developed a technique of comparative data analysis to evaluate
the health of a number of bearings in a fleet of 30 aircraft equipped
with a HUMS system. The technique developed is a post-processing tool
that performs a statistical comparison of bearing fault indicators of
all similar bearings in a fleet. Results to date isolated a damaged
input thrust bearing on an intermediate gearbox on one of the
helicopters in the fleet.

**Gearbox Vibration Database**

In early 1996 the Rotorcraft Industry Technology Association (RITA), in
cooperation with representatives from Department of Defense, NASA and
the Federal Aviation Administration (FAA) put together a "National HUMS
Technology Roadmap." This roadmap defined the existing HUMS efforts, the
state of HUMS technology and technology needs. One of the needs
identified as the result of this exercise was advancement of diagnostics
algorithms, damage and failure detection. The objective was to enhance
the effectiveness of diagnostics algorithms in detection and isolation
of fault in helicopter drive train. Early on the need for a central data
repository for vibration data and existing diagnostics algorithms was
identified. Sikorsky took the lead in developing a database storing the
existing diagnostics algorithms and vibration data collected from
multiple gearboxes for analysis. The database enables the user to apply
different raw data to multiple algorithms and compare the results.

**Metrics Evaluation Tool**

In another effort, Boeing investigated the evaluation of diagnostics
algorithms, identified metrics, criteria for threshold setting and
impact on the detection and false alarm rate. The result of the study
was compiled in a report, "Monitor the Monitors, RITA Metrics Document."
The Metrics Document formulates a set of criteria that can be used by
the rotorcraft industry and HUMS suppliers to evaluate and rate the
performance and effectiveness of diagnostic algorithms. In a subsequent
effort, Boeing developed a conceptual design for a tool adopting the
analytical methods and metrics identified in the RITA metrics document.
The tool, referred to as the Metrics Evaluation Tool, is a client-
server application that extracts the data from the RITA Database. A
paper by Safa-Bakhsh et al. (2003) describes the development of a
Metrics Evaluation Tool for this purpose. The paper provides an overview
of features to be evaluated using probability of detection and false
alarm metrics as well as diagnostic accuracy metrics. Unfortunately, as
of the date of this paper, a complete database of existing vibration
algorithms and their capabilities or limitations is not available. This
is due, in large part, to the limited amount of transmission seeded
fault data that are available for full assessment and validation of
vibration algorithm performance.

**Environmental Effects on Vibration Methods**

In addition to evaluating the performance of vibration features to
detecting damage, it is equally important to verify the sensitivity of
the features to typical environmental effects. The effects of speed and
load have also been studied and will be required for utilizing HUMS for
on-condition maintenance in varying flight regimes. Preliminary work
performed by Decker (2002), Dempsey (2001), and Mosher et al. (2002)
have evaluated gear diagnostic features under varying load, speed and
flight conditions.

**Condition Indicators** **in** **Certified HUMS**

Providing clear information to the end user regarding the health of the
transmission from the features extracted is a difficult process. All
major HUMS currently certified for civil helicopters compute a number of
condition indicators from vibration to characterization of the health of
the component. In the Goodrich HUMS, selected condition indicators are
fused into a Health Indicator (HI) to give an overall assessment of the
state of the component. When the HI exceeds some threshold, the
component is deemed to be in warning (perform additional analysis) or
alarm (component is faulted and requires maintenance) (Bechhoefer et
al., 2003).

**Acoustic** **Emissions**

As discussed by Evans (2003), although many component defects exhibit
'classic' vibration signatures, new defect indications still occur and
require expert interpretation and/or physical investigation is usually
necessary to detect incipient failures that would otherwise remain
undetected. One of the new technologies that is being developed is the
use of acoustic emission data to detect the very early stages of crack
initiation and propagation. As the crack tip advances through the
material, the grains "snap" which creates a stress wave that propagates
through the steel. In simple four point bending tests, this stress wave
is measurable. As damage progresses, the number of events grows
exponentially. A review of the literature suggests that this technology
should be applicable to both crack and pitting damage on both gears and
bearings (Tam and Mba, 2004 and Mba, 2005). They also report that this
technology has the potential to be used for prognosis (Tam et al., 2004
and Tam et al., 2005) In addition, Li and Choi (2002) are using fracture
mechanics and gear dynamic modeling codes to train forward neural
networks to estimate crack sizes of vibration and acoustic emission
data.

**Data Fusion**

One technique that has shown great promise for improving current HUMS
performance is to integrate two measurement technologies, oil debris
analysis and vibration, instead of relying only on vibration-based
features. There are a number of on-line oil debris monitoring systems
that can detect metallic debris in an oil flow line due to surface
degradation of oil wetted components. Some of these systems can also
determine particle size of the debris detected (Roylance and Hunt,
1999). Results from testing in NASA Glenn drive train test facilities
show the potential for integrating oil and vibration results in a health
monitoring system with improved damage detection and decision-making
capabilities as compared to using individual measurement technologies
(Dempsey, 2004).

**Auto Rotor Imbalance Detection and Rotor Smoothing**

A critical maintenance action specific to rotorcraft is reducing rotor
imbalance related vibration. Maintaining smooth rotor operation is
desirable for not only pilot and passenger comfort, but also critical to
reducing excessive loading to the life-limited dynamic components of the
rotor. Traditional methods of rotor smoothing require costly dedicated
maintenance flights in which the pilot must fly specific flight regimes
to obtain data for rotor adjustments. Many current HUMS installations
automatically collect data from normal operational flights, and are used
in conjunction with an automatic flight regime recognition system to
obtain data for maintenance actions. Branhof et al. (2005) developed a
new technique of automated rotor smoothing using continuous vibration
measurements. Results demonstrated that rotor smoothing adjustments can
be determined and applied based on continuous data to keep aircraft
smooth in the flight regimes they spend most of their time. With
continuous data collection, rotor smoothing data can be collected on
every flight without pilot input or using regime recognition
instrumentation. With this collection of continuous rotor smoothing
data, adjustments can be applied to the aircraft at the discretion of
the maintenance crew without special maintenance test flights. Some
variability in the continuous vibration data was seen when the method
was flight tested on an AH-64 Apache helicopter; however, the resulting
rotor smoothing adjustments reduced the vibration to within goals where
the aircraft flies the most.

**LESSONS LEARNED**

A number of the technologies discussed in the previous section have been
incorporated into industry and government developed on-board rotorcraft
HUMS systems. The following paragraphs summarize results of fielding
these systems, and subsequent lessons learned.

Eurocopter reports an assessment of their experience in HUMS development
over the past 10 years in a paper by Pouradier and Trouvé (2001).
Several shortfalls of today's HUMS are identified as well as ideas to
correct them. As shown in Table 1, the reasons for the shortfalls, such
as system complexity, or damage never or inconsistently detected,
summarize the challenges to improving the performance of current HUMS.
This table also indicates the diagnostic system will be used as a
maintenance tool, and for this reason it must provide the end user with
a facile decision making tool for assessing system health.

+---------------------+---------------------+-------------------------+
| TABLE 1             |                     |                         |
|                     |                     |                         |
| Eurocopter's list   |                     |                         |
| of shortfalls       |                     |                         |
| (Pouradier and      |                     |                         |
| Trouve, 2001).      |                     |                         |
+---------------------+---------------------+-------------------------+
| Sh                  | Reasons identified  | Eurocopter's answer     |
| ortfalls/Unforeseen |                     |                         |
| Difficulties        |                     |                         |
+---------------------+---------------------+-------------------------+
| Integration with    | 1.  System          | -   Adaptation of       |
| operator's          |     complexity      |     organizations       |
| maintenance and     |                     |                         |
| logistic            | 2.  New operator    | -   Training            |
| organization        |     skills          |                         |
|                     |                     | -   Improved            |
|                     |                     |     documentation       |
|                     |                     |                         |
|                     |                     | -   Support from        |
|                     |                     |     aircraft            |
|                     |                     |     manufacturer        |
+---------------------+---------------------+-------------------------+
| Limited maintenance | Performance         | Performance             |
| credits             |                     |                         |
|                     | -   Lack of         | -   Cooperation with    |
| -   Limited         |     evidence of     |     operators on        |
|     maintenance     |     performance     |     database            |
|     alleviation     |                     |     gathering/analysis  |
|                     | -   Incomplete      |                         |
| -   Time Between    |     defect coverage | -   Research activity   |
|     Overhauls       |                     |     to increase defect  |
|     unchanged       | -   Limited         |     coverage and        |
|                     |     prognosis       |     prognosis           |
|                     |     performance     |     performance         |
|                     |                     |                         |
|                     | Regulation          | -                       |
|                     |                     |                         |
|                     | > Requirements more | -   Economic benefit of |
|                     | > demanding than    |     structural usage    |
|                     | > those for         |     monitoring to be    |
|                     | > maintenance tools |     assessed            |
|                     |                     |                         |
|                     |                     | Regulation              |
|                     |                     |                         |
|                     |                     | > Consider HUMS a       |
|                     |                     | > maintenance tool      |
+---------------------+---------------------+-------------------------+
| Some mechanical     | Performance         | Performance             |
| damage is still     |                     |                         |
| missed              | -   Incomplete      | -   Research activity   |
|                     |     defect coverage |     to increase defect  |
| -   Monitoring of   |                     |     coverage            |
|     epicyclic       |                     |                         |
|     stages to be    |                     | -   Techniques other    |
|     improved        |                     |     than vibration      |
|                     |                     |     analysis to be      |
| -   Some damage is  |                     |     considered          |
|     never or is     |                     |                         |
|     inconsistently  |                     |                         |
|     detected        |                     |                         |
+---------------------+---------------------+-------------------------+
| Operating cost      | Performance         | Performance             |
| higher than         |                     |                         |
| anticipated         | -   Limited         | -   Improved diagnostic |
|                     |     diagnosis       |     procedures          |
| -   Decision making |     perform-ance    |                         |
|     sometimes       |     because of not  | -   Research activity   |
|     difficult       |     "defect         |     to improve          |
|                     |     specific"       |     diagnosis           |
|                     |     monitoring      |     performance         |
|                     |     techniques      |                         |
+---------------------+---------------------+-------------------------+
| Acquisition cost    | Technology          | Technology              |
|                     |                     |                         |
| -   Most of the     | -   Not enough      | -   Standardization     |
|     Civil           |     standardization |                         |
|     applications in |                     | -   Integration into    |
|     the North Sea   | -   Difficulty in   |     digital avionics    |
|     sector          |     retrofitting    |     systems             |
|                     |     HUMS in         |                         |
| -   HUMS mostly     |     aircraft with   | Regulation              |
|     installed on    |     analogue        |                         |
|     heavy aircraft  |     avionics        | -   Consider HUMS a     |
|                     |                     |     maintenance tool    |
|                     | -   Rapid           |                         |
|                     |     obsolescence    |                         |
|                     |                     |                         |
|                     | -   Regulation      |                         |
|                     |                     |                         |
|                     | -   High integrity  |                         |
|                     |     requirement     |                         |
+---------------------+---------------------+-------------------------+
| Support cost higher | Performance         | Performance             |
| than anticipated    |                     |                         |
|                     | -   Monitoring      | -   Streamlining        |
| -   Long maturing   |     techniques not  |     ongoing development |
|     process         |     "defect         |     activity through    |
|                     |     specific"       |     support contracts   |
| -   Help for        |                     |                         |
|     diagnostics     | -   Regulation      | -   Improved diagnostic |
|                     |                     |     procedures          |
| -   Threshold       | -   High integrity  |                         |
|     adjustment      |     requirement     | -   Research activity   |
|                     |                     |                         |
| -   Continuous      |                     | Regulation              |
|     development     |                     |                         |
|                     |                     | -   Consider HUMS a     |
|                     |                     |     maintenance tool    |
+---------------------+---------------------+-------------------------+

A paper by Heather (2003), presents an update on fielded experience for
over 300 Smiths Aerospace HUMS in service. Although safety is the major
benefit, the following maintenance and improved operational benefit have
also been obtained.

-   Accurate record of helicopter usage for maintenance and lifing

-   Reduced consequential damage from a mechanical fault due to timely
    removal of component based on HUMS data

-   Accurate recording of aircraft exceedances

-   Improved aircraft troubleshooting

-   Reduction of unscheduled maintenance

-   Maintenance credits and extension of component life

-   Fleet health verification through mining aircraft health data.

In particular, the Canadian Forces had a formal Maintenance Credit
program developed by their OEM (Augustin and Bradley, 2004) for the
CH-146 Griffon fleet. Many benefits were established, and the use of
HUMS became second nature to the pilots and maintainers of this fleet of
100 aircraft. In addition, a review of 180 HUMS-related maintenance
actions yielded the following results:

-   41 percent allowed the determination of an appropriate level of
    maintenance following an exceedance

-   19 percent allowed for installation condition improvements so as to
    preclude accelerated wearing of components

-   17 percent precluded the need for additional troubleshooting flights
    by having the second source of data available

-   12 percent precluded expensive (\$100,000 plus) component
    replacement as result of an exceedance

-   11 percent possibly prevented serious faults from loose mounts,
    shifted hubs, worn tail shaft bearings, tail rotor pitch links and
    main rotor components

Another rotorcraft health management system being field tested on a
several aircraft platforms has also yielded important maintenance
information and lessons learned. The Integrated Mechanical Diagnostics
Health and Usage Management System (IMD-HUMS) is a commercial system
developed by the U.S. Navy and the Goodrich Corporation to be a common
health management system for all Navy and Marine helicopters. The
IMD-HUMS performs automatic rotor track and balance data collection to
reduce aircraft vibrations caused by unbalanced rotor system. The
IMD-HUMS also performs automated monitoring of all major dynamic
components of the main drive train and tail rotor, and automated engine
performance monitoring. In addition, IMD-HUMS performs usage and
exceedances monitoring on critical structural components.

The U.S. Army and the Goodrich Corporation have undertaken a
battalion-level demonstration of the Goodrich Integrated Mechanical
Diagnostics Health and Usage Monitoring System (IMD HUMS). A paper by
Dora et al. (2004) addresses the status of the demonstration. The paper
identifies significant benefits of the system due to maintenance
man-hour reductions (on the order of 58 percent reduction compared to
current practices and automatic detection of drive train faults (Dora et
al. 2004).

Little (2005) provides an overview of results from a recently completed
operational test and evaluation program on three U.S. Marine CH53E
helicopters. Results showed the IMD-HUMS system was able to detect a
number of mechanical anomalies, including improperly balanced tail drive
shafts, degraded main rotor damper bearings, and degraded engine
performance which, if not detected, may have resulted in engine turbine
section failure. Wright (2005) also gives an overview of current results
from installing the IMD-HUMS system in 2003 on 30 U.S. Army UH-60L Black
Hawk helicopters stationed in Mosul, Iraq. The installed IMD-HUMS units
were able to detect prematurely worn generator shaft and adaptors on
several units. It was also able to pinpoint tail rotor vibration
problems to the tail rotor gearbox on one unit, which, upon inspection,
had severely corroded components. The system also identified a number of
sensor failures that were not apparent under routine inspections.
Overall, the IMD-HUMS system has provided more specific diagnostic
information on high vibration signals than previously available with
standard techniques. Wright indicates that setting accurate threshold
levels for the various health indicators in the system is underway, and
will be completed mid 2005.

Another rotorcraft health management system is currently fielded on a
number of helicopters in the U.S. Army. The Vibration Management
Enhancement Program (VMEP) is a health management system developed by
the U.S. Army and the Intelligent Automation Corporation to provide
automated track and balancing of the main rotor, and diagnostic
information on the primary drive system and tail rotor drives of the
helicopter. It is currently under field testing and has been installed
on over 100 aircraft which include the UH60, MH60, AH64, CH47, and the
Bell 412. Branhof (2005) gives an overview of the mechanical faults
detected to date using the VMEP system, and illustrates how this data
collected can be used to develop a true condition- based maintenance
program. Since 1999, the VMEP system has developed a large database of
drive train diagnostics indicators that have been recorded. Some of the
faults discovered were on critical areas of the drive train, and
included a swash plate bearing, hangar bearing, engine nose gearbox, oil
cooler fan bearing, and tail rotor drive shaft. The damaged units were
removed and disassembled to accurately document the damage for direct
correlation with the damage indicator. This validation step is critical
for the development of reliable threshold levels, and on-condition based
maintenance. This project also developed a web-based system where
statistical analysis is performed on a data warehouse of Army HUMS
parameters recorded from over 100 aircraft (Brotherton et al., 2003).
This system has been used effectively to let engineers set condition
indicator limits and find "outlier" aircraft from remote locations such
as Iraq and Kosovo.

Another paper by Draper (2003) discusses the Smiths Aerospace GenHUMS
(Generic HUMS) that has been in operational service in the UK Chinook
fleet since 2000. This paper reviews the lessons learned and benefits
from the Chinook program and how they are being incorporated in the Sea
King, Puma, and Lynx program. One of the most notable benefits from
GenHUMS to date was the use of the system to perform fleet wide health
check monitoring following a break up of a combiner transmission bearing
(Cook, 2002). The failure vibration characteristic was converted into a
HUMS condition indicator and it was possible to screen all of the other
HUMS equipped aircraft within 12 hours. The screening established that
no other transmissions displayed similar failure characteristics,
allowing aircraft with GenHUMS installed to remain available for
operations. As HUMS technology matures, fleet-wide database and analysis
capabilities will continue to provide significant benefits to the
rotorcraft industry.

**FUTURE CHALLENGES**

Although rotorcraft health monitoring technologies have matured
significantly over the last few decades, further work is needed to
achieve the full safety and economic potential of an integrated health
monitoring system.

The ultimate goal of rotorcraft health management systems is to achieve
true condition-based maintenance and operational quality while
increasing the safety of rotorcraft to that comparable to large fixed
wing aircraft. To achieve these goals, the following future challenges
will need to be addressed:

-   Increase the fault detection coverage from today's rate of
    70 percent

-   Increase the reliability of damage detection

-   

-   Decrease false alarm rates from historic average rates of about 1
    per 100 flight hours by an order of magnitude and at least no worse
    than the current misdiagnosis rate for component removals on non
    HUMS-equipped helicopters

-   Develop technology to accurately detect onset of failure and isolate
    damage, and assess severity of damage magnitude. With this, develop
    life prediction technologies to assess effects of the damage on the
    system and predict remaining useful life and maintenance actions
    required

-   Integrate the health monitoring outputs with the maintenance
    processes and procedures

-   Develop data management and automated techniques to obtain and
    process the diagnostic information with minimal specialist
    involvement

-   Develop system models, material failure models and correlation of
    failure under bench fatigue, seeded fault test and operational data

-   

-   Development of a generic data collection and management scheme for
    analysis of operational data is required. This requirement stems
    from the fact that establishing the threshold, false alarm and
    detection rates requires a large body of data with rich statistical
    content

-   

-   Development of mature and verifiable techniques to detect
    catastrophic failures and give in-flight pilot cueing and warning in
    near-real time

**CONCLUSIONS**

This paper gives an overview of health management issues and challenges
specific to rotorcraft. This paper presented a review of standard
practices used for rotorcraft health management in addition to lessons
learned from a number of fielded systems. In addition, this paper
outlines future challenges that need to be addressed to fulfill the
safety and maintainability opportunities an advanced health management
system represents. Although the technologies described in this paper
were developed for rotorcraft, many of these technologies could be
beneficial for other aerospace platforms. Areas such as data fusion and
sensor integration would indeed be useful for any application where
reliability and accuracy of damage detection is critical.

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