James J. Zakrajsek and Paula J. Dempsey

Glenn Research Center, Cleveland, Ohio

Edward M. Huff

Ames Research Center, Moffett Field, California

Michael Augustin

Bell Helicopter Textron, Fort Worth, Texas

Robab Safa-Bakhsh

Boeing Phantom Works–M&CT, Philadelphia, Pennsylvania

Alan Duke

Goodrich Fuel and Utilities Division, Vergennes, Vermont

Piet Ephraim

Smiths Aerospace, Grand Rapids, Michigan

Paul Grabil

Intelligent Automation Corporation, Poway, California

Harry J. Decker

U.S. Army Research Laboratory, Glenn Research Center, Cleveland, Ohio

Rotorcraft Health Management Issues

and Challenges

NASA/TM—2006-214022

February 2006
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James J. Zakrajsek and Paula J. Dempsey

Glenn Research Center, Cleveland, Ohio

Edward M. Huff

Ames Research Center, Moffett Field, California

Michael Augustin

Bell Helicopter Textron, Fort Worth, Texas

Robab Safa-Bakhsh

Boeing Phantom Works–M&CT, Philadelphia, Pennsylvania

Alan Duke

Goodrich Fuel and Utilities Division, Vergennes, Vermont

Piet Ephraim

Smiths Aerospace, Grand Rapids, Michigan

Paul Grabil

Intelligent Automation Corporation, Poway, California

Harry J. Decker

U.S. Army Research Laboratory, Glenn Research Center, Cleveland, Ohio

Rotorcraft Health Management Issues

and Challenges

NASA/TM—2006-214022

February 2006

National Aeronautics and

Space Administration

Glenn Research Center

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and Management in Aerospace

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NASA/TM—2006-214022 1

Rotorcraft Health Management Issues and Challenges 

James Zakrajsek and Paula Dempsey 

National Aeronautics and Space Administration 

Glenn Research Center 

Cleveland, Ohio 44135 

Edward Huff 

National Aeronautics and Space Administration 

Ames Research Center 

Moffett Field, California 94035 

Michael Augustin 

Bell Helicopter Textron 

Forth Worth, Texas 76101 

Robab Safa-Bakhsh 

Boeing Phantom Works—M&CT 

Philadelphia, Pennsylvania 19142 

Alan Duke 

Goodrich Fuel and Utilities Division 

Vergennes, Vermont 05491 

Piet Ephraim 

Smiths Aerospace 

Grand Rapids, Michigan 49512 

Paul Grabill 

Intelligent Automation Corporation 

Poway, California 92064 

Harry Decker 

U.S. Army Research Laboratory 

Glenn Research Center 

Cleveland, Ohio 44135 

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. 
NASA/TM—2006-214022 2

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 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. 

Figure 1.—Economic and Safety Benefits of Diagnostics 

and Prognostics (Romero et al. 1996). 
NASA/TM—2006-214022 3

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). 
NASA/TM—2006-214022 4

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 inflight 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. 
NASA/TM—2006-214022 5

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. 
NASA/TM—2006-214022 6

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 vibrationbased 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. 
NASA/TM—2006-214022 7

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).

Shortfalls/Unforeseen Difficulties Reasons identified Eurocopter’s answer 

Integration with operator’s 

maintenance and logistic 

organization 

1. System complexity 

2. New operator skills 

• Adaptation of organizations 

• Training 

• Improved documentation 

• Support from aircraft manufacturer 

Limited maintenance credits 

• Limited maintenance alleviation 

• Time Between Overhauls 

 unchanged 

Performance 

• Lack of evidence of performance 

• Incomplete defect coverage 

• Limited prognosis performance 

Regulation 

Requirements more demanding 

than those for maintenance tools 

Performance 

• Cooperation with operators on database 

gathering/analysis 

• Research activity to increase defect 

coverage and prognosis performance 

• Economic benefit of structural usage 

monitoring to be assessed 

Regulation 

Consider HUMS a maintenance tool 

Some mechanical damage is still 

missed 

• Monitoring of epicyclic stages to 

be improved 

• Some damage is never or is 

inconsistently detected 

Performance 

• Incomplete defect coverage 

Performance 

• Research activity to increase defect 

coverage 

• Techniques other than vibration analysis 

to be considered 

Operating cost higher than 

anticipated 

• Decision making sometimes 

difficult 

Performance 

• Limited diagnosis perform-ance 

because of not “defect specific” 

monitoring techniques 

Performance 

• Improved diagnostic procedures 

• Research activity to improve diagnosis 

performance 

Acquisition cost 

• Most of the Civil applications in 

the North Sea sector 

• HUMS mostly installed on heavy 

aircraft 

Technology 

• Not enough standardization 

• Difficulty in retrofitting HUMS 

in aircraft with analogue avionics 

• Rapid obsolescence 

• Regulation 

• High integrity requirement 

Technology 

• Standardization 

• Integration into digital avionics systems 

Regulation 

• Consider HUMS a maintenance tool 

Support cost higher than 

anticipated 

• Long maturing process 

• Help for diagnostics 

• Threshold adjustment 

• Continuous development 

Performance 

• Monitoring techniques not 

“defect specific” 

• Regulation 

• High integrity requirement 

Performance 

• Streamlining ongoing development 

activity through support contracts 

• Improved diagnostic procedures 

• Research activity 

Regulation 

• Consider HUMS a maintenance tool 
NASA/TM—2006-214022 8

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 
NASA/TM—2006-214022 9

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 
NASA/TM—2006-214022 10

• 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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19

Rotorcraft Health Management Issues and Challenges

James J. Zakrajsek, Paula J. Dempsey, Edward M. Huff, Michael Augustin,

Robab Safa-Bakhsh, Alan Duke, Piet Ephraim, Paul Grabil, and Harry J. Decker

Diagnostics; Rotorcraft; Health monitoring

Unclassified -Unlimited

Subject Categories: 01, 03, and 07

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.

Prepared for the First International Forum on Integrated System Health Engineering and Management in Aerospace cosponsored by

NASA Ames Research Center and NASA Marshall Space Flight Center, Napa, California, November 7–10, 2005. James J. Zakrajsek,

e-mail: James.J.Zakrajsek@nasa.gov; and Paula J. Dempsey, e-mail: paula.j.dempsey@nasa.gov, NASA Glenn Research Center;

Edward M. Huff, e-mail: edhuff@phc.net, NASA Ames Research Center; Michael Augustin, e-mail:

MAugustin@bellhelicopter.textron.com, Bell Helicopter Textron, P.O. Box 482, Fort Worth, Texas 76101; Robab Safa-Bakhsh,

e-mail: robab.safa-bakhsh@boeing.com, Boeing Phantom Works-M&CT, P.O. Box 16858 MC P29–99, Philadelphia, Pennsylvania

19142; Alan Duke, e-mail: Al.Duke@goodrich.com, Goodrich Fuel and Utilities Division, 100 Panton Road, Vergennes, Vermont

05491; Piet Ephraim, e-mail: Piet.Ephraim@SMITHS-AEROSPACE.COM, Smiths Aerospace, 3290 Patterson Avenue, Grand Rapids,

Michigan 49512; Paul Grabil, e-mail: paul.grabill@iac-online.com, Intelligent Automation Corporation, 13029 Danielson Street,

Suite 200, Poway, California 92064; and Harry J. Decker, e-mail: harry.j.decker@grc.nasa.gov, U.S. Army Research Laboratory,

NASA Glenn Research Center. Responsible person, Paula J. Dempsey, organization code RXM, 216–433–3968.