# Presentation: 592698

## ROTORCRAFT HEALTH MANAGEMENT ISSUES AND CHALLENGES

**James Zakrajsek and Paula Dempsey**
- NASA Glenn Research Center
**Edward Huff **
- NASA Ames Research Center (retired)
**Mike Augustin **
- Bell Helicopter Textron
**Robab Safa-Bakhsh**
- Boeing Phantom Works
**Alan Duke **
- Goodrich Fuel and Utilities Division
**Piet Ephraim **
- Smiths Aerospace
**Paul Grabill **
- Intelligent Automation Corporation
**Harry Decker**
- U.S. Army Research Laboratory

## OVERVIEW

**OVERVIEW**

- Background
- Standard Practices
- Lessons Learned
- Future Challenges

## BACKGROUND

**BACKGROUND**

- In **Rotorcraft, **the propulsion system is used for lift, propulsion and flight maneuvering.
- Helicopter safety is heavily dependent on the reliability and integrity of the power train.
**This paper focuses on health management issues related to the dynamic mechanical components in the power train.**

## BACKGROUND

**BACKGROUND**

**Rotorcraft Accident Statistics**
- Survey of rotorcraft accidents from 1937-1981 due to fatigue fracture found 32% caused by damaged engine and transmission components.
- Study of  1168 accidents from 1990-1996 found structural failures the 2nd most common cause of accidents.
- Continuation of this study from 1998-2004 found failure of the propulsion system the primary cause of vehicle related accidents.
- 1999 world total of 192 helicopter accidents, found 28 directly due to mechanical failures with the gearbox drive train most common.

## BACKGROUND

**BACKGROUND**

**Economic & Safety Benefits of Diagnostics & Prognostics**
- Service life extended if actual usage lower than predicted
- Safety benefit if actual usage more severe than predicted

- Reference:  Romero,  Summers  and Cronkhite, 1996 .

## BACKGROUND

**BACKGROUND**

- Starting to provide safety and economic benefits
- CAA (UK Civil Aviation Authority) shows a 70% fault  detection rate in fielded HUMS
- Historic average false alarm rates of 1 per 100 flight hours

**Current Status of Commercially Available **
**Health Usage Monitoring Systems (HUMS)**

## STANDARD PRACTICES

**Vibration-Based Methods**
- Damage in transmission components produce changes in  vibration signatures.
- Various vibration signature analysis methods developed to detect damage to bearings, gears, etc.
- Gears produce vibration signals synchronous with speed.
- Noise in their signal area reduced using time synchronous averaging.

## STANDARD PRACTICES

**Vibration-Based Gear Fault Detection Methods**

**STANDARD PRACTICES**

**FM4**

**NA4**

- Reference: Zakrajsek, 1989 and 1994.

## STANDARD PRACTICES

**Vibration-Based Bearing Fault Detection Methods**
- Reference: Howard, 1994
- Fault/defect frequencies (calculated by bearing dimensions and speed) generated when bearing fails.
- Several methods exist for extracting bearing defect frequencies from vibration data.
**Time domain: **
- Statistical parameters: RMS, peak, kurtosis
**Frequency Domain:**
- FFT used to identify characteristic bearing defect frequencies and their change in amplitude.
- Envelope analysis used to identify bearing resonances excited by periodic impacts (correlate to defect frequencies) when defect contacts another bearing surface.

## STANDARD PRACTICES

**Metrics Evaluation Tool**
- Reference: Safa-Bakhsh, Byington, Watson, and Kalgren, 2003.
- Need to evaluate performance of vibration-based fault detection methods for damage detection and false alarms.
- Metrics Evaluation Tool developed by Boeing to evaluate fault detection methods using probability of detection, false alarm metrics and diagnostic accuracy metrics.
- Database required to store vibration data collected from multiple gearboxes for analysis with existing diagnostic algorithms.
- To date, a complete database of vibration algorithms and their capabilities or limitations does not exist due to the limited amount of transmission fault data available.

## STANDARD PRACTICES

**Environmental Effects on Fault Detection Methods**
- Sensitivity of the diagnostics to environmental effects required for utilizing HUMS in varying flight regimes.
- HUMS manufacturers have observed significant variances of indicator levels between gearbox components.
- Due to limited damage data in flight, diagnostic tools must be developed in controlled ground test environments.
- Thresholds defined in test rigs can be used to define thresholds in flight to correctly classify the transmission operation as normal.**  **
- Flight fault data is required to verify damage detection sensitivity demonstrated in test rigs can be maintained in flight.

- References: Larder, 1997; Zakrajsek and Dempsey, 2001; Huff, Mosher and Dempsey 2003.

## STANDARD PRACTICES

- Planet 3

- Planet 2

**Carrier Direction**
**of Rotation**

- Planet 1

- As the transmission rotates, each individual planet passes the sensor.
- When a given planet gear is near a sensor, the vibrations measured by the sensor are dominated by the meshing of the planet gear with the ring gear and the sun gear.
**Goal: **Develop a** **method for separating vibration signatures of individual sun, planet, and ring gears.

- Accelerometer

**Vibration Based Planet Carrier Fault Detection Methods**

**STANDARD PRACTICES**

- References:  Samuel and Pines, 2003; McFadden, 1991; Mosher, 2005; Garga, 2005.

- Sun

- Ring

## STANDARD PRACTICES

**Data Fusion**
- Fusing oil debris analysis and vibration data, instead of relying only on vibration, has shown great promise for improving damage detection and decision-making capabilities in current HUMS.

- Wear debris and vibration signatures generated during failures.
- Data fusion concept validated in ground tests on Spur/Spiral Bevel Gear and 500-HP Transmission gears and bearings.
- Improved diagnostic tool performance using fused system over individual features.

- References:  Dempsey, Handschuh, and Afjeh 2003.

- Time

## LESSONS LEARNED

| Shortfalls/Difficulties | Reasons identified |
| --- | --- |
| Integration with operator’s maintenance and logistics | System complexity
New operator skills |
| Limited maintenance credits
Limited maintenance alleviation
Time Between Overhauls unchanged | Performance
Lack of evidence of performance
Incomplete defect coverage
Limited prognosis performance |
| 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 |

**Eurocopter’s list of shortfalls**

**Challenges to improving HUMS performance**

- Reference:  Pouradier and Trouvé, 2001.

## LESSONS LEARNED

| Shortfalls/Difficulties | Reasons identified |
| --- | --- |
| Operating cost higher than anticipated
Decision making sometimes difficult | Performance
Limited diagnosis performance because of not “defect specific” monitoring techniques |
| 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
Rapid obsolescence |
| Support cost higher than anticipated
Long maturing process
Help for diagnostics
Threshold adjustment
Continuous development | Performance
Monitoring techniques not “defect specific” |

**Eurocopter’s list of shortfalls (cont.)**

## LESSONS LEARNED

**Smiths Aerospace HUMS **
- Maintenance and improved operational benefits from 300 HUMS
- Operational service in the UK Chinook fleet since 2000
- Accurate record of helicopter usage for maintenance and lifing
- Reduced consequential damage from a mechanical fault
- Improved aircraft troubleshooting
- Reduction of unscheduled maintenance
- Maintenance credits and extension of component life
- Performs fleet wide health check monitoring of all HUMS equipped aircraft for a specific fault in a short amount of time

## LESSONS LEARNED

**Review of 180 HUMS-related maintenance actions**
- Canadian Forces Maintenance Program for CH-146 Griffon Fleet
- Enabled maintenance following an exceedance (41%)
- Installation improvements precluded accelerated wear (19%)
- Precluded the need for additional troubleshooting (17%)
- Precluded expensive (+$100K) component replacement (12%)
- Possibly prevented serious faults (11%)

## LESSONS LEARNED

**Goodrich Integrated Mechanical Diagnostics **
**Health and Usage Management System (IMD-HUMS)**
- Demonstration of the IMD-HUMS with the Army achieved 58% maintenance man-hour reductions compared to current practices.
- Results from 3 U.S. Marine CH53E and 30 U.S. Army UH-60L helicopters showed the IMD-HUMS was able to detect a number of mechanical anomalies.
- The IMD-HUMS system has provided more specific diagnostic information than previously available with standard techniques.
- Setting accurate threshold levels for the various health indicators is a challenge.

## LESSONS LEARNED

**Vibration Management Enhancement Program (VMEP) **
- Installed on over 100 helicopters
- Developed a large database of drive train diagnostic indicators of faults on critical areas of the drive train
- A web-based system for statistical analysis of Army HUMS parameters from over 100 aircraft is used to let engineers set condition indicator limits from remote locations

## FUTURE CHALLENGES

- Increase the fault detection coverage from today’s rate of 70 %
- 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
- Develop technology to accurately detect on-set of failure and isolate damage, and assess severity of damage magnitude
- 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

## FUTURE CHALLENGES

- Develop data management and automated techniques to obtain and process diagnostic information with minimal specialist involvement
- Develop system models, material failure models and correlation of failure under bench fatigue tests, seeded fault tests and operational data
- Development of a generic data collection and management scheme for analysis of operational data (Establishing 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