Integrated Software Platform for Fleet Data Analysis, Enhanced Diagnostics, and Safe Transition to Prognostics for Helicopter Component CBM



Romano Patrick Matthew J. Smith Carl S. Byington George J. Vachtsevanos Kwok Tom Canh Ly


Although typical Health and Usage Monitoring Systems (HUMS) intend to support a transition from scheduled part replacements to performing maintenance upon evidence of need, they generally exhibit a limited ability to diagnose component faults early and accurately in complex systems such as a helicopter drive train. Consequently, the traditional approach to implementing Condition Based Maintenance (CBM) programs is slow, requires substantial amounts of human supervision (including case-by-case data analysis and results verification), and ultimately shuns prognostic activities. Causes of these limitations, which ultimately lead to an underrepresentation of prognostics in fielded CBM systems, include: (i) the sensitivity of sensors and condition indicators to signal noise and operating modes; (ii) use of empirical condition indicators not fully understood at the fleet-wide level; (iii) uncertainty in damage progression tracking; (iv) the inherent risk of condition prognosis; and (v) the lack diagnostic and prognostic validation with known fault cases.

To improve the performance of CBM systems and facilitate transition from scheduled maintenance to reliable implementation of diagnostics and prognostics, a team of developers from Impact Technologies, the U.S. Army Research Laboratory and the Georgia Institute of Technology, with support from the U.S. Army have been working over the past 21⁄2 years to develop a methodology that is capable of addressing the challenges listed. This work has been a part of the Air Vehicle Diagnostics and Prognostics Improvement

Program (A VDPIP), a collaborative agreement to develop, test and evaluate modular software components that provide enhancements to diagnostic systems already in service, as well as add failure prognosis capabilities for critical Army aircraft components. This paper presents the integrated diagnostic enhancement and prognostic architecture, as well as the software suite developed under the collaborative program, and discusses how a hybrid and systematic approach to sensing, data processing, fault feature extraction, fault diagnosis, and parallel health- based and usage-based failure prognosis can be used to improve the performance of a wide variety of HUMS and CBM activities in support of implementing prognostics. The software architecture contains generic components and algorithms building on model based and data driven methodologies that are applicable to a variety of critical components in complex systems such as those found in a helicopter drive train

How to Cite

Patrick, R. ., J. Smith, M. ., S. Byington, C. ., J. Vachtsevanos, G. ., Tom, K., & Ly, C. (2010). Integrated Software Platform for Fleet Data Analysis, Enhanced Diagnostics, and Safe Transition to Prognostics for Helicopter Component CBM. Annual Conference of the PHM Society, 2(1).
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Branhof, R.W., Grabill, P., Grant, L., and Keller, J.A. “Application of Automated Rotor Smoothing Using Continuous Vibration Measurements”. American Helicopter Society 61st annual forum, Grapevine, Texas, June 1–3, 2005.

Byington, Orsagh, Kallappa, Sheldon, DeChristopher, Amin, and Hines, “Recent Case Studies in Bearing Fault Detection and Prognosis,” IEEE Aerospace Conference, Big Sky, MT, March 2006a.

Byington, Orsagh, Sheldon, Kallappa, DeChristopher, Amin, “V erification and V alidation of Incipient Fault Detection Techniques for Engines and Drivetrains,” 60th Meeting of the Society for MFPT, April 2006b.

Byington, C. S., Smith, M. J., and Kalgren, P. W., “Improving Diagnostic Isolation in Drivetrain Components: A Swashplate Bearing Example,” 5th DSTO International Conference on Health and Usage Monitoring (HUMS), Melbourne, Australia, March 20-22, 2007.

Byington, C.S., Watson, M.J., Amin, S.R., and Begin, M., “False Alarm Mitigation of Vibration Diagnostic Systems,” IEEE Aerospace 2008, Big Sky, MT, March 1-8, 2008a.

Byington, C.S., Watson, M.J., Lee, H., and Hollins, M., “Sensor-Level Fusion to Enhance Health and Usage Monitoring Systems,” AHS 64th Annual Forum and Technology Display, Montreal, Canada, April 29 – May 1, 2008b.

Dora, R., Wright, J., Hess, R., and Boydstun, B. “Utility of the IMD HUMS in an Operational Setting on the UH-60L Blackhawk”. American
Helicopter Society 60th annual forum, Baltimore, Maryland, May 7–10, 2004.

Patrick, R., Smith, M.J., Zhang, B., Byington, C.S., V achtsevanos, G.J., and Del Rosario, R., “Diagnostic Enhancements for Air Vehicle HUMS to Increase Prognostic System Effectiveness”. IEEEAC paper #1608, IEEE, 2009

Sheldon, J. S., Klenke, C., and Byington, C. S., “Detection of Incipient Bearing Faults in a Helicopter Gas Turbine Engine,” AHS 63rd Annual Forum and Technology Display, Virginia
Beach, VA, May 1-3, 2007.

Smith, M.J., Zhang, B., Patrick, R., Byington, C.S., Vachtsevanos, G.J., Del Rosario, R., Wade, D.R., and Suggs, D.T.; “Combination of Fusion and Preprocessing Techniques to Enhance Air Vehicle HUMS”. Sixth DSTO International Conference on Health & Usage Monitoring, 2009.

Watson, M. J., Sheldon, J. S., Amin, S. R., Lee, H., Byington, C. S., and Begin, M., “A Comprehensive High Frequency Vibration Monitoring System for Incipient Fault Detection and Isolation of Gears, Bearings and Shafts/Couplings in Turbine Engines and Accessories,” Proceedings of ASME Turbo Expo 2007: Power for Land, Sea and Air, Montreal, Canada, May 14-17, 2007.

Zakrajsek, J.J., Dempsey, P.J., et al. “Rotorcraft Health Management Issues and Challenges”. NASA report TM—2006-214022. February, 2006.

Zhang, B., Sconyers, C., Byington, C.S., Patrick, R., Orchard, M.E., and Vachtsevanos, G.J. “Anomaly Detection: A Robust Approach to Detection of Unanticipated Faults”. International Conference on Prognostics and Health Management, Denver, Colorado, October 6-9, 2008.
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