This paper develops a health monitoring scheme to detect and trend degradation in dynamic systems that are characterised by multiple parameter time-series data. The presented scheme provides early detection of degradation and ability to score its significance in order to inform maintenance planning and consequently reduce disruption. Non-parametric statistics are proposed to provide this early detection and scoring. The non-parametric statistics approximate the data distribution for a sliding time window, with the change in distribution is indicated using the two-sample Kolmogorov-Smirnov test. Trending the changes to the signal distribution is shown to provide diagnostic capabilities, with deviations indicating the precursors to failure. The paper applies the equipment health monitoring scheme to address the growing concerns for future gas turbine fuel metering valve availability. The fuel metering unit within a gas turbine is a complex electro-mechanical system, failures of which can be a major source of airline dis- ruption. The application is performed on data acquired from a series of industrial tests performed on large civil aero-engine fuel metering units subjected to varying levels of contaminant. The data exhibits characteristics of degradation, which are identified and trended by the equipment health monitoring scheme presented in this paper.
How to Cite
Data Based Diagnostics, Data Driven, Engine Health Monitoring, non-parametric density estimation
Eleffendi, M. A., Purshouse, R., & Mills, A. R. (2012). Gas Turbine Fuel Valve Diagnostics. In Proceedings of 2012 IEEE aerospace conference.
Greenwell, R. N., & Finch, S. J. (2004). Randomized rejection procedure for the two-sample kolmogorov-smirnov statistic. Computational Statistics & Data Analysis, 46(2), 257-267.
Hall, L. D., & Mba, D. (2004). Acoustic emissions diagnosis of rotor-stator rubs using the ks statistic. Mechanical Systems and Signal Processing, 18, 849-868. DOI: 10.1016/S0888-3270(03)00050-5.
Kar, C., & Mohanty, A. R. (2006). Multistage gearbox condition monitoring using motor current signature analysis and kolmogorov-smirnov test. Journal of Sound and Vibration, 290(1-2), 337-368. doi: 10.1016/j.jsv.2005.04.020
Marsland, S. (2003). Novelty detection in learning systems. Neural Computing Survey, 3, 157-195.
Modenesi, A. P., & Braga, A. P. (2009). Analysis of time series novelty detection strategies for synthetic and real data. Neural Processing Letters, 30(1), 1-17. DOI: 10.1007/s11063-009-9106-4
Salgado, D. R., & Alonso, F. J. (2006, February). Tool wear detection in turning operations using singular spectrum analysis. Journal of Materials Processing Technology, 171, 451-458.
Scheffer, C., & Heyns, P. S. (2001). Wear monitoring in turning operations using vibration and strain measurements. Mechanical Systems and Signal Processing, 15(6), 1185- 1202.
Sohn, H., Farrar, C. R., Hunter, N. F., & Worden, K. (2001). Structural health monitoring using statistical pattern recognition techniques. Journal of Dynamic Systems, Measurement, and Control, 123(4), 706-711. DOI: 10.1115/1.1410933
Subramaniam, S., Palpanas, T., Papadopoulos, D., Kalogeraki, V., & Gunopulos, D. (2006). Online outlier detection in sensor data using non-parametric models. In Proceedings of the 32nd international conference on very large databases (vldb ’06) (p. 187-198).
Waters, N. (2009, June). Engine health management. The Ingenia Magazine(39), 37-42.
Zhan, Y., & Mechefske, C. K. (2007). Robust detection of gearbox deterioration using compromised autoregressive modeling and kolmogorov-smirnov test statistic-part i: Compromised autoregressive modeling with the aid of hypothesis tests and simulation analysis. Mechanical Systems and Signal Processing, 21, 1953-1982.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.