I/P Transducer Application of Model-Based Wear Detection and Estimation using Steady State Conditions

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Published Oct 14, 2013
Christopher Teubert Matthew Daigle

Abstract

For modern systems, wear estimation plays an important role in preventing failure, scheduling maintenance, and improving utility. Wear estimation relies on a series of sensors, measuring the state of the system. In some components, the sensors used to estimate wear may not be fast enough to capture brief transient states that are indicative of wear. For this reason it is beneficial to be capable of detecting and estimating the extent of component wear using steady-state measurements. This paper details a method for estimating component wear using steady-state measurements, and describes a case study of a current/pressure (I/P) transducer. I/P Transducer nominal and off-nominal behavior are characterized using a physics- based model, and validated against expected component behavior. This model is used to determine steady state responses to many common I/P Transducer wear modes, isolate the ac- tive wear mode, and estimate its magnitude.

How to Cite

Teubert, . C., & Daigle, M. . (2013). I/P Transducer Application of Model-Based Wear Detection and Estimation using Steady State Conditions. Annual Conference of the PHM Society, 5(1). https://doi.org/10.36001/phmconf.2013.v5i1.2233
Abstract 379 | PDF Downloads 164

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Keywords

PHM

References
Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 50(2), 174–188.

Daigle, M., & Goebel, K. (2011, August). A model-based prognostics approach applied to pneumatic valves. International Journal of Prognostics and Health Management, 2(2).

Daigle, M., & Goebel, K. (2013, May). Model-based prognostics with concurrent damage progression processes. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43(4), 535-546.

Daigle, M., Saha, B., & Goebel, K. (2012, March). A comparison of filter-based approaches for model-based prognostics. In Proceedings of the IEEE aerospace conference.

Kulkarni, C., Daigle, M., & Goebel, K. (2013, September). Implementation of prognostic methodologies to cryogenic propellant loading testbed. Proceedings of 2013 IEEE Autotestcon.

Luo, J., Pattipati, K. R., Qiao, L., & Chigusa, S. (2008, September). Model-based prognostic techniques applied to a suspension system. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 38(5), 1156 -1168.

Marsh Bellofram. (n.d.). Type 1000 i/p & e/p transducers [Computer software manual].

Orchard, M., & Vachtsevanos, G. (2009, June). A particle filtering approach for on-line fault diagnosis and failure prognosis. Transactions of the Institute of Measurement and Control(3-4), 221-246.

Saha, B., & Goebel, K. (2009, September). Modeling Li-ion battery capacity depletion in a particle filtering framework. In Proceedings of the annual conference of the prognostics and health management society 2009.

Zio, E., & Peloni, G. (2011). Particle filtering prognostic estimation of the remaining useful life of nonlinear components. Reliability Engineering & System Safety, 96(3), 403-409.
Section
Technical Research Papers

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