Uncertainty in Steady-State Diagnostics of a Current-Pressure Transducer: How Confident are We in Diagnosing Faults?



Shankar Sankararaman Christopher Teubert Kai Goebel


Current-Pressure (I/P) transducers are effective pressure regulators that can vary the output pressure depending on the supplied electrical current signal, and are commonly used in pneumatic actuators and valves. Faults in current-pressure transducers have a significant impact on the regulation mechanism, and therefore, it is important to perform diagnosis to identify such faults. However, there are different sources of uncertainty that significantly affect the diagnostics procedure, and therefore, it may not be possible to perform fault diagnosis and prognosis accurately, with complete confidence. These sources of uncertainty include natural variability, sensor errors (gain, bias, noise), model uncertainty, etc. This paper presents a computational methodology to quantify the uncertainty and thereby estimate the confidence in the fault diagnosis of a current-pressure transducer. First, experiments are conducted to study the nominal and off-nominal behavior of the I/P transducer; however, sensor measurements are not fast enough to capture brief transient states that are indicative of wear, and hence, steady-state measurements are directly used for fault diagnosis. Second, the results of these experiments are used to train a Gaussian process model using machine learning principles. Finally, a Bayesian inference methodology is developed to quantify the uncertainty and assess the confidence in fault diagnosis by systematically accounting for the aforementioned sources of uncertainty.

How to Cite

Sankararaman, S. ., Teubert, C., & Goebel, K. . (2014). Uncertainty in Steady-State Diagnostics of a Current-Pressure Transducer: How Confident are We in Diagnosing Faults?. Annual Conference of the PHM Society, 6(1). https://doi.org/10.36001/phmconf.2014.v6i1.2432
Abstract 45 | PDF Downloads 22



diagnosis, uncertainty, Confidence, Transducer, Probability

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.

Chiles, J., & Delfiner, P. (1999). Geostatistics: modeling spatial uncertainty (Vol. 344). Wiley-Interscience.

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. (2013, 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, Sep). Implementation of prognostic methodologies to cryogenic propellant loading testbed. In IEEE AUTOTESTCON 2013.

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].

Neal, R. M. (2003). Slice sampling. Annals of Statistics, 705–741.

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.

Rasmussen, C. (2004). Gaussian processes in machine learn- ing. Advanced Lectures on Machine Learning, 63–71.

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.

Sankararaman, S., Ling, Y., & Mahadevan, S. (2010). Statis- tical inference of equivalent initial flaw size with complicated structural geometry and multi-axial variable amplitude loading. International Journal of Fatigue, 32(10), 1689–1700.

Sankararaman, S., & Mahadevan, S. (2011). Uncertainty quantification in structural damage diagnosis. Structural Control and Health Monitoring, 18(8), 807–824.

Sankararaman, S., & Mahadevan, S. (2013). Bayesian methodology for diagnosis uncertainty quantification and health monitoring. Structural Control and Health Monitoring, 20(1), 88–106.

Santner, T., Williams, B., & Notz, W. (2003). The de- sign and analysis of computer experiments. New York: Springer-Verlag.

Teubert, C., & Daigle, M. (2013, October). I/p transducer application of model-based wear detection and estimation using steady state conditions. In Proceedings of the annual conference of the prognostics and health management society (p. 134-140).

Teubert, C., & Daigle, M. (2014, March). Current/pressure transducer application of model-based prognostics using steady state conditions. In Proceedings of the IEEE aerospace conference.
Technical Papers

Most read articles by the same author(s)

1 2 3 4 5 > >>