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

Shankar Sankararaman, Christopher Teubert, and Kai Goebel
Submission Type: 
Full Paper
Supporting Agencies (optional): 
NASA Ames Research Center
AttachmentSizeTimestamp
phmc_14_038.pdf531.95 KBSeptember 5, 2014 - 4:28pm

Current-Pressure (I/P) Transducers are effective pressure regulators that can vary the output pressure depending on the supplied electrical current signal, and therefore, are often used for supplying precise pressures to control pneumatic actuators and valves. Faults in the current pressure transducers have a significant impact on the regulation mechanism and therefore, it is important detect, isolate, and estimate faults that affect the performance of the I/P transducer. However, it may not be possible to accurately perform fault diagnosis due to different sources of uncertainty that affect the fault diagnosis procedure. 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 in the fault diagnosis of a current-pressure transducer. A data-driven approach is pursued for fault diagnosis, where the I/P transducer nominal and off-nominal behaviors are characterized using a Gaussian process model trained using experimental data. Since the sensors used to estimate wear may not be fast enough to capture brief transient states that are indicative of wear, steady-state measurements are directly used for fault diagnosis, and the uncertainty in fault estimation is rigorously quantified using the principle of Bayesian inference. Finally, the proposed methodology is illustrated using a numerical example.

Publication Year: 
2014
Publication Volume: 
5
Publication Control Number: 
038
Page Count: 
11
Submission Keywords: 
diagnosis
uncertainty
Confidence
Transducer
Probability
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Uncertainty Quantification and Management in PHM
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