Assessment of Dynamic Bayesian Models for Gas Turbine Diagnostics, Part 1: Prior Probability Analysis
The reliability and cost-effectiveness of energy conversion in gas turbine systems are strongly dependent on an accurate diagnosis of possible process and sensor anomalies. Because data collected from a gas turbine system for diagnosis are inherently uncertain due to measurement noise and errors, pr...
Saved in:
Main Authors: | Valentina Zaccaria, Amare Desalegn Fentaye, Konstantinos Kyprianidis |
---|---|
Format: | article |
Language: | EN |
Published: |
MDPI AG
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/db04f4b5508d4be4ad02e06d2c63308a |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Fusion-Learning of Bayesian Network Models for Fault Diagnostics
by: Toyosi Ademujimi, et al.
Published: (2021) -
Future Trends in Semiconducting Gas-Selective Sensing Probes for Skin Diagnostics
by: Anthony Annerino, et al.
Published: (2021) -
Second law approach in the reduction of gas emission from gas turbine plant
by: M.N. Eke, et al.
Published: (2021) -
Emission characteristics of a lean-premixed ammonia/natural-gas gas-turbine combustor and effect of secondary ammonia injection
by: Shintaro ITO, et al.
Published: (2019) -
Journal of clinical and diagnostic research JCDR.
Published: (2007)