corrosion {bnRep} | R Documentation |
corrosion Bayesian Network
Description
Dynamic Bayesian network model to study under-deposit corrosion.
Format
A discrete Bayesian network to understand different risk factors and their interdependencies in under-deposit corrosion and how the interaction of these risk factors leads to asset failure due to under-deposit corrosion. Probabilities were given within the referenced paper. The vertices are:
- BurstPressure
(High, Low);
- Chloride
(High, Moderate, Low);
- DefectDepth
(Yes, No);
- DefectLength
(Yes, No);
- FlowVelocity
(High, Moderate, Low);
- InorganicDeposits
(Absent, Present);
- MEG
(Absent, Present);
- MixedDeposits
(Absent, Present);
- OD
(High, Low);
- OperatingPressure
(High, Moderate, Low);
- OperatingTemperature
(High, Moderate, Low);
- OrganicDeposits
(Absent, Present);
- PartialPressureCO2
(High, Moderate, Low);
- pH
(Acid, Neutral, Basic);
- PipeFailure
(Yes, No);
- ShearingForce
(High, Moderate, Low);
- SolidDeposits
(High, Moderate, Low);
- SteelGrade
(High, Low);
- SuspendedDeposits
(High, Moderate, Low);
- UDCCorrRate
(High, Moderate, Low);
- UnderDepositGalvanicCell
(Poor, Fair, Good, Excellent);
- WallThicknessLoss
(Yes, No).
Value
An object of class bn.fit
. Refer to the documentation of bnlearn
for details.
References
Dao, U., Sajid, Z., Khan, F., & Zhang, Y. (2023). Dynamic Bayesian network model to study under-deposit corrosion. Reliability Engineering & System Safety, 237, 109370.