powerplant_bayesian — Power plant sensor Bayesian network (2007)
- Description
Bayesian network modelling the correlations among the sensors of a coal-fired power plant, from T. D. Nielsen and F. V. Jensen, distributed with the book "Bayesian Networks and Decision Graphs" (F. V. Jensen and T. D. Nielsen) at Aalborg University. The network was used to design an on-line alert system that detects abnormal plant behavior as conflicts between sensor readings and the model. Nodes are variables (sensor measurements, discretized into three intervals), and a directed edge from $i$ to $j$ means that variable $i$ is a parent of variable $j$ in the directed acyclic graph of the Bayesian network, i.e., that the conditional probability distribution of $j$ is specified given the value of $i$. The network has 46 sparsely connected nodes (six of which have no connections); the conditional probability tables are not included.
The nodes have the following properties:
name: the name of the variable, as in the source (e.g. "V26"; the variables are not described further).states: the list of states of the variable (intervals of the sensor value, e.g. "(2.42-4.66]").1
- Tags
- Informational Unweighted Metadata
- Citation
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- T. D. Nielsen, F. V. Jensen, "On-line alert systems for production plants: A conflict based approach." International Journal of Approximate Reasoning 45(2), 255-270 (2007) 10.1016/j.ijar.2006.06.010 Sci-Hub
- Upstream URL OK
- https://web.archive.org/web/20070626181030/http://bndg.cs.aau.dk/html/bayesian_networks.html
- Networks
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Tip: hover over a column header for a description.
Column legend
- $\left<k\right>$
- Mean degree
- $\sigma_k$
- Standard deviation of the degrees
- $\lambda_h$
- Largest eigenvalue of the Hashimoto (non-backtracking) matrix
- $\tau$
- Random walk mixing time
- $r$
- Degree assortativity
- $c$
- Global clustering coefficient
- $\oslash$
- (Pseudo-) diameter
- $S$
- Relative size of the largest component
- NPs
- Node properties
- EPs
- Edge properties