System and methods for fault-isolation and fault-mitigation based on network modeling

A method for identifying an information monitoring point in an electrical and electronic system (EES) on a vehicle, said method comprising:

• defining a layered network model of the EES that includes nodes identifying one or more of electronic control units and software components in the EES;
• determining, using a microprocessor, a betweenness centrality metric for each node in the model as a summation of a ratio of a number of shortest paths between each pair of nodes in the model that pass through the node whose betweenness centrality metric is being determined to a total number of shortest paths between each pair of nodes;
• selecting a minimum betweenness centrality metric threshold;
• identifying those nodes having a determined betweenness centrality metric is greater than the threshold to be potential monitoring points;
• defining a minimum degree of neighbor factor that defines a predetermined coverage of a node relative to all of the other nodes;
• and
• identifying one of the potential monitoring points which has a degree of neighbor factor greater than the minimum degree of neighbor factor and has a highest betweenness centrality metric as the information monitoring point.

A system for identifying an information monitoring point in an electrical and electronic system (EES) on a vehicle, said system comprising:

• a microprocessor coupled to a memory, where said microprocessor is configured with an algorithm, said algorithm including:
• defining a network model of the EES where potential monitoring points in the model are identified as targets;
• determining a betweenness centrality metric for each target in the model as a summation of a ratio of a number of shortest paths between each pair of targets in the model that pass through the target whose betweenness centrality metric is being determined to a total number of shortest paths between each pair of targets;
• selecting a minimum betweenness centrality metric threshold;
• identifying which of the determined betweenness centrality metrics for each of the targets is greater than the threshold;
• defining a minimum degree of neighbor factor that defines a predetermined coverage of a target relative to all of the other targets;
• determining which of the targets having a betweenness centrality metric greater than the threshold has a degree of neighbor factor greater than the minimum degree of neighbor factor;
• and
• identifying a target, from the determined targets having a betweenness centrality metric greater than the threshold and having a degree of neighbor factor greater than the minimum degree of neighbor factor, that has the highest betweenness centrality metric as the information monitoring point.