SYSTEM AND METHODS FOR FAULT-ISOLATION AND FAULT-MITIGATION BASED ON NETWORK MODELING

• US 20120303348A1
• Filed: 05/23/2011
• Published: 11/29/2012
• Est. Priority Date: 05/23/2011
• Status: Active Grant

• Current Assignee: GM Global Technology Operations LLC (General Motors Company)
• Original Assignee: GM Global Technology Operations Incorporated (General Motors Company)
• Inventors: Zhang, Yilu, Nijamkin, Alejandro, Salman, Mutasim A., Lu, Tsai-Ching, Allen, David L., Moon, Hankyu
• Granted Patent: US 8,577,663 B2
• Time in Patent Office: Days
• US Class Current: 703/13
• CPC Class Codes: B60W 50/0205 Diagnosing or detecting failures; Failure detection models
• CPC Class Codes: B60W 50/045 Monitoring control system parameters
• CPC Class Codes: G05B 23/0235 based on a comparison with predetermined threshold or range, e.g. "classical methods", carried out during normal operation; threshold adaptation or choice; when or how to compare with the threshold
• CPC Class Codes: G06F 11/0739 in a data processing system embedded in automotive or aircraft systems
• CPC Class Codes: G06F 11/079 Root cause analysis, i.e. error or fault diagnosis in a hardware test environment G06F11/22; in a software test environment G06F11/36
• CPC Class Codes: G06F 11/3013 where the computing system is an embedded system, i.e. a combination of hardware and software dedicated to perform a certain function in mobile devices, printers, automotive or aircraft systems testing or monitoring of control systems or parts thereof G05B23/02
• CPC Class Codes: G06F 11/3093 Configuration details thereof, e.g. installation, enabling, spatial arrangement of the probes

Abstract

A system and method for identifying a monitoring point in an electrical and electronic system (EES) in a vehicle. The method includes defining a network model of the EES where potential monitoring point locations in the model are identified as targets, such as nodes. The method then computes a betweenness centrality metric for each target in the model as a summation of a ratio of a total number of shortest paths between each pair of targets and a number of shortest paths that pass through the target whose betweenness centrality metric is being determined. The method identifies which of the betweenness centrality metrics are greater than a threshold that defines a minimum acceptable metric and determines which of those targets meets a predetermined model coverage. The monitoring point is selected as the target that best satisfies the minimum metric and the desired coverage.

Claims

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

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 total number of shortest paths between each pair of targets in the model and a number of those shortest paths that pass through the target whose betweenness centrality metric is being determined;

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 as the monitoring point that has the highest betweenness centrality metric above the threshold and that has a degree of neighbor factor that satisfies the predetermined coverage.

2. The method according to claim 1 wherein the model includes nodes that are selected from the group consisting of electronic control units, software components and transmission paths, and edges that are connections between the nodes.

3. The method according to claim 2 wherein the targets include edges in the network model.

4. The method according to claim 2 wherein the targets include nodes in the network model.

5. The method according to claim 1 wherein the network model is a layered model.

6. The method according to claim 5 wherein the network model includes a data flow network layer, a message network layer, a functional network layer, a physical network layer and an electrical network layer.

7. The method according to claim 1 wherein the betweenness centrality metric is a homogenous multi-partite betweenness centrality metric, an inverse multi-partite betweenness centrality metric, a designated multi-partite betweenness centrality metric, a homogenous multi-attribute betweenness centrality metric, an inverse multi-attribute betweenness centrality metric or an edge betweenness centrality metric.

8. The method according to claim 1 wherein the minimum degree of neighbor factor is a second degree of neighbor factor where a particular target is within two connections of another target.

9. The method according to claim 1 further comprising reducing the minimum betweenness centrality metric threshold if none of the targets satisfies the predetermined coverage provided by the minimum degree of neighbor factor.

10. The method according to claim 1 further comprising performing a target failure simulation for each target to determine whether a target has failed.

11. 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, software components and transmission paths in the EES;

determining a betweenness centrality metric for each node in the model as a summation of a ratio of a total number of shortest paths between each pair of nodes in the model and a number of those shortest paths that pass through the node whose betweenness centrality metric is being determined;

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.

12. The method according to claim 11 further comprising defining a minimum degree of neighbor factor that defines a predetermined coverage of a node relative to all of the other nodes, and determining which of the nodes having a betweenness centrality metric greater than the threshold has a degree of neighbor factor greater than the minimum degree of neighbor factor, wherein identifying those nodes having a determined betweenness centrality metric greater than the threshold includes identifying those nodes having a determined betweenness centrality metric greater than the threshold and that have a degree of neighbor factor that satisfies the predetermined coverage as defined potential monitoring points.

13. The method according to claim 12 wherein the minimum degree of neighbor factor is a second degree of neighbor factor where a particular node is within two connections of another node.

14. The method according to claim 11 wherein the betweenness centrality metric is a homogenous multi-partite betweenness centrality metric, an inverse multi-partite betweenness centrality metric, a designated multi-partite betweenness centrality metric, a homogenous multi-attribute betweenness centrality metric, an inverse multi-attribute betweenness centrality metric or an edge betweenness centrality metric.

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

means for defining a network model of the EES where potential monitoring points in the model are identified as targets;

means for determining a betweenness centrality metric for each target in the model as a summation of a ratio of a total number of shortest paths between each pair of targets in the model and a number of those shortest paths that pass through the target whose betweenness centrality metric is being determined;

means for selecting a minimum betweenness centrality metric threshold;

means for identifying which of the determined betweenness centrality metrics for each of the targets is greater than the threshold;

means for defining a minimum degree of neighbor factor that defines a predetermined coverage of a target relative to all of the other targets;

means for 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

means for identifying a target as the monitoring point that has the highest betweenness centrality metric above the threshold and that has a degree of neighbor factor that satisfies the predetermined coverage.

16. The system according to claim 15 wherein the means for defining a network model of the EES the model defines a model including nodes that are selected from the group consisting of electronic control units, software components and transmission paths, and edges that are connections between the nodes.

17. The system according to claim 16 wherein the targets include edges in the network model.

18. The system according to claim 16 wherein the targets include nodes in the network model.

19. The system according to claim 15 wherein the means for determining a betweenness centrality metric selects the betweenness centrality metric as a homogenous multi-partite betweenness centrality metric, an inverse multi-partite betweenness centrality metric, a designated multi-partite betweenness centrality metric, a homogenous multi-attribute betweenness centrality metric, an inverse multi-attribute betweenness centrality metric or an edge betweenness centrality metric.

20. The system according to claim 15 wherein the means for defining a minimum degree of neighbor factor defines the minimum degree of neighbor factor as a second degree of neighbor factor where a particular target is within two connections of another target.